Publications
1.
Priyatikanto, Rhorom; Chamberlain, Heather; Bondarenko, Maksym; Zhang, Wenbin; Garavito, Natalia Tejedor; Tatem, Andrew
High Spatial Resolution Building Characteristics for the Global South: Insights from the Google Open Buildings Temporal Dataset (2016-2023) Journal Article
In: Gates Open Research, vol. 10, iss. 33, 2026.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {High Spatial Resolution Building Characteristics for the Global South: Insights from the Google Open Buildings Temporal Dataset (2016-2023)},
author = {Rhorom Priyatikanto and Heather Chamberlain and Maksym Bondarenko and Wenbin Zhang and Natalia Tejedor Garavito and Andrew Tatem},
url = {https://doi.org/10.12688/gatesopenres.16386.1},
doi = {10.12688/gatesopenres.16386.1},
year = {2026},
date = {2026-06-02},
urldate = {2026-06-02},
journal = {Gates Open Research},
volume = {10},
issue = {33},
abstract = {The need for detailed built-up area data for applications such as population modelling, urban planning, and environmental research is growing due to the pace of global population changes, particularly in the Global South, where existing datasets have limitations.
Here, we processed the Google Open Buildings Temporal (OBT) dataset to derive six 100-m spatial resolution datasets per year on building characteristics. The characteristics include building count, total perimeter, total area, total volume, height variance, and mean distance to the nearest building edges. These were calculated using arithmetic operations, convolutions, and spatial aggregation. The derived data was validated against a set of existing largescale open spatial datasets on buildings and human settlement extents for single timepoints. Additionally, temporal consistency was assessed, with polynomial fitting explored to test suitability for smoothing the data where significant fluctuations were seen.
The new dataset strongly correlated with the Google Open Buildings Polygons dataset (e.g., building count: r = 0.88; building area: r = 0.90) but showed systematic perimeter underestimation in dense areas due to blending effects. Weaker correlations were found with other datasets due to methodological differences. Internally, building height variance correlated moderately with total volume (r = 0.47). A strong positive correlation (r > 0.8) existed between building count, area, volume, and population. Temporal analysis revealed significant fluctuations in most characteristics, especially height-related metrics, with second-order polynomial fitting proving optimal for smoothing.
A validated 100-m resolution building characteristics dataset for the Global South, covering each year from 2016 to 2023, derived from Google OBT, was produced. While showing consistency with similar largescale spatial datasets, temporal fluctuations indicate a need for further processing for time-series applications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The need for detailed built-up area data for applications such as population modelling, urban planning, and environmental research is growing due to the pace of global population changes, particularly in the Global South, where existing datasets have limitations.
Here, we processed the Google Open Buildings Temporal (OBT) dataset to derive six 100-m spatial resolution datasets per year on building characteristics. The characteristics include building count, total perimeter, total area, total volume, height variance, and mean distance to the nearest building edges. These were calculated using arithmetic operations, convolutions, and spatial aggregation. The derived data was validated against a set of existing largescale open spatial datasets on buildings and human settlement extents for single timepoints. Additionally, temporal consistency was assessed, with polynomial fitting explored to test suitability for smoothing the data where significant fluctuations were seen.
The new dataset strongly correlated with the Google Open Buildings Polygons dataset (e.g., building count: r = 0.88; building area: r = 0.90) but showed systematic perimeter underestimation in dense areas due to blending effects. Weaker correlations were found with other datasets due to methodological differences. Internally, building height variance correlated moderately with total volume (r = 0.47). A strong positive correlation (r > 0.8) existed between building count, area, volume, and population. Temporal analysis revealed significant fluctuations in most characteristics, especially height-related metrics, with second-order polynomial fitting proving optimal for smoothing.
A validated 100-m resolution building characteristics dataset for the Global South, covering each year from 2016 to 2023, derived from Google OBT, was produced. While showing consistency with similar largescale spatial datasets, temporal fluctuations indicate a need for further processing for time-series applications.
Here, we processed the Google Open Buildings Temporal (OBT) dataset to derive six 100-m spatial resolution datasets per year on building characteristics. The characteristics include building count, total perimeter, total area, total volume, height variance, and mean distance to the nearest building edges. These were calculated using arithmetic operations, convolutions, and spatial aggregation. The derived data was validated against a set of existing largescale open spatial datasets on buildings and human settlement extents for single timepoints. Additionally, temporal consistency was assessed, with polynomial fitting explored to test suitability for smoothing the data where significant fluctuations were seen.
The new dataset strongly correlated with the Google Open Buildings Polygons dataset (e.g., building count: r = 0.88; building area: r = 0.90) but showed systematic perimeter underestimation in dense areas due to blending effects. Weaker correlations were found with other datasets due to methodological differences. Internally, building height variance correlated moderately with total volume (r = 0.47). A strong positive correlation (r > 0.8) existed between building count, area, volume, and population. Temporal analysis revealed significant fluctuations in most characteristics, especially height-related metrics, with second-order polynomial fitting proving optimal for smoothing.
A validated 100-m resolution building characteristics dataset for the Global South, covering each year from 2016 to 2023, derived from Google OBT, was produced. While showing consistency with similar largescale spatial datasets, temporal fluctuations indicate a need for further processing for time-series applications.
2.
Wang, Siqin; Liu, Haiyan; Wu, Connor Y. H.; Huang, Xiao; Wang, Ruomei; Yang, Yifan; Corcoran, Jonathan; Lai, Shengjie; Xia, Xinming; Liu, Yan
The Boiling Frog Effect: Global Warming Delays Emotional Impacts of Air Pollution in Warmer Climates Journal Article
In: Journal of Hazardous Materials, no. 142440, 2026.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {The Boiling Frog Effect: Global Warming Delays Emotional Impacts of Air Pollution in Warmer Climates},
author = {Siqin Wang and Haiyan Liu and Connor Y.H. Wu and Xiao Huang and Ruomei Wang and Yifan Yang and Jonathan Corcoran and Shengjie Lai and Xinming Xia and Yan Liu},
url = {https://doi.org/10.1016/j.jhazmat.2026.142440},
doi = {10.1016/j.jhazmat.2026.142440},
year = {2026},
date = {2026-05-22},
journal = {Journal of Hazardous Materials},
number = {142440},
abstract = {Climate change, air pollution, and extreme weather interact in complex ways that impact emotional states of populations. These dynamics are crucial in effective health planning and risk profiling, however, remain poorly understood in real time contexts, hampering timely responses by governmental agencies. Here we conduct a long-term large-scale investigation of the synthetic and lagged effects of environmental stressors on expressed sentiment, as a proxy of emotional states and subjective wellbeing, derived from over 850 million geotagged tweets using natural language processing across the continental United States from 2016 to 2022. Our spatiotemporal Bayesian hierarchical model, optimized with a distributed lag non-linear algorithm, reveals that combined exposure to air pollution and heatwaves produces significant lagged effects on sentiment, with the population in warmer climates showing more gradual emotional responses than those in colder regions. This evidence corroborates the ‘boiling frog effect’ – a metaphor implying how populations adapt to environmental stressors in ways that delay emotional responses. These findings provide empirical, spatially explicit support for the long-established environmental psychology conjectures including Environmental Stress Theory and Adaptation Level Theory. Our results offer tangible pathways for wellbeing related interventions, climate adaption strategies and public health emergency response systems in the face of increasing global environmental challenges.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Climate change, air pollution, and extreme weather interact in complex ways that impact emotional states of populations. These dynamics are crucial in effective health planning and risk profiling, however, remain poorly understood in real time contexts, hampering timely responses by governmental agencies. Here we conduct a long-term large-scale investigation of the synthetic and lagged effects of environmental stressors on expressed sentiment, as a proxy of emotional states and subjective wellbeing, derived from over 850 million geotagged tweets using natural language processing across the continental United States from 2016 to 2022. Our spatiotemporal Bayesian hierarchical model, optimized with a distributed lag non-linear algorithm, reveals that combined exposure to air pollution and heatwaves produces significant lagged effects on sentiment, with the population in warmer climates showing more gradual emotional responses than those in colder regions. This evidence corroborates the ‘boiling frog effect’ – a metaphor implying how populations adapt to environmental stressors in ways that delay emotional responses. These findings provide empirical, spatially explicit support for the long-established environmental psychology conjectures including Environmental Stress Theory and Adaptation Level Theory. Our results offer tangible pathways for wellbeing related interventions, climate adaption strategies and public health emergency response systems in the face of increasing global environmental challenges.
3.
Gayawan, Ezra; Egbon, Osafu Augustine; Utazi, Chigozie Edson; Umar, Jamila Abubakar; Trotter, Caroline
The influence of women’s empowerment on childhood vaccination coverage in Nigeria: a spatio-temporal analysis. Journal Article
In: Scientific Reports, 2026.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {The influence of women’s empowerment on childhood vaccination coverage in Nigeria: a spatio-temporal analysis.},
author = {Ezra Gayawan and Osafu Augustine Egbon and Chigozie Edson Utazi and Jamila Abubakar Umar and Caroline Trotter },
url = {https://doi.org/10.1038/s41598-026-51266-8},
doi = {10.1038/s41598-026-51266-8},
year = {2026},
date = {2026-05-15},
journal = {Scientific Reports},
abstract = {Immunization is one of the most impactful public health achievements, significantly reducing childhood morbidity and mortality worldwide. However, gender disparity and women’s disempowerment constitute structural barriers in accessing vaccine services in low- and middle-income countries. In Nigeria, widespread differences in social norms and cultural values affect gender roles and influence women’s ability to decide their own healthcare needs and participate in household decision-making. This leads to attitudinal differences in uptake of immunization depending on the child’s location of residence. Using data from four waves of the Nigeria Demographic and Health Survey, we constructed two empowerment indices that determine whether caregivers participate in household decision-making and have the ability to decide on their healthcare needs. We used a structured spatiotemporal statistical model to determine whether a significant part of childhood vaccination coverage disparities can be attributed to these women’s empowerment measures and predicted events at the third administrative level of the country. We considered five vaccination indicators: Bacillus Calmette-Guerin (BCG), zero-dose, receiving a complete dose of DPT, MCV-1 (first dose of measles-containing vaccine), and receipt of all basic vaccinations. The adopted model was validated by comparing the empirical estimates of vaccination coverage level from the data with model projections at the second administrative level. The findings indicate that although empowerment regarding participation in household decision-making and agency over healthcare access is generally associated with increased vaccine uptake, their effects vary considerably across locations and notably among the highly empowered category of women. Although there are efforts to bridge immunization gaps within the country, the study emphasizes the need for tailored strategies that target up-scaling the ability of women and the wider community to participate in the decision-making process and be able to decide on healthcare needs to address regional disparities and improve vaccination coverage.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Immunization is one of the most impactful public health achievements, significantly reducing childhood morbidity and mortality worldwide. However, gender disparity and women’s disempowerment constitute structural barriers in accessing vaccine services in low- and middle-income countries. In Nigeria, widespread differences in social norms and cultural values affect gender roles and influence women’s ability to decide their own healthcare needs and participate in household decision-making. This leads to attitudinal differences in uptake of immunization depending on the child’s location of residence. Using data from four waves of the Nigeria Demographic and Health Survey, we constructed two empowerment indices that determine whether caregivers participate in household decision-making and have the ability to decide on their healthcare needs. We used a structured spatiotemporal statistical model to determine whether a significant part of childhood vaccination coverage disparities can be attributed to these women’s empowerment measures and predicted events at the third administrative level of the country. We considered five vaccination indicators: Bacillus Calmette-Guerin (BCG), zero-dose, receiving a complete dose of DPT, MCV-1 (first dose of measles-containing vaccine), and receipt of all basic vaccinations. The adopted model was validated by comparing the empirical estimates of vaccination coverage level from the data with model projections at the second administrative level. The findings indicate that although empowerment regarding participation in household decision-making and agency over healthcare access is generally associated with increased vaccine uptake, their effects vary considerably across locations and notably among the highly empowered category of women. Although there are efforts to bridge immunization gaps within the country, the study emphasizes the need for tailored strategies that target up-scaling the ability of women and the wider community to participate in the decision-making process and be able to decide on healthcare needs to address regional disparities and improve vaccination coverage.
4.
Qader, Sarchil; Darin, Edith; Dicko, Ahmadou Hamady; Galal, Hisham; Park, Hyunju; Jimenez, Rebeca Moreno; Harfoot, Andrew; Tatem, Andrew J
Developing A Customized, Enumeration Area-Based Sampling Frame Tailored to a Specific Population Subgroup Using Geospatial Methods Journal Article
In: Journal of Survey Statistics and Methodology, 2026.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Developing A Customized, Enumeration Area-Based Sampling Frame Tailored to a Specific Population Subgroup Using Geospatial Methods},
author = {Sarchil Qader and Edith Darin and Ahmadou Hamady Dicko and Hisham Galal and Hyunju Park and Rebeca Moreno Jimenez and Andrew Harfoot and Andrew J Tatem},
url = {https://doi.org/10.1093/jssam/smaf027},
doi = {10.1093/jssam/smaf027},
year = {2026},
date = {2026-04-10},
journal = {Journal of Survey Statistics and Methodology},
abstract = {A national sampling frame typically comprises a list of Primary Sampling Units (PSUs), such as enumeration areas derived from census data, which are commonly used in household surveys. Both national statistical offices and non-governmental organizations often rely on this framework when conducting surveys related to forced displacement. However, these frames are generally developed without considering the estimated number or geographic distribution of displaced populations. As a result, achieving the desired sample size becomes difficult and cost-intensive, as selected units frequently contain no individuals of interest. This study aimed to evaluate the potential of geospatial methodologies to develop a digital national sample frame tailored to a specific population subgroup or the general population, with the goal of ensuring applicability across diverse settings. For the first time, this work produced publicly accessible, digitized boundaries for urban and rural areas in Cameroon that are aligned with official administrative divisions and do not follow a grid-based system. According to our classification and estimated number from the ProGres database, 46 percent of refugees in Cameroon resided in rural areas, while 31 percent lived in camps and 23 percent in urban settings. The proposed geospatial approach offers a cost-effective alternative to traditional manual methods, particularly in data-scarce environments, and eliminates common geometric inconsistencies found in manual mapping efforts. All sampling units were nested within administrative boundaries, and in populated areas, their delineations aligned with observable ground features and respected major physical barriers. Importantly, including the refugee population in the customized national sampling frame was essential, as it enhanced the representativeness of refugees within it. This approach can be easily adapted to other countries. Notably, it was implemented in preparation for 2024’s Forced Displacement Survey in Cameroon, highlighting its practical application and relevance in real-world survey contexts.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A national sampling frame typically comprises a list of Primary Sampling Units (PSUs), such as enumeration areas derived from census data, which are commonly used in household surveys. Both national statistical offices and non-governmental organizations often rely on this framework when conducting surveys related to forced displacement. However, these frames are generally developed without considering the estimated number or geographic distribution of displaced populations. As a result, achieving the desired sample size becomes difficult and cost-intensive, as selected units frequently contain no individuals of interest. This study aimed to evaluate the potential of geospatial methodologies to develop a digital national sample frame tailored to a specific population subgroup or the general population, with the goal of ensuring applicability across diverse settings. For the first time, this work produced publicly accessible, digitized boundaries for urban and rural areas in Cameroon that are aligned with official administrative divisions and do not follow a grid-based system. According to our classification and estimated number from the ProGres database, 46 percent of refugees in Cameroon resided in rural areas, while 31 percent lived in camps and 23 percent in urban settings. The proposed geospatial approach offers a cost-effective alternative to traditional manual methods, particularly in data-scarce environments, and eliminates common geometric inconsistencies found in manual mapping efforts. All sampling units were nested within administrative boundaries, and in populated areas, their delineations aligned with observable ground features and respected major physical barriers. Importantly, including the refugee population in the customized national sampling frame was essential, as it enhanced the representativeness of refugees within it. This approach can be easily adapted to other countries. Notably, it was implemented in preparation for 2024’s Forced Displacement Survey in Cameroon, highlighting its practical application and relevance in real-world survey contexts.
5.
Aheto, Justice Moses K.; Auzenbergs, Megan; Ferrari, Matthew J.; Portnoy, Allison; Utazi, Chigozie Edson; Kakaï, Romain Glèlè; Gayawan, Ezra; Azam, James M.; Nonvignon, Justice
Rebalancing power in infectious disease modelling: Toward inclusive and contextual approaches Journal Article
In: PLOS Global Public Health, vol. 6, iss. 4, 2026.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Rebalancing power in infectious disease modelling: Toward inclusive and contextual approaches},
author = {Justice Moses K. Aheto and Megan Auzenbergs and Matthew J. Ferrari and Allison Portnoy and Chigozie Edson Utazi and Romain Glèlè Kakaï and Ezra Gayawan and James M. Azam and Justice Nonvignon},
url = {https://doi.org/10.1371/journal.pgph.0006220},
doi = {10.1371/journal.pgph.0006220},
year = {2026},
date = {2026-04-03},
journal = {PLOS Global Public Health},
volume = {6},
issue = {4},
abstract = {Over the past several decades, infectious disease modelling has become a central tool in global health decision‑making, shaping financing decisions, vaccination strategies, and disease control policies [1]; for measles alone, our review identified over 400 modelling studies published since 2000 [2]. However, many of the modelling analyses that have guided these decisions originate in high‑income countries (HICs), even when they intend to inform policy in low- and middle-income countries (LMICs) [3]. With the rapid expansion of Large Language Model (LLM)‑enabled modelling, concerns are intensified about analyses produced without adequate contextual understanding. Models developed at a distance can rely on assumptions that fail to reflect local epidemiology or realities, carrying real‑world consequences for feasibility, equity, and impact.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Over the past several decades, infectious disease modelling has become a central tool in global health decision‑making, shaping financing decisions, vaccination strategies, and disease control policies [1]; for measles alone, our review identified over 400 modelling studies published since 2000 [2]. However, many of the modelling analyses that have guided these decisions originate in high‑income countries (HICs), even when they intend to inform policy in low- and middle-income countries (LMICs) [3]. With the rapid expansion of Large Language Model (LLM)‑enabled modelling, concerns are intensified about analyses produced without adequate contextual understanding. Models developed at a distance can rely on assumptions that fail to reflect local epidemiology or realities, carrying real‑world consequences for feasibility, equity, and impact.
6.
Zhang, Wen-Bin; Ge, Yong; Wan, Xuan; Lai, Shengjie; Atkinson, Peter M.
An entropogram-based Random Field model for categorical geospatial data prediction Journal Article
In: International Journal of Geographical Information Science, pp. 1–18, 2026.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {An entropogram-based Random Field model for categorical geospatial data prediction},
author = {Wen-Bin Zhang and Yong Ge and Xuan Wan and Shengjie Lai and Peter M. Atkinson},
url = {https://doi.org/10.1080/13658816.2026.2650365},
doi = {10.1080/13658816.2026.2650365},
year = {2026},
date = {2026-03-30},
journal = {International Journal of Geographical Information Science},
pages = {1–18},
abstract = {Categorical geospatial data underpin applications from biodiversity monitoring to land-use planning, yet existing approaches often fail to recover rare classes while preserving realistic patch structures. We introduced an Entropogram-based Random Field (ERF) model that integrates intrinsic randomness from local class probabilities with entropogram-derived spatial dependence, balancing local class proportions with global neighborhood associations. Using a 10-class, 1-km land-cover map of Northern Ireland, we compared ERF against Indicator Kriging (IK), multi-phase Indicator Kriging (MIK), Compositional Data Analysis (CoDA) and a spatial multinomial logistic (SMLM) model. ERF matches IK and MIK in overall accuracy but achieves higher recall and F1 scores for minority classes, reducing the loss of small, coherent patches. While CoDA ensures compositional validity, it underperforms on rare classes and increases spatial aggregation; MIK improves rare-class recovery but still favors dominant types. SMLM performs comparably to ERF but with far higher computational demand. Landscape metrics showed that ERF and SMLM best preserved patch diversity and realistic geometry, whereas IK and CoDA produced more aggregated patterns. Together, these results highlight ERF as a computationally efficient, scalable and balanced solution for categorical mapping, particularly in applications where minority-class recovery and spatial realism are critical for biodiversity monitoring, habitat connectivity and land-use planning.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Categorical geospatial data underpin applications from biodiversity monitoring to land-use planning, yet existing approaches often fail to recover rare classes while preserving realistic patch structures. We introduced an Entropogram-based Random Field (ERF) model that integrates intrinsic randomness from local class probabilities with entropogram-derived spatial dependence, balancing local class proportions with global neighborhood associations. Using a 10-class, 1-km land-cover map of Northern Ireland, we compared ERF against Indicator Kriging (IK), multi-phase Indicator Kriging (MIK), Compositional Data Analysis (CoDA) and a spatial multinomial logistic (SMLM) model. ERF matches IK and MIK in overall accuracy but achieves higher recall and F1 scores for minority classes, reducing the loss of small, coherent patches. While CoDA ensures compositional validity, it underperforms on rare classes and increases spatial aggregation; MIK improves rare-class recovery but still favors dominant types. SMLM performs comparably to ERF but with far higher computational demand. Landscape metrics showed that ERF and SMLM best preserved patch diversity and realistic geometry, whereas IK and CoDA produced more aggregated patterns. Together, these results highlight ERF as a computationally efficient, scalable and balanced solution for categorical mapping, particularly in applications where minority-class recovery and spatial realism are critical for biodiversity monitoring, habitat connectivity and land-use planning.
7.
Liu, Yonghong; Wang, Xiaoli; Li, Mengyao; Cleary, Eimear; Cheng, Zhifeng; Zhang, Wenbin; Shen, Ying; Yao, Hui; Han, Jiatong; Ruktanonchai, Nick W.; Tatem, Andrew J.; Lai, Shengjie; Wang, Quanyi; Yang, Peng (Ed.)
Interactions of SARS-CoV-2, influenza and respiratory syncytial virus influence epidemic timing and risk Journal Article
In: Communications Medicine, 2026.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Interactions of SARS-CoV-2, influenza and respiratory syncytial virus influence epidemic timing and risk},
editor = {Yonghong Liu and Xiaoli Wang and Mengyao Li and Eimear Cleary and Zhifeng Cheng and Wenbin Zhang and Ying Shen and Hui Yao and Jiatong Han and Nick W. Ruktanonchai and Andrew J. Tatem and Shengjie Lai and Quanyi Wang and Peng Yang},
url = {https://doi.org/10.1038/s43856-026-01504-x},
doi = {10.1038/s43856-026-01504-x},
year = {2026},
date = {2026-03-14},
journal = {Communications Medicine},
abstract = {Interactions between SARS-CoV-2, influenza virus, and respiratory syncytial virus (RSV) at the population level remain poorly understood. This study aimed to quantify potential interactions among these viruses and assess their influence on transmission dynamics.
We analyzed weekly surveillance data on SARS-CoV-2, influenza A and B viruses (IAV and IBV), and RSV from seven regions from October 2021 to May 2024. Distributed lag nonlinear models within a spatiotemporal Bayesian hierarchical framework were used to assess the exposure-lag-response associations among virus pairs. Additionally, we developed a two-pathogen, meta-population mechanistic transmission model to capture the co-epidemic dynamics of IAV and SARS-CoV-2, and to quantify the strength and duration of their bidirectional interactions.
Among all virus pairs examined, a statistically significant association is identified only between IAV positivity and subsequent SARS-CoV-2 risk. When IAV positive rate percentile is between the 52nd and 88th percentiles, the relative risk (RR) of SARS-CoV-2 infection is significantly reduced. The lowest RR for SARS-CoV-2 (0.58, 95% CrI: 0.40-0.85) occurs at a 5-week lag when IAV positivity reaches the 70th percentile. The fitted mechanistic model using incidence data in Beijing shows that IAV infection substantially reduces infection to SARS-CoV-2 by 94.24% (95% CrI: 88.50%–99.24%), with the protective effect lasting 38.24 days (95% CrI: 35.50–41.29 days). Conversely, SARS-CoV-2 infection is associated with a slight increase in infection to IAV.
Our findings indicate that IAV circulation may transiently reduce population-level infection to SARS-CoV-2, potential through ecological or immunological mechanisms.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Interactions between SARS-CoV-2, influenza virus, and respiratory syncytial virus (RSV) at the population level remain poorly understood. This study aimed to quantify potential interactions among these viruses and assess their influence on transmission dynamics.
We analyzed weekly surveillance data on SARS-CoV-2, influenza A and B viruses (IAV and IBV), and RSV from seven regions from October 2021 to May 2024. Distributed lag nonlinear models within a spatiotemporal Bayesian hierarchical framework were used to assess the exposure-lag-response associations among virus pairs. Additionally, we developed a two-pathogen, meta-population mechanistic transmission model to capture the co-epidemic dynamics of IAV and SARS-CoV-2, and to quantify the strength and duration of their bidirectional interactions.
Among all virus pairs examined, a statistically significant association is identified only between IAV positivity and subsequent SARS-CoV-2 risk. When IAV positive rate percentile is between the 52nd and 88th percentiles, the relative risk (RR) of SARS-CoV-2 infection is significantly reduced. The lowest RR for SARS-CoV-2 (0.58, 95% CrI: 0.40-0.85) occurs at a 5-week lag when IAV positivity reaches the 70th percentile. The fitted mechanistic model using incidence data in Beijing shows that IAV infection substantially reduces infection to SARS-CoV-2 by 94.24% (95% CrI: 88.50%–99.24%), with the protective effect lasting 38.24 days (95% CrI: 35.50–41.29 days). Conversely, SARS-CoV-2 infection is associated with a slight increase in infection to IAV.
Our findings indicate that IAV circulation may transiently reduce population-level infection to SARS-CoV-2, potential through ecological or immunological mechanisms.
We analyzed weekly surveillance data on SARS-CoV-2, influenza A and B viruses (IAV and IBV), and RSV from seven regions from October 2021 to May 2024. Distributed lag nonlinear models within a spatiotemporal Bayesian hierarchical framework were used to assess the exposure-lag-response associations among virus pairs. Additionally, we developed a two-pathogen, meta-population mechanistic transmission model to capture the co-epidemic dynamics of IAV and SARS-CoV-2, and to quantify the strength and duration of their bidirectional interactions.
Among all virus pairs examined, a statistically significant association is identified only between IAV positivity and subsequent SARS-CoV-2 risk. When IAV positive rate percentile is between the 52nd and 88th percentiles, the relative risk (RR) of SARS-CoV-2 infection is significantly reduced. The lowest RR for SARS-CoV-2 (0.58, 95% CrI: 0.40-0.85) occurs at a 5-week lag when IAV positivity reaches the 70th percentile. The fitted mechanistic model using incidence data in Beijing shows that IAV infection substantially reduces infection to SARS-CoV-2 by 94.24% (95% CrI: 88.50%–99.24%), with the protective effect lasting 38.24 days (95% CrI: 35.50–41.29 days). Conversely, SARS-CoV-2 infection is associated with a slight increase in infection to IAV.
Our findings indicate that IAV circulation may transiently reduce population-level infection to SARS-CoV-2, potential through ecological or immunological mechanisms.
8.
Nsubuga, Rogers; Kananura, Rornald Muhumuza; Wasswa, Ronald; Birabwa, Catherine; Ogwal, Jimmy; Dotse-Gborgbortsi, Winfred; Mwinnyaa, George; Abajobir, Amanuel; Kisozi, Julius; Nyandwi, Alypio; Boerma, Ties; Waiswa, Peter; Nilsen, Kristine
Maternal and child healthcare coverage and trends: refugee vs. non-refugee districts in Uganda Journal Article
In: Conflict and Health, vol. 20, iss. 42, 2026.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Maternal and child healthcare coverage and trends: refugee vs. non-refugee districts in Uganda},
author = {Rogers Nsubuga and Rornald Muhumuza Kananura and Ronald Wasswa and Catherine Birabwa and Jimmy Ogwal and Winfred Dotse-Gborgbortsi and George Mwinnyaa and Amanuel Abajobir and Julius Kisozi and Alypio Nyandwi and Ties Boerma and Peter Waiswa and Kristine Nilsen },
url = {https://doi.org/10.1186/s13031-026-00780-7},
doi = {10.1186/s13031-026-00780-7},
year = {2026},
date = {2026-03-11},
journal = {Conflict and Health},
volume = {20},
issue = {42},
abstract = {Uganda hosts the largest refugee population in Africa, which exerts much pressure on the district health systems. While refugee-hosting districts (RH) receive targeted investments, the extent to which these influence maternal and child health (MCH) service coverage remains unclear. Using routine facility data, we examined differences in MCH coverage and trends between RH and non-refugee-hosting (non-RH) districts and also explored the effects of government health financing and health system performance on MCH coverage.
We conducted a retrospective analysis utilizing routine health facility MCH data from the Uganda District Health Information System and district-level government Primary Healthcare (PHC) expenditure data from 2020 to 2023. MCH indicators were ANC1st trimester, ANC4, Institutional deliveries, mothers’ Post-natal care (PNC), Measles1 and DPT3 vaccination. We computed a composite coverage index (CCI), health systems performance z-score and compared trends across RH and non-RH districts. Mixed Effects Models assessed the association between government expenditure, RH-status, health system performance over the years.
RH districts consistently had modestly higher coverage of ANC1st trimester, ANC4, Institutional deliveries, PNC, Measles vaccination and CCI trends. Government expenditure was significantly higher in RH districts and refugee-dominant (RD) districts (p < 0.001 vs. p = 0.007). Refugee-dominant districts had higher but non-significant MCH coverage. Unadjusted models of MCH indicators and CCI were positively influenced by government financing and health systems performance z-score except for DPT3 and Measles, respectively. Adjusted models revealed that ANC4 coverage was 7.4% points higher in RH districts (7.42; 95% CI:0.753, 14.090; p = 0.029) and increased by 3.6% points for every unit increase in z-score (3.60; 95% CI: 0.729, 6.462; p = 0.014). CCI increased by 1.6% points and 2.3% points for every unit increased in the government expenditure and z-score respectively (1.55; 95% CI: 0.310, 2.788; p = 0.014) vs. (2.31; 95% CI: 0.642, 3.975; p = 0.007).
Novel approach - leveraging routine facility data, revealed MCH coverage was modestly consistently higher in RH districts over the years and RH status influenced ANC4 coverage. Overall district-CCI depended on Government investment and health systems performance implying increase in PHC financing could be a key driver to universal district-level improvement.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Uganda hosts the largest refugee population in Africa, which exerts much pressure on the district health systems. While refugee-hosting districts (RH) receive targeted investments, the extent to which these influence maternal and child health (MCH) service coverage remains unclear. Using routine facility data, we examined differences in MCH coverage and trends between RH and non-refugee-hosting (non-RH) districts and also explored the effects of government health financing and health system performance on MCH coverage.
We conducted a retrospective analysis utilizing routine health facility MCH data from the Uganda District Health Information System and district-level government Primary Healthcare (PHC) expenditure data from 2020 to 2023. MCH indicators were ANC1st trimester, ANC4, Institutional deliveries, mothers’ Post-natal care (PNC), Measles1 and DPT3 vaccination. We computed a composite coverage index (CCI), health systems performance z-score and compared trends across RH and non-RH districts. Mixed Effects Models assessed the association between government expenditure, RH-status, health system performance over the years.
RH districts consistently had modestly higher coverage of ANC1st trimester, ANC4, Institutional deliveries, PNC, Measles vaccination and CCI trends. Government expenditure was significantly higher in RH districts and refugee-dominant (RD) districts (p < 0.001 vs. p = 0.007). Refugee-dominant districts had higher but non-significant MCH coverage. Unadjusted models of MCH indicators and CCI were positively influenced by government financing and health systems performance z-score except for DPT3 and Measles, respectively. Adjusted models revealed that ANC4 coverage was 7.4% points higher in RH districts (7.42; 95% CI:0.753, 14.090; p = 0.029) and increased by 3.6% points for every unit increase in z-score (3.60; 95% CI: 0.729, 6.462; p = 0.014). CCI increased by 1.6% points and 2.3% points for every unit increased in the government expenditure and z-score respectively (1.55; 95% CI: 0.310, 2.788; p = 0.014) vs. (2.31; 95% CI: 0.642, 3.975; p = 0.007).
Novel approach - leveraging routine facility data, revealed MCH coverage was modestly consistently higher in RH districts over the years and RH status influenced ANC4 coverage. Overall district-CCI depended on Government investment and health systems performance implying increase in PHC financing could be a key driver to universal district-level improvement.
We conducted a retrospective analysis utilizing routine health facility MCH data from the Uganda District Health Information System and district-level government Primary Healthcare (PHC) expenditure data from 2020 to 2023. MCH indicators were ANC1st trimester, ANC4, Institutional deliveries, mothers’ Post-natal care (PNC), Measles1 and DPT3 vaccination. We computed a composite coverage index (CCI), health systems performance z-score and compared trends across RH and non-RH districts. Mixed Effects Models assessed the association between government expenditure, RH-status, health system performance over the years.
RH districts consistently had modestly higher coverage of ANC1st trimester, ANC4, Institutional deliveries, PNC, Measles vaccination and CCI trends. Government expenditure was significantly higher in RH districts and refugee-dominant (RD) districts (p < 0.001 vs. p = 0.007). Refugee-dominant districts had higher but non-significant MCH coverage. Unadjusted models of MCH indicators and CCI were positively influenced by government financing and health systems performance z-score except for DPT3 and Measles, respectively. Adjusted models revealed that ANC4 coverage was 7.4% points higher in RH districts (7.42; 95% CI:0.753, 14.090; p = 0.029) and increased by 3.6% points for every unit increase in z-score (3.60; 95% CI: 0.729, 6.462; p = 0.014). CCI increased by 1.6% points and 2.3% points for every unit increased in the government expenditure and z-score respectively (1.55; 95% CI: 0.310, 2.788; p = 0.014) vs. (2.31; 95% CI: 0.642, 3.975; p = 0.007).
Novel approach - leveraging routine facility data, revealed MCH coverage was modestly consistently higher in RH districts over the years and RH status influenced ANC4 coverage. Overall district-CCI depended on Government investment and health systems performance implying increase in PHC financing could be a key driver to universal district-level improvement.
9.
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.
10.
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.
11.
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.
12.
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.
13.
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.
14.
Lu, Xin; Feng, Jiawei; Lai, Shengjie; Holme, Petter; Liu, Shuo; Du, Zhanwei; Yuan, Xiaoqian; Wang, Siqing; Li, Yunxuan; Zhang, Xiaoyu; Bai, Yuan; Duan, Xiaojun; Mei, Wenjun; Yu, Hongjie; Tan, Suoyi; Liljeros, Fredrik
Human mobility in epidemic modeling Journal Article
In: Physics Reports, vol. 1157, pp. 1-45, 2025.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Human mobility in epidemic modeling},
author = {Xin Lu and Jiawei Feng and Shengjie Lai and Petter Holme and Shuo Liu and Zhanwei Du and Xiaoqian Yuan and Siqing Wang and Yunxuan Li and Xiaoyu Zhang and Yuan Bai and Xiaojun Duan and Wenjun Mei and Hongjie Yu and Suoyi Tan and Fredrik Liljeros},
url = {https://doi.org/10.1016/j.physrep.2025.10.010},
doi = {10.1016/j.physrep.2025.10.010},
year = {2025},
date = {2025-11-07},
journal = {Physics Reports},
volume = {1157},
pages = {1-45},
abstract = {Human mobility forms the backbone of contact patterns through which infectious diseases propagate, fundamentally shaping the spatio-temporal dynamics of epidemics and pandemics. While traditional models are often based on the assumption that all individuals have the same probability of infecting every other individual in the population, a so-called random homogeneous mixing, they struggle to catch the complex and heterogeneous nature of real-world human interactions. Recent advancements in data-driven methodologies and computational capabilities have unlocked the potential of integrating high-resolution human mobility data into epidemic modeling, significantly improving the accuracy, timeliness, and applicability of epidemic risk assessment, contact tracing, and intervention strategies. This review provides a comprehensive synthesis of the current landscape in human mobility-informed epidemic modeling. We explore several data sources and representations of human mobility, and examine the behavioral and structural roles of mobility and contact in shaping disease transmission dynamics. Furthermore, the review spans a wide range of epidemic modeling approaches, ranging from classical compartmental models to network-based, agent-based, and machine learning models. It also discusses how mobility integration enhances risk management and response strategies during epidemics. By synthesizing these insights, the review can serve as a foundational resource for researchers and practitioners, bridging the gap between epidemiological theory and the dynamic complexities of human interaction while charting clear directions for future research.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Human mobility forms the backbone of contact patterns through which infectious diseases propagate, fundamentally shaping the spatio-temporal dynamics of epidemics and pandemics. While traditional models are often based on the assumption that all individuals have the same probability of infecting every other individual in the population, a so-called random homogeneous mixing, they struggle to catch the complex and heterogeneous nature of real-world human interactions. Recent advancements in data-driven methodologies and computational capabilities have unlocked the potential of integrating high-resolution human mobility data into epidemic modeling, significantly improving the accuracy, timeliness, and applicability of epidemic risk assessment, contact tracing, and intervention strategies. This review provides a comprehensive synthesis of the current landscape in human mobility-informed epidemic modeling. We explore several data sources and representations of human mobility, and examine the behavioral and structural roles of mobility and contact in shaping disease transmission dynamics. Furthermore, the review spans a wide range of epidemic modeling approaches, ranging from classical compartmental models to network-based, agent-based, and machine learning models. It also discusses how mobility integration enhances risk management and response strategies during epidemics. By synthesizing these insights, the review can serve as a foundational resource for researchers and practitioners, bridging the gap between epidemiological theory and the dynamic complexities of human interaction while charting clear directions for future research.
15.
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.
16.
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.
17.
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.
18.
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.
19.
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.
20.
Wariri, Oghenebrume; Muhammad, Abdul Khalie; Sowe, Alieu; Strandmark, Julia; Utazi, Chigozie Edson; Metcalf, C Jessica E; Kampmann, Beate
Serological survey to determine measles and rubella immunity gaps across age and geographic locations in The Gambia: a study protocol Journal Article
In: Global Health Action, vol. 18, iss. 1, 2025.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Serological survey to determine measles and rubella immunity gaps across age and geographic locations in The Gambia: a study protocol},
author = {Oghenebrume Wariri and Abdul Khalie Muhammad and Alieu Sowe and Julia Strandmark and Chigozie Edson Utazi and C Jessica E Metcalf and Beate Kampmann},
doi = {10.1080/16549716.2025.2540135},
year = {2025},
date = {2025-08-20},
journal = {Global Health Action},
volume = {18},
issue = {1},
abstract = {Vaccine coverage and disease surveillance data are valuable for monitoring protection against vaccine-preventable diseases; however, they do not directly measure population immunity. High-quality, representative serological studies can provide key insights into immunity gaps, outbreak susceptibility, and inform targeted vaccination strategies, even in high-performing immunization programs. This study aims to estimate location-specific and age-specific immunity profiles for measles and rubella while evaluating the predictive value of indirect immunity estimates derived from vaccination and surveillance data against direct serological measurements. Additionally, it seeks to model the risk of measles outbreaks and assess the impact of mitigation strategies. A multi-stage, stratified cluster sampling design will be implemented across six districts in The Gambia's North Bank and Upper River Regions. Survey clusters (i.e. 5 km × 5 km areas) encompassing all settlements within their boundaries will be selected, proportionally to district population sizes. Cluster selection ensures representativeness of both the population and vaccine coverage within each district. Based on detecting a 10% difference in protective immunity across vaccine coverage levels, power analysis assumes an intraclass correlation coefficient (ICC) of 0.01. In each cluster, 70 children aged 9 months to 14 years will be recruited, yielding a total sample size of 1,750 children across 25 selected clusters. Dried blood samples will be collected and tested for anti-measles and anti-rubella IgG using enzyme immunoassays (EIA). District-specific measles seroprevalence will be estimated using a hierarchical spatial model. This study will generate key evidence needed to refine immunization strategies and reduce the risk of measles and rubella outbreaks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Vaccine coverage and disease surveillance data are valuable for monitoring protection against vaccine-preventable diseases; however, they do not directly measure population immunity. High-quality, representative serological studies can provide key insights into immunity gaps, outbreak susceptibility, and inform targeted vaccination strategies, even in high-performing immunization programs. This study aims to estimate location-specific and age-specific immunity profiles for measles and rubella while evaluating the predictive value of indirect immunity estimates derived from vaccination and surveillance data against direct serological measurements. Additionally, it seeks to model the risk of measles outbreaks and assess the impact of mitigation strategies. A multi-stage, stratified cluster sampling design will be implemented across six districts in The Gambia's North Bank and Upper River Regions. Survey clusters (i.e. 5 km × 5 km areas) encompassing all settlements within their boundaries will be selected, proportionally to district population sizes. Cluster selection ensures representativeness of both the population and vaccine coverage within each district. Based on detecting a 10% difference in protective immunity across vaccine coverage levels, power analysis assumes an intraclass correlation coefficient (ICC) of 0.01. In each cluster, 70 children aged 9 months to 14 years will be recruited, yielding a total sample size of 1,750 children across 25 selected clusters. Dried blood samples will be collected and tested for anti-measles and anti-rubella IgG using enzyme immunoassays (EIA). District-specific measles seroprevalence will be estimated using a hierarchical spatial model. This study will generate key evidence needed to refine immunization strategies and reduce the risk of measles and rubella outbreaks.
