@article{nokey,

title = {Mapping urban physical distancing constraints, sub-Saharan Africa: a case study from Kenya},

author = {Chamberlain, Heather R and Macharia, Peter M and Tatem, Andrew J},

doi = {10.2471%2FBLT.21.287572},

year  = {2022},

date = {2022-06-22},

urldate = {2022-06-22},

journal = {Bulletin of  the World Health Organisation},

volume = {100},

issue = {9},

abstract = {With the onset of the coronavirus disease 2019 (COVID-19) pandemic, public health measures such as physical distancing were recommended to reduce transmission of the virus causing the disease. However, the same approach in all areas, regardless of context, may lead to measures being of limited effectiveness and having unforeseen negative consequences, such as loss of livelihoods and food insecurity. A prerequisite to planning and implementing effective, context-appropriate measures to slow community transmission is an understanding of any constraints, such as the locations where physical distancing would not be possible. Focusing on sub-Saharan Africa, we outline and discuss challenges that are faced by residents of urban informal settlements in the ongoing COVID-19 pandemic. We describe how new geospatial data sets can be integrated to provide more detailed information about local constraints on physical distancing and can inform planning of alternative ways to reduce transmission of COVID-19 between people. We include a case study for Nairobi County, Kenya, with mapped outputs which illustrate the intra-urban variation in the feasibility of physical distancing and the expected difficulty for residents of many informal settlement areas. Our examples demonstrate the potential of new geospatial data sets to provide insights and support to policy-making for public health measures, including COVID-19.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Small area population denominators for improved disease surveillance and response},

author = {Andrew J Tatem

},

doi = {10.1016/j.epidem.2022.100597},

year  = {2022},

date = {2022-06-17},

journal = {Epidemics},

volume = {40},

abstract = {The Covid-19 pandemic has highlighted the value of strong surveillance systems in supporting our abilities to respond rapidly and effectively in mitigating the impacts of infectious diseases. A cornerstone of such systems is basic subnational scale data on populations and their demographics, which enable the scale of outbreaks to be assessed, risk to specific groups to be determined and appropriate interventions to be designed. Ongoing weaknesses and gaps in such data have however been highlighted by the pandemic. These can include outdated or inaccurate census data and a lack of administrative and registry systems to update numbers, particularly in low and middle income settings. Efforts to design and implement globally consistent geospatial modelling methods for the production of small area demographic data that can be flexibly integrated into health-focussed surveillance and information systems have been made, but these often remain based on outdated population data or uncertain projections. In recent years, efforts have been made to capitalise on advances in computing power, satellite imagery and new forms of digital data to construct methods for estimating small area population distributions across national and regional scales in the absence of full enumeration. These are starting to be used to complement more traditional data collection approaches, especially in the delivery of health interventions, but barriers remain to their widespread adoption and use in disease surveillance and response. Here an overview of these approaches is presented, together with discussion of future directions and needs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Delineating natural catchment health districts with routinely collected health data from women’s travel to give birth in Ghana},

author = {Winfred Dotse-Gborgbortsi and Andrew J. Tatem and Zoë Matthews and Victor Alegana and Anthony Ofosu and Jim Wright },

doi = {10.1186/s12913-022-08125-9},

year  = {2022},

date = {2022-06-13},

urldate = {2022-06-13},

journal = {BMC Health Services Research},

volume = {22},

number = {772},

abstract = {Health service areas are essential for planning, policy and managing public health interventions. In this study, we delineate health service areas from routinely collected health data as a robust geographic basis for presenting access to maternal care indicators.



Methods

A zone design algorithm was adapted to delineate health service areas through a cross-sectional, ecological study design. Health sub-districts were merged into health service areas such that patient flows across boundaries were minimised. Delineated zones and existing administrative boundaries were used to provide estimates of access to maternal health services. We analysed secondary data comprising routinely collected health records from 32,921 women attending 27 hospitals to give birth, spatial demographic data, a service provision assessment on the quality of maternal healthcare and health sub-district boundaries from Eastern Region, Ghana.



Results

Clear patterns of cross border movement to give birth emerged from the analysis, but more women originated closer to the hospitals. After merging the 250 sub-districts in 33 districts, 11 health service areas were created. The minimum percent of internal flows of women giving birth within any health service area was 97.4%. Because the newly delineated boundaries are more “natural” and sensitive to observed flow patterns, when we calculated areal indicator estimates, they showed a marked improvement over the existing administrative boundaries, with the inclusion of a hospital in every health service area.



Conclusion

Health planning can be improved by using routine health data to delineate natural catchment health districts. In addition, data-driven geographic boundaries derived from public health events will improve areal health indicator estimates, planning and interventions.},

keywords = {Ghana, Health, maternal health, Public health},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Untangling the changing impact of non-pharmaceutical interventions and vaccination on European COVID-19 trajectories},

author = {Ge, Yong and Zhang, Wen-Bin and Wu, Xilin and Ruktanonchai, Corrine W and Liu, Haiyan and Wang, Jianghao and Song, Yongze and Liu, Mengxiao and Yan, Wei and Yang, Juan and Cleary, Eimear and Qader, Sarchil H. and Atuhaire, Fatumah and Ruktanonchai, Nick W. and Tatem, Andrew J. and Lai Shengjie },

doi = {10.1038/s41467-022-30897-1},

year  = {2022},

date = {2022-06-03},

urldate = {2022-06-03},

journal = {Nature Communications},

volume = {13},

issue = {3106},

abstract = {Non-pharmaceutical interventions (NPIs) and vaccination are two fundamental approaches for mitigating the coronavirus disease 2019 (COVID-19) pandemic. However, the real-world impact of NPIs versus vaccination, or a combination of both, on COVID-19 remains uncertain. To address this, we built a Bayesian inference model to assess the changing effect of NPIs and vaccination on reducing COVID-19 transmission, based on a large-scale dataset including epidemiological parameters, virus variants, vaccines, and climate factors in Europe from August 2020 to October 2021. We found that (1) the combined effect of NPIs and vaccination resulted in a 53% (95% confidence interval: 42–62%) reduction in reproduction number by October 2021, whereas NPIs and vaccination reduced the transmission by 35% and 38%, respectively; (2) compared with vaccination, the change of NPI effect was less sensitive to emerging variants; (3) the relative effect of NPIs declined 12% from May 2021 due to a lower stringency and the introduction of vaccination strategies. Our results demonstrate that NPIs were complementary to vaccination in an effort to reduce COVID-19 transmission, and the relaxation of NPIs might depend on vaccination rates, control targets, and vaccine effectiveness concerning extant and emerging variants.},

keywords = {covid-19, Europe, NPIs, vaccination},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Multilevel analysis of predictors of multiple indicators of childhood vaccination in Nigeria},

author = {Justice M. K. Aheto and Oliver Pannell and Winfred Dotse-Gborgbortsi and Mary K. Trimner and Andrew J. Tatem and Dale A. Rhoda and Felicity T. Cutts and C Edson Utazi},

doi = {https://doi.org/10.1371/journal.pone.0269066},

year  = {2022},

date = {2022-05-25},

urldate = {2022-05-25},

journal = {PLoS ONE},

volume = {15},

number = {5},

pages = {e0269066},

abstract = {Substantial inequalities exist in childhood vaccination coverage levels. To increase vaccine uptake, factors that predict vaccination coverage in children should be identified and addressed.



Methods

Using data from the 2018 Nigeria Demographic and Health Survey and geospatial data sets, we fitted Bayesian multilevel binomial and multinomial logistic regression models to analyse independent predictors of three vaccination outcomes: receipt of the first dose of Pentavalent vaccine (containing diphtheria-tetanus-pertussis, Hemophilus influenzae type B and Hepatitis B vaccines) (PENTA1) (n = 6059) and receipt of the third dose having received the first (PENTA3/1) (n = 3937) in children aged 12–23 months, and receipt of measles vaccine (MV) (n = 11839) among children aged 12–35 months.



Results

Factors associated with vaccination were broadly similar for documented versus recall evidence of vaccination. Based on any evidence of vaccination, we found that health card/document ownership, receipt of vitamin A and maternal educational level were significantly associated with each outcome. Although the coverage of each vaccine dose was higher in urban than rural areas, urban residence was not significant in multivariable analyses that included travel time. Indicators relating to socio-economic status, as well as ethnic group, skilled birth attendance, lower travel time to the nearest health facility and problems seeking health care were significantly associated with both PENTA1 and MV. Maternal religion was related to PENTA1 and PENTA3/1 and maternal age related to MV and PENTA3/1; other significant variables were associated with one outcome each. Substantial residual community level variances in different strata were observed in the fitted models for each outcome.



Conclusion

Our analysis has highlighted socio-demographic and health care access factors that affect not only beginning but completing the vaccination series in Nigeria. Other factors not measured by the DHS such as health service quality and community attitudes should also be investigated and addressed to tackle inequities in coverage.},

keywords = {Nigeria, Predictive clustering, vaccination},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Who and which regions are at high risk of returning to poverty during the COVID-19 pandemic?},

author = {Ge, Yong and Liu, Mengxiao and Hu, Shan and Wang, Daoping and Wang, Jinfeng and Wang, Xiaolin and Qader, Sarchil and Cleary, Eimear and Tatem, Andrew J. and Lai, Shengjie},

doi = {https://doi.org/10.1057/s41599-022-01205-5},

year  = {2022},

date = {2022-05-25},

urldate = {2022-05-25},

journal = {Humanities and Social Sciences Communications},

volume = {9},

number = {183},

abstract = {Pandemics such as COVID-19 and their induced lockdowns/travel restrictions have a significant impact on people’s lives, especially for lower-income groups who lack savings and rely heavily on mobility to fulfill their daily needs. Taking the COVID-19 pandemic as an example, this study analysed the risk of returning to poverty for low-income households in Hubei Province in China as a result of the COVID-19 lockdown. Employing a dataset including information on 78,931 government-identified poor households, three scenarios were analysed in an attempt to identify who is at high risk of returning to poverty, where they are located, and how the various risk factors influence their potential return to poverty. The results showed that the percentage of households at high risk of returning to poverty (falling below the poverty line) increased from 5.6% to 22% due to a 3-month lockdown. This vulnerable group tended to have a single source of income, shorter working hours, and more family members. Towns at high risk (more than 2% of households returning to poverty) doubled (from 27.3% to 46.9%) and were mainly located near railway stations; an average decrease of 10–50 km in the distance to the nearest railway station increased the risk from 1.8% to 9%. These findings, which were supported by the representativeness of the sample and a variety of robustness tests, provide new information for policymakers tasked with protecting vulnerable groups at high risk of returning to poverty and alleviating the significant socio-economic consequences of future pandemics.},

keywords = {covid-19},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Characterising the Land Surface Phenology of Middle Eastern Countries Using Moderate Resolution Landsat Data},

author = {Qader, Sarchil Hama and Priyatikanto, Rhorom and Khwarahm, Nabaz R. and Tatem, Andrew J. and Dash, Jadunandan},

doi = {10.3390/rs14092136},

year  = {2022},

date = {2022-04-28},

urldate = {2022-04-28},

journal = {Remote Sensing},

volume = {14},

number = {9},

abstract = {Global change impacts including climate change, increased CO2 and nitrogen deposition can be determined through a more precise characterisation of Land Surface Phenology (LSP) parameters. In addition, accurate estimation of LSP dates is being increasingly used in applications such as mapping vegetation types, yield forecasting, and irrigation management. However, there has not been any attempt to characterise Middle East vegetation phenology at the fine spatial resolution appropriate for such applications. Remote-sensing based approaches have proved to be a useful tool in such regions since access is restricted in some areas due to security issues and their inter-annual vegetation phenology parameters vary considerably because of high uncertainty in rainfall. This study aims to establish for the first time a comprehensive characterisation of the vegetation phenological characteristics of the major vegetation types in the Middle East at a fine spatial resolution of 30 m using Landsat Normalized Difference Vegetation Index (NDVI) time series data over a temporal range of 20 years (2000 - 2020). Overall, a progressive pattern in phenophases was observed from low to high latitude. The earliest start of the season was concentrated in the central and east of the region associated mainly with grassland and cultivated land, while the significantly delayed end of the season was mainly distributed in northern Turkey and Iran corresponding to the forest, resulting in the prolonged length of the season in the study area. There was a significant positive correlation between LSP parameters and latitude, which indicates a delay in the start of the season of 4.83 days (R2 = 0.86, p < 0.001) and a delay in the end of the season of 6.54 days (R2 = 0.83, p < 0.001) per degree of latitude increase. In addition, we have discussed the advantages of fine resolution LSP parameters over the available coarse datasets and showed how such outputs can improve many applications in the region. This study shows the potential of Landsat data to quantify the LSP of major land cover types in heterogeneous landscapes of the Middle East which enhances our understanding of the spatial-temporal dynamics of vegetation dynamics in arid and semi-arid settings in the world.},

keywords = {Iran, Landsat, phenology, Turkey},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Assessing the characteristics of un- and under-vaccinated children in low- and middle-income countries: A multi-level cross-sectional study},

author = {Utazi, C Edson and Pannell, Oliver and Aheto, Justice MK and Wigley, Adelle and Tejedor-Garavito, Natalia and Wunderlich, Josh and Hagedorn, Brittany and Hogan, Dan and and Tatem, Andrew J. },

doi = {https://doi.org/10.1371/journal.pgph.0000244},

year  = {2022},

date = {2022-04-27},

urldate = {2022-04-27},

journal = {PLoS Global Public Health},

volume = {2},

number = {4},

pages = {e0000244},

abstract = {Achieving equity in vaccination coverage has been a critical priority within the global health community. Despite increased efforts recently, certain populations still have a high proportion of un- and under-vaccinated children in many low- and middle-income countries (LMICs). These populations are often assumed to reside in remote-rural areas, urban slums and conflict-affected areas. Here, we investigate the effects of these key community-level factors, alongside a wide range of other individual, household and community level factors, on vaccination coverage. Using geospatial datasets, including cross-sectional data from the most recent Demographic and Health Surveys conducted between 2008 and 2018 in nine LMICs, we fitted Bayesian multi-level binary logistic regression models to determine key community-level and other factors significantly associated with non- and under-vaccination. We analyzed the odds of receipt of the first doses of diphtheria-tetanus-pertussis (DTP1) vaccine and measles-containing vaccine (MCV1), and receipt of all three recommended DTP doses (DTP3) independently, in children aged 12–23 months. In bivariate analyses, we found that remoteness increased the odds of non- and under-vaccination in nearly all the study countries. We also found evidence that living in conflict and urban slum areas reduced the odds of vaccination, but not in most cases as expected. However, the odds of vaccination were more likely to be lower in urban slums than formal urban areas. Our multivariate analyses revealed that the key community variables–remoteness, conflict and urban slum–were sometimes associated with non- and under-vaccination, but they were not frequently predictors of these outcomes after controlling for other factors. Individual and household factors such as maternal utilization of health services, maternal education and ethnicity, were more common predictors of vaccination. Reaching the Immunisation Agenda 2030 target of reducing the number of zero-dose children by 50% by 2030 will require country tailored analyses and strategies to identify and reach missed communities with reliable immunisation services.},

keywords = {Demographic and Health Surveys, LMICs, vaccination},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {High-resolution population estimation using household survey data and building footprints},

author = {Boo, Gianluca and Darin, Edith and Leasure, Douglas R and Dooley, Claire A and Chamberlain, Heather R and and Lázár, Attila N and Tschirhart, Kevin and Sinai, Cyrus and Hoff, Nicole A and Fuller, Trevon},

doi = {https://doi.org/10.1038/s41467-022-29094-x},

year  = {2022},

date = {2022-03-14},

urldate = {2022-03-14},

journal = {Nature Communications},

volume = {13},

number = {1330},

abstract = {The national census is an essential data source to support decision-making in many areas of public interest. However, this data may become outdated during the intercensal period, which can stretch up to several decades. In this study, we develop a Bayesian hierarchical model leveraging recent household surveys and building footprints to produce up-to-date population estimates. We estimate population totals and age and sex breakdowns with associated uncertainty measures within grid cells of approximately 100 m in five provinces of the Democratic Republic of the Congo, a country where the last census was completed in 1984. The model exhibits a very good fit, with an R2 value of 0.79 for out-of-sample predictions of population totals at the microcensus-cluster level and 1.00 for age and sex proportions at the province level. This work confirms the benefits of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses.},

keywords = {Bayesian inference, Demographic and Health Surveys, Population},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Understanding factors associated with attending secondary school in Tanzania using household survey data},

author = {Carla Pezzulo and Victor A Alegana and Andrew Christensen and Omar Bakari and Andrew Tatem},

doi = {http://dx.doi.org/10.1371/journal.pone.0263734},

year  = {2022},

date = {2022-02-25},

urldate = {2022-02-25},

journal = {PLoS ONE},

volume = {17},

number = {2},

abstract = {Sustainable Development Goal (SDG) 4 aims to ensure inclusive and equitable access for all by 2030, leaving no one behind. One indicator selected to measure progress towards achievement is the participation rate of youth in education (SDG 4.3.1). Here we aim to understand drivers of school attendance using one country in East Africa as an example.



Methods

Nationally representative household survey data (2015–16 Tanzania Demographic and Health Survey) were used to explore individual, household and contextual factors associated with secondary school attendance in Tanzania. These included, age, head of household’s levels of education, gender, household wealth index and total number of children under five. Contextual factors such as average pupil to qualified teacher ratio and geographic access to school were also tested at cluster level. A two-level random intercept logistic regression model was used in exploring association of these factors with attendance in a multi-level framework.



Results

Age of household head, educational attainments of either of the head of the household or parent, child characteristics such as gender, were important predictors of secondary school attendance. Being in a richer household and with fewer siblings of lower age (under the age of 5) were associated with increased odds of attendance (OR = 0.91, CI 95%: 0.86; 0.96). Contextual factors were less likely to be associated with secondary school attendance.



Conclusions

Individual and household level factors are likely to impact secondary school attendance rates more compared to contextual factors, suggesting an increased focus of interventions at these levels is needed. Future studies should explore the impact of interventions targeting these levels. Policies should ideally promote gender equality in accessing secondary school as well as support those families where the dependency ratio is high. Strategies to reduce poverty will also increase the likelihood of attending school.},

keywords = {Africa, Demographic and Health Surveys, education, SDG4, Tanzania},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Mapping the prevalence of severe acute malnutrition in Papua, Indonesia by using geostatistical models},

author = {Jasper, Paul and Jochem, Warren C and Lambert-Porter, Emma and Naeem, Umer and Utazi, Chigozie Edson},

doi = {https://doi.org/10.1186/s40795-022-00504-z},

year  = {2022},

date = {2022-02-14},

urldate = {2022-02-14},

journal = {BMC Nutrition},

volume = {8},

number = {13},

abstract = {Severe acute malnutrition (SAM) is the most life-threatening form of malnutrition, and in 2019, approximately 14.3 million children under the age of 5 were considered to have SAM. The prevalence of child malnutrition is recorded through large-scale household surveys run at multi-year intervals. However, these surveys are expensive, yield estimates with high levels of aggregation, are run over large time intervals, and may show gaps in area coverage. Geospatial modelling approaches could address some of these challenges by combining geo-located survey data with geospatial data to produce mapped estimates that predict malnutrition risk in both surveyed and non-surveyed areas.



Methods

A secondary analysis of cluster-level program evaluation data (n = 123 primary sampling units) was performed to map severe acute malnutrition (SAM) in Papuan children under 2 years (0–23 months) of age with a spatial resolution of 1 × 1 km in Papua, Indonesia. The approach used Bayesian geostatistical modelling techniques and publicly available geospatial data layers.



Results

In Papua, Indonesia, SAM was predicted in geostatistical models by using six geospatial covariates related primarily to conditions of remoteness and inaccessibility. The predicted 1-km spatial resolution maps of SAM showed substantial spatial variation across the province. By combining the predicted rates of SAM with estimates of the population under 2 years of age, the prevalence of SAM in late 2018 was estimated to be around 15,000 children (95% CI 10,209–26,252). Further tests of the predicted levels suggested that in most areas of Papua, more than 5% of Papuan children under 2 years of age had SAM, while three districts likely had more than 15% of children with SAM.



Conclusions

Eradication of hunger and malnutrition remains a key development goal, and more spatially detailed data can guide efficient intervention strategies. The application of additional household survey datasets in geostatistical models is one way to improve the monitoring and timely estimation of populations at risk of malnutrition. Importantly, geospatial mapping can yield insights for both surveyed and non-surveyed areas and can be applied in low-income country contexts where data is scarce and data collection is expensive or regions are inaccessible.},

keywords = {Asia, Bayesian geostatistics, Demographic and Health Surveys, Indonesia, malnutrition, Papua},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Unmet need for COVID-19 vaccination coverage in Kenya},

author = {Samuel K. Muchiri and Rose Muthee and Hellen Kiarie and Joseph Sitienei and Ambrose Agweyu and Peter M. Atkinson and C. {Edson Utazi} and Andrew J. Tatem and Victor A. Alegana},

doi = {https://doi.org/10.1016/j.vaccine.2022.02.035},

issn = {0264-410X},

year  = {2022},

date = {2022-02-14},

urldate = {2022-02-14},

journal = {Vaccine},

volume = {40},

number = {13},

abstract = {COVID-19 has impacted the health and livelihoods of billions of people since it emerged in 2019. Vaccination for COVID-19 is a critical intervention that is being rolled out globally to end the pandemic. Understanding the spatial inequalities in vaccination coverage and access to vaccination centres is important for planning this intervention nationally. Here, COVID-19 vaccination data, representing the number of people given at least one dose of vaccine, a list of the approved vaccination sites, population data and ancillary GIS data were used to assess vaccination coverage, using Kenya as an example. Firstly, physical access was modelled using travel time to estimate the proportion of population within 1 hour of a vaccination site. Secondly, a Bayesian conditional autoregressive (CAR) model was used to estimate the COVID-19 vaccination coverage and the same framework used to forecast coverage rates for the first quarter of 2022. Nationally, the average travel time to a designated COVID-19 vaccination site (n = 622) was 75.5 min (Range: 62.9 – 94.5 min) and over 87% of the population >18 years reside within 1 hour to a vaccination site. The COVID-19 vaccination coverage in December 2021 was 16.70% (95% CI: 16.66 – 16.74) – 4.4 million people and was forecasted to be 30.75% (95% CI: 25.04 – 36.96) – 8.1 million people by the end of March 2022. Approximately 21 million adults were still unvaccinated in December 2021 and, in the absence of accelerated vaccine uptake, over 17.2 million adults may not be vaccinated by end March 2022 nationally. Our results highlight geographic inequalities at sub-national level and are important in targeting and improving vaccination coverage in hard-to-reach populations. Similar mapping efforts could help other countries identify and increase vaccination coverage for such populations.},

keywords = {Africa, covid-19, Kenya, travel time, vaccination},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Influenza’s plummeting during the COVID-19 pandemic: The roles of mask-wearing, mobility change, and SARS-CoV-2 interference},

author = {Shasha Han and Ting Zhang and Yan Lyu and Shengjie Lai and Peixi Dai and Jiandong Zheng and Weizhong Yang and Xiao-Hua Zhou and Luzhao Feng},

doi = {https://doi.org/10.1016/j.eng.2021.12.011},

issn = {2095-8099},

year  = {2022},

date = {2022-02-02},

urldate = {2022-02-02},

journal = {Engineering},

abstract = {Seasonal influenza activity typically peaks in the winter months but plummeted globally during the current coronavirus disease 2019 (COVID-19) pandemic. Unraveling lessons from influenza’s unprecedented low profile is critical in informing preparedness for incoming influenza seasons. Here, we explored a country-specific inference model to estimate the effects of mask-wearing, mobility changes (international and domestic), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) interference in China, England, and the United States. We found that a one-week increase in mask-wearing intervention had a percent reduction of 11.3%–35.2% in influenza activity in these areas. The one-week mobility mitigation had smaller effects for the international (1.7%–6.5%) and the domestic community (1.6%–2.8%). In 2020–2021, the mask-wearing intervention alone could decline percent positivity by 13.3–19.8. The mobility change alone could reduce percent positivity by 5.2–14.0, of which 79.8%–98.2% were attributed to the deflected international travel. Only in 2019–2020, SARS-CoV-2 interference had statistically significant effects. There was a reduction in percent positivity of 7.6 (2.4–14.4) and 10.2 (7.2–13.6) in northern China and England, respectively. Our results have implications for understanding how influenza evolves under non-pharmaceutical interventions and other respiratory diseases and will inform health policy and the design of tailored public health measures.},

keywords = {covid-19, influenza, NPIs},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Global holiday datasets for understanding seasonal human mobility and population dynamics},

author = {Lai, Shengjie and Sorichetta, Alessandro and Steele, Jessica and Ruktanonchai, Corrine W and Cunningham, Alexander D and Rogers, Grant and Koper, Patrycja and Woods, Dorothea and Bondarenko, Maksym and Ruktanonchai, Nick W and Shi, Weifeng and and Tatem, Andrew J.},

doi = {https://doi.org/10.1038/s41597-022-01120-z},

year  = {2022},

date = {2022-01-20},

urldate = {2022-01-20},

journal = {Scientific Data},

volume = {9},

number = {17},

abstract = {Public and school holidays have important impacts on population mobility and dynamics across multiple spatial and temporal scales, subsequently affecting the transmission dynamics of infectious diseases and many socioeconomic activities. However, worldwide data on public and school holidays for understanding their changes across regions and years have not been assembled into a single, open-source and multitemporal dataset. To address this gap, an open access archive of data on public and school holidays in 2010–2019 across the globe at daily, weekly, and monthly timescales was constructed. Airline passenger volumes across 90 countries from 2010 to 2018 were also assembled to illustrate the usage of the holiday data for understanding the changing spatiotemporal patterns of population movements.},

keywords = {holidays, Mobility, Population},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling},

author = {Palacios-Lopez, Daniela and Esch, Thomas and MacManus, Kytt and Marconcini, Mattia and Sorichetta, Alessandro and Yetman, Greg and Zeidler, Julian and Dech, Stefan and Tatem, Andrew J. and and Reinartz, Peter},

doi = {10.3390/rs14020325},

issn = {2072-4292},

year  = {2022},

date = {2022-01-20},

urldate = {2022-01-20},

journal = {Remote Sensing},

volume = {14},

number = {2},

abstract = {Large-scale gridded population datasets available at the global or continental scale have become an important source of information in applications related to sustainable development. In recent years, the emergence of new population models has leveraged the inclusion of more accurate and spatially detailed proxy layers describing the built-up environment (e.g., built-area and building footprint datasets), enhancing the quality, accuracy and spatial resolution of existing products. However, due to the consistent lack of vertical and functional information on the built-up environment, large-scale gridded population datasets that rely on existing built-up land proxies still report large errors of under- and overestimation, especially in areas with predominantly high-rise buildings or industrial/commercial areas, respectively. This research investigates, for the first time, the potential contributions of the new World Settlement Footprint—3D (WSF3D) dataset in the field of large-scale population modelling. First, we combined a Random Forest classifier with spatial metrics derived from the WSF3D to predict the industrial versus non-industrial use of settlement pixels at the Pan-European scale. We then examined the effects of including volume and settlement use information into frameworks of dasymetric population modelling. We found that the proposed classification method can predict industrial and non-industrial areas with overall accuracies and a kappa-coefficient of ~84% and 0.68, respectively. Additionally, we found that both, integrating volume and settlement use information considerably increased the accuracy of population estimates between 10% and 30% over commonly employed models (e.g., based on a binary settlement mask as input), mainly by eliminating systematic large overestimations in industrial/commercial areas. While the proposed method shows strong promise for overcoming some of the main limitations in large-scale population modelling, future research should focus on improving the quality of the WFS3D dataset and the classification method alike, to avoid the false detection of built-up settlements and to reduce misclassification errors of industrial and high-rise buildings.},

keywords = {Europe, Population, Random forest},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Etiological and epidemiological features of acute meningitis or encephalitis in China: a nationwide active surveillance study},

author = {Wang, Li-Ping and Yuan, Yang and Liu, Ying-Le and Lu, Qing-Bin and Shi, Lu-Sha and Ren, Xiang and Zhou, Shi-Xia and Zhang, Hai-Yang and Zhang, Xiao-Ai and Wang, Xin and Wang, Yi-Fei and Lin, Sheng-Hong and Zhang, Cui-Hong and Geng, Meng-Jie and Li, Jun and Zhao, Shi-Wen and Yi, Zhi-Gang and Chen, Xiao and Yang, Zuo-Sen and Meng, Lei and Wang, Xin-Hua and Cui, Ai-Li and Lai, Sheng-Jie and and others},

doi = {https://doi.org/10.1016/j.lanwpc.2021.100361},

year  = {2022},

date = {2022-01-03},

urldate = {2022-01-03},

journal = {The Lancet Regional Health-Western Pacific},

volume = {20},

number = {100361},

abstract = {Acute meningitis or encephalitis (AME) results from a neurological infection causing high case fatality and severe sequelae. AME lacked comprehensive surveillance in China.

Methods

Nation-wide surveillance of all-age patients with AME syndromes was conducted in 144 sentinel hospitals of 29 provinces in China. Eleven AME-causative viral and bacterial pathogens were tested with multiple diagnostic methods.

Findings

Between 2009 and 2018, 20,454 AME patients were recruited for tests. Based on 9,079 patients with all-four-virus tested, 28.43% (95% CI: 27.50%‒29.36%) of them had at least one virus-positive detection. Enterovirus was the most frequently determined virus in children <18 years, herpes simplex virus and Japanese encephalitis virus were the most frequently determined in 18−59 and ≥60 years age groups, respectively. Based on 6,802 patients with all-seven-bacteria tested, 4.43% (95% CI: 3.94%‒4.91%) had at least one bacteria-positive detection, Streptococcus pneumoniae and Neisseria meningitidis were the leading bacterium in children aged <5 years and 5−17 years, respectively. Staphylococcus aureus was the most frequently detected in adults aged 18−59 and ≥60 years. The pathogen spectrum also differed statistically significantly between northern and southern China. Joinpoint analysis revealed age-specific positive rates, with enterovirus, herpes simplex virus and mumps virus peaking at 3−6 years old, while Japanese encephalitis virus peaked in the ≥60 years old. As age increased, the positive rate for Streptococcus pneumoniae and Escherichia coli statistically significantly decreased, while for Staphylococcus aureus and Streptococcus suis it increased.

Interpretation

The current findings allow enhanced identification of the predominant AME-related pathogen candidates for diagnosis in clinical practice and more targeted application of prevention and control measures in China, and a possible reassessment of vaccination strategy.},

keywords = {Asia, China, Demographic and Health Surveys, infectious disease},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Impacts of worldwide individual non-pharmaceutical interventions on COVID-19 transmission across waves and space},

author = {Yong Ge and Wen-Bin Zhang and Haiyan Liu and Corrine W Ruktanonchai and Maogui Hu and Xilin Wu and Yongze Song and Nick W Ruktanonchai and Wei Yan and Eimear Cleary and Luzhao Feng and Zhongjie Li and Weizhong Yang and Mengxiao Liu and Andrew J Tatem and Jin-Feng Wang and and Shengjie Lai},

doi = {https://doi.org/10.1016/j.jag.2021.102649},

year  = {2021},

date = {2021-12-13},

urldate = {2021-12-13},

journal = {International Journal of Applied Earth Observation and Geoinformation},

volume = {106},

abstract = {Governments worldwide have rapidly deployed non-pharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic. However, the effect of these individual NPI measures across space and time has yet to be sufficiently assessed, especially with the increase of policy fatigue and the urge for NPI relaxation in the vaccination era. Using the decay ratio in the suppression of COVID-19 infections and multi-source big data, we investigated the changing performance of different NPIs across waves from global and regional levels (in 133 countries) to national and subnational (in the United States of America [USA]) scales before the implementation of mass vaccination. The synergistic effectiveness of all NPIs for reducing COVID-19 infections declined along waves, from 95.4% in the first wave to 56.0% in the third wave recently at the global level and similarly from 83.3% to 58.7% at the USA national level, while it had fluctuating performance across waves on regional and subnational scales. Regardless of geographical scale, gathering restrictions and facial coverings played significant roles in epidemic mitigation before the vaccine rollout. Our findings have important implications for continued tailoring and implementation of NPI strategies, together with vaccination, to mitigate future COVID-19 waves, caused by new variants, and other emerging respiratory infectious diseases.},

keywords = {covid-19, NPIs},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {The emergence, genomic diversity and global spread of SARS-CoV-2},

author = {Li, Juan and Lai, Shengjie and Gao, George F and and Shi, Weifeng},

doi = {https://doi.org/10.1038/s41586-021-04188-6},

year  = {2021},

date = {2021-12-08},

urldate = {2021-12-08},

journal = {Nature},

volume = {600},

pages = {408–418},

abstract = {Since the first cases of COVID-19 were documented in Wuhan, China in 2019, the world has witnessed a devastating global pandemic, with more than 238 million cases, nearly 5 million fatalities and the daily number of people infected increasing rapidly. Here we describe the currently available data on the emergence of the SARS-CoV-2 virus, the causative agent of COVID-19, outline the early viral spread in Wuhan and its transmission patterns in China and across the rest of the world, and highlight how genomic surveillance, together with other data such as those on human mobility, has helped to trace the spread and genetic variation of the virus and has also comprised a key element for the control of the pandemic. We pay particular attention to characterizing and describing the international spread of the major variants of concern of SARS-CoV-2 that were first identified in late 2020 and demonstrate that virus evolution has entered a new phase. More broadly, we highlight our currently limited understanding of coronavirus diversity in nature, the rapid spread of the virus and its variants in such an increasingly connected world, the reduced protection of vaccines, and the urgent need for coordinated global surveillance using genomic techniques. In summary, we provide important information for the prevention and control of both the ongoing COVID-19 pandemic and any new diseases that will inevitably emerge in the human population in future generations.},

keywords = {covid-19, Mobility},

pubstate = {published},

tppubtype = {article}

}