Publications
1.
Utazi, C. Edson; Chaudhuri, Somnath; Wariri, Oghenebrume; Olowe, Iyanuloluwa D.; Megheib, Mohamed; Tatem, Andrew J.
An age-structured spatially varying coefficient model for high-resolution mapping of vaccination coverage Journal Article
In: PLoS Computational Biology, vol. 22, iss. 2, no. e1013989, 2026.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
2.
Duan, Qianwen; Lai, Shengjie; Sorichetta, Alessandro; Tatem, Andrew J.; snd Felix Eigenbrod, Jessica Steele
COVID-19 and urban exodus: diverging population redistribution patterns across countries from 2020 to 2022 Journal Article
In: npj urban sustainability, 2026.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
3.
Jiatong Han Haiyan Liu, Jianghao Wang
Combined benefits of multi-hazard early warnings on human mobility resilience to tropical cyclones Journal Article
In: Global Environmental Change, vol. 96, no. 103111, 2026.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
4.
Liu, Haiyan; Wang, Siqin; Wei, Chunzhu; Zhang, Wenbin; Tatem, Andrew J; Lai, Shengjie
Assessing context-dependent effectiveness of heat adaptation through human mobility under different heatwave regimes Journal Article
In: Sustainable Cities and Society, vol. 136, no. 107066, 2025.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
5.
Cheng, Zhifeng; Ruktanonchai, Nick W.; Wesolowski, Amy; Pei, Sen; Wang, Jianghao; Cockings, Samantha; Tatem, Andrew J.; Lai, Shengjie
Social, mobility and contact networks in shaping health behaviours and infectious disease dynamics: a scoping review Journal Article
In: Infectious Diseases of Poverty, vol. 14, no. 123, 2025.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
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.
6.
van Kleef, Esther; Borte, Wim Van; Arsevska, Elena; Busani, Luca; Dellicour, Simon; Domenico, Laura Di; Gilbert, Marius; van Elsland, Sabine L; Kraemer11, Moritz UG; Lai1, Shengjie; Lemey, Philippe; Merler1, Stefano; Milosavljevic, Zoran; Rizzoli1, Annapaola; Simic1, Danijela; and, Andrew J Tatem
In: Eurosurveillance, vol. 30, iss. 42, 2025.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
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.
7.
Zhang, Wenbin; Sorichetta, Alessandro; Frye, Charlie; Tejedor-Garavito, Natalia; Fang, Weixuan; Cihan, Duygu; Woods, Dorothea; Yetman, Gregory; Hilton, Jason; Tatem, Andrew J.; Bondarenko, Maksym
A stochastic approach to integerize floating-point estimates in gridded population mapping Journal Article
In: International Journal of Geographical Information Science, pp. 1–17, 2025.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
8.
Woods, Dorothea; McKeen, Tom; Cunningham, Alexander; Priyatikanto, Rhorom; Tatem, Andrew J.; Sorichetta, Alessandro; Bondarenko, Maksym
Global gridded multi-temporal datasets to support human population distribution modelling Journal Article
In: Gates Open Research, vol. 9, iss. 72, 2025.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
9.
Boo, Gianluca; Darin, Edith; Chamberlain, Heather R.; Hosner, Roland; Akilimali, Pierre K.; Kazadi, Henri Marie; Nnanatu, Chibuzor C.; Lázár, Attila N.; Tatem, Andrew J.
In: PLOS Global Public Health , 2025.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
10.
Utazi, C. Edson; Yankey, Ortis; Chaudhuri, Somnath; Olowe, Iyanuloluwa D.; Danovaro-Holliday, M. Carolina; Lazar, Attila N.; Tatem, Andrew J.
Geostatistical and machine learning approaches for high-resolution mapping of vaccination coverage Journal Article
In: Spatial and Spatio-temporal Epidemiology, vol. 54, no. 100744, 2025.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
11.
Zhang, Wen-Bin; Woods, Dorothea; Olowe, Iyanuloluwa Deborah; Schiavina, Marcello; Fang, Weixuan; Hornby, Graeme; Bondarenko, Maksym; Maes, Joachim; Dijkstra, Lewis; Tatem, Andrew J.; Sorichetta, Alessandro
Assessing the impacts of gridded population model choice on degree of urbanisation metrics Journal Article
In: Cities, vol. 166, no. 106293, 2025.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
12.
Espey, Jessica M.; Tatem, Andrew J.; Thomson, Dana R.
Disappearing people: A global demographic data crisis threatens public policy Journal Article
In: Science, vol. 388, iss. 6753, pp. 1277-1280, 2025.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
13.
Cleary, Eimear; Atuhaire, Fatumah; Sorichetta, Alessandro; Ruktanonchai, Nick; Ruktanonchai, Cori; Cunningham, Alexander; Pasqui, Massimiliano; Schiavina, Marcello; Melchiorri, Michele; Bondarenko, Maksym; Shepherd, Harry E R; Padmadas, Sabu S; Wesolowski, Amy; Cummings, Derek A T; Tatem, Andrew J; Lai, Shengjie
In: PLOS Global Public Health, vol. 5, iss. 4, no. e0003431, 2025.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
14.
Amouzou, Agbessi; Barros, Aluisio J D; Requejo, Jennifer; Faye, Cheikh; Akseer, Nadia; Bendavid, Eran; Blumenberg, Cauane; Borghi, Josephine; Baz, Sama El; Federspiel, Frederik; Ferreira, Leonardo Z; Hazel, Elizabeth; Heft-Neal, Sam; Hellwig, Franciele; Liu, Li; Maïga, Abdoulaye; Munos, Melinda; Pitt, Catherine; Shawar, Yusra Ribhi; Shiffman, Jeremy; Tam, Yvonne; Walker, Neff; Akilimali, Pierre; Alkema, Leontine; Behanzin, Paoli; Binyaruka, Peter; Bhutta, Zulfiqar; Blanchard, Andrea; Blencowe, Hannah; Bradley, Ellen; Brikci, Nouria; Caicedo-Velásquez, Beatriz; Costello, Anthony; Dotse-Gborgbortsi, Winfred; Arifeen, Shams El; Ezzati, Majid; Freedman, Lynn P; Guillot, Michel; Hanson, Claudia; Heidkamp, Rebecca; Huicho, Luis; Izugbara, Chimaraoke; Jiwani, Safia S; Kabiru, Caroline; Kiarie, Helen; Kinney, Mary; Kirakoya-Samadoulougou, Fati; Lawn, Joy; Madise, Nyovani; Mady, Gouda Roland Mesmer; Masquelier, Bruno; Melesse, Dessalegn; Nilsen, Kristine; Perin, Jamie; Ram, Usha; Romanello, Marina; Saad, Ghada E; Sharma, Sudha; Sidze, Estelle M; Spiegel, Paul; Tappis, Hannah; Tatem, Andrew J; and others,
The 2025 report of the Lancet Countdown to 2030 for women's, children's, and adolescents' health: tracking progress on health and nutrition Journal Article
In: The Lancet, vol. 405, iss. 10488, no. 10488, pp. 1505–1554, 2025.
Abstract | Links | BibTeX | Tags:
@article{amouzou20252025,
title = {The 2025 report of the Lancet Countdown to 2030 for women's, children's, and adolescents' health: tracking progress on health and nutrition},
author = {Agbessi Amouzou and Aluisio J D Barros and Jennifer Requejo and Cheikh Faye and Nadia Akseer and Eran Bendavid and Cauane Blumenberg and Josephine Borghi and Sama El Baz and Frederik Federspiel and Leonardo Z Ferreira and Elizabeth Hazel and Sam Heft-Neal and Franciele Hellwig and Li Liu and Abdoulaye Maïga and Melinda Munos and Catherine Pitt and Yusra Ribhi Shawar and Jeremy Shiffman and Yvonne Tam and Neff Walker and Pierre Akilimali and Leontine Alkema and Paoli Behanzin and Peter Binyaruka and Zulfiqar Bhutta and Andrea Blanchard and Hannah Blencowe and Ellen Bradley and Nouria Brikci and Beatriz Caicedo-Velásquez and Anthony Costello and Winfred Dotse-Gborgbortsi and Shams El Arifeen and Majid Ezzati and Lynn P Freedman and Michel Guillot and Claudia Hanson and Rebecca Heidkamp and Luis Huicho and Chimaraoke Izugbara and Safia S Jiwani and Caroline Kabiru and Helen Kiarie and Mary Kinney and Fati Kirakoya-Samadoulougou and Joy Lawn and Nyovani Madise and Gouda Roland Mesmer Mady and Bruno Masquelier and Dessalegn Melesse and Kristine Nilsen and Jamie Perin and Usha Ram and Marina Romanello and Ghada E Saad and Sudha Sharma and Estelle M Sidze and Paul Spiegel and Hannah Tappis and Andrew J Tatem and and others},
doi = {10.1016/S0140-6736(25)00151-5},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {The Lancet},
volume = {405},
number = {10488},
issue = {10488},
pages = {1505–1554},
publisher = {Elsevier},
abstract = {In line with previous progress reports by Countdown to 2030 for Women's, Children's, and Adolescents' Health, this report analyses global and regional trends and inequalities in health determinants, survival, nutritional status, intervention coverage, and quality of care in reproductive, maternal, newborn, child and adolescent health (RMNCAH) and nutrition, as well as country health systems, policies, financing, and prioritisation. The focus is on low-income and middle-income countries (LMICs) where 99% of maternal deaths and 98% of child and adolescent deaths (individuals aged 0–19 years) occur, with special attention to sub-Saharan Africa and South Asia.
Recognising the urgency of reaching the Sustainable Development Goal (SDG) for health, SDG 3, and health-related targets by 2030, the report assesses whether the momentum needed to reach these goals has been sustained, accelerated, stagnated, or regressed in comparison with the Millennium Development Goal (MDG) period (2000–15). Although most health and health-related indicators continue to show progress, there has been a notable slowdown in the rate of improvement after 2015, falling well short of the pace needed to achieve the 2030 SDG targets. This deceleration in pace contrasts sharply with the aspired grand convergence in health, characterised by drastic reductions in mortality and RMNCAH inequalities, which was expected to occur during the SDG period based on the assumption that the spectacular progress achieved during the MDG period would continue unabated. Multiple threats, external and internal to the RMNCAH health community, must be addressed to safeguard the gains in RMNCAH and nutrition and to accelerate progress. Furthermore, a large gap between sub-Saharan Africa, especially West and Central Africa, and other parts of the world persists for many indicators, necessitating further prioritisation of this region.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In line with previous progress reports by Countdown to 2030 for Women's, Children's, and Adolescents' Health, this report analyses global and regional trends and inequalities in health determinants, survival, nutritional status, intervention coverage, and quality of care in reproductive, maternal, newborn, child and adolescent health (RMNCAH) and nutrition, as well as country health systems, policies, financing, and prioritisation. The focus is on low-income and middle-income countries (LMICs) where 99% of maternal deaths and 98% of child and adolescent deaths (individuals aged 0–19 years) occur, with special attention to sub-Saharan Africa and South Asia.
Recognising the urgency of reaching the Sustainable Development Goal (SDG) for health, SDG 3, and health-related targets by 2030, the report assesses whether the momentum needed to reach these goals has been sustained, accelerated, stagnated, or regressed in comparison with the Millennium Development Goal (MDG) period (2000–15). Although most health and health-related indicators continue to show progress, there has been a notable slowdown in the rate of improvement after 2015, falling well short of the pace needed to achieve the 2030 SDG targets. This deceleration in pace contrasts sharply with the aspired grand convergence in health, characterised by drastic reductions in mortality and RMNCAH inequalities, which was expected to occur during the SDG period based on the assumption that the spectacular progress achieved during the MDG period would continue unabated. Multiple threats, external and internal to the RMNCAH health community, must be addressed to safeguard the gains in RMNCAH and nutrition and to accelerate progress. Furthermore, a large gap between sub-Saharan Africa, especially West and Central Africa, and other parts of the world persists for many indicators, necessitating further prioritisation of this region.
Recognising the urgency of reaching the Sustainable Development Goal (SDG) for health, SDG 3, and health-related targets by 2030, the report assesses whether the momentum needed to reach these goals has been sustained, accelerated, stagnated, or regressed in comparison with the Millennium Development Goal (MDG) period (2000–15). Although most health and health-related indicators continue to show progress, there has been a notable slowdown in the rate of improvement after 2015, falling well short of the pace needed to achieve the 2030 SDG targets. This deceleration in pace contrasts sharply with the aspired grand convergence in health, characterised by drastic reductions in mortality and RMNCAH inequalities, which was expected to occur during the SDG period based on the assumption that the spectacular progress achieved during the MDG period would continue unabated. Multiple threats, external and internal to the RMNCAH health community, must be addressed to safeguard the gains in RMNCAH and nutrition and to accelerate progress. Furthermore, a large gap between sub-Saharan Africa, especially West and Central Africa, and other parts of the world persists for many indicators, necessitating further prioritisation of this region.
15.
Seidler, Valentin; Utazi, Edson C; Finaret, Amelia B; Luckeneder, Sebastian; Zens, Gregor; Bodarenko, Maksym; Smith, Abigail W; Bradley, Sarah EK; Tatem, Andrew J; Webb, Patrick
Subnational variations in the quality of household survey data in sub-Saharan Africa Journal Article
In: Nature Communications, vol. 16, no. 1, pp. 3771, 2025.
Abstract | Links | BibTeX | Tags:
@article{seidler2025subnational,
title = {Subnational variations in the quality of household survey data in sub-Saharan Africa},
author = {Valentin Seidler and Edson C Utazi and Amelia B Finaret and Sebastian Luckeneder and Gregor Zens and Maksym Bodarenko and Abigail W Smith and Sarah EK Bradley and Andrew J Tatem and Patrick Webb},
url = {https://doi.org/10.1038/s41467-025-58776-5},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Nature Communications},
volume = {16},
number = {1},
pages = {3771},
publisher = {Nature Publishing Group UK London},
abstract = {Nationally representative household surveys collect geocoded data that are vital to tackling health and other development challenges in sub-Saharan Africa. Scholars and practitioners generally assume uniform data quality but subnational variation of errors in household data has never been investigated at high spatial resolution. Here, we explore within-country variation in the quality of most recent household surveys for 35 African countries at 5 × 5 km resolution and district levels. Findings show a striking heterogeneity in the subnational distribution of sampling and measurement errors. Data quality degrades with greater distance from settlements, and missing data as well as imprecision of estimates add to quality problems that can result in vulnerable remote populations receiving less than optimal services and needed resources. Our easy-to-access geospatial estimates of survey data quality highlight the need to invest in better targeting of household surveys in remote areas.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Nationally representative household surveys collect geocoded data that are vital to tackling health and other development challenges in sub-Saharan Africa. Scholars and practitioners generally assume uniform data quality but subnational variation of errors in household data has never been investigated at high spatial resolution. Here, we explore within-country variation in the quality of most recent household surveys for 35 African countries at 5 × 5 km resolution and district levels. Findings show a striking heterogeneity in the subnational distribution of sampling and measurement errors. Data quality degrades with greater distance from settlements, and missing data as well as imprecision of estimates add to quality problems that can result in vulnerable remote populations receiving less than optimal services and needed resources. Our easy-to-access geospatial estimates of survey data quality highlight the need to invest in better targeting of household surveys in remote areas.
16.
Chamberlain, Heather R; Pollard, Derek; Winters, Anna; Renn, Silvia; Borkovska, Olena; Musuka, Chisenga Abel; Membele, Garikai; Lazar, Attila N; Tatem, Andrew J
In: International Journal of Health Geographics, vol. 24, no. 1, pp. 13, 2025.
Abstract | Links | BibTeX | Tags:
@article{chamberlain2025assessing,
title = {Assessing the impact of building footprint dataset choice for health programme planning: a case study of indoor residual spraying (IRS) in Zambia},
author = {Heather R Chamberlain and Derek Pollard and Anna Winters and Silvia Renn and Olena Borkovska and Chisenga Abel Musuka and Garikai Membele and Attila N Lazar and Andrew J Tatem},
url = {https://doi.org/10.1186/s12942-025-00398-7},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {International Journal of Health Geographics},
volume = {24},
number = {1},
pages = {13},
publisher = {Springer},
abstract = {The increasing availability globally of building footprint datasets has brought new opportunities to support a geographic approach to health programme planning. This is particularly acute in settings with high disease burdens but limited geospatial data available to support targeted planning. The comparability of building footprint datasets has recently started to be explored, but the impact of utilising a particular dataset in analyses to support decision making for health programme planning has not been studied. In this study, we quantify the impact of utilising four different building footprint datasets in analyses to support health programme planning, with an example of malaria vector control initiatives in Zambia.
Using the example of planning indoor residual spraying (IRS) campaigns in Zambia, we identify priority locations for deployment of this intervention based on criteria related to the area, proximity and counts of building footprints per settlement. We apply the same criteria to four different building footprint datasets and quantify the count and geographic variability in the priority settlements that are identified.
We show that nationally the count of potential priority settlements for IRS varies by over 230% with different building footprint datasets, considering a minimum threshold of 25 sprayable buildings per settlement. Differences are most pronounced for rural settlements, indicating that the choice of dataset may bias the selection to include or exclude settlements, and consequently population groups, in some areas.
The results of this study show that the choice of building footprint dataset can have a considerable impact on the potential settlements identified for IRS, in terms of (i) their location and count, and (ii) the count of building footprints within priority settlements. The choice of dataset potentially has substantial implications for campaign planning, implementation and coverage assessment. Given the magnitude of the differences observed, further work should more broadly assess the sensitivity of health programme planning metrics to different building footprint datasets, and across a range of geographic contexts and health campaign types.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The increasing availability globally of building footprint datasets has brought new opportunities to support a geographic approach to health programme planning. This is particularly acute in settings with high disease burdens but limited geospatial data available to support targeted planning. The comparability of building footprint datasets has recently started to be explored, but the impact of utilising a particular dataset in analyses to support decision making for health programme planning has not been studied. In this study, we quantify the impact of utilising four different building footprint datasets in analyses to support health programme planning, with an example of malaria vector control initiatives in Zambia.
Using the example of planning indoor residual spraying (IRS) campaigns in Zambia, we identify priority locations for deployment of this intervention based on criteria related to the area, proximity and counts of building footprints per settlement. We apply the same criteria to four different building footprint datasets and quantify the count and geographic variability in the priority settlements that are identified.
We show that nationally the count of potential priority settlements for IRS varies by over 230% with different building footprint datasets, considering a minimum threshold of 25 sprayable buildings per settlement. Differences are most pronounced for rural settlements, indicating that the choice of dataset may bias the selection to include or exclude settlements, and consequently population groups, in some areas.
The results of this study show that the choice of building footprint dataset can have a considerable impact on the potential settlements identified for IRS, in terms of (i) their location and count, and (ii) the count of building footprints within priority settlements. The choice of dataset potentially has substantial implications for campaign planning, implementation and coverage assessment. Given the magnitude of the differences observed, further work should more broadly assess the sensitivity of health programme planning metrics to different building footprint datasets, and across a range of geographic contexts and health campaign types.
Using the example of planning indoor residual spraying (IRS) campaigns in Zambia, we identify priority locations for deployment of this intervention based on criteria related to the area, proximity and counts of building footprints per settlement. We apply the same criteria to four different building footprint datasets and quantify the count and geographic variability in the priority settlements that are identified.
We show that nationally the count of potential priority settlements for IRS varies by over 230% with different building footprint datasets, considering a minimum threshold of 25 sprayable buildings per settlement. Differences are most pronounced for rural settlements, indicating that the choice of dataset may bias the selection to include or exclude settlements, and consequently population groups, in some areas.
The results of this study show that the choice of building footprint dataset can have a considerable impact on the potential settlements identified for IRS, in terms of (i) their location and count, and (ii) the count of building footprints within priority settlements. The choice of dataset potentially has substantial implications for campaign planning, implementation and coverage assessment. Given the magnitude of the differences observed, further work should more broadly assess the sensitivity of health programme planning metrics to different building footprint datasets, and across a range of geographic contexts and health campaign types.
17.
Kostandova, Natalya; Schluth, Catherine; Arambepola, Rohan; Atuhaire, Fatumah; Bérubé, Sophie; Chin, Taylor; Cleary, Eimear; Cortes-Azuero, Oscar; García-Carreras, Bernardo; Grantz, Kyra H.; Hitchings, Matt D. T.; Huang, Angkana T.; Kishore, Nishant; Lai, Shengjie; Larsen, Soren L.; Loisate, Stacie; Martinez, Pamela; Meredith, Hannah R.; Purbey, Ritika; Ramiadantsoa, Tanjona; Read, Jonathan; Rice, Benjamin L.; Rosman, Lori; Ruktanonchai, Nick; Salje, Henrik; Schaber, Kathryn L.; Tatem, Andrew J.; Wang, Jasmine; Cummings, Derek A. T.; Wesolowski, Amy
A systematic review of using population-level human mobility data to understand SARS-CoV-2 transmission Journal Article
In: Nature Communications, vol. 15, no. 10504, 2024.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {A systematic review of using population-level human mobility data to understand SARS-CoV-2 transmission},
author = {Natalya Kostandova and Catherine Schluth and Rohan Arambepola and Fatumah Atuhaire and Sophie Bérubé and Taylor Chin and Eimear Cleary and Oscar Cortes-Azuero and Bernardo García-Carreras and Kyra H. Grantz and Matt D. T. Hitchings and Angkana T. Huang and Nishant Kishore and Shengjie Lai and Soren L. Larsen and Stacie Loisate and Pamela Martinez and Hannah R. Meredith and Ritika Purbey and Tanjona Ramiadantsoa and Jonathan Read and Benjamin L. Rice and Lori Rosman and Nick Ruktanonchai and Henrik Salje and Kathryn L. Schaber and Andrew J. Tatem and Jasmine Wang and Derek A. T. Cummings and Amy Wesolowski },
url = {https://doi.org/10.1038/s41467-024-54895-7},
year = {2024},
date = {2024-12-03},
journal = {Nature Communications},
volume = {15},
number = {10504},
abstract = {The emergence of SARS-CoV-2 into a highly susceptible global population was primarily driven by human mobility-induced introduction events. Especially in the early stages, understanding mobility was vital to mitigating the pandemic prior to widespread vaccine availability. We conducted a systematic review of studies published from January 1, 2020, to May 9, 2021, that used population-level human mobility data to understand SARS-CoV-2 transmission. Of the 5505 papers with abstracts screened, 232 were included in the analysis. These papers focused on a range of specific questions but were dominated by analyses focusing on the USA and China. The majority included mobile phone data, followed by Google Community Mobility Reports, and few included any adjustments to account for potential biases in population sampling processes. There was no clear relationship between methods used to integrate mobility and SARS-CoV-2 data and goals of analysis. When considering papers focused only on the estimation of the effective reproductive number within the US, there was no clear relationship identified between this measure and changes in mobility patterns. Our findings underscore the need for standardized, systematic ways to identify the source of mobility data, select an appropriate approach to using it in analysis, and reporting.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The emergence of SARS-CoV-2 into a highly susceptible global population was primarily driven by human mobility-induced introduction events. Especially in the early stages, understanding mobility was vital to mitigating the pandemic prior to widespread vaccine availability. We conducted a systematic review of studies published from January 1, 2020, to May 9, 2021, that used population-level human mobility data to understand SARS-CoV-2 transmission. Of the 5505 papers with abstracts screened, 232 were included in the analysis. These papers focused on a range of specific questions but were dominated by analyses focusing on the USA and China. The majority included mobile phone data, followed by Google Community Mobility Reports, and few included any adjustments to account for potential biases in population sampling processes. There was no clear relationship between methods used to integrate mobility and SARS-CoV-2 data and goals of analysis. When considering papers focused only on the estimation of the effective reproductive number within the US, there was no clear relationship identified between this measure and changes in mobility patterns. Our findings underscore the need for standardized, systematic ways to identify the source of mobility data, select an appropriate approach to using it in analysis, and reporting.
18.
Utazi, C. Edson; Olowe, Iyanuloluwa D.; Chan, H. M. Theophilus; Dotse-Gborgbortsi, Winfred; Wagai, John; Umar, Jamila A.; Etamesor, Sulaiman; Atuhaire, Brian; Fafunmi, Biyi; Crawford, Jessica; Adeniran, Adeyemi; Tatem, Andrew J.
In: Vaccines, vol. 12, no. 1299, 2024.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Geospatial Variation in Vaccination Coverage and Zero-Dose Prevalence at the District, Ward and Health Facility Levels Before and After a Measles Vaccination Campaign in Nigeria},
author = {C. Edson Utazi and Iyanuloluwa D. Olowe and H. M. Theophilus Chan and Winfred Dotse-Gborgbortsi and John Wagai and Jamila A. Umar and Sulaiman Etamesor and Brian Atuhaire and Biyi Fafunmi and Jessica Crawford and Adeyemi Adeniran and Andrew J. Tatem},
url = {https://doi.org/10.3390/vaccines12121299},
year = {2024},
date = {2024-11-21},
journal = {Vaccines},
volume = {12},
number = {1299},
abstract = {Many measles endemic countries with suboptimal coverage levels still rely on vaccination campaigns to fill immunity gaps and boost control efforts. Depending on local epidemiological patterns, national or targeted campaigns are implemented, following which post-campaign coverage surveys (PCCSs) are conducted to evaluate their performance, particularly in terms of reaching previously unvaccinated children. Due to limited resources, PCCS surveys are designed to be representative at coarse spatial scales, often masking important heterogeneities in coverage that could enhance the identification of areas of poor performance for follow-up via routine immunization strategies. Here, we undertake geospatial analyses of the 2021 measles PCCS in Nigeria to map indicators of coverage measuring the individual and combined performance of the campaign and routine immunization (RI) at 1 × 1 km resolution and the ward and district levels in 13 states. Using additional geospatial datasets, we also produced estimates of numbers of unvaccinated children during the campaign and numbers of measles-containing vaccine (MCV) zero-dose children before and after the campaign at these levels and within health facility catchment areas. Our study revealed that although the campaign reduced the numbers of MCV zero-dose children in all the districts, areas of suboptimal campaign and RI performance with considerable numbers of zero-dose children remained. Our analyses further identified wards and health facility catchment areas with higher numbers of unvaccinated children within these areas. Our outputs provide a robust evidence base to plan and implement follow-up RI strategies and to guide future campaigns at flexible and operationally relevant spatial scales.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Many measles endemic countries with suboptimal coverage levels still rely on vaccination campaigns to fill immunity gaps and boost control efforts. Depending on local epidemiological patterns, national or targeted campaigns are implemented, following which post-campaign coverage surveys (PCCSs) are conducted to evaluate their performance, particularly in terms of reaching previously unvaccinated children. Due to limited resources, PCCS surveys are designed to be representative at coarse spatial scales, often masking important heterogeneities in coverage that could enhance the identification of areas of poor performance for follow-up via routine immunization strategies. Here, we undertake geospatial analyses of the 2021 measles PCCS in Nigeria to map indicators of coverage measuring the individual and combined performance of the campaign and routine immunization (RI) at 1 × 1 km resolution and the ward and district levels in 13 states. Using additional geospatial datasets, we also produced estimates of numbers of unvaccinated children during the campaign and numbers of measles-containing vaccine (MCV) zero-dose children before and after the campaign at these levels and within health facility catchment areas. Our study revealed that although the campaign reduced the numbers of MCV zero-dose children in all the districts, areas of suboptimal campaign and RI performance with considerable numbers of zero-dose children remained. Our analyses further identified wards and health facility catchment areas with higher numbers of unvaccinated children within these areas. Our outputs provide a robust evidence base to plan and implement follow-up RI strategies and to guide future campaigns at flexible and operationally relevant spatial scales.
19.
Yankey, Ortis; Utazi, Chigozie E.; Nnanatu, Christopher C.; Gadiaga, Assane N.; Abbot, Thomas; Lazar, Attila N.; Tatem, Andrew J.
Disaggregating census data for population mapping using a Bayesian Additive Regression Tree model Journal Article
In: Applied Geography, vol. 174, 2024.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Disaggregating census data for population mapping using a Bayesian Additive Regression Tree model},
author = {Ortis Yankey and Chigozie E. Utazi and Christopher C. Nnanatu and Assane N. Gadiaga and Thomas Abbot and Attila N. Lazar and Andrew J. Tatem},
url = {https://doi.org/10.1016/j.apgeog.2024.103416},
year = {2024},
date = {2024-09-14},
journal = {Applied Geography},
volume = {174},
abstract = {Population data is crucial for policy decisions, but fine-scale population numbers are often lacking due to the challenge of sharing sensitive data. Different approaches, such as the use of the Random Forest (RF) model, have been used to disaggregate census data from higher administrative units to small area scales. A major limitation of the RF model is its inability to quantify the uncertainties associated with the predicted populations, which can be important for policy decisions. In this study, we applied a Bayesian Additive Regression Tree (BART) model for population disaggregation and compared the result with a RF model using both simulated data and the 2021 census data for Ghana. The BART model consistently outperforms the RF model in out-of-sample predictions for all metrics, such as bias, mean squared error (MSE), and root mean squared error (RMSE). The BART model also addresses the limitations of the RF model by providing uncertainty estimates around the predicted population, which is often lacking with the RF model. Overall, the study demonstrates the superiority of the BART model over the RF model in disaggregating population data and highlights its potential for gridded population estimates.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Population data is crucial for policy decisions, but fine-scale population numbers are often lacking due to the challenge of sharing sensitive data. Different approaches, such as the use of the Random Forest (RF) model, have been used to disaggregate census data from higher administrative units to small area scales. A major limitation of the RF model is its inability to quantify the uncertainties associated with the predicted populations, which can be important for policy decisions. In this study, we applied a Bayesian Additive Regression Tree (BART) model for population disaggregation and compared the result with a RF model using both simulated data and the 2021 census data for Ghana. The BART model consistently outperforms the RF model in out-of-sample predictions for all metrics, such as bias, mean squared error (MSE), and root mean squared error (RMSE). The BART model also addresses the limitations of the RF model by providing uncertainty estimates around the predicted population, which is often lacking with the RF model. Overall, the study demonstrates the superiority of the BART model over the RF model in disaggregating population data and highlights its potential for gridded population estimates.
20.
Duan, Qianwen; Steele, Jessica; Cheng, Zhifeng; Cleary, Eimear; Ruktanonchai, Nick; Voepel, Hal; O'Riordan, Tim; Tatem, Andrew J.; Sorichetta, Alessandro; Lai, Shengjie; Eigenbrod, Felix
Identifying counter-urbanisation using Facebook's user count data Journal Article
In: Habitat International, vol. 150, 2024.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Identifying counter-urbanisation using Facebook's user count data},
author = {Qianwen Duan and Jessica Steele and Zhifeng Cheng and Eimear Cleary and Nick Ruktanonchai and Hal Voepel and Tim O'Riordan and Andrew J. Tatem and Alessandro Sorichetta and Shengjie Lai and Felix Eigenbrod},
url = {https://doi.org/10.1016/j.habitatint.2024.103113},
doi = {10.1016/j.habitatint.2024.103113},
year = {2024},
date = {2024-06-04},
journal = {Habitat International},
volume = {150},
abstract = {Identifying the growing widespread phenomenon of counter-urbanisation, where people relocate from urban centres to rural areas, is essential for understanding the social and ecological consequences of the associated changes. However, its nuanced dynamics and complex characteristics pose challenges for quantitative analysis. Here, we used near real-time Facebook user count data for Belgium and Thailand, with missing data imputed, and applied the Seasonal-Trend decomposition using Loess (STL) model to capture subtle urban and rural population dynamics and assess counter-urbanisation. We identified counter-urbanisation in both Belgium and Thailand, evidenced by increases of 1.80% and 2.14% in rural residents (night-time user counts) and decreases of 3.08% and 5.04% in urban centre night-time user counts from March 2020 to May 2022, respectively. However, the counter-urbanisation in Thailand appears to be transitory, with rural users beginning to decline during both day and night as COVID-19 restrictions were lifted. By contrast, in Belgium, at the country level, there is as yet no evidence of a return to urban residences, though daytime numbers in rural areas are decreasing and in urban centres are increasing, suggesting an increase in commuting post-pandemic. These variation characteristics observed both between Belgium and Thailand and between day and night, extend the current understanding of counter-urbanisation. The use of novel social media data provides an effective quantitative perspective to comprehend counter-urbanisation in different settings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Identifying the growing widespread phenomenon of counter-urbanisation, where people relocate from urban centres to rural areas, is essential for understanding the social and ecological consequences of the associated changes. However, its nuanced dynamics and complex characteristics pose challenges for quantitative analysis. Here, we used near real-time Facebook user count data for Belgium and Thailand, with missing data imputed, and applied the Seasonal-Trend decomposition using Loess (STL) model to capture subtle urban and rural population dynamics and assess counter-urbanisation. We identified counter-urbanisation in both Belgium and Thailand, evidenced by increases of 1.80% and 2.14% in rural residents (night-time user counts) and decreases of 3.08% and 5.04% in urban centre night-time user counts from March 2020 to May 2022, respectively. However, the counter-urbanisation in Thailand appears to be transitory, with rural users beginning to decline during both day and night as COVID-19 restrictions were lifted. By contrast, in Belgium, at the country level, there is as yet no evidence of a return to urban residences, though daytime numbers in rural areas are decreasing and in urban centres are increasing, suggesting an increase in commuting post-pandemic. These variation characteristics observed both between Belgium and Thailand and between day and night, extend the current understanding of counter-urbanisation. The use of novel social media data provides an effective quantitative perspective to comprehend counter-urbanisation in different settings.
