Publications
1.
Chamberlain, Heather R.; Darin, Edith; Adewole, Wole Ademola; Jochem, Warren C.; Lazar, Attila N.; Tatem, Andrew J.
Building footprint data for countries in Africa: To what extent are existing data products comparable? Journal Article
In: Computers, Environment and Urban Systems, vol. 110, 2024.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Building footprint data for countries in Africa: To what extent are existing data products comparable?},
author = {Heather R. Chamberlain and Edith Darin and Wole Ademola Adewole and Warren C. Jochem and Attila N. Lazar and Andrew J. Tatem},
url = {https://doi.org/10.1016/j.compenvurbsys.2024.102104},
doi = {10.1016/j.compenvurbsys.2024.102104},
year = {2024},
date = {2024-03-22},
journal = {Computers, Environment and Urban Systems},
volume = {110},
abstract = {Growth and developments in computing power, machine-learning algorithms and satellite imagery spatiotemporal resolution have led to rapid developments in automated feature-extraction. These methods have been applied to create geospatial datasets of features such as roads, trees and building footprints, at a range of spatial scales, with national and multi-country datasets now available as open data from multiple sources. Building footprint data is particularly useful in a range of applications including mapping population distributions, planning resource distribution campaigns and in humanitarian response. In settings with well-developed geospatial data systems, such datasets may complement existing authoritative sources, but in data-scarce settings, they may be the only source of data. However, knowledge on the degree to which building footprint data products are comparable and can be used interchangeably, and the impact of selecting a particular dataset on subsequent analyses remains limited. For all countries in Africa, we review the available multi-country building footprint data products and analyse their similarities and differences in terms of building area and count metrics. We explore the variation between building footprint data products across a range of spatial scales, including sub-national administrative units and different settlement types. Our results show that the available building footprint data products are not interchangeable. There are clear differences in counts and total area of building footprints between the assessed data products, as well as considerable spatial heterogeneity in building footprint coverage and completeness.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Growth and developments in computing power, machine-learning algorithms and satellite imagery spatiotemporal resolution have led to rapid developments in automated feature-extraction. These methods have been applied to create geospatial datasets of features such as roads, trees and building footprints, at a range of spatial scales, with national and multi-country datasets now available as open data from multiple sources. Building footprint data is particularly useful in a range of applications including mapping population distributions, planning resource distribution campaigns and in humanitarian response. In settings with well-developed geospatial data systems, such datasets may complement existing authoritative sources, but in data-scarce settings, they may be the only source of data. However, knowledge on the degree to which building footprint data products are comparable and can be used interchangeably, and the impact of selecting a particular dataset on subsequent analyses remains limited. For all countries in Africa, we review the available multi-country building footprint data products and analyse their similarities and differences in terms of building area and count metrics. We explore the variation between building footprint data products across a range of spatial scales, including sub-national administrative units and different settlement types. Our results show that the available building footprint data products are not interchangeable. There are clear differences in counts and total area of building footprints between the assessed data products, as well as considerable spatial heterogeneity in building footprint coverage and completeness.
2.
Aheto, Justice Moses K.; Olowe, Iyanuloluwa Deborah; Chan, Ho Man Theophilus; Ekeh, Adachi; Dieng, Boubacar; Fafunmi, Biyi; Setayesh, Hamidreza; Atuhaire, Brian; Crawford, Jessica; Tatem, Andrew J.; Utazi, Chigozie Edson
In: Vaccines, vol. 11, iss. 12, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Geospatial Analyses of Recent Household Surveys to Assess Changes in the Distribution of Zero-Dose Children and Their Associated Factors before and during the COVID-19 Pandemic in Nigeria},
author = {Justice Moses K. Aheto and Iyanuloluwa Deborah Olowe and Ho Man Theophilus Chan and Adachi Ekeh and Boubacar Dieng and Biyi Fafunmi and Hamidreza Setayesh and Brian Atuhaire and Jessica Crawford and Andrew J. Tatem and Chigozie Edson Utazi},
url = {https://doi.org/10.3390/vaccines11121830},
doi = {10.3390/vaccines11121830 },
year = {2023},
date = {2023-12-08},
journal = {Vaccines},
volume = {11},
issue = {12},
abstract = {The persistence of geographic inequities in vaccination coverage often evidences the presence of zero-dose and missed communities and their vulnerabilities to vaccine-preventable diseases. These inequities were exacerbated in many places during the coronavirus disease 2019 (COVID-19) pandemic, due to severe disruptions to vaccination services. Understanding changes in zero-dose prevalence and its associated risk factors in the context of the COVID-19 pandemic is, therefore, critical to designing effective strategies to reach vulnerable populations. Using data from nationally representative household surveys conducted before the COVID-19 pandemic, in 2018, and during the pandemic, in 2021, in Nigeria, we fitted Bayesian geostatistical models to map the distribution of three vaccination coverage indicators: receipt of the first dose of diphtheria-tetanus-pertussis-containing vaccine (DTP1), the first dose of measles-containing vaccine (MCV1), and any of the four basic vaccines (bacilli Calmette-Guerin (BCG), oral polio vaccine (OPV0), DTP1, and MCV1), and the corresponding zero-dose estimates independently at a 1 × 1 km resolution and the district level during both time periods. We also explored changes in the factors associated with non-vaccination at the national and regional levels using multilevel logistic regression models. Our results revealed no increases in zero-dose prevalence due to the pandemic at the national level, although considerable increases were observed in a few districts. We found substantial subnational heterogeneities in vaccination coverage and zero-dose prevalence both before and during the pandemic, showing broadly similar patterns in both time periods. Areas with relatively higher zero-dose prevalence occurred mostly in the north and a few places in the south in both time periods. We also found consistent areas of low coverage and high zero-dose prevalence using all three zero-dose indicators, revealing the areas in greatest need. At the national level, risk factors related to socioeconomic/demographic status (e.g., maternal education), maternal access to and utilization of health services, and remoteness were strongly associated with the odds of being zero dose in both time periods, while those related to communication were mostly relevant before the pandemic. These associations were also supported at the regional level, but we additionally identified risk factors specific to zero-dose children in each region; for example, communication and cross-border migration in the northwest. Our findings can help guide tailored strategies to reduce zero-dose prevalence and boost coverage levels in Nigeria.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The persistence of geographic inequities in vaccination coverage often evidences the presence of zero-dose and missed communities and their vulnerabilities to vaccine-preventable diseases. These inequities were exacerbated in many places during the coronavirus disease 2019 (COVID-19) pandemic, due to severe disruptions to vaccination services. Understanding changes in zero-dose prevalence and its associated risk factors in the context of the COVID-19 pandemic is, therefore, critical to designing effective strategies to reach vulnerable populations. Using data from nationally representative household surveys conducted before the COVID-19 pandemic, in 2018, and during the pandemic, in 2021, in Nigeria, we fitted Bayesian geostatistical models to map the distribution of three vaccination coverage indicators: receipt of the first dose of diphtheria-tetanus-pertussis-containing vaccine (DTP1), the first dose of measles-containing vaccine (MCV1), and any of the four basic vaccines (bacilli Calmette-Guerin (BCG), oral polio vaccine (OPV0), DTP1, and MCV1), and the corresponding zero-dose estimates independently at a 1 × 1 km resolution and the district level during both time periods. We also explored changes in the factors associated with non-vaccination at the national and regional levels using multilevel logistic regression models. Our results revealed no increases in zero-dose prevalence due to the pandemic at the national level, although considerable increases were observed in a few districts. We found substantial subnational heterogeneities in vaccination coverage and zero-dose prevalence both before and during the pandemic, showing broadly similar patterns in both time periods. Areas with relatively higher zero-dose prevalence occurred mostly in the north and a few places in the south in both time periods. We also found consistent areas of low coverage and high zero-dose prevalence using all three zero-dose indicators, revealing the areas in greatest need. At the national level, risk factors related to socioeconomic/demographic status (e.g., maternal education), maternal access to and utilization of health services, and remoteness were strongly associated with the odds of being zero dose in both time periods, while those related to communication were mostly relevant before the pandemic. These associations were also supported at the regional level, but we additionally identified risk factors specific to zero-dose children in each region; for example, communication and cross-border migration in the northwest. Our findings can help guide tailored strategies to reduce zero-dose prevalence and boost coverage levels in Nigeria.
3.
Dwomoh, Duah; Iddi, Samuel; Afagbedzi, Seth Kwaku; Tejedor-Garavito, Natalia; Dotse-Gborgbortsi, Winfred; Wright, Jim; Tatem, Andrew J; Nilsen, Kristine
In: Journal of Urban Health, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Impact of Urban Slum Residence on Coverage of Maternal, Neonatal, and Child Health Service Indicators in the Greater Accra Region of Ghana: an Ecological Time-Series Analysis, 2018-2021},
author = {Duah Dwomoh and Samuel Iddi and Seth Kwaku Afagbedzi and Natalia Tejedor-Garavito and Winfred Dotse-Gborgbortsi and Jim Wright and Andrew J Tatem and Kristine Nilsen },
url = {https://doi.org/10.1007/s11524-023-00812-0
},
doi = {10.1007/s11524-023-00812-0},
year = {2023},
date = {2023-11-16},
journal = {Journal of Urban Health},
abstract = {Among other focus areas, the global Sustainable Development Goals (SDGs) 3 and 11 seek to advance progress toward universal coverage of maternal, neonatal, and child health (MNCH) services and access to safe and affordable housing and basic services by 2030. Governments and development agencies have historically neglected the health and well-being associated with living in urban slums across major capital cities in sub-Saharan Africa since health policies and programs have tended to focus on people living in rural communities. This study assessed the trends and compared inequities in MNCH service utilization between slum and non-slum districts in the Greater Accra region of Ghana. It analyzed information from 29 districts using monthly time-series Health Management Information System (HMIS) data on MNCH service utilization between January 2018 and December 2021. Multivariable quantile regression models with robust standard errors were used to quantify the impact of urban slum residence on MNCH service utilization. We assessed the inequality of MNCH coverage indicators between slum and non-slum districts using the Gini index with bootstrapped standard errors and the generalized Lorenz curve. The results indicate that rates of vaccination coverage and antenatal care (ANC) attendance have declined significantly in slum districts compared to those in non-slum districts. However, skilled birth delivery and postnatal care (PNC) were found to be higher in urban slum areas compared to those in non-urban slum areas. To help achieve the SDGs’ targets, it is important for the government of Ghana and other relevant stakeholders to prioritize the implementation of effective policies, programs, and interventions that will improve access to and utilization of ANC and immunization services among urban slum dwellers.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Among other focus areas, the global Sustainable Development Goals (SDGs) 3 and 11 seek to advance progress toward universal coverage of maternal, neonatal, and child health (MNCH) services and access to safe and affordable housing and basic services by 2030. Governments and development agencies have historically neglected the health and well-being associated with living in urban slums across major capital cities in sub-Saharan Africa since health policies and programs have tended to focus on people living in rural communities. This study assessed the trends and compared inequities in MNCH service utilization between slum and non-slum districts in the Greater Accra region of Ghana. It analyzed information from 29 districts using monthly time-series Health Management Information System (HMIS) data on MNCH service utilization between January 2018 and December 2021. Multivariable quantile regression models with robust standard errors were used to quantify the impact of urban slum residence on MNCH service utilization. We assessed the inequality of MNCH coverage indicators between slum and non-slum districts using the Gini index with bootstrapped standard errors and the generalized Lorenz curve. The results indicate that rates of vaccination coverage and antenatal care (ANC) attendance have declined significantly in slum districts compared to those in non-slum districts. However, skilled birth delivery and postnatal care (PNC) were found to be higher in urban slum areas compared to those in non-urban slum areas. To help achieve the SDGs’ targets, it is important for the government of Ghana and other relevant stakeholders to prioritize the implementation of effective policies, programs, and interventions that will improve access to and utilization of ANC and immunization services among urban slum dwellers.
4.
Rogers, Grant; Koper, Patrycja; Ruktanonchai, Cori; and Nick Ruktanonchai,; Utazi, Edson; Woods, Dorothea; Cunningham, Alexander; Tatem, Andrew J.; Steele, Jessica; Lai, Shengjie; Sorichetta, Alessandro
Exploring the Relationship between Temporal Fluctuations in Satellite Nightlight Imagery and Human Mobility across Africa Journal Article
In: Remote Sensing, vol. 15, iss. 17, no. 4252;, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Exploring the Relationship between Temporal Fluctuations in Satellite Nightlight Imagery and Human Mobility across Africa},
author = {Grant Rogers and Patrycja Koper and Cori Ruktanonchai and and Nick Ruktanonchai and Edson Utazi and Dorothea Woods and Alexander Cunningham and Andrew J. Tatem and Jessica Steele and Shengjie Lai and Alessandro Sorichetta},
url = {https://doi.org/10.3390/rs15174252},
doi = {10.3390/rs15174252},
year = {2023},
date = {2023-09-30},
journal = {Remote Sensing},
volume = {15},
number = {4252;},
issue = {17},
abstract = {Mobile phone data have been increasingly used over the past decade or more as a pretty reliable indicator of human mobility to measure population movements and the associated changes in terms of population presence and density at multiple spatial and temporal scales. However, given the fact mobile phone data are not available everywhere and are generally difficult to access and share, mostly because of commercial restrictions and privacy concerns, more readily available data with global coverage, such as night-time light (NTL) imagery, have been alternatively used as a proxy for population density changes due to population movements. This study further explores the potential to use NTL brightness as a short-term mobility metric by analysing the relationship between NTL and smartphone-based Google Aggregated Mobility Research Dataset (GAMRD) data across twelve African countries over two periods: 2018–2019 and 2020. The data were stratified by a measure of the degree of urbanisation, whereby the administrative units of each country were assigned to one of eight classes ranging from low-density rural to high-density urban. Results from the correlation analysis, between the NTL Sum of Lights (SoL) radiance values and three different GAMRD-based flow metrics calculated at the administrative unit level, showed significant differences in NTL-GAMRD correlation values across the eight rural/urban classes. The highest correlations were typically found in predominantly rural areas, suggesting that the use of NTL data as a mobility metric may be less reliable in predominantly urban settings. This is likely due to the brightness saturation and higher brightness stability within the latter, showing less of an effect than in rural or peri-urban areas of changes in brightness due to people leaving or arriving. Human mobility in 2020 (during COVID-19-related restrictions) was observed to be significantly different than in 2018–2019, resulting in a reduced NTL-GAMRD correlation strength, especially in urban settings, most probably because of the monthly NTL SoL radiance values remaining relatively similar in 2018–2019 and 2020 and the human mobility, especially in urban settings, significantly decreasing in 2020 with respect to the previous considered period. The use of NTL data on its own to assess monthly mobility and the associated fluctuations in population density was therefore shown to be promising in rural and peri-urban areas but problematic in urban settings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Mobile phone data have been increasingly used over the past decade or more as a pretty reliable indicator of human mobility to measure population movements and the associated changes in terms of population presence and density at multiple spatial and temporal scales. However, given the fact mobile phone data are not available everywhere and are generally difficult to access and share, mostly because of commercial restrictions and privacy concerns, more readily available data with global coverage, such as night-time light (NTL) imagery, have been alternatively used as a proxy for population density changes due to population movements. This study further explores the potential to use NTL brightness as a short-term mobility metric by analysing the relationship between NTL and smartphone-based Google Aggregated Mobility Research Dataset (GAMRD) data across twelve African countries over two periods: 2018–2019 and 2020. The data were stratified by a measure of the degree of urbanisation, whereby the administrative units of each country were assigned to one of eight classes ranging from low-density rural to high-density urban. Results from the correlation analysis, between the NTL Sum of Lights (SoL) radiance values and three different GAMRD-based flow metrics calculated at the administrative unit level, showed significant differences in NTL-GAMRD correlation values across the eight rural/urban classes. The highest correlations were typically found in predominantly rural areas, suggesting that the use of NTL data as a mobility metric may be less reliable in predominantly urban settings. This is likely due to the brightness saturation and higher brightness stability within the latter, showing less of an effect than in rural or peri-urban areas of changes in brightness due to people leaving or arriving. Human mobility in 2020 (during COVID-19-related restrictions) was observed to be significantly different than in 2018–2019, resulting in a reduced NTL-GAMRD correlation strength, especially in urban settings, most probably because of the monthly NTL SoL radiance values remaining relatively similar in 2018–2019 and 2020 and the human mobility, especially in urban settings, significantly decreasing in 2020 with respect to the previous considered period. The use of NTL data on its own to assess monthly mobility and the associated fluctuations in population density was therefore shown to be promising in rural and peri-urban areas but problematic in urban settings.
5.
Gebrechorkos, Solomon; Leyland, Julian; Slater, Louise; Wortmann, Michel; Ashworth, Philip J.; Bennett, Georgina L.; Boothroyd, Richard; Cloke, Hannah; Delorme, Pauline; Griffith, Helen; Hardy, Richard; Hawker, Laurence; McLelland, Stuart; Neal, Jeffrey; Nicholas, Andrew; Tatem, Andrew J.; Vahidi, Ellie; Parsons, Daniel R.; Darby, Stephen E.
A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses Journal Article
In: Scientific Data, iss. 10, no. 611, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses},
author = {Solomon Gebrechorkos and Julian Leyland and Louise Slater and Michel Wortmann and Philip J. Ashworth and Georgina L. Bennett and Richard Boothroyd and Hannah Cloke and Pauline Delorme and Helen Griffith and Richard Hardy and Laurence Hawker and Stuart McLelland and Jeffrey Neal and Andrew Nicholas and Andrew J. Tatem and Ellie Vahidi and Daniel R. Parsons and Stephen E. Darby},
url = {https://doi.org/10.1038/s41597-023-02528-x
},
doi = {10.1038/s41597-023-02528-x},
year = {2023},
date = {2023-09-11},
urldate = {2023-09-11},
journal = {Scientific Data},
number = {611},
issue = {10},
abstract = {A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis (https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317) for the historical (1981–2014) and future (2015–2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis (https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317) for the historical (1981–2014) and future (2015–2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models.
6.
Utazi, C. E.; Chan, H. M. T.; Olowe, I.; Wigley, A.; Tejedor-Garavito, N.; Cunningham, A.; Bondarenko, M.; Lorin, J.; Boyda, D.; Hogan, D.; Tatem, A. J.
A zero-dose vulnerability index for equity assessment and spatial prioritization in low- and middle-income countries Journal Article
In: Spatial Statistics, no. 100772, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {A zero-dose vulnerability index for equity assessment and spatial prioritization in low- and middle-income countries},
author = {C.E. Utazi and H.M.T. Chan and I. Olowe and A. Wigley and N. Tejedor-Garavito and A. Cunningham and M. Bondarenko and J. Lorin and D. Boyda and D. Hogan and A.J. Tatem},
url = {https://doi.org/10.1016/j.spasta.2023.100772
},
doi = {10.1016/j.spasta.2023.100772},
year = {2023},
date = {2023-09-05},
journal = {Spatial Statistics},
number = {100772},
abstract = {Many low- and middle-income countries (LMICs) continue to experience substantial inequities in vaccination coverage despite recent efforts to reach missed communities and reduce zero-dose prevalence. Geographic inequities in vaccination coverage are often characterized by a multiplicity of risk factors which should be operationalized through data integration to inform more effective and equitable vaccination policies and programmes. Here, we explore approaches for integrating information from multiple risk factors to create a zero-dose vulnerability index to improve the identification and prioritization of vulnerable communities and understanding of inequities in vaccination coverage. We assembled geolocated data on vaccination coverage and associated risk factors in six LMICs, focusing on the coverage of DTP1, DTP3 and MCV1 vaccines as indicators of zero dose and under-vaccination. Using geospatial modelling techniques built on a suite of geospatial covariate information, we produced 1 × 1 km and district level maps of the previously unmapped risk factors and vaccination coverage. We then integrated data from the maps of the risk factors using different approaches to construct a zero-dose vulnerability index to classify districts within the countries into different vulnerability groups, ranging from the least vulnerable (1) to the most vulnerable (5) areas. Through integration with population data, we estimated numbers of children aged under 1 living within the different vulnerability classes. Our results show substantial variation in the spatial distribution of the index, revealing the most vulnerable areas despite little variation in coverage in some cases. We found that the most distinguishing characteristics of the most vulnerable areas cut across the different subdomains (health, socioeconomic, demographic and geographic) of the risk factors included in our study. We also demonstrated that the index can be robustly estimated with fewer risk factors and without linkage to information on vaccination coverage. The index constitutes a practical and effective tool to guide targeted vaccination strategies in LMICs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Many low- and middle-income countries (LMICs) continue to experience substantial inequities in vaccination coverage despite recent efforts to reach missed communities and reduce zero-dose prevalence. Geographic inequities in vaccination coverage are often characterized by a multiplicity of risk factors which should be operationalized through data integration to inform more effective and equitable vaccination policies and programmes. Here, we explore approaches for integrating information from multiple risk factors to create a zero-dose vulnerability index to improve the identification and prioritization of vulnerable communities and understanding of inequities in vaccination coverage. We assembled geolocated data on vaccination coverage and associated risk factors in six LMICs, focusing on the coverage of DTP1, DTP3 and MCV1 vaccines as indicators of zero dose and under-vaccination. Using geospatial modelling techniques built on a suite of geospatial covariate information, we produced 1 × 1 km and district level maps of the previously unmapped risk factors and vaccination coverage. We then integrated data from the maps of the risk factors using different approaches to construct a zero-dose vulnerability index to classify districts within the countries into different vulnerability groups, ranging from the least vulnerable (1) to the most vulnerable (5) areas. Through integration with population data, we estimated numbers of children aged under 1 living within the different vulnerability classes. Our results show substantial variation in the spatial distribution of the index, revealing the most vulnerable areas despite little variation in coverage in some cases. We found that the most distinguishing characteristics of the most vulnerable areas cut across the different subdomains (health, socioeconomic, demographic and geographic) of the risk factors included in our study. We also demonstrated that the index can be robustly estimated with fewer risk factors and without linkage to information on vaccination coverage. The index constitutes a practical and effective tool to guide targeted vaccination strategies in LMICs.
7.
Ge, Yong; Wu, Xilin; Zhang, Wenbin; Wang, Xiaoli; Zhang, Die; Wang, Jianghao; Liu, Haiyan; Ren, Zhoupeng; Ruktanonchai, Nick W.; Ruktanonchai, Corrine W.; Cleary, Eimear; Yao, Yongcheng; Wesolowski, Amy; Cummings, Derek A. T.; Li, Zhongjie; Tatem, Andrew J.; La, Shengjie
Effects of public-health measures for zeroing out different SARS-CoV-2 variants Journal Article
In: Nature Communications, vol. 14, no. 5270, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Effects of public-health measures for zeroing out different SARS-CoV-2 variants},
author = {Yong Ge and Xilin Wu and Wenbin Zhang and Xiaoli Wang and Die Zhang and Jianghao Wang and Haiyan Liu and Zhoupeng Ren and Nick W. Ruktanonchai and Corrine W. Ruktanonchai and Eimear Cleary and Yongcheng Yao and Amy Wesolowski and Derek A. T. Cummings and Zhongjie Li and Andrew J. Tatem and Shengjie La},
url = {https://doi.org/10.1038/s41467-023-40940-4
},
doi = {10.1038/s41467-023-40940-4},
year = {2023},
date = {2023-08-29},
urldate = {2023-08-29},
journal = {Nature Communications},
volume = {14},
number = {5270},
abstract = {Targeted public health interventions for an emerging epidemic are essential for preventing pandemics. During 2020-2022, China invested significant efforts in strict zero-COVID measures to contain outbreaks of varying scales caused by different SARS-CoV-2 variants. Based on a multi-year empirical dataset containing 131 outbreaks observed in China from April 2020 to May 2022 and simulated scenarios, we ranked the relative intervention effectiveness by their reduction in instantaneous reproduction number. We found that, overall, social distancing measures (38% reduction, 95% prediction interval 31-45%), face masks (30%, 17-42%) and close contact tracing (28%, 24-31%) were most effective. Contact tracing was crucial in containing outbreaks during the initial phases, while social distancing measures became increasingly prominent as the spread persisted. In addition, infections with higher transmissibility and a shorter latent period posed more challenges for these measures. Our findings provide quantitative evidence on the effects of public-health measures for zeroing out emerging contagions in different contexts.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Targeted public health interventions for an emerging epidemic are essential for preventing pandemics. During 2020-2022, China invested significant efforts in strict zero-COVID measures to contain outbreaks of varying scales caused by different SARS-CoV-2 variants. Based on a multi-year empirical dataset containing 131 outbreaks observed in China from April 2020 to May 2022 and simulated scenarios, we ranked the relative intervention effectiveness by their reduction in instantaneous reproduction number. We found that, overall, social distancing measures (38% reduction, 95% prediction interval 31-45%), face masks (30%, 17-42%) and close contact tracing (28%, 24-31%) were most effective. Contact tracing was crucial in containing outbreaks during the initial phases, while social distancing measures became increasingly prominent as the spread persisted. In addition, infections with higher transmissibility and a shorter latent period posed more challenges for these measures. Our findings provide quantitative evidence on the effects of public-health measures for zeroing out emerging contagions in different contexts.
8.
Liu, Haiyan; Wang, Jianghao; Liu, Jian; Ge, Yong; Wang, Xiaoli; Zhang, Chi; Cleary, Eimear; Ruktanonchai, Nick W.; Ruktanonchai, Corrine W.; Yao, Yongcheng; Wesolowski, Amy; Lu, Xin; Tatem, Andrew J.; Bai, Xuemei; Lai, Shengjie
Combined and delayed impacts of epidemics and extreme weather on urban mobility recovery Journal Article
In: Sustainable Cities and Society, vol. 99, no. 104872, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Combined and delayed impacts of epidemics and extreme weather on urban mobility recovery},
author = {Haiyan Liu and Jianghao Wang and Jian Liu and Yong Ge and Xiaoli Wang and Chi Zhang and Eimear Cleary and Nick W. Ruktanonchai and Corrine W. Ruktanonchai and Yongcheng Yao and Amy Wesolowski and Xin Lu and Andrew J. Tatem and Xuemei Bai and Shengjie Lai},
url = {https://doi.org/10.1016/j.scs.2023.104872
},
doi = {10.1016/j.scs.2023.104872},
year = {2023},
date = {2023-08-22},
journal = {Sustainable Cities and Society},
volume = {99},
number = {104872},
abstract = {The ever-increasing pandemic and natural disasters might spatial-temporal overlap to trigger compound disasters that disrupt urban life, including human movements. In this study, we proposed a framework for data-driven analyses on mobility resilience to uncover the compound effects of COVID-19 and extreme weather events on mobility recovery across cities with varied socioeconomic contexts. The concept of suppression risk (SR) is introduced to quantify the relative risk of mobility being reduced below the pre-pandemic baseline when certain variables deviate from their normal values. By analysing daily mobility data within and between 313 Chinese cities, we consistently observed that the highest SR under outbreaks occurred at high temperatures and abnormal precipitation levels, regardless of the type of travel, incidences, and time. Specifically, extremely high temperatures (at 35 °C) increased SR during outbreaks by 12.5%-120% but shortened the time for mobility recovery. Increased rainfall (at 20 mm/day) added SRs by 12.5%-300%, with delayed effects reflected in cross-city movements. These compound impacts, with varying lagged responses, were aggravated in cities with high population density and low GDP levels. Our findings provide quantitative evidence to inform the design of preparedness and response strategies for enhancing urban resilience in the face of future pandemics and compound disasters.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The ever-increasing pandemic and natural disasters might spatial-temporal overlap to trigger compound disasters that disrupt urban life, including human movements. In this study, we proposed a framework for data-driven analyses on mobility resilience to uncover the compound effects of COVID-19 and extreme weather events on mobility recovery across cities with varied socioeconomic contexts. The concept of suppression risk (SR) is introduced to quantify the relative risk of mobility being reduced below the pre-pandemic baseline when certain variables deviate from their normal values. By analysing daily mobility data within and between 313 Chinese cities, we consistently observed that the highest SR under outbreaks occurred at high temperatures and abnormal precipitation levels, regardless of the type of travel, incidences, and time. Specifically, extremely high temperatures (at 35 °C) increased SR during outbreaks by 12.5%-120% but shortened the time for mobility recovery. Increased rainfall (at 20 mm/day) added SRs by 12.5%-300%, with delayed effects reflected in cross-city movements. These compound impacts, with varying lagged responses, were aggravated in cities with high population density and low GDP levels. Our findings provide quantitative evidence to inform the design of preparedness and response strategies for enhancing urban resilience in the face of future pandemics and compound disasters.
9.
McKeen, Tom; Bondarenko, Maksym; Kerr, David; Esch, Thomas; Marconcini, Mattia; Palacios-Lopez, Daniela; Zeidler, Julian; Valle, R. Catalina; Juran, Sabrina; Tatem, Andrew J.; Sorichetta, Alessandro
High-resolution gridded population datasets for Latin America and the Caribbean using official statistics Journal Article
In: Scientific Data, vol. 10, no. 436, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {High-resolution gridded population datasets for Latin America and the Caribbean using official statistics},
author = {Tom McKeen and Maksym Bondarenko and David Kerr and Thomas Esch and Mattia Marconcini and Daniela Palacios-Lopez and Julian Zeidler and R. Catalina Valle and Sabrina Juran and Andrew J. Tatem and Alessandro Sorichetta},
url = {https://doi.org/10.1038/s41597-023-02305-w},
doi = {10.1038/s41597-023-02305-w},
year = {2023},
date = {2023-07-07},
journal = {Scientific Data},
volume = {10},
number = {436},
abstract = {“Leaving no one behind” is the fundamental objective of the 2030 Agenda for Sustainable Development. Latin America and the Caribbean is marked by social inequalities, whilst its total population is projected to increase to almost 760 million by 2050. In this context, contemporary and spatially detailed datasets that accurately capture the distribution of residential population are critical to appropriately inform and support environmental, health, and developmental applications at subnational levels. Existing datasets are under-utilised by governments due to the non-alignment with their own statistics. Therefore, official statistics at the finest level of administrative units available have been implemented to construct an open-access repository of high-resolution gridded population datasets for 40 countries in Latin American and the Caribbean. These datasets are detailed here, alongside the ‘top-down’ approach and methods to generate and validate them. Population distribution datasets for each country were created at a resolution of 3 arc-seconds (approximately 100 m at the equator), and are all available from the WorldPop Data Repository.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
“Leaving no one behind” is the fundamental objective of the 2030 Agenda for Sustainable Development. Latin America and the Caribbean is marked by social inequalities, whilst its total population is projected to increase to almost 760 million by 2050. In this context, contemporary and spatially detailed datasets that accurately capture the distribution of residential population are critical to appropriately inform and support environmental, health, and developmental applications at subnational levels. Existing datasets are under-utilised by governments due to the non-alignment with their own statistics. Therefore, official statistics at the finest level of administrative units available have been implemented to construct an open-access repository of high-resolution gridded population datasets for 40 countries in Latin American and the Caribbean. These datasets are detailed here, alongside the ‘top-down’ approach and methods to generate and validate them. Population distribution datasets for each country were created at a resolution of 3 arc-seconds (approximately 100 m at the equator), and are all available from the WorldPop Data Repository.
10.
Sanchez-Cespedes, Lina Maria; Leasure, Douglas Ryan; Tejedor-Garavito, Natalia; Cruz, Glenn Harry Amaya; Velez, Gustavo Adolfo Garcia; Mendoza, Andryu Enrique; Salazar, Yenny Andrea Marín; Esch, Thomas; Tatem, Andrew J.; Bohórquez, Mariana Ospina
Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia Journal Article
In: Population Studies, pp. 1-18, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia},
author = {Lina Maria Sanchez-Cespedes and Douglas Ryan Leasure and Natalia Tejedor-Garavito and Glenn Harry Amaya Cruz and Gustavo Adolfo Garcia Velez and Andryu Enrique Mendoza and Yenny Andrea Marín Salazar and Thomas Esch and Andrew J. Tatem and Mariana Ospina Bohórquez},
url = {https://doi.org/10.1080/00324728.2023.2190151},
doi = {10.1080/00324728.2023.2190151},
year = {2023},
date = {2023-03-28},
journal = {Population Studies},
pages = {1-18},
abstract = {Effective government services rely on accurate population numbers to allocate resources. In Colombia and globally, census enumeration is challenging in remote regions and where armed conflict is occurring. During census preparations, the Colombian National Administrative Department of Statistics conducted social cartography workshops, where community representatives estimated numbers of dwellings and people throughout their regions. We repurposed this information, combining it with remotely sensed buildings data and other geospatial data. To estimate building counts and population sizes, we developed hierarchical Bayesian models, trained using nearby full-coverage census enumerations and assessed using 10-fold cross-validation. We compared models to assess the relative contributions of community knowledge, remotely sensed buildings, and their combination to model fit. The Community model was unbiased but imprecise; the Satellite model was more precise but biased; and the Combination model was best for overall accuracy. Results reaffirmed the power of remotely sensed buildings data for population estimation and highlighted the value of incorporating local knowledge.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Effective government services rely on accurate population numbers to allocate resources. In Colombia and globally, census enumeration is challenging in remote regions and where armed conflict is occurring. During census preparations, the Colombian National Administrative Department of Statistics conducted social cartography workshops, where community representatives estimated numbers of dwellings and people throughout their regions. We repurposed this information, combining it with remotely sensed buildings data and other geospatial data. To estimate building counts and population sizes, we developed hierarchical Bayesian models, trained using nearby full-coverage census enumerations and assessed using 10-fold cross-validation. We compared models to assess the relative contributions of community knowledge, remotely sensed buildings, and their combination to model fit. The Community model was unbiased but imprecise; the Satellite model was more precise but biased; and the Combination model was best for overall accuracy. Results reaffirmed the power of remotely sensed buildings data for population estimation and highlighted the value of incorporating local knowledge.
11.
Qader, Sarchil; Chamberlain, Heather; Kuepie, Mathias; Hunt, Freja K.; and Andrew J. Tatem, Attila Lazar
Field testing of pre-Enumeration Areas created using semi-automated delineation approach, Democratic Republic of Congo Technical Report
2023.
Abstract | Links | BibTeX | Tags:
@techreport{nokey,
title = {Field testing of pre-Enumeration Areas created using semi-automated delineation approach, Democratic Republic of Congo},
author = {Sarchil Qader and Heather Chamberlain and Mathias Kuepie and Freja K. Hunt and Attila Lazar and Andrew J. Tatem},
url = {https://eprints.soton.ac.uk/475327/},
doi = {10.5258/SOTON/WP00759},
year = {2023},
date = {2023-03-15},
urldate = {2023-03-15},
abstract = {This report details the main outcomes of the field testing of pre-Enumeration Areas (EAs) created from WorldPop and Flominder’s semi-automated EA approach that took place across three test sites in the provinces of Kinshasa and Kongo-Central, Democratic Republic of the Congo in December 2019. The field testing was conducted over four days by the BCR technical staff with participation from UNFPA and WorldPop staff.
Generally, EA boundaries from one census will form the basis for the EAs in the next census, with updates needed to account for new settlements and changes in population density. However, in countries where there hasn’t been a census for many years, often due to conflict or insecurity, EA boundaries can be incomplete, outdated, or missing altogether. The delineation of EAs is, therefore, a crucial pre-census activity but can often be particularly challenging and highly resource intensive. Creating EAs requires consideration of population and area size within each unit to ensure that they have approximately equal-sized populations and are a manageable size to be covered by census enumeration staff. To respond to this challenge, WorldPop has developed a semi-automatic approach of delineating pre-EAs to support census cartography. This approach utilises high-resolution gridded population estimates and digitised geographic features, including administrative boundaries, and natural and man-made features, such as rivers and roads, to divide the regions into small areas which are then merged to meet criteria specified for population size and geographic area.
The last census in DRC was conducted in 1984; consequently, a recent, national, digital EA dataset which can be used for cartography planning does not exist. GRID3 is supporting the realisation of a fully digital 2020 round census in the DRC and is working closely with the National Institute of Statistics and the DRC Census Bureau (Bureau Central de Recensement, BCR) to provide technical guidance regarding options for incorporating geospatial methodologies into census planning and census cartography. As the DRC Census Bureau prepares for the 2nd National Population and Housing Census (RGPH2), a new dataset of EA boundaries is needed. As part of GRID3’s work with the BCR, a field test was conducted to assess the feasibility of using a semi-automated approach for the delineation of pre-EA boundaries.
A preliminary pre-EA dataset was produced for the three test sites (Site 1: Quartier Kingu, Kinshasa (urban), Site 2: Quartier Dumi, Kinshasa (sub-urban), Site 3: Secteur Kasangulu, Kongo-Central (rural)) that span both rural and urban contexts. The geographic area covered by the three sites totalled 1,190 km2 and was sub-divided into approximately 312 pre-EAs. The pre-EAs created for the three test sites were classified as classes 1-3 depending on the degree to which the pre-EA boundaries followed visible features (e.g. roads). Class 1 being those pre-EAs with boundaries which fully followed visible features, class 2 boundaries followed visible features in part, and class 3 which didn’t follow visible features at all. A visual assessment was carried out by comparing the pre-EA boundaries with recent high-resolution satellite imagery. A subset of the pre-EAs (15 pre-EAs), covering classes 1, 2 and 3 were selected, and assessed in the field to check how the boundaries related to ground features and their feasibility as units for population enumeration. Class 1 pre-EAs were only found in urban contexts and tended to be bounded fully by roads, which were found to be simple for the field teams to follow. In class 2 and class 3 pre-EAs, the field teams were generally able to follow roads or tracks throughout the pre-EA to reach settlements, and ascertain when they had reached the boundary of the pre-EA using the maps and GPS location indicator on the tablets. The pre-EA boundaries were also created to avoid splitting settlements and therefore even in rural areas, the field teams were able to know where housing units needed to be enumerated.
A range of limitations with this work have been identified, both with the methods and equipment used in the field data collection and the methods and input data used to produce the pre-EA boundaries. Despite the identified limitations and the challenges encountered in the field, the findings from the field test were generally consistent, with the pre-EAs created by the semi-automated approach found to be suitable for population enumeration in the field. Overall the fieldwork was successfully conducted and expectations were met and even exceeded: the BCR found that the pre-EA outputs were found to help facilitate enumeration, as the BCR team could navigate within the pre-EA boundaries and know which housing units to enumerate. The findings of the field test indicate this semi-automated approach to creating pre-EAs has the potential to be used by the BCR to create pre-EAs in preparation for census cartography, and offers large savings in terms of time, labour and cost. Nonetheless, it would be expected that the pre-EA outputs created in the approach are carefully reviewed in the lab, and manually edited as needed prior to census cartography. Then whilst in the field, the pre-EA boundaries should be validated. Limitations associated with input datasets can be addressed through a comprehensive review of existing datasets, incorporating newly available feature extraction datasets as appropriate. Further development of the approach and potential solutions and suggestions to overcome the identified limitations are outlined and discussed in detail in the report.
We expect the findings of the field test in DRC to be transferable to other similar contexts, with the approach having applicability in countries with no recent digital EAs. We also expect the approach could be adapted to update digital EA boundaries in contexts with outdated EA datasets, but this should be explored through further research and testing in such contexts.
Worth noting that in close collaboration with GeoData at the University of Southampton, UNFPA and multiple national statistical offices around the world, WorldPop has now converted the automatic delineation script to a user-friendly tool which require minimal GIS skill to run.},
howpublished = {eprints Soton},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
This report details the main outcomes of the field testing of pre-Enumeration Areas (EAs) created from WorldPop and Flominder’s semi-automated EA approach that took place across three test sites in the provinces of Kinshasa and Kongo-Central, Democratic Republic of the Congo in December 2019. The field testing was conducted over four days by the BCR technical staff with participation from UNFPA and WorldPop staff.
Generally, EA boundaries from one census will form the basis for the EAs in the next census, with updates needed to account for new settlements and changes in population density. However, in countries where there hasn’t been a census for many years, often due to conflict or insecurity, EA boundaries can be incomplete, outdated, or missing altogether. The delineation of EAs is, therefore, a crucial pre-census activity but can often be particularly challenging and highly resource intensive. Creating EAs requires consideration of population and area size within each unit to ensure that they have approximately equal-sized populations and are a manageable size to be covered by census enumeration staff. To respond to this challenge, WorldPop has developed a semi-automatic approach of delineating pre-EAs to support census cartography. This approach utilises high-resolution gridded population estimates and digitised geographic features, including administrative boundaries, and natural and man-made features, such as rivers and roads, to divide the regions into small areas which are then merged to meet criteria specified for population size and geographic area.
The last census in DRC was conducted in 1984; consequently, a recent, national, digital EA dataset which can be used for cartography planning does not exist. GRID3 is supporting the realisation of a fully digital 2020 round census in the DRC and is working closely with the National Institute of Statistics and the DRC Census Bureau (Bureau Central de Recensement, BCR) to provide technical guidance regarding options for incorporating geospatial methodologies into census planning and census cartography. As the DRC Census Bureau prepares for the 2nd National Population and Housing Census (RGPH2), a new dataset of EA boundaries is needed. As part of GRID3’s work with the BCR, a field test was conducted to assess the feasibility of using a semi-automated approach for the delineation of pre-EA boundaries.
A preliminary pre-EA dataset was produced for the three test sites (Site 1: Quartier Kingu, Kinshasa (urban), Site 2: Quartier Dumi, Kinshasa (sub-urban), Site 3: Secteur Kasangulu, Kongo-Central (rural)) that span both rural and urban contexts. The geographic area covered by the three sites totalled 1,190 km2 and was sub-divided into approximately 312 pre-EAs. The pre-EAs created for the three test sites were classified as classes 1-3 depending on the degree to which the pre-EA boundaries followed visible features (e.g. roads). Class 1 being those pre-EAs with boundaries which fully followed visible features, class 2 boundaries followed visible features in part, and class 3 which didn’t follow visible features at all. A visual assessment was carried out by comparing the pre-EA boundaries with recent high-resolution satellite imagery. A subset of the pre-EAs (15 pre-EAs), covering classes 1, 2 and 3 were selected, and assessed in the field to check how the boundaries related to ground features and their feasibility as units for population enumeration. Class 1 pre-EAs were only found in urban contexts and tended to be bounded fully by roads, which were found to be simple for the field teams to follow. In class 2 and class 3 pre-EAs, the field teams were generally able to follow roads or tracks throughout the pre-EA to reach settlements, and ascertain when they had reached the boundary of the pre-EA using the maps and GPS location indicator on the tablets. The pre-EA boundaries were also created to avoid splitting settlements and therefore even in rural areas, the field teams were able to know where housing units needed to be enumerated.
A range of limitations with this work have been identified, both with the methods and equipment used in the field data collection and the methods and input data used to produce the pre-EA boundaries. Despite the identified limitations and the challenges encountered in the field, the findings from the field test were generally consistent, with the pre-EAs created by the semi-automated approach found to be suitable for population enumeration in the field. Overall the fieldwork was successfully conducted and expectations were met and even exceeded: the BCR found that the pre-EA outputs were found to help facilitate enumeration, as the BCR team could navigate within the pre-EA boundaries and know which housing units to enumerate. The findings of the field test indicate this semi-automated approach to creating pre-EAs has the potential to be used by the BCR to create pre-EAs in preparation for census cartography, and offers large savings in terms of time, labour and cost. Nonetheless, it would be expected that the pre-EA outputs created in the approach are carefully reviewed in the lab, and manually edited as needed prior to census cartography. Then whilst in the field, the pre-EA boundaries should be validated. Limitations associated with input datasets can be addressed through a comprehensive review of existing datasets, incorporating newly available feature extraction datasets as appropriate. Further development of the approach and potential solutions and suggestions to overcome the identified limitations are outlined and discussed in detail in the report.
We expect the findings of the field test in DRC to be transferable to other similar contexts, with the approach having applicability in countries with no recent digital EAs. We also expect the approach could be adapted to update digital EA boundaries in contexts with outdated EA datasets, but this should be explored through further research and testing in such contexts.
Worth noting that in close collaboration with GeoData at the University of Southampton, UNFPA and multiple national statistical offices around the world, WorldPop has now converted the automatic delineation script to a user-friendly tool which require minimal GIS skill to run.
Generally, EA boundaries from one census will form the basis for the EAs in the next census, with updates needed to account for new settlements and changes in population density. However, in countries where there hasn’t been a census for many years, often due to conflict or insecurity, EA boundaries can be incomplete, outdated, or missing altogether. The delineation of EAs is, therefore, a crucial pre-census activity but can often be particularly challenging and highly resource intensive. Creating EAs requires consideration of population and area size within each unit to ensure that they have approximately equal-sized populations and are a manageable size to be covered by census enumeration staff. To respond to this challenge, WorldPop has developed a semi-automatic approach of delineating pre-EAs to support census cartography. This approach utilises high-resolution gridded population estimates and digitised geographic features, including administrative boundaries, and natural and man-made features, such as rivers and roads, to divide the regions into small areas which are then merged to meet criteria specified for population size and geographic area.
The last census in DRC was conducted in 1984; consequently, a recent, national, digital EA dataset which can be used for cartography planning does not exist. GRID3 is supporting the realisation of a fully digital 2020 round census in the DRC and is working closely with the National Institute of Statistics and the DRC Census Bureau (Bureau Central de Recensement, BCR) to provide technical guidance regarding options for incorporating geospatial methodologies into census planning and census cartography. As the DRC Census Bureau prepares for the 2nd National Population and Housing Census (RGPH2), a new dataset of EA boundaries is needed. As part of GRID3’s work with the BCR, a field test was conducted to assess the feasibility of using a semi-automated approach for the delineation of pre-EA boundaries.
A preliminary pre-EA dataset was produced for the three test sites (Site 1: Quartier Kingu, Kinshasa (urban), Site 2: Quartier Dumi, Kinshasa (sub-urban), Site 3: Secteur Kasangulu, Kongo-Central (rural)) that span both rural and urban contexts. The geographic area covered by the three sites totalled 1,190 km2 and was sub-divided into approximately 312 pre-EAs. The pre-EAs created for the three test sites were classified as classes 1-3 depending on the degree to which the pre-EA boundaries followed visible features (e.g. roads). Class 1 being those pre-EAs with boundaries which fully followed visible features, class 2 boundaries followed visible features in part, and class 3 which didn’t follow visible features at all. A visual assessment was carried out by comparing the pre-EA boundaries with recent high-resolution satellite imagery. A subset of the pre-EAs (15 pre-EAs), covering classes 1, 2 and 3 were selected, and assessed in the field to check how the boundaries related to ground features and their feasibility as units for population enumeration. Class 1 pre-EAs were only found in urban contexts and tended to be bounded fully by roads, which were found to be simple for the field teams to follow. In class 2 and class 3 pre-EAs, the field teams were generally able to follow roads or tracks throughout the pre-EA to reach settlements, and ascertain when they had reached the boundary of the pre-EA using the maps and GPS location indicator on the tablets. The pre-EA boundaries were also created to avoid splitting settlements and therefore even in rural areas, the field teams were able to know where housing units needed to be enumerated.
A range of limitations with this work have been identified, both with the methods and equipment used in the field data collection and the methods and input data used to produce the pre-EA boundaries. Despite the identified limitations and the challenges encountered in the field, the findings from the field test were generally consistent, with the pre-EAs created by the semi-automated approach found to be suitable for population enumeration in the field. Overall the fieldwork was successfully conducted and expectations were met and even exceeded: the BCR found that the pre-EA outputs were found to help facilitate enumeration, as the BCR team could navigate within the pre-EA boundaries and know which housing units to enumerate. The findings of the field test indicate this semi-automated approach to creating pre-EAs has the potential to be used by the BCR to create pre-EAs in preparation for census cartography, and offers large savings in terms of time, labour and cost. Nonetheless, it would be expected that the pre-EA outputs created in the approach are carefully reviewed in the lab, and manually edited as needed prior to census cartography. Then whilst in the field, the pre-EA boundaries should be validated. Limitations associated with input datasets can be addressed through a comprehensive review of existing datasets, incorporating newly available feature extraction datasets as appropriate. Further development of the approach and potential solutions and suggestions to overcome the identified limitations are outlined and discussed in detail in the report.
We expect the findings of the field test in DRC to be transferable to other similar contexts, with the approach having applicability in countries with no recent digital EAs. We also expect the approach could be adapted to update digital EA boundaries in contexts with outdated EA datasets, but this should be explored through further research and testing in such contexts.
Worth noting that in close collaboration with GeoData at the University of Southampton, UNFPA and multiple national statistical offices around the world, WorldPop has now converted the automatic delineation script to a user-friendly tool which require minimal GIS skill to run.
12.
Pezzulo, Carla; Tejedor-Garavito, Natalia; Chan, Ho Man Theophilus; Dreoni, Ilda; Kerr, David; Ghosh, Samik; Bonnie, Amy; Bondarenko, Maksym; Salasibew, Mihretab; Tatem, Andrew J.
A subnational reproductive, maternal, newborn, child, and adolescent health and development atlas of India Journal Article
In: Scientific Data, vol. 10, iss. 86, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {A subnational reproductive, maternal, newborn, child, and adolescent health and development atlas of India},
author = {Carla Pezzulo and Natalia Tejedor-Garavito and Ho Man Theophilus Chan and Ilda Dreoni and David Kerr and Samik Ghosh and Amy Bonnie and Maksym Bondarenko and Mihretab Salasibew and Andrew J. Tatem },
url = {https://www.nature.com/articles/s41597-023-01961-2},
doi = {10.1038/s41597-023-01961-2},
year = {2023},
date = {2023-02-10},
journal = {Scientific Data},
volume = {10},
issue = {86},
abstract = {Understanding the fine scale and subnational spatial distribution of reproductive, maternal, newborn, child, and adolescent health and development indicators is crucial for targeting and increasing the efficiency of resources for public health and development planning. National governments are committed to improve the lives of their people, lift the population out of poverty and to achieve the Sustainable Development Goals. We created an open access collection of high resolution gridded and district level health and development datasets of India using mainly the 2015–16 National Family Health Survey (NFHS-4) data, and provide estimates at higher granularity than what is available in NFHS-4, to support policies with spatially detailed data. Bayesian methods for the construction of 5 km × 5 km high resolution maps were applied for a set of indicators where the data allowed (36 datasets), while for some other indicators, only district level data were produced. All data were summarised using the India district administrative boundaries. In total, 138 high resolution and district level datasets for 28 indicators were produced and made openly available.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Understanding the fine scale and subnational spatial distribution of reproductive, maternal, newborn, child, and adolescent health and development indicators is crucial for targeting and increasing the efficiency of resources for public health and development planning. National governments are committed to improve the lives of their people, lift the population out of poverty and to achieve the Sustainable Development Goals. We created an open access collection of high resolution gridded and district level health and development datasets of India using mainly the 2015–16 National Family Health Survey (NFHS-4) data, and provide estimates at higher granularity than what is available in NFHS-4, to support policies with spatially detailed data. Bayesian methods for the construction of 5 km × 5 km high resolution maps were applied for a set of indicators where the data allowed (36 datasets), while for some other indicators, only district level data were produced. All data were summarised using the India district administrative boundaries. In total, 138 high resolution and district level datasets for 28 indicators were produced and made openly available.
13.
Qader, Sarchil Hama; Utazi, Chigozie Edson; Priyatikanto, Rhorom; Najmaddin, Peshawa; Hama-Ali, Emad Omer; Khwarahm, Nabaz R.; Tatem, Andrew J.; Dash, Jadu
Exploring the use of Sentinel-2 datasets and environmental variables to model wheat crop yield in smallholder arid and semi-arid farming systems Journal Article
In: Science of The Total Environment, vol. 869, no. 161716, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Exploring the use of Sentinel-2 datasets and environmental variables to model wheat crop yield in smallholder arid and semi-arid farming systems},
author = {Sarchil Hama Qader and Chigozie Edson Utazi and Rhorom Priyatikanto and Peshawa Najmaddin and Emad Omer Hama-Ali and Nabaz R. Khwarahm and Andrew J. Tatem and Jadu Dash},
url = {https://doi.org/10.1016/j.scitotenv.2023.161716},
doi = {10.1016/j.scitotenv.2023.161716},
year = {2023},
date = {2023-01-24},
urldate = {2023-01-24},
journal = {Science of The Total Environment},
volume = {869},
number = {161716},
abstract = {Low levels of agricultural productivity are associated with the persistence of food insecurity, poverty, and other socio-economic stresses. Mapping and monitoring agricultural dynamics and production in real-time at high spatial resolution are essential for ensuring food security and shaping policy interventions. However, an accurate yield estimation might be challenging in some arid and semi-arid regions since input datasets are generally scarce, and access is restricted due to security challenges. This work examines how well Sentinel-2 satellite sensor-derived data, topographic and climatic variables, can be used as covariates to accurately model and predict wheat crop yield at the farm level using statistical models in low data settings of arid and semi-arid regions, using Sulaimani governorate in Iraq as an example. We developed a covariate selection procedure that assessed the correlations between the covariates and their relationships with wheat crop yield. Potential non-linear relationships were investigated in the latter case using regression splines. In the absence of substantial non-linear relationships between the covariates and crop yield, and residual spatial autocorrelation, we fitted a Bayesian multiple linear regression model to model and predict crop yield at 10 m resolution. Out of the covariates tested, our results showed significant relationships between crop yield and mean cumulative NDVI during the growing season, mean elevation, mean end of the season, mean maximum temperature and mean the start of the season at the farm level. For in-sample prediction, we estimated an R2 value of 51 % for the model, whereas for out-of-sample prediction, this was 41 %, both of which indicate reasonable predictive performance. The calculated root-mean-square error for out-of-sample prediction was 69.80, which is less than the standard deviation of 89.23 for crop yield, further showing that the model performed well by reducing prediction variability. Besides crop yield estimates, the model produced uncertainty metrics at 10 m resolution. Overall, this study showed that Sentinel-2 data can be valuable for upscaling field measurement of crop yield in arid and semi-arid regions. In addition, the environmental covariates can strengthen the model predictive power. The method may be applicable in other areas with similar environments, particularly in conflict zones, to increase the availability of agricultural statistics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Low levels of agricultural productivity are associated with the persistence of food insecurity, poverty, and other socio-economic stresses. Mapping and monitoring agricultural dynamics and production in real-time at high spatial resolution are essential for ensuring food security and shaping policy interventions. However, an accurate yield estimation might be challenging in some arid and semi-arid regions since input datasets are generally scarce, and access is restricted due to security challenges. This work examines how well Sentinel-2 satellite sensor-derived data, topographic and climatic variables, can be used as covariates to accurately model and predict wheat crop yield at the farm level using statistical models in low data settings of arid and semi-arid regions, using Sulaimani governorate in Iraq as an example. We developed a covariate selection procedure that assessed the correlations between the covariates and their relationships with wheat crop yield. Potential non-linear relationships were investigated in the latter case using regression splines. In the absence of substantial non-linear relationships between the covariates and crop yield, and residual spatial autocorrelation, we fitted a Bayesian multiple linear regression model to model and predict crop yield at 10 m resolution. Out of the covariates tested, our results showed significant relationships between crop yield and mean cumulative NDVI during the growing season, mean elevation, mean end of the season, mean maximum temperature and mean the start of the season at the farm level. For in-sample prediction, we estimated an R2 value of 51 % for the model, whereas for out-of-sample prediction, this was 41 %, both of which indicate reasonable predictive performance. The calculated root-mean-square error for out-of-sample prediction was 69.80, which is less than the standard deviation of 89.23 for crop yield, further showing that the model performed well by reducing prediction variability. Besides crop yield estimates, the model produced uncertainty metrics at 10 m resolution. Overall, this study showed that Sentinel-2 data can be valuable for upscaling field measurement of crop yield in arid and semi-arid regions. In addition, the environmental covariates can strengthen the model predictive power. The method may be applicable in other areas with similar environments, particularly in conflict zones, to increase the availability of agricultural statistics.
14.
Dotse-Gborgbortsi, Winfred; Tatem, Andrew J; Matthews, Zoe; Alegana, Victor A; Ofosu, Anthony; Wright, Jim A
In: BMJ Open, vol. 13, iss. 1, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Quality of maternal healthcare and travel time influence birthing service utilisation in Ghanaian health facilities: a geographical analysis of routine health data},
author = {Winfred Dotse-Gborgbortsi and Andrew J Tatem and Zoe Matthews and Victor A Alegana and Anthony Ofosu and Jim A Wright},
url = {http://dx.doi.org/10.1136/bmjopen-2022-066792},
doi = {10.1136/bmjopen-2022-066792},
year = {2023},
date = {2023-01-18},
journal = {BMJ Open},
volume = {13},
issue = {1},
abstract = {Objectives: To investigate how the quality of maternal health services and travel times to health facilities affect birthing service utilisation in Eastern Region, Ghana.
Design: The study is a cross-sectional spatial interaction analysis of birth service utilisation patterns. Routine birth data were spatially linked to quality care, service demand and travel time data.
Setting: 131 Health facilities (public, private and faith-based) in 33 districts in Eastern Region, Ghana.
Participants: Women who gave birth in health facilities in the Eastern Region, Ghana in 2017.
Outcome measures: The count of women giving birth, the quality of birthing care services and the geographic coverage of birthing care services.
Results: As travel time from women’s place of residence to the health facility increased up to two (2) hours, the utilisation rate markedly decreased. Higher quality of maternal health services haves a larger, positive effect on utilisation rates than service proximity. The quality of maternal health services was higher in hospitals than in primary care facilities. Most women (88.6%) travelling via mechanised transport were within two2 hours of any birthing service. The majority (56.2%) of women were beyond the two2 -hour threshold of critical comprehensive emergency obstetric and newborn care (CEmONC) services. Few CEmONC services were in urban centres, disadvantaging rural populations.
Conclusions: To increase birthing service utilisation in Ghana, higher quality health facilities should be located closer to women, particularly in rural areas. Beyond Ghana, routinely collected birth records could be used to understand the interaction of service proximity and quality.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objectives: To investigate how the quality of maternal health services and travel times to health facilities affect birthing service utilisation in Eastern Region, Ghana.
Design: The study is a cross-sectional spatial interaction analysis of birth service utilisation patterns. Routine birth data were spatially linked to quality care, service demand and travel time data.
Setting: 131 Health facilities (public, private and faith-based) in 33 districts in Eastern Region, Ghana.
Participants: Women who gave birth in health facilities in the Eastern Region, Ghana in 2017.
Outcome measures: The count of women giving birth, the quality of birthing care services and the geographic coverage of birthing care services.
Results: As travel time from women’s place of residence to the health facility increased up to two (2) hours, the utilisation rate markedly decreased. Higher quality of maternal health services haves a larger, positive effect on utilisation rates than service proximity. The quality of maternal health services was higher in hospitals than in primary care facilities. Most women (88.6%) travelling via mechanised transport were within two2 hours of any birthing service. The majority (56.2%) of women were beyond the two2 -hour threshold of critical comprehensive emergency obstetric and newborn care (CEmONC) services. Few CEmONC services were in urban centres, disadvantaging rural populations.
Conclusions: To increase birthing service utilisation in Ghana, higher quality health facilities should be located closer to women, particularly in rural areas. Beyond Ghana, routinely collected birth records could be used to understand the interaction of service proximity and quality.
Design: The study is a cross-sectional spatial interaction analysis of birth service utilisation patterns. Routine birth data were spatially linked to quality care, service demand and travel time data.
Setting: 131 Health facilities (public, private and faith-based) in 33 districts in Eastern Region, Ghana.
Participants: Women who gave birth in health facilities in the Eastern Region, Ghana in 2017.
Outcome measures: The count of women giving birth, the quality of birthing care services and the geographic coverage of birthing care services.
Results: As travel time from women’s place of residence to the health facility increased up to two (2) hours, the utilisation rate markedly decreased. Higher quality of maternal health services haves a larger, positive effect on utilisation rates than service proximity. The quality of maternal health services was higher in hospitals than in primary care facilities. Most women (88.6%) travelling via mechanised transport were within two2 hours of any birthing service. The majority (56.2%) of women were beyond the two2 -hour threshold of critical comprehensive emergency obstetric and newborn care (CEmONC) services. Few CEmONC services were in urban centres, disadvantaging rural populations.
Conclusions: To increase birthing service utilisation in Ghana, higher quality health facilities should be located closer to women, particularly in rural areas. Beyond Ghana, routinely collected birth records could be used to understand the interaction of service proximity and quality.
15.
CE, Utazi; JM, Aheto; A, Wigley; N, Tejedor-Garavito; A, Bonnie; CC, Nnanatu; J, Wagai; C, Williams; H, Setayesh; AJ, Tatem; FT, Cutts
In: Vaccine, vol. 41, iss. 1, pp. 170-181, 2022.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Mapping the distribution of zero-dose children to assess the performance of vaccine delivery strategies and their relationships with measles incidence in Nigeria},
author = {Utazi CE and Aheto JM and Wigley A and Tejedor-Garavito N and Bonnie A and Nnanatu CC and Wagai J and Williams C and Setayesh H and Tatem AJ and Cutts FT},
url = {https://doi.org/10.1016/j.vaccine.2022.11.026
},
doi = {10.1016/j.vaccine.2022.11.026},
year = {2022},
date = {2022-11-19},
urldate = {2023-11-19},
journal = {Vaccine},
volume = {41},
issue = {1},
pages = {170-181},
abstract = {Geographically precise identification and targeting of populations at risk of vaccine-preventable diseases has gained renewed attention within the global health community over the last few years. District level estimates of vaccination coverage and corresponding zero-dose prevalence constitute a potentially useful evidence base to evaluate the performance of vaccination strategies. These estimates are also valuable for identifying missed communities, hence enabling targeted interventions and better resource allocation. Here, we fit Bayesian geostatistical models to map the routine coverage of the first doses of diphtheria-tetanus-pertussis vaccine (DTP1) and measles-containing vaccine (MCV1) and corresponding zero-dose estimates in Nigeria at 1x1 km resolution and the district level using geospatial data sets. We also map MCV1 coverage before and after the 2019 measles vaccination campaign in the northern states to further explore variations in routine vaccine coverage and to evaluate the effectiveness of both routine immunization (RI) and campaigns in reaching zero-dose children. Additionally, we map the spatial distributions of reported measles cases during 2018 to 2020 and explore their relationships with MCV zero-dose prevalence to highlight the public health implications of varying performance of vaccination strategies across the country. Our analysis revealed strong similarities between the spatial distributions of DTP and MCV zero dose prevalence, with districts with the highest prevalence concentrated mostly in the northwest and the northeast, but also in other areas such as Lagos state and the Federal Capital Territory. Although the 2019 campaign reduced MCV zero-dose prevalence substantially in the north, pockets of vulnerabilities remained in areas that had among the highest prevalence prior to the campaign. Importantly, we found strong correlations between measles case counts and MCV RI zero-dose estimates, which provides a strong indication that measles incidence in the country is mostly affected by RI coverage. Our analyses reveal an urgent and highly significant need to strengthen the country’s RI program as a longer-term measure for disease control, whilst ensuring effective campaigns in the short term.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Geographically precise identification and targeting of populations at risk of vaccine-preventable diseases has gained renewed attention within the global health community over the last few years. District level estimates of vaccination coverage and corresponding zero-dose prevalence constitute a potentially useful evidence base to evaluate the performance of vaccination strategies. These estimates are also valuable for identifying missed communities, hence enabling targeted interventions and better resource allocation. Here, we fit Bayesian geostatistical models to map the routine coverage of the first doses of diphtheria-tetanus-pertussis vaccine (DTP1) and measles-containing vaccine (MCV1) and corresponding zero-dose estimates in Nigeria at 1x1 km resolution and the district level using geospatial data sets. We also map MCV1 coverage before and after the 2019 measles vaccination campaign in the northern states to further explore variations in routine vaccine coverage and to evaluate the effectiveness of both routine immunization (RI) and campaigns in reaching zero-dose children. Additionally, we map the spatial distributions of reported measles cases during 2018 to 2020 and explore their relationships with MCV zero-dose prevalence to highlight the public health implications of varying performance of vaccination strategies across the country. Our analysis revealed strong similarities between the spatial distributions of DTP and MCV zero dose prevalence, with districts with the highest prevalence concentrated mostly in the northwest and the northeast, but also in other areas such as Lagos state and the Federal Capital Territory. Although the 2019 campaign reduced MCV zero-dose prevalence substantially in the north, pockets of vulnerabilities remained in areas that had among the highest prevalence prior to the campaign. Importantly, we found strong correlations between measles case counts and MCV RI zero-dose estimates, which provides a strong indication that measles incidence in the country is mostly affected by RI coverage. Our analyses reveal an urgent and highly significant need to strengthen the country’s RI program as a longer-term measure for disease control, whilst ensuring effective campaigns in the short term.
16.
Utazi, C. Edson; Aheto, Justice M. K.; Wigley, Adelle; Tejedor-Garavito, Natalia; Bonnie, Amy; Nnanatu, Chris; Wagai, John; Williams, Cheryl; Setayesh, Hamidrez; J.Tatem, Andrew
In: Vaccine, 2022.
Abstract | Links | BibTeX | Tags: Measles vaccination, Nigeria, zero dose
@article{nokey,
title = {Mapping the distribution of zero-dose children to assess the performance of vaccine delivery strategies and their relationships with measles incidence in Nigeria},
author = {C. Edson Utazi and Justice M. K. Aheto and Adelle Wigley and Natalia Tejedor-Garavito and Amy Bonnie and Chris Nnanatu and John Wagai and Cheryl Williams and Hamidrez Setayesh and Andrew J.Tatem},
doi = {10.1016/j.vaccine.2022.11.026},
year = {2022},
date = {2022-11-19},
urldate = {2022-11-19},
journal = {Vaccine},
abstract = {Geographically precise identification and targeting of populations at risk of vaccine-preventable diseases has gained renewed attention within the global health community over the last few years. District level estimates of vaccination coverage and corresponding zero-dose prevalence constitute a potentially useful evidence base to evaluate the performance of vaccination strategies. These estimates are also valuable for identifying missed communities, hence enabling targeted interventions and better resource allocation. Here, we fit Bayesian geostatistical models to map the routine coverage of the first doses of diphtheria-tetanus-pertussis vaccine (DTP1) and measles-containing vaccine (MCV1) and corresponding zero-dose estimates in Nigeria at 1x1 km resolution and the district level using geospatial data sets. We also map MCV1 coverage before and after the 2019 measles vaccination campaign in the northern states to further explore variations in routine vaccine coverage and to evaluate the effectiveness of both routine immunization (RI) and campaigns in reaching zero-dose children. Additionally, we map the spatial distributions of reported measles cases during 2018 to 2020 and explore their relationships with MCV zero-dose prevalence to highlight the public health implications of varying performance of vaccination strategies across the country. Our analysis revealed strong similarities between the spatial distributions of DTP and MCV zero dose prevalence, with districts with the highest prevalence concentrated mostly in the northwest and the northeast, but also in other areas such as Lagos state and the Federal Capital Territory. Although the 2019 campaign reduced MCV zero-dose prevalence substantially in the north, pockets of vulnerabilities remained in areas that had among the highest prevalence prior to the campaign. Importantly, we found strong correlations between measles case counts and MCV RI zero-dose estimates, which provides a strong indication that measles incidence in the country is mostly affected by RI coverage. Our analyses reveal an urgent and highly significant need to strengthen the country’s RI program as a longer-term measure for disease control, whilst ensuring effective campaigns in the short term.},
keywords = {Measles vaccination, Nigeria, zero dose},
pubstate = {published},
tppubtype = {article}
}
Geographically precise identification and targeting of populations at risk of vaccine-preventable diseases has gained renewed attention within the global health community over the last few years. District level estimates of vaccination coverage and corresponding zero-dose prevalence constitute a potentially useful evidence base to evaluate the performance of vaccination strategies. These estimates are also valuable for identifying missed communities, hence enabling targeted interventions and better resource allocation. Here, we fit Bayesian geostatistical models to map the routine coverage of the first doses of diphtheria-tetanus-pertussis vaccine (DTP1) and measles-containing vaccine (MCV1) and corresponding zero-dose estimates in Nigeria at 1x1 km resolution and the district level using geospatial data sets. We also map MCV1 coverage before and after the 2019 measles vaccination campaign in the northern states to further explore variations in routine vaccine coverage and to evaluate the effectiveness of both routine immunization (RI) and campaigns in reaching zero-dose children. Additionally, we map the spatial distributions of reported measles cases during 2018 to 2020 and explore their relationships with MCV zero-dose prevalence to highlight the public health implications of varying performance of vaccination strategies across the country. Our analysis revealed strong similarities between the spatial distributions of DTP and MCV zero dose prevalence, with districts with the highest prevalence concentrated mostly in the northwest and the northeast, but also in other areas such as Lagos state and the Federal Capital Territory. Although the 2019 campaign reduced MCV zero-dose prevalence substantially in the north, pockets of vulnerabilities remained in areas that had among the highest prevalence prior to the campaign. Importantly, we found strong correlations between measles case counts and MCV RI zero-dose estimates, which provides a strong indication that measles incidence in the country is mostly affected by RI coverage. Our analyses reveal an urgent and highly significant need to strengthen the country’s RI program as a longer-term measure for disease control, whilst ensuring effective campaigns in the short term.
17.
Chamberlain, Heather R.; Lazar, Attila N.; Tatem, Andrew J.
High-resolution estimates of social distancing feasibility, mapped for urban areas in sub-Saharan Africa Journal Article
In: Scientific Data, vol. 9, no. 711 (2022), 2022.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {High-resolution estimates of social distancing feasibility, mapped for urban areas in sub-Saharan Africa},
author = {Heather R. Chamberlain and Attila N. Lazar and Andrew J. Tatem },
url = {https://doi.org/10.1038/s41597-022-01799-0
},
doi = {10.1038/s41597-022-01799-0},
year = {2022},
date = {2022-11-18},
urldate = {2023-11-18},
journal = {Scientific Data},
volume = {9},
number = {711 (2022)},
abstract = {Social distancing has been widely-implemented as a public health measure during the COVID-19 pandemic. Despite widespread application of social distancing guidance, the feasibility of people adhering to such guidance varies in different settings, influenced by population density, the built environment and a range of socio-economic factors. Social distancing constraints however have only been identified and mapped for limited areas. Here, we present an ease of social distancing index, integrating metrics on urban form and population density derived from new multi-country building footprint datasets and gridded population estimates. The index dataset provides estimates of social distancing feasibility, mapped at high-resolution for urban areas across 50 countries in sub-Saharan Africa.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Social distancing has been widely-implemented as a public health measure during the COVID-19 pandemic. Despite widespread application of social distancing guidance, the feasibility of people adhering to such guidance varies in different settings, influenced by population density, the built environment and a range of socio-economic factors. Social distancing constraints however have only been identified and mapped for limited areas. Here, we present an ease of social distancing index, integrating metrics on urban form and population density derived from new multi-country building footprint datasets and gridded population estimates. The index dataset provides estimates of social distancing feasibility, mapped at high-resolution for urban areas across 50 countries in sub-Saharan Africa.
18.
Chamberlain, Heather R.; Lazar, Attila N.; Tatem, Andrew J.
High-resolution estimates of social distancing feasibility, mapped for urban areas in sub-Saharan Africa Journal Article
In: Scientific Data, vol. 9, no. 711, 2022.
Abstract | Links | BibTeX | Tags: Africa, covid-19, NPIs
@article{nokey,
title = {High-resolution estimates of social distancing feasibility, mapped for urban areas in sub-Saharan Africa},
author = {Heather R. Chamberlain and Attila N. Lazar and Andrew J. Tatem },
doi = {10.1038/s41597-022-01799-0},
year = {2022},
date = {2022-11-18},
journal = {Scientific Data},
volume = {9},
number = {711},
abstract = {Social distancing has been widely-implemented as a public health measure during the COVID-19 pandemic. Despite widespread application of social distancing guidance, the feasibility of people adhering to such guidance varies in different settings, influenced by population density, the built environment and a range of socio-economic factors. Social distancing constraints however have only been identified and mapped for limited areas. Here, we present an ease of social distancing index, integrating metrics on urban form and population density derived from new multi-country building footprint datasets and gridded population estimates. The index dataset provides estimates of social distancing feasibility, mapped at high-resolution for urban areas across 50 countries in sub-Saharan Africa.},
keywords = {Africa, covid-19, NPIs},
pubstate = {published},
tppubtype = {article}
}
Social distancing has been widely-implemented as a public health measure during the COVID-19 pandemic. Despite widespread application of social distancing guidance, the feasibility of people adhering to such guidance varies in different settings, influenced by population density, the built environment and a range of socio-economic factors. Social distancing constraints however have only been identified and mapped for limited areas. Here, we present an ease of social distancing index, integrating metrics on urban form and population density derived from new multi-country building footprint datasets and gridded population estimates. The index dataset provides estimates of social distancing feasibility, mapped at high-resolution for urban areas across 50 countries in sub-Saharan Africa.
19.
Ferreira, Leonardo Z.; Utazi, C. Edson; Huicho, Luis; Nilsen, Kristine; Hartwig, Fernando P.; Tatem, Andrew J.; Barros, Aluisio J. D.
Geographic inequalities in health intervention coverage – mapping the composite coverage index in Peru using geospatial modelling Journal Article
In: BMC Public Health 22, vol. 22, no. 2104 (2022), 2022.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Geographic inequalities in health intervention coverage – mapping the composite coverage index in Peru using geospatial modelling},
author = {Leonardo Z. Ferreira and C. Edson Utazi and Luis Huicho and Kristine Nilsen and Fernando P. Hartwig and Andrew J. Tatem and Aluisio J. D. Barros},
url = {https://doi.org/10.1186/s12889-022-14371-7
},
doi = {10.1186/s12889-022-14371-7},
year = {2022},
date = {2022-11-17},
urldate = {2023-11-17},
journal = {BMC Public Health 22},
volume = {22},
number = {2104 (2022)},
abstract = {The composite coverage index (CCI) provides an integrated perspective towards universal health coverage in the context of reproductive, maternal, newborn and child health. Given the sample design of most household surveys does not provide coverage estimates below the first administrative level, approaches for achieving more granular estimates are needed. We used a model-based geostatistical approach to estimate the CCI at multiple resolutions in Peru.
We generated estimates for the eight indicators on which the CCI is based for the departments, provinces, and areas of 5 × 5 km of Peru using data from two national household surveys carried out in 2018 and 2019 plus geospatial covariates. Bayesian geostatistical models were fit using the INLA-SPDE approach. We assessed model fit using cross-validation at the survey cluster level and by comparing modelled and direct survey estimates at the department-level.
CCI coverage in the provinces along the coast was consistently higher than in the remainder of the country. Jungle areas in the north and east presented the lowest coverage levels and the largest gaps between and within provinces. The greatest inequalities were found, unsurprisingly, in the largest provinces where populations are scattered in jungle territory and are difficult to reach.
Our study highlighted provinces with high levels of inequality in CCI coverage indicating areas, mostly low-populated jungle areas, where more attention is needed. We also uncovered other areas, such as the border with Bolivia, where coverage is lower than the coastal provinces and should receive increased efforts. More generally, our results make the case for high-resolution estimates to unveil geographic inequities otherwise hidden by the usual levels of survey representativeness.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The composite coverage index (CCI) provides an integrated perspective towards universal health coverage in the context of reproductive, maternal, newborn and child health. Given the sample design of most household surveys does not provide coverage estimates below the first administrative level, approaches for achieving more granular estimates are needed. We used a model-based geostatistical approach to estimate the CCI at multiple resolutions in Peru.
We generated estimates for the eight indicators on which the CCI is based for the departments, provinces, and areas of 5 × 5 km of Peru using data from two national household surveys carried out in 2018 and 2019 plus geospatial covariates. Bayesian geostatistical models were fit using the INLA-SPDE approach. We assessed model fit using cross-validation at the survey cluster level and by comparing modelled and direct survey estimates at the department-level.
CCI coverage in the provinces along the coast was consistently higher than in the remainder of the country. Jungle areas in the north and east presented the lowest coverage levels and the largest gaps between and within provinces. The greatest inequalities were found, unsurprisingly, in the largest provinces where populations are scattered in jungle territory and are difficult to reach.
Our study highlighted provinces with high levels of inequality in CCI coverage indicating areas, mostly low-populated jungle areas, where more attention is needed. We also uncovered other areas, such as the border with Bolivia, where coverage is lower than the coastal provinces and should receive increased efforts. More generally, our results make the case for high-resolution estimates to unveil geographic inequities otherwise hidden by the usual levels of survey representativeness.
We generated estimates for the eight indicators on which the CCI is based for the departments, provinces, and areas of 5 × 5 km of Peru using data from two national household surveys carried out in 2018 and 2019 plus geospatial covariates. Bayesian geostatistical models were fit using the INLA-SPDE approach. We assessed model fit using cross-validation at the survey cluster level and by comparing modelled and direct survey estimates at the department-level.
CCI coverage in the provinces along the coast was consistently higher than in the remainder of the country. Jungle areas in the north and east presented the lowest coverage levels and the largest gaps between and within provinces. The greatest inequalities were found, unsurprisingly, in the largest provinces where populations are scattered in jungle territory and are difficult to reach.
Our study highlighted provinces with high levels of inequality in CCI coverage indicating areas, mostly low-populated jungle areas, where more attention is needed. We also uncovered other areas, such as the border with Bolivia, where coverage is lower than the coastal provinces and should receive increased efforts. More generally, our results make the case for high-resolution estimates to unveil geographic inequities otherwise hidden by the usual levels of survey representativeness.
20.
Gore-Langton, Georgia R.; Simpson, Jorge Canoand Hope; Tejedor-Garavito, Andrew Tatemand Natalia; Wigley, Adelle; Carioli, Alessandra; Gething, Peter; Weiss, Daniel J.; Chandramohan, Daniel; Walker, Patrick G. T.; Cairns, Matthew E.; Chico, R. Matthew
Global estimates of pregnancies at risk of Plasmodium falciparum and Plasmodium vivax infection in 2020 and changes in risk patterns since 2000 Journal Article
In: PLOS Global Public Health, 2022.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Global estimates of pregnancies at risk of Plasmodium falciparum and Plasmodium vivax infection in 2020 and changes in risk patterns since 2000},
author = {Georgia R. Gore-Langton and Jorge Canoand Hope Simpson and Andrew Tatemand Natalia Tejedor-Garavito and Adelle Wigley and Alessandra Carioli and Peter Gething and Daniel J. Weiss and Daniel Chandramohan and Patrick G. T. Walker and Matthew E. Cairns and R. Matthew Chico},
url = {https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0001061},
doi = {10.1371/journal.pgph.0001061},
year = {2022},
date = {2022-11-09},
urldate = {2023-11-09},
journal = {PLOS Global Public Health},
abstract = {Women are at risk of severe adverse pregnancy outcomes attributable to Plasmodium spp. infection in malaria-endemic areas. Malaria control efforts since 2000 have aimed to reduce this burden of disease.
We used data from the Malaria Atlas Project and WorldPop to calculate global pregnancies at-risk of Plasmodium spp. infection. We categorised pregnancies as occurring in areas of stable and unstable P. falciparum and P. vivax transmission. We further stratified stable endemicity as hypo-endemic, meso-endemic, hyper-endemic, or holo-endemic, and estimated pregnancies at risk in 2000, 2005, 2010, 2015, 2017, and 2020.
In 2020, globally 120.4M pregnancies were at risk of P. falciparum, two-thirds (81.0M, 67.3%) were in areas of stable transmission; 85 2M pregnancies were at risk of P. vivax, 93.9% (80.0M) were in areas of stable transmission. An estimated 64.6M pregnancies were in areas with both P. falciparum and P. vivax transmission. The number of pregnancies at risk of each of P. falciparum and P. vivax worldwide decreased between 2000 and 2020, with the exception of sub-Saharan Africa, where the total number of pregnancies at risk of P. falciparum increased from 37 3M in 2000 to 52 4M in 2020.
Historic investments in malaria control have reduced the number of women at risk of malaria in pregnancy in all endemic regions except sub-Saharan Africa. Population growth in Africa has outpaced reductions in malaria prevalence. Interventions that reduce the risk of malaria in pregnancy are needed as much today as ever.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Women are at risk of severe adverse pregnancy outcomes attributable to Plasmodium spp. infection in malaria-endemic areas. Malaria control efforts since 2000 have aimed to reduce this burden of disease.
We used data from the Malaria Atlas Project and WorldPop to calculate global pregnancies at-risk of Plasmodium spp. infection. We categorised pregnancies as occurring in areas of stable and unstable P. falciparum and P. vivax transmission. We further stratified stable endemicity as hypo-endemic, meso-endemic, hyper-endemic, or holo-endemic, and estimated pregnancies at risk in 2000, 2005, 2010, 2015, 2017, and 2020.
In 2020, globally 120.4M pregnancies were at risk of P. falciparum, two-thirds (81.0M, 67.3%) were in areas of stable transmission; 85 2M pregnancies were at risk of P. vivax, 93.9% (80.0M) were in areas of stable transmission. An estimated 64.6M pregnancies were in areas with both P. falciparum and P. vivax transmission. The number of pregnancies at risk of each of P. falciparum and P. vivax worldwide decreased between 2000 and 2020, with the exception of sub-Saharan Africa, where the total number of pregnancies at risk of P. falciparum increased from 37 3M in 2000 to 52 4M in 2020.
Historic investments in malaria control have reduced the number of women at risk of malaria in pregnancy in all endemic regions except sub-Saharan Africa. Population growth in Africa has outpaced reductions in malaria prevalence. Interventions that reduce the risk of malaria in pregnancy are needed as much today as ever.
We used data from the Malaria Atlas Project and WorldPop to calculate global pregnancies at-risk of Plasmodium spp. infection. We categorised pregnancies as occurring in areas of stable and unstable P. falciparum and P. vivax transmission. We further stratified stable endemicity as hypo-endemic, meso-endemic, hyper-endemic, or holo-endemic, and estimated pregnancies at risk in 2000, 2005, 2010, 2015, 2017, and 2020.
In 2020, globally 120.4M pregnancies were at risk of P. falciparum, two-thirds (81.0M, 67.3%) were in areas of stable transmission; 85 2M pregnancies were at risk of P. vivax, 93.9% (80.0M) were in areas of stable transmission. An estimated 64.6M pregnancies were in areas with both P. falciparum and P. vivax transmission. The number of pregnancies at risk of each of P. falciparum and P. vivax worldwide decreased between 2000 and 2020, with the exception of sub-Saharan Africa, where the total number of pregnancies at risk of P. falciparum increased from 37 3M in 2000 to 52 4M in 2020.
Historic investments in malaria control have reduced the number of women at risk of malaria in pregnancy in all endemic regions except sub-Saharan Africa. Population growth in Africa has outpaced reductions in malaria prevalence. Interventions that reduce the risk of malaria in pregnancy are needed as much today as ever.
