@article{nokey,

title = {Quantifying Heterogeneous Impacts of Geolocated mHealth Tracing on Spatiotemporal Transmission of Emerging Infections},

author = {Zhifeng Cheng and Hao Liang and Fei Gao and Xuyao Chen and Xiao Liu and Yuping Zhou and Xu Chen and Hui Jiang and Samantha Cockings and Andrew Tatem and Shengjie Lai and Chu He},

url = {https://doi.org/10.1029/2026GH001838},

doi = {10.1029/2026GH001838},

year  = {2026},

date = {2026-08-10},

journal = {GeoHealth},

abstract = {Digital health tools, including mobile device location-based health (mHealth) applications, have been adopted to augment conventional contact tracing during recent epidemics and pandemics, yet quantitative evidence about their added epidemiological value remains limited. Using aggregated, privacy-preserving mHealth data from six Chinese cities (July 2021–May 2022), we modelled transmission dynamics of emerging respiratory infections to quantify the heterogeneous effects of mHealth-triggered movement restrictions and risk-based quarantines across viral variants. We found that the mHealth-assisted interventions, together with population-wide lockdowns, could rapidly reduce the effective infection rate and drive daily incidence toward zero within two weeks, which was rarely achieved through traditional contact tracing alone. Scenario simulations further showed that in the absence of mHealth interventions, infections consistently propagated to connected cities across a wide range of epidemiological assumptions, contradicting the reality. These results suggest that early integration of geolocated mHealth tools with traditional epidemiological investigations enhances outbreak containment at both local and regional scales, providing quantitative, spatially explicit evidence to inform scalable mHealth integration strategies for future infectious threats with pandemic potential.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Where and why malaria persists in heterogeneous transmission settings: insights from routine health facility data},

author = {Camille Morlighem and Corentin Visée and Chibuzor Christopher Nnanatu and Mame Wodji Tine and Aminata Niang Diène and Catherine Linard},

url = {https://doi.org/10.1136/bmjgh-2025-023003},

doi = {10.1136/bmjgh-2025-023003},

year  = {2026},

date = {2026-08-07},

urldate = {2026-08-07},

journal = {BMJ Global Health},

volume = {11},

issue = {e023003},

abstract = {Malaria distribution in sub-Saharan Africa is becoming increasingly spatially heterogeneous. Pre-elimination areas, high-transmission settings and urban areas with increasing transmission may all coexist within the same country. Effective intervention planning requires identifying high-risk areas and their drivers—an area in which geospatial models can provide valuable insights. Yet these models often assume stationary risk factors across transmission levels and frequently rely on prevalence survey data, which are unreliable in low-transmission settings. This study uses routine public-sector health facility data (ie, DHIS2) to model malaria incidence in Senegal from 2017 to 2021, accounting for transmission levels, and introduces novel fine-scale estimates of reported malaria incidence derived from dasymetric disaggregation.



Health facility catchment populations were estimated using a probabilistic approach, allowing individuals to attend multiple facilities based on relative travel time. Malaria incidence per catchment was modelled using high-resolution environmental and socioeconomic covariates within a Bayesian hierarchical modelling framework. Separate models were fitted by season and endemicity-urbanisation level to assess the heterogeneity of risk factors. We adapted dasymetric disaggregation methods from population mapping to redistribute facility-level cases to 1 km pixels.



Estimated malaria incidence increased from 60 cases per 1000 people in 2017 to 71 cases in 2021. Key risk factors varied across endemicity-urbanisation levels; some factors were uniquely associated with low (eg, distance to roads), moderate (eg, cropland) and high-transmission settings (eg, flooded vegetation). Fine-scale maps represent malaria cases that the public-sector health system routinely reports, rather than the total malaria burden, and revealed potential underdiagnosis in remote areas.



Our findings emphasise the importance of considering endemicity levels when evaluating malaria risk factors in heterogeneous transmission contexts. As health system data quality continues to improve, this approach offers a scalable alternative for mapping reported malaria incidence, supporting better targeted interventions and the identification of underdiagnosed areas.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Responsible use of artificial intelligence and machine learning for food security early warning systems},

author = {Weston Anderson and Inbal Becker-Reshef and Nicholas Bodanac and Bia Carneiro and Dave English and Andrew Hoell and Tim Hoffine and YanYan Liu and Mélissande Machefer and Michele Meroni and Clionadh Raleigh and Andrew J. Tatem and Robert Trigwell and Paola Vesco and Rob Vos },

url = {https://doi.org/10.1038/s43016-026-01400-6},

doi = {10.1038/s43016-026-01400-6},

year  = {2026},

date = {2026-08-06},

journal = {Nature Food},

abstract = {Artificial intelligence and machine learning are already enhancing food security early warning systems by improving data collection, event monitoring and forecasting. However, to avoid costly errors, artificial intelligence and machine learning should be applied selectively alongside expert oversight, robust governance and transparent practices to maintain accountable and effective humanitarian decision-making.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A spatiotemporal dataset of multi-hazard early warnings in China},

author = {Haiyan Liu and Shengjie Lai and Zhifeng Cheng and Jianghao Wang},

url = {https://doi.org/10.1038/s41597-026-07994-7},

doi = {10.1038/s41597-026-07994-7},

year  = {2026},

date = {2026-07-30},

journal = {Scientific Data},

abstract = {Effective disaster risk reduction depends not only on hazard detection, but also on the timely dissemination of administrative early warnings. However, datasets that systematically document how warnings are issued across administrative hierarchies remain limited. Here, we present the Chinese Multi-Hazard Early Warning Dataset, a standardized database containing 1,057,817 warning records issued across mainland China between 1 January 2022 and 31 December 2025. The dataset integrates records from the national emergency broadcasting platform and official social media accounts operated within China’s warning dissemination system. Raw warning texts were processed through a standardized workflow including spatiotemporal geocoding, hierarchical hazard classification, and record cleaning and integration. The final dataset provides hourly records across four administrative tiers (national, provincial, city-level, and county-level) using a unified taxonomy of hydrometeorological, geophysical, oceanographic, and environmental hazards, each graded according to four levels of severity. Comparisons with official statistical reports show strong temporal agreement across administrative levels (r > 0.94, p < 0.001), supporting the overall credibility of the dataset. This dataset complements traditional hazard observations by documenting the operational layer of warning dissemination and provides empirical resources for studying warning governance, dissemination timing, administrative coordination, and risk communication.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Intercity connectivity enhances urban mobility resilience to extreme rainfall},

author = {Haiyan Liu and Xuemei Bai},

url = {https://doi.org/10.1038/s41558-026-02713-x},

doi = {10.1038/s41558-026-02713-x},

year  = {2026},

date = {2026-07-17},

journal = {Nature Climate Change},

abstract = {It is important to strengthen urban resilience amid the growing extreme weather events under climate change, yet the role of intercity connectivity in shaping such resilience remains understudied. Here using mobility data from 1.1 billion devices across 366 Chinese cities, we show that stronger connectivity enhances intracity mobility resilience, a core indicator of urban functionality, during extreme rainfall. Cities with higher network centrality and stronger ties to wealthier cities exhibit higher resilience, characterized by shorter recovery times, faster rebound and lower total performance loss (TPL). Counterfactual simulations indicate that less developed or constrained networks could increase predicted TPL by 527.1% and 54.7%, respectively. Projections to 2050 suggest that socioeconomic development combined with strategic railway development would reduce median city-level TPL by 72.5–76.1%, avoiding indirect economic losses of 12.8–14.2 billion yuan. Notably, smaller cities benefit more from improved connectivity and prioritizing network enhancement in poorer cities renders the largest gains.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Spatio-temporal modelling of COVID-19 infection and associated risk factors in Dakar, Senegal},

author = {Assane Niang Gadiaga and Mame Wodji Tine and Aminata Niang Diene and Catherine Linard and Niko Speybroeck and Ortis Yankey and Somnath Chaudhuri and Chibuzor Christopher Nnanatu and Eimear Cleary and Shengjie Lai and Attila N. Lazar and Andrew J. Tatem},

url = {https://doi.org/https://doi.org/10.1371/journal.pgph.0004945},

doi = {10.1371/journal.pgph.0004945},

year  = {2026},

date = {2026-06-11},

urldate = {2026-06-11},

journal = {PLOS Global Public Health},

volume = {6},

number = {e0004945},

issue = {6},

abstract = {Infectious diseases are a major threat to global health and economy and the recent COVID-19 pandemic is a perfect example of this. Appropriate modelling and accurate prediction of the outcome of disease spread over time and across space are critical steps towards informed development of effective strategies for public health interventions. In low and middle-income countries, however, the scarcity of spatially disaggregated time-series infectious diseases data often limits the analysis of the burden of infectious disease at a broad-scale, and the effects of the contextual risk factors is not often fully captured. In this study, we investigate the spatio-temporal patterns of COVID-19 infection in Dakar at the neighbourhood level, and evaluate the impact of potential risk factors. Geostatistical models based on COVID-19 infection data were used to explain and predict the spatio-temporal distribution of infections between June 2020 and June 2021. We specified a Bayesian regression model that incorporates a spatio-temporally autocorrelated random effect in order to quantify the evolution of the spatial patterns of the COVID-19 infection overtime. Results show significant strong spatial heterogeneity but relatively small temporal variations of the COVID-19 distribution, and a positive association between adjusted population density (mean of the posterior probability: 0.29, credible interval: 0.24-0.34) and residential areas (mean of the posterior probability: 1.25, credible interval: 0.66-1.83) with COVID-19 infection. Western areas are at higher risk of COVID-19 infection compared to eastern and less densely populated peripheral neighbourhoods. Measuring the role of contextual risk factors and mapping the at-risk areas can provide valuable insights for policymakers, enabling more targeted public health interventions. These efforts also support the management of endemic diseases and preparedness for future outbreaks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@article{nokey,

title = {Urban–Rural Disparities in Geographic Healthcare Accessibility: A Comparative Study of Nigeria and Zambia},

author = {Weixuan Fang and Wen-Bin Zhang and Dorothea Woods and Heather Chamberlain and Winfred Dotse-Gborgbortsi and Iyanuloluwa Deborah Olowe and Marcello Schiavina and Borys Nosatiuk and Rhorom Priyatikanto and Maksym Bondarenko and Joachim Maes and Lewis Dijkstra and Andrew J. Tatem and Alessandro Sorichetta},

url = {https://doi.org/10.1007/s11524-026-01077-z},

doi = {10.1007/s11524-026-01077-z},

year  = {2026},

date = {2026-04-16},

journal = {Journal of Urban Health},

volume = {103},

pages = {431-442},

abstract = {Geographic access to healthcare remains a critical barrier to health equity in low- and middle-income countries, where infrastructure and service provision are unevenly distributed. Using the UN-endorsed Degree of Urbanisation framework, we assessed disparities in geographic healthcare accessibility across Nigeria and Zambia in 2020. Travel times to health facilities were modeled for both walking and motorized transport at 1 km resolution, and stratified by settlement type and demographic group. Results showed marked urban–rural disparities: while city residents could typically reach hospitals within minutes, rural populations faced journeys exceeding 4 h on foot. Motorized transport substantially improved accessibility but remained unavailable to many, leaving only 8% of Nigeria’s population and 6% of Zambia’s population within a 15-min walking distance of a hospital. Children in rural Nigeria were particularly disadvantaged, with longer hospital travel times compared to reproductive-age women and elderly adults, whereas demographic differences were less pronounced in Zambia. These findings highlight persistent geographic and demographic inequalities in access to care, underscoring the need for targeted investment in rural transport networks, strategic placement of health facilities, and tailored support for vulnerable populations. By linking harmonized urban–rural classifications with demographic health needs, this study provides actionable evidence for advancing universal health coverage and reducing inequities in rapidly urbanizing African contexts.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

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

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

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

doi = {10.1093/jssam/smaf027},

year  = {2026},

date = {2026-04-10},

journal = {Journal of Survey Statistics and Methodology},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

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

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

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

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

year  = {2026},

date = {2026-04-03},

journal = {PLOS Global Public Health},

volume = {6},

issue = {4},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

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

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

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

doi = {10.1080/13658816.2026.2650365},

year  = {2026},

date = {2026-03-30},

journal = {International Journal of Geographical Information Science},

pages = {1–18},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

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

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

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

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

year  = {2026},

date = {2026-03-14},

journal = {Communications Medicine},

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

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

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

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

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

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

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

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

year  = {2026},

date = {2026-03-11},

journal = {Conflict and Health},

volume = {20},

issue = {42},

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



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



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



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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

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

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

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

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

year  = {2026},

date = {2026-02-17},

journal = {PLoS Computational Biology},

volume = {22},

number = {e1013989},

issue = {2},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

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

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

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

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

year  = {2026},

date = {2026-02-05},

urldate = {2026-02-05},

journal = {npj urban sustainability},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

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

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

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

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

year  = {2026},

date = {2026-01-07},

urldate = {2026-01-07},

journal = {Global Environmental Change},

volume = {96},

number = {103111},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

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

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

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

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

year  = {2025},

date = {2025-12-17},

urldate = {2025-12-17},

journal = {Sustainable Cities and Society},

volume = {136},

number = {107066},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

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

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

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

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

year  = {2025},

date = {2025-12-03},

journal = {Infectious Diseases of Poverty},

volume = {14},

number = {123},

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



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



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



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

keywords = {},

pubstate = {published},

tppubtype = {article}

}