@article{nokey,

title = {Developing A Customized, Enumeration Area-Based Sampling Frame Tailored to a Specific Population Subgroup Using Geospatial Methods},

author = {Sarchil Qader and Edith Darin and Ahmadou Hamady Dicko and Hisham Galal and Hyunju Park and Rebeca Moreno Jimenez and Andrew Harfoot and Andrew J Tatem},

url = {https://doi.org/10.1093/jssam/smaf027},

doi = {10.1093/jssam/smaf027},

year  = {2026},

date = {2026-04-10},

journal = {Journal of Survey Statistics and Methodology},

abstract = {A national sampling frame typically comprises a list of Primary Sampling Units (PSUs), such as enumeration areas derived from census data, which are commonly used in household surveys. Both national statistical offices and non-governmental organizations often rely on this framework when conducting surveys related to forced displacement. However, these frames are generally developed without considering the estimated number or geographic distribution of displaced populations. As a result, achieving the desired sample size becomes difficult and cost-intensive, as selected units frequently contain no individuals of interest. This study aimed to evaluate the potential of geospatial methodologies to develop a digital national sample frame tailored to a specific population subgroup or the general population, with the goal of ensuring applicability across diverse settings. For the first time, this work produced publicly accessible, digitized boundaries for urban and rural areas in Cameroon that are aligned with official administrative divisions and do not follow a grid-based system. According to our classification and estimated number from the ProGres database, 46 percent of refugees in Cameroon resided in rural areas, while 31 percent lived in camps and 23 percent in urban settings. The proposed geospatial approach offers a cost-effective alternative to traditional manual methods, particularly in data-scarce environments, and eliminates common geometric inconsistencies found in manual mapping efforts. All sampling units were nested within administrative boundaries, and in populated areas, their delineations aligned with observable ground features and respected major physical barriers. Importantly, including the refugee population in the customized national sampling frame was essential, as it enhanced the representativeness of refugees within it. This approach can be easily adapted to other countries. Notably, it was implemented in preparation for 2024’s Forced Displacement Survey in Cameroon, highlighting its practical application and relevance in real-world survey contexts.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Rebalancing power in infectious disease modelling: Toward inclusive and contextual approaches},

author = {Justice Moses K. Aheto and Megan Auzenbergs and Matthew J. Ferrari and Allison Portnoy and Chigozie Edson Utazi and Romain Glèlè Kakaï and Ezra Gayawan and James M. Azam and Justice Nonvignon},

url = {https://doi.org/10.1371/journal.pgph.0006220},

doi = {10.1371/journal.pgph.0006220},

year  = {2026},

date = {2026-04-03},

journal = {PLOS Global Public Health},

volume = {6},

issue = {4},

abstract = {Over the past several decades, infectious disease modelling has become a central tool in global health decision‑making, shaping financing decisions, vaccination strategies, and disease control policies [1]; for measles alone, our review identified over 400 modelling studies published since 2000 [2]. However, many of the modelling analyses that have guided these decisions originate in high‑income countries (HICs), even when they intend to inform policy in low- and middle-income countries (LMICs) [3]. With the rapid expansion of Large Language Model (LLM)‑enabled modelling, concerns are intensified about analyses produced without adequate contextual understanding. Models developed at a distance can rely on assumptions that fail to reflect local epidemiology or realities, carrying real‑world consequences for feasibility, equity, and impact.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {An entropogram-based Random Field model for categorical geospatial data prediction},

author = {Wen-Bin Zhang and Yong Ge and Xuan Wan and Shengjie Lai and Peter M. Atkinson},

url = {https://doi.org/10.1080/13658816.2026.2650365},

doi = {10.1080/13658816.2026.2650365},

year  = {2026},

date = {2026-03-30},

journal = {International Journal of Geographical Information Science},

pages = {1–18},

abstract = {Categorical geospatial data underpin applications from biodiversity monitoring to land-use planning, yet existing approaches often fail to recover rare classes while preserving realistic patch structures. We introduced an Entropogram-based Random Field (ERF) model that integrates intrinsic randomness from local class probabilities with entropogram-derived spatial dependence, balancing local class proportions with global neighborhood associations. Using a 10-class, 1-km land-cover map of Northern Ireland, we compared ERF against Indicator Kriging (IK), multi-phase Indicator Kriging (MIK), Compositional Data Analysis (CoDA) and a spatial multinomial logistic (SMLM) model. ERF matches IK and MIK in overall accuracy but achieves higher recall and F1 scores for minority classes, reducing the loss of small, coherent patches. While CoDA ensures compositional validity, it underperforms on rare classes and increases spatial aggregation; MIK improves rare-class recovery but still favors dominant types. SMLM performs comparably to ERF but with far higher computational demand. Landscape metrics showed that ERF and SMLM best preserved patch diversity and realistic geometry, whereas IK and CoDA produced more aggregated patterns. Together, these results highlight ERF as a computationally efficient, scalable and balanced solution for categorical mapping, particularly in applications where minority-class recovery and spatial realism are critical for biodiversity monitoring, habitat connectivity and land-use planning.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Interactions of SARS-CoV-2, influenza and respiratory syncytial virus influence epidemic timing and risk},

editor = {Yonghong Liu and Xiaoli Wang and Mengyao Li and Eimear Cleary and Zhifeng Cheng and Wenbin Zhang and Ying Shen and Hui Yao and Jiatong Han and Nick W. Ruktanonchai and Andrew J. Tatem and Shengjie Lai and Quanyi Wang and Peng Yang},

url = {https://doi.org/10.1038/s43856-026-01504-x},

doi = {10.1038/s43856-026-01504-x},

year  = {2026},

date = {2026-03-14},

journal = {Communications Medicine},

abstract = {Interactions between SARS-CoV-2, influenza virus, and respiratory syncytial virus (RSV) at the population level remain poorly understood. This study aimed to quantify potential interactions among these viruses and assess their influence on transmission dynamics.

We analyzed weekly surveillance data on SARS-CoV-2, influenza A and B viruses (IAV and IBV), and RSV from seven regions from October 2021 to May 2024. Distributed lag nonlinear models within a spatiotemporal Bayesian hierarchical framework were used to assess the exposure-lag-response associations among virus pairs. Additionally, we developed a two-pathogen, meta-population mechanistic transmission model to capture the co-epidemic dynamics of IAV and SARS-CoV-2, and to quantify the strength and duration of their bidirectional interactions.

Among all virus pairs examined, a statistically significant association is identified only between IAV positivity and subsequent SARS-CoV-2 risk. When IAV positive rate percentile is between the 52nd and 88th percentiles, the relative risk (RR) of SARS-CoV-2 infection is significantly reduced. The lowest RR for SARS-CoV-2 (0.58, 95% CrI: 0.40-0.85) occurs at a 5-week lag when IAV positivity reaches the 70th percentile. The fitted mechanistic model using incidence data in Beijing shows that IAV infection substantially reduces infection to SARS-CoV-2 by 94.24% (95% CrI: 88.50%–99.24%), with the protective effect lasting 38.24 days (95% CrI: 35.50–41.29 days). Conversely, SARS-CoV-2 infection is associated with a slight increase in infection to IAV.

Our findings indicate that IAV circulation may transiently reduce population-level infection to SARS-CoV-2, potential through ecological or immunological mechanisms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Maternal and child healthcare coverage and trends: refugee vs. non-refugee districts in Uganda},

author = {Rogers Nsubuga and Rornald Muhumuza Kananura and Ronald Wasswa and Catherine Birabwa and Jimmy Ogwal and Winfred Dotse-Gborgbortsi and George Mwinnyaa and Amanuel Abajobir and Julius Kisozi and Alypio Nyandwi and Ties Boerma and Peter Waiswa and Kristine Nilsen },

url = {https://doi.org/10.1186/s13031-026-00780-7},

doi = {10.1186/s13031-026-00780-7},

year  = {2026},

date = {2026-03-11},

journal = {Conflict and Health},

volume = {20},

issue = {42},

abstract = {Uganda hosts the largest refugee population in Africa, which exerts much pressure on the district health systems. While refugee-hosting districts (RH) receive targeted investments, the extent to which these influence maternal and child health (MCH) service coverage remains unclear. Using routine facility data, we examined differences in MCH coverage and trends between RH and non-refugee-hosting (non-RH) districts and also explored the effects of government health financing and health system performance on MCH coverage.



We conducted a retrospective analysis utilizing routine health facility MCH data from the Uganda District Health Information System and district-level government Primary Healthcare (PHC) expenditure data from 2020 to 2023. MCH indicators were ANC1st trimester, ANC4, Institutional deliveries, mothers’ Post-natal care (PNC), Measles1 and DPT3 vaccination. We computed a composite coverage index (CCI), health systems performance z-score and compared trends across RH and non-RH districts. Mixed Effects Models assessed the association between government expenditure, RH-status, health system performance over the years.



RH districts consistently had modestly higher coverage of ANC1st trimester, ANC4, Institutional deliveries, PNC, Measles vaccination and CCI trends. Government expenditure was significantly higher in RH districts and refugee-dominant (RD) districts (p < 0.001 vs. p = 0.007). Refugee-dominant districts had higher but non-significant MCH coverage. Unadjusted models of MCH indicators and CCI were positively influenced by government financing and health systems performance z-score except for DPT3 and Measles, respectively. Adjusted models revealed that ANC4 coverage was 7.4% points higher in RH districts (7.42; 95% CI:0.753, 14.090; p = 0.029) and increased by 3.6% points for every unit increase in z-score (3.60; 95% CI: 0.729, 6.462; p = 0.014). CCI increased by 1.6% points and 2.3% points for every unit increased in the government expenditure and z-score respectively (1.55; 95% CI: 0.310, 2.788; p = 0.014) vs. (2.31; 95% CI: 0.642, 3.975; p = 0.007).



Novel approach - leveraging routine facility data, revealed MCH coverage was modestly consistently higher in RH districts over the years and RH status influenced ANC4 coverage. Overall district-CCI depended on Government investment and health systems performance implying increase in PHC financing could be a key driver to universal district-level improvement.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}