@article{nokey,

title = {A stochastic approach to integerize floating-point estimates in gridded population mapping},

author = {Wenbin Zhang and Alessandro Sorichetta and Charlie Frye and Natalia Tejedor-Garavito and Weixuan Fang and Duygu Cihan and Dorothea Woods and Gregory Yetman and Jason Hilton and Andrew J. Tatem and Maksym Bondarenko},

url = {https://doi.org/10.1080/13658816.2025.2568721},

doi = {10.1080/13658816.2025.2568721},

year  = {2025},

date = {2025-10-01},

journal = {International Journal of Geographical Information Science},

pages = {1–17},

abstract = {Gridded population datasets are increasingly relied upon for spatial planning, resource allocation, and disaster response due to their flexible integration with other spatial data layers. These datasets are typically produced by disaggregating population counts from administrative units into grid cells, yielding non-integer values that preserve overall counts. However, floating-point cell values are often difficult for users to interpret, and standard rounding approaches may introduce aggregation errors at administrative levels that affect planning decisions. Here, we present a stochastic integerisation method that preserves total population and demographic proportions, and compare it with existing approaches. The method separates the value of each cell into integer and decimal parts, and probabilistically allocates the residual based on decimal magnitudes. Applying the method to gridded population data shows that it effectively reduces unrealistic population predictions in uninhabited areas. The results also demonstrate that the new integerisation method can effectively convert floating-point population estimates into integers while preserving both spatial distribution and demographic proportions, such as age-sex structures. These findings highlight the performance of the proposed integerisation method to generate reliable gridded population distribution datasets across diverse contexts that are easier to interpret, particularly for areas with sparse populations or complex geometries of underlying administrative units.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Tackling public health data gaps through Bayesian high-resolution population estimation: A case study of Kasaï-Oriental, Democratic Republic of the Congo},

author = {Gianluca Boo and Edith Darin and Heather R. Chamberlain and Roland Hosner and Pierre K. Akilimali and Henri Marie Kazadi and Chibuzor C. Nnanatu and Attila N. Lázár and Andrew J. Tatem},

url = {https://doi.org/10.1371/journal.pgph.0005072},

year  = {2025},

date = {2025-09-04},

journal = {PLOS Global Public Health },

abstract = {Most low- and middle-income countries face significant public health challenges, exacerbated by the lack of reliable demographic data supporting effective planning and intervention. In such data-scarce settings, statistical models combining geolocated survey data with geospatial datasets enable the estimation of population counts at high spatial resolution in the absence of dependable demographic data sources. This study introduces a Bayesian model jointly estimating building and population counts, combining geolocated survey data and gridded geospatial datasets. The model provides population estimates for the Kasaï-Oriental province, Democratic Republic of the Congo (DRC), at a spatial resolution of approximately one hectare. Posterior estimates are aggregated across health zones and health areas to offer probabilistic insights into their respective populations. The model exhibits a –0.28 bias, 0.47 inaccuracy, and 0.95 imprecision using scaled residuals, with robust 95% credible intervals. The estimated population of Kasaï-Oriental for 2024 is approximately 4.1 million, with a credible range of 3.4 to 4.8 million. Aggregations by health zones and health areas reveal significant variations in population estimates and uncertainty levels, particularly between the provincial capital, Mbuji-Mayi and the rural hinterland. High-resolution Bayesian population estimates allow flexible aggregation across spatial units while providing probabilistic insights into model uncertainty. These estimates offer a unique resource for the public health community working in Kasaï-Oriental, for instance, in support of a better-informed allocation of vaccines to different operational boundaries based on the upper bound of the 95% credible intervals.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Geostatistical and machine learning approaches for high-resolution mapping of vaccination coverage},

author = {C. Edson Utazi and Ortis Yankey and Somnath Chaudhuri and Iyanuloluwa D. Olowe and M. Carolina Danovaro-Holliday and Attila N. Lazar and Andrew J. Tatem},

url = {https://doi.org/10.1016/j.sste.2025.100744},

year  = {2025},

date = {2025-08-23},

journal = {Spatial and Spatio-temporal Epidemiology},

volume = {54},

number = {100744},

abstract = {Recently, there has been a growing interest in the production of high-resolution maps of vaccination coverage. These maps have been useful for uncovering geographic inequities in coverage and improving targeting of interventions to reach marginalized populations. Different methodological approaches have been developed for producing these maps using mostly geolocated household survey data and geospatial covariate information. However, it remains unclear how much the predicted coverage maps produced by the various methods differ, and which methods yield more reliable estimates. Here, we explore the predictive performance of these methods and resulting implications for spatial prioritization to fill this gap. Using Nigeria Demographic and Health Survey as a case study, we generate 1 × 1 km and district level maps of indicators of vaccination coverage using geostatistical, machine learning (ML) and hybrid methods and evaluate predictive performance via cross-validation. Our results show similar predictive performance for five of the seven methods investigated, although two geostatistical approaches are the best performing methods. The worst-performing methods are two ML approaches. We find marked differences in spatial prioritization using these methods, which could potentially result in missing important underserved populations, although broad similarities exist. Our study can help guide map production for other health and development metrics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Serological survey to determine measles and rubella immunity gaps across age and geographic locations in The Gambia: a study protocol},

author = {Oghenebrume Wariri and Abdul Khalie Muhammad and Alieu Sowe and Julia Strandmark and Chigozie Edson Utazi and C Jessica E Metcalf and Beate Kampmann},

doi = {10.1080/16549716.2025.2540135},

year  = {2025},

date = {2025-08-20},

journal = {Global Health Action},

volume = {18},

issue = {1},

abstract = {Vaccine coverage and disease surveillance data are valuable for monitoring protection against vaccine-preventable diseases; however, they do not directly measure population immunity. High-quality, representative serological studies can provide key insights into immunity gaps, outbreak susceptibility, and inform targeted vaccination strategies, even in high-performing immunization programs. This study aims to estimate location-specific and age-specific immunity profiles for measles and rubella while evaluating the predictive value of indirect immunity estimates derived from vaccination and surveillance data against direct serological measurements. Additionally, it seeks to model the risk of measles outbreaks and assess the impact of mitigation strategies. A multi-stage, stratified cluster sampling design will be implemented across six districts in The Gambia's North Bank and Upper River Regions. Survey clusters (i.e. 5 km × 5 km areas) encompassing all settlements within their boundaries will be selected, proportionally to district population sizes. Cluster selection ensures representativeness of both the population and vaccine coverage within each district. Based on detecting a 10% difference in protective immunity across vaccine coverage levels, power analysis assumes an intraclass correlation coefficient (ICC) of 0.01. In each cluster, 70 children aged 9 months to 14 years will be recruited, yielding a total sample size of 1,750 children across 25 selected clusters. Dried blood samples will be collected and tested for anti-measles and anti-rubella IgG using enzyme immunoassays (EIA). District-specific measles seroprevalence will be estimated using a hierarchical spatial model. This study will generate key evidence needed to refine immunization strategies and reduce the risk of measles and rubella outbreaks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Integrating vulnerability and hazard in malaria risk mapping: the elimination context of Senegal},

author = {Camille Morlighem and Chibuzor Christopher Nnanatu and Justice Moses K. Aheto and Catherine Linard },

url = {https://doi.org/10.1186/s12879-025-11412-5},

year  = {2025},

date = {2025-08-18},

journal = {BMC Infectious Diseases},

volume = {25},

number = {1031},

abstract = {Significant efforts over the past decades have successfully reduced the global burden of malaria. However, progress has stalled since 2015. In low-transmission settings, the traditional distribution of malaria along vector suitability gradients is shifting to a new profile, with the emergence of hotspots where the disease persists. To support elimination in this context, it is essential that malaria risk maps consider not only environmental and climatic factors, but also societal vulnerabilities, in order to identify remaining hotspots and ensure that no contributing factors are overlooked. In this paper, we present an integrated approach to malaria risk mapping based on the decomposition of malaria risk into two components: ‘hazard’, which refers to the potential presence of infected vectors (e.g. influenced by rainfall and temperature), and ‘vulnerability’, which is the predisposition of the population to the burden of malaria (e.g. related to health care access and housing conditions). We focus on Senegal, which has a heterogeneous malaria epidemiological profile, ranging from high transmission in the south-east to very low transmission in the north, and which aims to eliminate malaria by 2030.



We combined data from several sources: the 2017 Demographic and Health Survey (DHS) (national coverage) and the 2020-21 Malaria Indicator Survey (MIS) (south-east regions), as well as remotely sensed, high-resolution covariate data. Using Bayesian geostatistical models, we predicted the prevalence of malaria in children under five years of age with a spatial resolution of 1 km.



Including vulnerability factors alongside hazard factors in the 2017 DHS data model improved the accuracy of predictive maps, achieving a median predictive R² of 0.64. Furthermore, models including only vulnerability factors outperformed those including only hazard factors. However, the models trained on the 2020-21 MIS data performed poorly, achieving a median R² of 0.13 at best for the model based on hazard factors, likely due to data collection during the dry season.



These findings highlight the importance of integrating both vulnerability and hazard factors into predictive maps. Future work could validate this approach further using routine malaria data from health management information systems, such as DHIS2.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Future heat-related mortality in Europe driven by compound day-night heatwaves and demographic shifts},

author = {Xilin Wu and Jun Wang and Yong Ge and Shengjie Lai and Die Zhang and Zhoupeng Ren and Jianghao Wang },

url = {https://doi.org/10.1038/s41467-025-62871-y},

year  = {2025},

date = {2025-08-11},

journal = {Nature Communications},

volume = {16},

number = {7420},

abstract = {Anthropogenic climate change is driving summer heat toward more humid conditions, accompanied by more frequent day-night compound heat extremes (high temperatures during both day and night). As the fast-warming and aging continent, Europe faces escalating heat-related health risks. Here, we projected future heat-related mortality in Europe using a distributed lag nonlinear model that incorporates humid heat and compound heat extremes, strengthened by a health risk-based definition of extreme heat and a scenario matrix integrating time-varying adaptation trajectories. Under 2010–2019 adaptation baselines, future heat-related mortality is projected to increase annually by 103.7-135.1 deaths per million people by 2100 across various population-climate scenarios for every degree of global warming, with Western and Eastern Europe suffering the most. If global warming exceeds 2 °C, climate change will dominate (84.0–96.8%) projected increase in heat-related mortality. Across all socioeconomic pathways, even a 50% reduction in heat-related relative risk through physiological adaptation will be insufficient to offset the climate change-driven escalation of future heat-related mortality.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A Robust Cross-Sectional Assessment of the Impacts of COVID-19 Pandemic on the Prevalence of Female Genital Mutilation Among 0–14 Years Old Girls in Nigeria},

author = {Corentin Visée and Camille Morlighem and Chibuzor Christopher Nnanatu },

url = {https://doi.org/10.1177/17455057241311948},

year  = {2025},

date = {2025-05-26},

journal = {Women’s Health},

volume = {21},

abstract = {Female genital mutilation (FGM) is a human rights violation that still affects more than 3 million girls aged 0–14 years each year. To achieve the Sustainable Development Goal 2030 agenda, efforts have been made at the local, national and international levels to end the practice by the year 2030. However, the recent COVID-19 pandemic may have reversed the progress made due to increased rates of early marriage of girls, violence against children and school closures during lockdowns. Although some surveys have examined changes in FGM prevalence over the COVID-19 period, changes at the national and sub-national levels among 0–14 years old girls have not been quantified.



This study aimed to understand the potential impacts of the COVID-19 pandemic on the likelihood of FGM among girls aged 0–14 years, and whether it affected progress towards the elimination of FGM.



We used Bayesian hierarchical regression models implemented within the integrated nested Laplace approximations frameworks.



We modelled the likelihood and prevalence of FGM among girls aged 0–14 years before and after the COVID-19 pandemic in Nigeria, with respect to individual- and community-level characteristics, using Bayesian hierarchical models. We used the 2018 Demographic and Health Survey as the pre-COVID-19 period and the 2021 Multiple Indicator Cluster Survey as the post-COVID-19 period.



At the state level, FGM prevalence varied geographically and increased by 23% and 27% in the northwestern states of Katsina and Kana, respectively. There were 11% increase in Kwara and 14% increase in Oyo. However, at the national level, the prevalence of FGM was found to decrease from 19.5% to 12.3% between 2018 and 2021. Cultural factors were identified as the key drivers of FGM among 0–14 years old girls in Nigeria. The changes in the likelihood of girls undergoing FGM across the two time periods also varied across ethnic and religious groups following COVID-19 pandemic.



Our findings highlight that FGM is still a social norm in some states/regions and groups in Nigeria, thereby highlighting the need for a continued but accelerated FGM interventions throughout the country.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Beyond proximity: an observational study of stillbirth rates and emergency obstetric and newborn care accessibility in The Gambia},

author = {Oghenebrume Wariri and Winfred Dotse-Gborgbortsi and Schadrac C Agbla and Hawanatu Jah and Mamady Cham and Ba Foday Jawara and Mustapha Bittaye and Momodou T Nyassi and Musa Marena and Sainey Sanneh and Mariama Janneh and Beate Kampmann and Aduragbemi Banke-Thomas and Joy E Lawn and Uduak Okomo },

url = {https://doi.org/10.1136/bmjgh-2024-016579},

year  = {2025},

date = {2025-04-03},

journal = {BMJ Global Health},

abstract = {Stillbirths are disproportionately concentrated in sub-Saharan Africa, where geographical accessibility to basic/comprehensive emergency obstetric and newborn care (BEmONC and CEmONC) significantly influences maternal and perinatal outcomes. This study describes stillbirth rates within healthcare facilities in The Gambia and examines their distribution in relation to the geographical accessibility of these facilities.



We analysed 97 276 births recorded between 1 January 2013 and 31 December 2018, from 10 major public healthcare facilities in The Gambia. To standardise definitions, stillbirths were defined as fetal deaths with a birth weight of ≥500 g. Fresh stillbirths were reclassified as intrapartum, and macerated stillbirths were reclassified as antepartum. Linear regression with cubic splines was used to model trends, and AccessMod software estimated travel times to facilities.



Among recorded births, 5.1% (4873) were stillbirths, with an overall stillbirth rate of 51.3 per 1000 births (95% CI: 27.5 to 93.6). Intrapartum stillbirths accounted for 53.8% (27.6 per 1000 births; 95% CI: 14.4 to 49.8). Fully functional CEmONC facilities reported the highest stillbirth rates, including the National Teaching Hospital (101.7 per 1000 births, 95% CI: 96.8 to 106.8). Approximately 42.8%, 58.9% and 68.3% of women aged 15–49 lived within a 10, 20 and 30 min travel time, respectively, to fully functional CEmONC facilities, where high stillbirth rates were concentrated.



In The Gambia, intrapartum stillbirth rates remain alarmingly high, even in geographically accessible CEmONC facilities. Inadequate documentation of fetal heart rate on admission hampers accurate classification, complicating targeted interventions. Ensuring that EmONC-designated facilities—particularly those providing BEmONC services—are fully functional with essential equipment, trained staff and robust referral systems, while enhancing the timeliness and quality of obstetric care, is crucial to reducing stillbirth rates.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A scoping review of the methods used to estimate health facility catchment populations for child health indicators in sub-Saharan Africa},

author = {Matthew Johnson and Wole Ademola Adewole and Victor Alegana and C. Edson Utazi and Nuala McGrath and James Wright },

url = {https://doi.org/10.1186/s12963-025-00374-0},

year  = {2025},

date = {2025-03-29},

journal = {Population Health Metrics},

volume = {23},

issue = {11},

abstract = {Evidence indicating persistent geographic inequalities in health outcomes signifies a need for routine subnational monitoring of health-related Sustainable Development Goal targets in sub-Saharan Africa. Health facilities may be an appropriate subnational unit for monitoring purposes, but a lack of suitable demographic data complicates the production of baseline facility-level population denominators against which progress can be reliably measured. This scoping review aimed to map the methods and data sources used to estimate health facility catchment areas and translate them to population denominators for child health indicators in the region.



Peer-reviewed research publications and grey literature reports were identified by searching bibliographic databases and relevant organisational websites. The inclusion criteria required that studies were conducted in sub-Saharan Africa since January 2000, described quantitative method(s) for estimating health facility catchment areas and/or population denominators, and focussed on children as the population of interest. Following title/abstract then full text screening of search results, relevant data were extracted using a standard form. Thematic analysis was undertaken to extract themes and present a narrative synthesis.



Overall, 33 research publications and 3 grey literature reports were included. Of these, only 7 research studies and 1 technical guidance document outlined aims explicitly framed around methods development and/or evaluation. Studies increasingly estimated catchment areas using complex geostatistical or travel time-based modelling approaches rather than simpler proximity metrics, and produced denominators by intersecting catchment boundaries with gridded population surfaces rather than aggregating area-based administrative counts. Few studies used data produced by or describing health facilities to link estimation methods to service utilisation patterns, inter-facility competition or facility characteristics.



There is a need for catchment population estimation methods that can be scaled to national-level facility networks and replicated across the region. This could be achieved by leveraging routinely collected health data and other readily available and nationally consistent data sources. Future methodological development should emphasise modern geostatistical approaches drawing upon the relative strengths of multiple data sources and capturing the range of spatial, supply-side, individual-level and environmental factors with potential to influence catchments’ extent, shape and demographic composition.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Beyond physical accessibility, bypassing health facilities offering caesarean section: insights from women in Dakar’s slums },

author = {El Hadji Malick Sylla and Ndeye Awa Fall and Winfred Dotse-Gborgbortsi and Arsène Brunelle Sandie and Barrel Sow Gueye and Diarra Bousso Senghor and Birane Cissé and Fadima Yaya Bocoum and Ibrahima Ousmane Sy and Cheikh Faye},

doi = {10.1136/bmjopen-2024-088606},

year  = {2025},

date = {2025-02-14},

journal = {BMJ Open},

volume = {15},

abstract = {The study examines the geographic  accessibility of Comprehensive Emergency Obstetric Care (CEmONC) among women residing in the slums of Dakar.



Design: The study is a cross-sectional geographic analysis of caesarean care utilisation in health facilities offering the service in Dakar.



Setting: The study was conducted in urban slum areas in Dakar.



Participants: 763 women living in urban slums who had undergone a caesarean section in six health facilities in Dakar between July and December 2022.



Outcome measures: The proportion of women bypassing the nearest health facility and travel time to health facilities.



Key findings: show that most women in Dakar’s urban slums live within 5min from a health facility offering caesarean services, with an average travel time of 6.3min. However, 44.3% bypassed nearby facilities, often travelling outside their district. Medical referral was the primary reason for bypassing (43.2%), followed by the search for higher quality care (13.5%) and reliance on family or social networks (14.9%). Only a small proportion (1.4%) cited more affordable treatment costs as a reason for bypassing.



Conclusion: Despite the good geographical accessibility of health facilities offering caesarean sections in Dakar, many women bypass nearby facilities due to medical referrals and the search for higher quality care, resulting in increased travel time and costs. Strengthening the quality and capacity of local health centres in urban slums is crucial to minimising unnecessary bypassing and ensuring timely access to essential obstetric services.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Multi-level determinants of timely routine childhood vaccinations in The Gambia: Findings from a nationwide analysis},

author = {Oghenebrume Wariri and Chigozie Edson Utazi and Uduak Okomo and Winfred Dotse-Gborgbortsi and Malick Sogur and Sidat Fofana and Kris A. Murray and Chris Grundy and Beate Kampmann },

url = {https://doi.org/10.1016/j.vaccine.2024.126500},

year  = {2025},

date = {2025-01-01},

journal = {Vaccine},

volume = {43},

abstract = {Achieving the ambitious goals of the Immunisation Agenda 2030 (IA2030) requires a deeper understanding of factors influencing under-vaccination, including timely vaccination. This study investigates the demand- and supply-side determinants influencing the timely uptake of key childhood vaccines scheduled throughout the first year of life in The Gambia.



We used two nationally-representative datasets: the 2019–20 Gambian Demographic and Health Survey and the 2019 national immunisation facility mapping. Using Bayesian multi-level binary logistic regression models, we identified key factors significantly associated with timely vaccination for five key vaccines: birth dose of hepatitis-B (HepB0), first, second, and third doses of the pentavalent vaccine (Penta1, Penta2, Penta3), and first-dose of measles-containing vaccine (MCV1) in children 



We found that demand-side factors, such as ethnicity, household wealth status, maternal education, maternal parity, and the duration of the household's residency in its current location, were the most common drivers of timely childhood vaccination. However, supply-side factors such as travel time to the nearest immunisation clinic, availability of cold-storage and staffing numbers in the nearest immunisation clinic were also significant determinants. Furthermore, the determinants varied across specific vaccines and the timing of doses. For example, delivery in a health facility (aOR = 1.58, 95 %CI: 1.02–2.53), living less than 30 min (aOR = 2.11, 95 %CI: 1.2–8.84) and living between 30 and 60 min (aOR = 3.68, 95 %CI: 1.1–14.99) from a fixed-immunisation clinic was associated with timely HepB0, a time-sensitive vaccine that must be administered within 24 h of birth. On the other hand, children who received Penta1 and Penta2 on time were three- to five-fold more likely to receive subsequent doses on time (Penta2 and Penta3, respectively). Finally, proximity to an immunisation facility with functional vaccine cold-storage was a significant supply-side determinant of timely MCV1 (aOR = 1.4, 95 %CI: 1.09–1.99).



These findings provide valuable insights for programme managers and policymakers. By prioritising interventions and allocating scarce resources based on these identified determinants, they can maximize their impact and ensure children in The Gambia receive timely vaccinations throughout their first year of life, contributing to IA2030 goals.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A systematic review of using population-level human mobility data to understand SARS-CoV-2 transmission},

author = {Natalya Kostandova and Catherine Schluth and Rohan Arambepola and Fatumah Atuhaire and Sophie Bérubé and Taylor Chin and Eimear Cleary and Oscar Cortes-Azuero and Bernardo García-Carreras and Kyra H. Grantz and Matt D. T. Hitchings and Angkana T. Huang and Nishant Kishore and Shengjie Lai and Soren L. Larsen and Stacie Loisate and Pamela Martinez and Hannah R. Meredith and Ritika Purbey and Tanjona Ramiadantsoa and Jonathan Read and Benjamin L. Rice and Lori Rosman and Nick Ruktanonchai and Henrik Salje and Kathryn L. Schaber and Andrew J. Tatem and Jasmine Wang and Derek A. T. Cummings and Amy Wesolowski },

url = {https://doi.org/10.1038/s41467-024-54895-7},

year  = {2024},

date = {2024-12-03},

journal = {Nature Communications},

volume = {15},

number = {10504},

abstract = {The emergence of SARS-CoV-2 into a highly susceptible global population was primarily driven by human mobility-induced introduction events. Especially in the early stages, understanding mobility was vital to mitigating the pandemic prior to widespread vaccine availability. We conducted a systematic review of studies published from January 1, 2020, to May 9, 2021, that used population-level human mobility data to understand SARS-CoV-2 transmission. Of the 5505 papers with abstracts screened, 232 were included in the analysis. These papers focused on a range of specific questions but were dominated by analyses focusing on the USA and China. The majority included mobile phone data, followed by Google Community Mobility Reports, and few included any adjustments to account for potential biases in population sampling processes. There was no clear relationship between methods used to integrate mobility and SARS-CoV-2 data and goals of analysis. When considering papers focused only on the estimation of the effective reproductive number within the US, there was no clear relationship identified between this measure and changes in mobility patterns. Our findings underscore the need for standardized, systematic ways to identify the source of mobility data, select an appropriate approach to using it in analysis, and reporting.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A systematic review of using population-level human mobility data to understand SARS-CoV-2 transmission},

author = {Natalya Kostandova and Catherine Schluth and Rohan Arambepola and Fatumah Atuhaire and Sophie Bérubé and Taylor Chin and Eimear Cleary and Oscar Cortes-Azuero and Bernardo García-Carreras and Kyra H. Grantz and Matt D. T. Hitchings and Angkana T. Huang and Nishant Kishore and Shengjie Lai and Soren L. Larsen and Stacie Loisate and Pamela Martinez and Hannah R. Meredith and Ritika Purbey and Tanjona Ramiadantsoa and Jonathan Read and Benjamin L. Rice and Lori Rosman and Nick Ruktanonchai and Henrik Salje and Kathryn L. Schaber and Andrew J. Tatem and Jasmine Wang and Derek A. T. Cummings and Amy Wesolowski},

url = {https://doi.org/10.1038/s41467-024-54895-7},

year  = {2024},

date = {2024-12-03},

urldate = {2024-12-03},

journal = {Nature Communications},

volume = {15},

abstract = {The emergence of SARS-CoV-2 into a highly susceptible global population was primarily driven by human mobility-induced introduction events. Especially in the early stages, understanding mobility was vital to mitigating the pandemic prior to widespread vaccine availability. We conducted a systematic review of studies published from January 1, 2020, to May 9, 2021, that used population-level human mobility data to understand SARS-CoV-2 transmission. Of the 5505 papers with abstracts screened, 232 were included in the analysis. These papers focused on a range of specific questions but were dominated by analyses focusing on the USA and China. The majority included mobile phone data, followed by Google Community Mobility Reports, and few included any adjustments to account for potential biases in population sampling processes. There was no clear relationship between methods used to integrate mobility and SARS-CoV-2 data and goals of analysis. When considering papers focused only on the estimation of the effective reproductive number within the US, there was no clear relationship identified between this measure and changes in mobility patterns. Our findings underscore the need for standardized, systematic ways to identify the source of mobility data, select an appropriate approach to using it in analysis, and reporting.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Trade-offs among human, animal, and environmental health hinder the uniform progress of global One Health},

author = {Tian, Ya and Zhang, Junze and Li, Zonghan and Wu, Kai and Cao, Min and Lin, Jian and Pradhan, Prajal and Lai, Shengjie and Meng, Jia and Fu, Bojie and Chen, Min and Lin, Hui },

url = {https://doi.org/10.1016/j.isci.2024.111357},

doi = {10.1016/j.isci.2024.111357 },

year  = {2024},

date = {2024-11-22},

journal = {iScience},

volume = {27},

issue = {12},

abstract = {The One Health (OH) approach, integrating aspects of human, animal, and environmental health, still lacks robustly quantified insights into its complex relationships. To fill this knowledge gap, we devised a comprehensive assessment scheme for OH to assess its progress, synergies, trade-offs, and priority targets. From 2000 to 2020, we find evidence for global progress toward OH, albeit uneven, with its average score rising from 61.6 to 65.5, driven primarily by better human health although environmental health lags. Despite synergies prevalent within and between the three health dimensions, over half of the world’s countries, mainly low-income ones, still incur substantial trade-offs impeding OH’s advancement, especially between animal and environmental health. Our in-depth analysis of synergy and trade-off networks reveals that maternal, newborn, and child health are critical synergistic targets, whereas biodiversity and land resources dominate trade-offs. We provide key information for the synergetic and uniform development of global OH and policymaking.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Geospatial Variation in Vaccination Coverage and Zero-Dose Prevalence at the District, Ward and Health Facility Levels Before and After a Measles Vaccination Campaign in Nigeria},

author = {C. Edson Utazi and Iyanuloluwa D. Olowe and H. M. Theophilus Chan and Winfred Dotse-Gborgbortsi and John Wagai and Jamila A. Umar and Sulaiman Etamesor and Brian Atuhaire and Biyi Fafunmi and Jessica Crawford and Adeyemi Adeniran and Andrew J. Tatem},

url = {https://doi.org/10.3390/vaccines12121299},

year  = {2024},

date = {2024-11-21},

journal = {Vaccines},

volume = {12},

number = {1299},

abstract = {Many measles endemic countries with suboptimal coverage levels still rely on vaccination campaigns to fill immunity gaps and boost control efforts. Depending on local epidemiological patterns, national or targeted campaigns are implemented, following which post-campaign coverage surveys (PCCSs) are conducted to evaluate their performance, particularly in terms of reaching previously unvaccinated children. Due to limited resources, PCCS surveys are designed to be representative at coarse spatial scales, often masking important heterogeneities in coverage that could enhance the identification of areas of poor performance for follow-up via routine immunization strategies. Here, we undertake geospatial analyses of the 2021 measles PCCS in Nigeria to map indicators of coverage measuring the individual and combined performance of the campaign and routine immunization (RI) at 1 × 1 km resolution and the ward and district levels in 13 states. Using additional geospatial datasets, we also produced estimates of numbers of unvaccinated children during the campaign and numbers of measles-containing vaccine (MCV) zero-dose children before and after the campaign at these levels and within health facility catchment areas. Our study revealed that although the campaign reduced the numbers of MCV zero-dose children in all the districts, areas of suboptimal campaign and RI performance with considerable numbers of zero-dose children remained. Our analyses further identified wards and health facility catchment areas with higher numbers of unvaccinated children within these areas. Our outputs provide a robust evidence base to plan and implement follow-up RI strategies and to guide future campaigns at flexible and operationally relevant spatial scales.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Disaggregating census data for population mapping using a Bayesian Additive Regression Tree model},

author = {Ortis Yankey and Chigozie E. Utazi and Christopher C. Nnanatu and Assane N. Gadiaga and Thomas Abbot and Attila N. Lazar and Andrew J. Tatem},

url = {https://doi.org/10.1016/j.apgeog.2024.103416},

year  = {2024},

date = {2024-09-14},

journal = {Applied Geography},

volume = {174},

abstract = {Population data is crucial for policy decisions, but fine-scale population numbers are often lacking due to the challenge of sharing sensitive data. Different approaches, such as the use of the Random Forest (RF) model, have been used to disaggregate census data from higher administrative units to small area scales. A major limitation of the RF model is its inability to quantify the uncertainties associated with the predicted populations, which can be important for policy decisions. In this study, we applied a Bayesian Additive Regression Tree (BART) model for population disaggregation and compared the result with a RF model using both simulated data and the 2021 census data for Ghana. The BART model consistently outperforms the RF model in out-of-sample predictions for all metrics, such as bias, mean squared error (MSE), and root mean squared error (RMSE). The BART model also addresses the limitations of the RF model by providing uncertainty estimates around the predicted population, which is often lacking with the RF model. Overall, the study demonstrates the superiority of the BART model over the RF model in disaggregating population data and highlights its potential for gridded population estimates.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Identifying counter-urbanisation using Facebook's user count data},

author = {Qianwen Duan and Jessica Steele and Zhifeng Cheng and Eimear Cleary and Nick Ruktanonchai and Hal Voepel and Tim O'Riordan and Andrew J. Tatem and Alessandro Sorichetta and Shengjie Lai and Felix Eigenbrod},

url = {https://doi.org/10.1016/j.habitatint.2024.103113},

doi = {10.1016/j.habitatint.2024.103113},

year  = {2024},

date = {2024-06-04},

journal = {Habitat International},

volume = {150},

abstract = {Identifying the growing widespread phenomenon of counter-urbanisation, where people relocate from urban centres to rural areas, is essential for understanding the social and ecological consequences of the associated changes. However, its nuanced dynamics and complex characteristics pose challenges for quantitative analysis. Here, we used near real-time Facebook user count data for Belgium and Thailand, with missing data imputed, and applied the Seasonal-Trend decomposition using Loess (STL) model to capture subtle urban and rural population dynamics and assess counter-urbanisation. We identified counter-urbanisation in both Belgium and Thailand, evidenced by increases of 1.80% and 2.14% in rural residents (night-time user counts) and decreases of 3.08% and 5.04% in urban centre night-time user counts from March 2020 to May 2022, respectively. However, the counter-urbanisation in Thailand appears to be transitory, with rural users beginning to decline during both day and night as COVID-19 restrictions were lifted. By contrast, in Belgium, at the country level, there is as yet no evidence of a return to urban residences, though daytime numbers in rural areas are decreasing and in urban centres are increasing, suggesting an increase in commuting post-pandemic. These variation characteristics observed both between Belgium and Thailand and between day and night, extend the current understanding of counter-urbanisation. The use of novel social media data provides an effective quantitative perspective to comprehend counter-urbanisation in different settings.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Inter-district and Wealth-related Inequalities in Maternal and Child Health Service Coverage and Child Mortality within Addis Ababa City},

author = {Mekonnen, W. and Dechassa, W. and Melesse, D.Y. and Tejedor-Garavito, N. and Nilsen, K. and Getachew, T. and Mulu, S. and Wondrad, N},

url = {https://doi.org/10.1007/s11524-024-00836-0},

doi = {doi.org/10.1007/s11524-024-00836-0},

year  = {2024},

date = {2024-03-27},

journal = {Journal of Urban Health},

abstract = {In sub-Saharan Africa, urban areas generally have better access to and use of maternal, newborn, and child health (MNCH) services than rural areas, but previous research indicates that there are significant intra-urban disparities. This study aims to investigate temporal trends and geographic differences in maternal, newborn, and child health service utilization between Addis Ababa’s poorest and richest districts and households. A World Bank district-based poverty index was used to classify districts into the top 60% (non-poor) and bottom 40% (poor), and wealth index data from the Ethiopian Demographic and Health Survey (EDHS) was used to classify households into the top 60% (non-poor) and bottom 40% (poor). Essential maternal, newborn, and child health service coverage was estimated from routine health facility data for 2019–2021, and five rounds of the EDHS (2000–2019) were used to estimate child mortality. The results showed that service coverage was substantially higher in the top 60% than in the bottom 40% of districts. Coverage of four antenatal care visits, skill birth attendance, and postnatal care all exceeded 90% in the non-poor districts but only ranged from 54 to 67% in the poor districts. Inter-district inequalities were less pronounced for childhood vaccinations, with over 90% coverage levels across all districts. Inter-district inequalities in mortality rates were considerable. The neonatal mortality rate was nearly twice as high in the bottom 40% of households' as in the top 60% of households. Similarly, the under-5 mortality rate was three times higher in the bottom 40% compared to the top 60% of households. The substantial inequalities in MNCH service utilization and child mortality in Addis Ababa highlight the need for greater focus on the city’s women and children living in the poorest households and districts in maternal, newborn, and child health programs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}