@article{nokey,

title = {An age-structured spatially varying coefficient model for high-resolution mapping of vaccination coverage},

author = {C. Edson Utazi and Somnath Chaudhuri and Oghenebrume Wariri and Iyanuloluwa D. Olowe and Mohamed Megheib and Andrew J. Tatem},

url = {https://doi.org/10.1371/journal.pcbi.1013989},

doi = {10.1371/journal.pcbi.1013989},

year  = {2026},

date = {2026-02-17},

journal = {PLoS Computational Biology},

volume = {22},

number = {e1013989},

issue = {2},

abstract = {High-resolution maps of vaccination coverage are valuable for uncovering heterogeneities in coverage to inform vaccine delivery strategies. Coverage maps stratified by age can reveal additional heterogeneities in the timeliness of vaccination and critical immunity gaps among birth cohorts. Here, we propose a spatially varying coefficient model relying on a Bayesian approach for age-structured mapping of vaccination coverage using geolocated individual level household survey and geospatial covariate data. Our flexible modelling framework includes parameterizations capturing spatial (non-)stationarity in differences in coverage between age groups, as well as a modification to allow coverage mapping for single age points through the inclusion of a smoother over age. The proposed models are fitted using the INLA-SPDE approach implemented in the inlabru package in R. We choose between competing model parameterizations by examining their out-of-sample predictive performance via cross-validation and using Bayesian model choice criteria. The methodology is applied to age-structured mapping of measles vaccination coverage in Cote d’Ivoire using the 2021 Demographic and Health Survey. Our results reveal a significant delay in measles vaccination in the first year of life and substantial spatial differences in coverage by age, highlighting the need for targeted interventions to achieve equity and attain vaccine-derived immunity goals.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {COVID-19 and urban exodus: diverging population redistribution patterns across countries from 2020 to 2022},

author = {Qianwen Duan and Shengjie Lai and Alessandro Sorichetta and Andrew J. Tatem and Jessica Steele snd Felix Eigenbrod },

url = {https://doi.org/10.1038/s42949-026-00351-y},

doi = {10.1038/s42949-026-00351-y},

year  = {2026},

date = {2026-02-05},

urldate = {2026-02-05},

journal = {npj urban sustainability},

abstract = {While widespread urbanisation continues, emerging trends of population redistribution away from highly urbanised areas have been observed in some countries, with important implications for infrastructure planning, resource allocation, and environmental risk assessment. However, few studies have examined this trend in a timely and spatially comprehensive manner across diverse national contexts, particularly in response to the turbulence in migration patterns caused by the COVID-19 pandemic. Here, we analyse spatial Facebook population data from 2020 to 2022 across 35 countries to characterise two forms of population redistribution: shifts between urban and rural areas, and changes along the urban density gradient. During the early response phase of the pandemic, broader country-level trends of urban-to-rural redistribution and intra-urban deconcentration were evident. However, 20% and 4.8% of these trends, respectively, were temporary and reversed during the later phase of the pandemic. The extent and direction of these patterns varied across countries and were negatively associated with the Human Development Index, suggesting that developed nations experienced greater urban depopulation and spatial deconcentration. Our findings reveal a potential misalignment between population redistribution and existing physical urban densities in certain countries, as densely built-up areas are experiencing outflows, highlighting the need for adaptive urban planning strategies to address evolving population dynamics and related sustainability challenges.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Combined benefits of multi-hazard early warnings on human mobility  resilience to tropical cyclones},

author = {Haiyan Liu, Jiatong Han, Jianghao Wang, Phil J. Ashworth, Zhifeng Cheng, Steve Darby, Siqin Wang, Faith Ka Shun Chan, Andrew J. Tatem, Shengjie Lai},

url = {https://doi.org/10.1016/j.gloenvcha.2025.103111},

doi = {10.1016/j.gloenvcha.2025.103111},

year  = {2026},

date = {2026-01-07},

urldate = {2026-01-07},

journal = {Global Environmental Change},

volume = {96},

number = {103111},

abstract = {Multi-hazard early-warning systems (MHEWS) are critical for mitigating extreme weather impacts and enhancing disaster resilience. However, quantitative empirical evidence on how different types of early warnings individually and collectively trigger preventive actions and influence resilience remains limited. Here, using location- based human mobility data aggregated from over 1.1 billion mobile devices across Chinese cities, we quantified daily intracity human mobility responses to 21,126 early warning signals during 19 tropical cyclones (TCs) from 2021 to 2023. To represent disaster resilience under MHEWS protection, we developed a protected resilience index that integrates both the magnitude of mobility changes and recovery durations. We found that, compared with city-level TC warnings alone, combined multi-level, multi-hazard warnings resulted in a 52.4 % reduction in mobility during TC exposure days, thereby increasing avoided direct population exposure by around 57.1 %. Each additional warning type further shortened recovery times, collectively reducing recovery durations by at least 55.6 %, with larger effects observed for stronger TCs. Under MHEWS protection, protected resilience remained statistically similar between moderate-intensity TCs (34 kt and 50 kt) but declined significantly under severe (≥64 kt) conditions. Although absolute reductions in exposure were greater in high-frequency, coastal, and wealthier cities, relative improvements from MHEWS were more pronounced in less frequently affected, inland, and socioeconomically disadvantaged areas. Consequently, MHEWS significantly narrowed resilience disparities among cities facing equivalent hazard exposures. This study introduces a scalable, behaviour-based framework for quantifying early-warning effectiveness, highlighting the essential role of integrated multi-level and multi-hazard warnings in disaster preparedness across cities amid escalating climate risks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Assessing context-dependent effectiveness of heat adaptation through human mobility under different heatwave regimes},

author = {Haiyan Liu and Siqin Wang and Chunzhu Wei and Wenbin Zhang and Andrew J Tatem and Shengjie Lai},

url = {https://doi.org/10.1016/j.scs.2025.107066},

doi = {10.1016/j.scs.2025.107066},

year  = {2025},

date = {2025-12-17},

urldate = {2025-12-17},

journal = {Sustainable Cities and Society},

volume = {136},

number = {107066},

abstract = {As heatwaves intensify under climate change, cities increasingly rely on adaptation strategies to mitigate risk. Yet, the real-world effectiveness of climate adaptation measures in influencing human behavior to support daily functioning across cities remains limited. Using daily intracity mobility data aggregated from over 1.1 billion mobile devices across 366 Chinese cities in 2023, we apply a causal inference framework based on causal random forest to quantify the heterogeneous effects of three key adaptation measures: access to cooling centers, urban greenness (NDVI), and heat warnings during daytime-only and compound day-night heatwaves. We find that the adaptation effectiveness varies markedly by heatwave type and local socioeconomic conditions. Public cooling facilities reduced mobility during daytime-only heatwaves but promoted it under day-night heatwaves, especially in low GDP per capita, aging and agriculturally dependent cities. In contrast, greenness consistently failed to sustain mobility in elderly or agriculturally dominant cities. Heat warnings exhibited paradoxical effects: although intended to discourage heat exposure, they were often associated with increased mobility at extreme temperatures in vulnerable cities, while showing only modest suppressive effects in younger, less agricultural cities. These findings reveal that the benefits of adaptation are highly context-dependent and unequally distributed, highlighting the need for precision adaptation: strategies tailored not only to environmental conditions but also to behavioral, demographic, and socioeconomic variability. By linking adaptation measures to near real-time behavioral responses, our study offers a scalable, data-driven framework to guide more equitable and effective urban climate-resilient planning.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Social, mobility and contact networks in shaping health behaviours and infectious disease dynamics: a scoping review},

author = {Zhifeng Cheng and Nick W. Ruktanonchai and Amy Wesolowski and Sen Pei and Jianghao Wang and Samantha Cockings and Andrew J. Tatem and Shengjie Lai},

url = {https://doi.org/10.1186/s40249-025-01378-6},

doi = {10.1186/s40249-025-01378-6},

year  = {2025},

date = {2025-12-03},

journal = {Infectious Diseases of Poverty},

volume = {14},

number = {123},

abstract = {The interconnectedness of human society in this modern world can transform localised outbreaks into global pandemics, underscoring the pivotal roles of social, mobility and contact networks in shaping infectious disease dynamics. Although these networks share analogous contagion principles, they are often studied in isolation, hindering the incorporation of behavioural, informational, and epidemiological processes into disease models. This review synthesises current research on the interplay between social, mobility and contact networks in health behaviour contagion and infectious disease transmission.



We searched Web-of-Science and PubMed from January 2000 to June 2025 for research on health behaviour contagion and information dissemination in social networks, pathogen spread through mobility and contact networks, and their joint impacts on epidemic dynamics. This was first done by a preliminary literature screening based on predefined criteria. With potentially relevant publications retained, we performed keyword co-occurrence network analysis to identify the most common themes in studies. The results guide us to narrow down the reviewing scope to the social, mobility and contact network impacts on informational, behavioural, and epidemiological dynamics. We then further identified and reviewed the literature on these multidimensional network influences.



Our review finds that each network type plays a distinct yet interconnected role in shaping behaviours and disease dynamics. Social networks, comprising both online and offline interpersonal relationships, facilitate the dissemination of health information and influence behavioural responses to public health interventions. Concurrently, mobility and contact networks govern the spatiotemporal pathways of pathogen transmission, as demonstrated in recent pandemics. While traditional population-level models often overlook individual discrepancies and social network effects, significant efforts have been made through developing individual-level simulation-based models that integrate behavioural dynamics. With emerging new data sources and advanced computational techniques, two promising approaches—multiplex network analysis and generative agent-based modelling—offer frameworks for integrating the complex interdependencies among social, mobility and contact networks into epidemic dynamics estimation.



This review highlights the theoretical and methodological advances in network-based infectious disease modelling and identifies critical knowledge and research gaps. Future research should prioritise integrating multi-source behavioural and spatial data, unifying modelling strategies, and developing scalable approaches for incorporating multilayer network data. The integrated approach will strengthen public health strategies, enabling equitable and effective interventions against emerging infections.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Human mobility in epidemic modeling},

author = {Xin Lu and Jiawei Feng and Shengjie Lai and Petter Holme and Shuo Liu and Zhanwei Du and Xiaoqian Yuan and Siqing Wang and Yunxuan Li and Xiaoyu Zhang and Yuan Bai and Xiaojun Duan and Wenjun Mei and Hongjie Yu and Suoyi Tan and Fredrik Liljeros},

url = {https://doi.org/10.1016/j.physrep.2025.10.010},

doi = {10.1016/j.physrep.2025.10.010},

year  = {2025},

date = {2025-11-07},

journal = {Physics Reports},

volume = {1157},

pages = {1-45},

abstract = {Human mobility forms the backbone of contact patterns through which infectious diseases propagate, fundamentally shaping the spatio-temporal dynamics of epidemics and pandemics. While traditional models are often based on the assumption that all individuals have the same probability of infecting every other individual in the population, a so-called random homogeneous mixing, they struggle to catch the complex and heterogeneous nature of real-world human interactions. Recent advancements in data-driven methodologies and computational capabilities have unlocked the potential of integrating high-resolution human mobility data into epidemic modeling, significantly improving the accuracy, timeliness, and applicability of epidemic risk assessment, contact tracing, and intervention strategies. This review provides a comprehensive synthesis of the current landscape in human mobility-informed epidemic modeling. We explore several data sources and representations of human mobility, and examine the behavioral and structural roles of mobility and contact in shaping disease transmission dynamics. Furthermore, the review spans a wide range of epidemic modeling approaches, ranging from classical compartmental models to network-based, agent-based, and machine learning models. It also discusses how mobility integration enhances risk management and response strategies during epidemics. By synthesizing these insights, the review can serve as a foundational resource for researchers and practitioners, bridging the gap between epidemiological theory and the dynamic complexities of human interaction while charting clear directions for future research.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Modelling practices, data provisioning, sharing and dissemination needs for pandemic decision-making: a European survey-based modellers’ perspective, 2020 to 2022},

author = {Esther van Kleef and Wim Van Borte and Elena Arsevska and Luca Busani and Simon Dellicour and Laura Di Domenico and Marius Gilbert and Sabine L van Elsland and Moritz UG Kraemer11 and Shengjie Lai1 and Philippe Lemey and Stefano Merler1 and Zoran Milosavljevic and Annapaola Rizzoli1 and Danijela Simic1 and Andrew J Tatem and et al.},

url = {https://doi.org/10.2807/1560-7917.ES.2025.30.42.2500216},

doi = {10.2807/1560-7917.ES.2025.30.42.2500216},

year  = {2025},

date = {2025-10-23},

journal = {Eurosurveillance},

volume = {30},

issue = {42},

abstract = {Key public health message



What did you want to address in this study and why?



We wanted to know how COVID-19 modelling was used across Europe to support public health decisions. We evaluated changes in modelling practices, data access and collaboration with policymakers. To our knowledge, this is the first systematic and semiquantitative assessment of these elements during the pandemic, offering insights for better crisis response in the future.



What have we learnt from this study?



Modelling priorities shifted throughout the pandemic, from understanding the virus in the early stages to evaluating interventions such as vaccines later on. While timely case numbers were widely available, (real-time) behavioural, mobility and immunity data and sufficient population details were often missing. Collaboration between scientists and decision-makers evolved from informal network exchanges to formal advisory roles.



What are the implications of your findings for public health?



There is a need for rethinking the sustainability of existing and recently emerging collaborative platforms and advisory boards, including research consortia and modelling networks. This can help foster standardised data collection, sharing and coordination during pandemics, particularly for data that move beyond counting cases and come from diverse (including private) providers, so to act faster in future health emergencies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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 = {Global gridded multi-temporal datasets to support human population distribution modelling},

author = {Dorothea Woods and Tom McKeen and Alexander Cunningham and Rhorom Priyatikanto and Andrew J. Tatem and Alessandro Sorichetta and Maksym Bondarenko},

url = {https://doi.org/10.12688/gatesopenres.16363.1},

doi = {10.12688/gatesopenres.16363.1},

year  = {2025},

date = {2025-09-15},

journal = {Gates Open Research},

volume = {9},

issue = {72},

abstract = {Population distributions across countries and regions exhibit significant spatial and temporal variability. This variation highlights the need for high-resolution, small-area demographic data to address the challenges posed by shifting population dynamics, urbanization, and migration. Small area population modelling, particularly the production of gridded population estimates, has advanced rapidly over the past decade. Gridded population estimates rely heavily on the availability of detailed geospatial ancillary datasets to capture, inform and explain the variabilities in population densities and distributions at small area scales, enabling the disaggregation from areal unit-based counts. Here we describe an extensive geospatial collection of annual, high resolution, spatio-temporally harmonised, global datasets aimed at driving improvements in mapping small area population density variation. This article presents the spatio-temporal harmonisation process that results in an open access repository of 73 individual gridded datasets addressing topography, climate, nighttime lights, land cover, inland water, infrastructure, protected areas as well as the built-up environment on a global level at a spatial resolution of 3 arc-seconds (approximately 100 metres). Datasets are available as annual time series from 2015 up to and including at least 2020, and as recent as 2023 where source datasets allow. Such datasets not only support population modelling but also applications across environmental, economic, and health sectors, supporting informed policy-making and resource allocation for sustainable development.},

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 = {Assessing the impacts of gridded population model choice on degree of urbanisation metrics},

author = {Wen-Bin Zhang and Dorothea Woods and Iyanuloluwa Deborah Olowe and Marcello Schiavina and Weixuan Fang and Graeme Hornby and Maksym Bondarenko and Joachim Maes and Lewis Dijkstra and Andrew J. Tatem and Alessandro Sorichetta},

url = {https://doi.org/10.1016/j.cities.2025.106293},

doi = {10.1016/j.cities.2025.106293},

year  = {2025},

date = {2025-07-21},

journal = {Cities},

volume = {166},

number = {106293},

abstract = {Defining urban and rural areas is crucial for assessing the accessibility of services and opportunities that impact people worldwide. The Degree of Urbanisation framework, endorsed by the UN Statistical Commission, primarily uses population grids to classify areas through a harmonised, population-centric approach, enabling international comparisons. However, variations in the distribution of population counts at the grid-cell level across different population datasets can significantly influence the resulting patterns. We applied the Degree of Urbanisation to 16 countries in Africa and the Caribbean, using four population grids to evaluate these effects. It shows that differences primarily occur in the classification of urban cluster. On average, 27.5 % of the population falls into mixed classes, with 17.5 % in mixed rural and urban cluster areas and 7.8 % in mixed urban cluster and urban centre areas. Population grids that only model populations within satellite-detected settlements show limited disagreement, with mixed rural and urban cluster population classifications decreasing by 5.6 percentage points and mixed urban cluster and urban centre populations by 1.4. Population modelling approaches that distribute populations more broadly, including outside of detected built-up areas, substantially reduce settlement identifications, resulting in 42.3 % fewer urban centres and 66.2 % fewer dense urban clusters than the average across the study countries. Our analyses highlight the potential sensitivity of Degree of Urbanisation to gridded population modelling assumptions and provide guidance on its implementation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Disappearing people: A global demographic data crisis threatens public policy},

author = {Jessica M. Espey and Andrew J. Tatem and Dana R. Thomson},

url = {https://doi.org/10.1126/science.adx8683},

doi = {10.1126/science.adx8683},

year  = {2025},

date = {2025-06-19},

journal = {Science},

volume = {388},

issue = {6753},

pages = {1277-1280},

abstract = {Every day, decisions that affect our lives—such as where to locate hospitals and how to allocate resources for schools—depend on knowing how many people live where and who they are; for example, their ages, occupations, living conditions, and needs. Such core demographic data in most countries come from a census, a count of the population usually conducted every 10 years. But something alarming is happening to many of these critical data sources. As widely discussed at the United Nations (UN) Statistical Commission meeting in New York in March, fewer countries have managed to complete a census in recent years. And even when they are conducted, censuses have been shown to undercount members of certain groups in important ways. Redressing this predicament requires investment and technological solutions alongside extensive political outreach, citizen engagement, and new partnerships.},

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 = {Comparing lagged impacts of mobility changes and environmental factors on COVID-19 waves in rural and urban India: A Bayesian spatiotemporal modelling study},

author = {Eimear Cleary and Fatumah Atuhaire and Alessandro Sorichetta and Nick Ruktanonchai and Cori Ruktanonchai and Alexander Cunningham and Massimiliano Pasqui and Marcello Schiavina and Michele Melchiorri and Maksym Bondarenko and Harry E R Shepherd and Sabu S Padmadas and Amy Wesolowski and Derek A T Cummings and Andrew J Tatem and Shengjie Lai},

url = {https://doi.org/10.1371/journal.pgph.0003431},

doi = {10.1371/journal.pgph.0003431},

year  = {2025},

date = {2025-04-30},

journal = {PLOS Global Public Health},

volume = {5},

number = {e0003431},

issue = {4},

abstract = {Previous research in India has identified urbanisation, human mobility and population demographics as key variables associated with higher district level COVID-19 incidence. However, the spatiotemporal dynamics of mobility patterns in rural and urban areas in India, in conjunction with other drivers of COVID-19 transmission, have not been fully investigated. We explored travel networks within India during two pandemic waves using aggregated and anonymized weekly human movement datasets obtained from Google, and quantified changes in mobility before and during the pandemic compared with the mean baseline mobility for the 8-week time period at the beginning of 2020. We fit Bayesian spatiotemporal hierarchical models coupled with distributed lag non-linear models (DLNM) within the integrated nested Laplace approximation (INLA) package in R to examine the lag-response associations of drivers of COVID-19 transmission in urban, suburban and rural districts in India during two pandemic waves in 2020-2021. Model results demonstrate that recovery of mobility to 99% that of pre-pandemic levels was associated with an increase in relative risk of COVID-19 transmission during the Delta wave of transmission. This increased mobility, coupled with reduced stringency in public intervention policy and the emergence of the Delta variant, were the main contributors to the high COVID-19 transmission peak in India in April 2021. During both pandemic waves in India, reduction in human mobility, higher stringency of interventions, and climate factors (temperature and precipitation) had 2-week lag-response impacts on the  of COVID-19 transmission, with variations in drivers of COVID-19 transmission observed across urban, rural and suburban areas. With the increased likelihood of emergent novel infections and disease outbreaks under a changing global climate, providing a framework for understanding the lagged impact of spatiotemporal drivers of infection transmission will be crucial for informing interventions.},

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 

},

doi = {10.1186/s12963-025-00374-0},

year  = {2025},

date = {2025-03-03},

urldate = {2025-03-03},

journal = {Population Health Metrics},

volume = {23},

number = {11},

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}

}