@article{nokey,

title = {Accounting for a typology of travel behavior in modeling infectious disease transmission, detection, and community response},

author = {Nick W. Ruktanonchai and Nicholas Kortessis and Dale T. Clement and Omar Saucedo and Eimear Cleary and Shengjie Lai and Song Gao and Robert D. Holt},

url = {https://doi.org/10.1038/s41598-026-60383-3},

doi = {10.1038/s41598-026-60383-3},

year  = {2026},

date = {2026-08-22},

journal = {Scientific Reports},

abstract = {Modifying travel behavior is often critical for the public health response to infectious disease outbreaks, but depends on consistent, reliable detection. Often, this involves recommending that communities avoid high-risk areas, but if detection is low where transmission is high, these recommendations could lead to a counterproductive response if cases reported by health systems are mostly from low-risk areas. Mismatches between detection and transmission can be further exacerbated by behavioral associations between locations. For example, locations associated with high vectorborne disease (VBD) risk, such as green spaces and parks, are often distant from doctor’s offices, meaning that more time spent in areas with high VBD risk could mean decreased propensity to visit a doctor when sick. These kinds of behavioral correlations between high-risk areas and doctor’s offices carry especially strong risk of causing a counterproductive community response. We combine mathematical models with mobility data analysis to explore how correlations between trips to high-risk areas and healthcare providers can shape disease detection, behavioral response, and outbreak dynamics. Through modeling, we find that a negative correlation between high-risk places and healthcare facilities can cause community response to worsen the outbreak, due to many undetected cases in high-risk areas. We then use smartphone location data to show that there is a clear dichotomy in terms of travel behavior, where the number of trips to places typically associated with urban areas are strongly positively correlated through space and time, and are strongly negatively correlated with places associated with rural areas. This suggests a simple delineation (urban vs. rural) that can be used in future studies to further elucidate how changes in travel behavior could impact the spread and control of infectious diseases.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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 = {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 = {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 = {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 = {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 = {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}

}

@article{nokey,

title = {Human mobility in epidemic modeling},

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

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

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

year  = {2025},

date = {2025-11-07},

journal = {Physics Reports},

volume = {1157},

pages = {1-45},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

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

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

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

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

year  = {2025},

date = {2025-10-23},

journal = {Eurosurveillance},

volume = {30},

issue = {42},

abstract = {Key public health message



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



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



What have we learnt from this study?



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



What are the implications of your findings for public health?



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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Future heat-related mortality in Europe driven by compound day-night heatwaves and demographic shifts},

author = {Xilin Wu and Jun Wang and Yong Ge and Shengjie Lai and Die Zhang and Zhoupeng Ren and Jianghao Wang },

url = {https://doi.org/10.1038/s41467-025-62871-y},

year  = {2025},

date = {2025-08-11},

journal = {Nature Communications},

volume = {16},

number = {7420},

abstract = {Anthropogenic climate change is driving summer heat toward more humid conditions, accompanied by more frequent day-night compound heat extremes (high temperatures during both day and night). As the fast-warming and aging continent, Europe faces escalating heat-related health risks. Here, we projected future heat-related mortality in Europe using a distributed lag nonlinear model that incorporates humid heat and compound heat extremes, strengthened by a health risk-based definition of extreme heat and a scenario matrix integrating time-varying adaptation trajectories. Under 2010–2019 adaptation baselines, future heat-related mortality is projected to increase annually by 103.7-135.1 deaths per million people by 2100 across various population-climate scenarios for every degree of global warming, with Western and Eastern Europe suffering the most. If global warming exceeds 2 °C, climate change will dominate (84.0–96.8%) projected increase in heat-related mortality. Across all socioeconomic pathways, even a 50% reduction in heat-related relative risk through physiological adaptation will be insufficient to offset the climate change-driven escalation of future heat-related mortality.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

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

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

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

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

year  = {2025},

date = {2025-04-30},

journal = {PLOS Global Public Health},

volume = {5},

number = {e0003431},

issue = {4},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{liu2025lessons,

title = {Lessons from failure to success on malaria elimination in the Huai River Basin in China},

author = {Xiaobo Liu and Pi Guo and Ying Liang and Chuanwei Chen and Jince Sun and Haisheng Wu and Tianyun Su and Shengjie Lai and Qiyong Liu},

url = {https://doi.org/10.1136/bmj-2024-080658},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {bmj},

volume = {389},

publisher = {British Medical Journal Publishing Group},

abstract = {Malaria is still a significant public health threat. After generations of control efforts, China was certified as a malaria-free country by the World Health Organization in June 2021. Not without its challenges, China’s experience of eliminating malaria is informative for elimination strategies in other countries and regions.

The Huai River Basin (HRB) in central China was the epicentre of two epidemics in the 1960s and 1970s, accounting for 93.1% and 91.2% of total reported cases in China, respectively.5 A comprehensive control strategy focused on eliminating infection sources, supplemented by integrated mosquito control, was adopted, such that by 1987 most regions in the HRB had achieved “basic malaria elimination”—with incidence rates below 1/10 000.

Unfortunately, malaria resurged in the HRB in 2003 and peaked in 2006. At that time, 62.45% of China’s total cases (60 193 cases) were in the HRB.5 As a result, the government’s leadership in malaria control was re-enforced through the implementation of comprehensive measures, such as mass drug administration, case management,6 and sustainable vector management.7 Consequently, the incidence of malaria in the HRB decreased significantly. No indigenous malaria has occurred in the HRB since the end of 2012.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{liu2025stratified,

title = {Stratified sustainable vector control strategies and measures for malaria control and elimination in China: a 70 year journey},

author = {Qiyong Liu and Yiguan Wang and Xiaobo Liu and Simon I Hay and Shengjie Lai},

url = {https://doi.org/10.1136/bmj-2024-080656},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {bmj},

volume = {389},

publisher = {British Medical Journal Publishing Group},

abstract = {Malaria is a mosquito-borne infectious disease that significantly threatens global health. Considerable efforts and investments have led to a steady decline in incidence and mortality over recent decades. However, 249 million cases were reported from 85 countries and areas in 2022, resulting in 608 000 deaths.1 Notably, approximately 95% of these cases and deaths occurred in the African region. China has had a heavy disease burden of malaria for more than 3000 years, evidenced by the Chinese character for malaria—疟 or nüè—discovered on oracle bone and bronze inscriptions from between 1562 and 1066 BC.2 Chinese medicine has historically been used to treat people with malaria. However, in the 1940s, before the foundation of the People’s Republic of China, the burden of malaria was still immense, with an estimated 30 million annual cases, more than 90% of the population at risk, and a fatality rate of approximately 1%.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A systematic review of using population-level human mobility data to understand SARS-CoV-2 transmission},

author = {Natalya Kostandova and Catherine Schluth and Rohan Arambepola and Fatumah Atuhaire and Sophie Bérubé and Taylor Chin and Eimear Cleary and Oscar Cortes-Azuero and Bernardo García-Carreras and Kyra H. Grantz and Matt D. T. Hitchings and Angkana T. Huang and Nishant Kishore and Shengjie Lai and Soren L. Larsen and Stacie Loisate and Pamela Martinez and Hannah R. Meredith and Ritika Purbey and Tanjona Ramiadantsoa and Jonathan Read and Benjamin L. Rice and Lori Rosman and Nick Ruktanonchai and Henrik Salje and Kathryn L. Schaber and Andrew J. Tatem and Jasmine Wang and Derek A. T. Cummings and Amy Wesolowski },

url = {https://doi.org/10.1038/s41467-024-54895-7},

year  = {2024},

date = {2024-12-03},

journal = {Nature Communications},

volume = {15},

number = {10504},

abstract = {The emergence of SARS-CoV-2 into a highly susceptible global population was primarily driven by human mobility-induced introduction events. Especially in the early stages, understanding mobility was vital to mitigating the pandemic prior to widespread vaccine availability. We conducted a systematic review of studies published from January 1, 2020, to May 9, 2021, that used population-level human mobility data to understand SARS-CoV-2 transmission. Of the 5505 papers with abstracts screened, 232 were included in the analysis. These papers focused on a range of specific questions but were dominated by analyses focusing on the USA and China. The majority included mobile phone data, followed by Google Community Mobility Reports, and few included any adjustments to account for potential biases in population sampling processes. There was no clear relationship between methods used to integrate mobility and SARS-CoV-2 data and goals of analysis. When considering papers focused only on the estimation of the effective reproductive number within the US, there was no clear relationship identified between this measure and changes in mobility patterns. Our findings underscore the need for standardized, systematic ways to identify the source of mobility data, select an appropriate approach to using it in analysis, and reporting.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Trade-offs among human, animal, and environmental health hinder the uniform progress of global One Health},

author = {Tian, Ya and Zhang, Junze and Li, Zonghan and Wu, Kai and Cao, Min and Lin, Jian and Pradhan, Prajal and Lai, Shengjie and Meng, Jia and Fu, Bojie and Chen, Min and Lin, Hui },

url = {https://doi.org/10.1016/j.isci.2024.111357},

doi = {10.1016/j.isci.2024.111357 },

year  = {2024},

date = {2024-11-22},

journal = {iScience},

volume = {27},

issue = {12},

abstract = {The One Health (OH) approach, integrating aspects of human, animal, and environmental health, still lacks robustly quantified insights into its complex relationships. To fill this knowledge gap, we devised a comprehensive assessment scheme for OH to assess its progress, synergies, trade-offs, and priority targets. From 2000 to 2020, we find evidence for global progress toward OH, albeit uneven, with its average score rising from 61.6 to 65.5, driven primarily by better human health although environmental health lags. Despite synergies prevalent within and between the three health dimensions, over half of the world’s countries, mainly low-income ones, still incur substantial trade-offs impeding OH’s advancement, especially between animal and environmental health. Our in-depth analysis of synergy and trade-off networks reveals that maternal, newborn, and child health are critical synergistic targets, whereas biodiversity and land resources dominate trade-offs. We provide key information for the synergetic and uniform development of global OH and policymaking.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Identifying counter-urbanisation using Facebook's user count data},

author = {Qianwen Duan and Jessica Steele and Zhifeng Cheng and Eimear Cleary and Nick Ruktanonchai and Hal Voepel and Tim O'Riordan and Andrew J. Tatem and Alessandro Sorichetta and Shengjie Lai and Felix Eigenbrod},

url = {https://doi.org/10.1016/j.habitatint.2024.103113},

doi = {10.1016/j.habitatint.2024.103113},

year  = {2024},

date = {2024-06-04},

journal = {Habitat International},

volume = {150},

abstract = {Identifying the growing widespread phenomenon of counter-urbanisation, where people relocate from urban centres to rural areas, is essential for understanding the social and ecological consequences of the associated changes. However, its nuanced dynamics and complex characteristics pose challenges for quantitative analysis. Here, we used near real-time Facebook user count data for Belgium and Thailand, with missing data imputed, and applied the Seasonal-Trend decomposition using Loess (STL) model to capture subtle urban and rural population dynamics and assess counter-urbanisation. We identified counter-urbanisation in both Belgium and Thailand, evidenced by increases of 1.80% and 2.14% in rural residents (night-time user counts) and decreases of 3.08% and 5.04% in urban centre night-time user counts from March 2020 to May 2022, respectively. However, the counter-urbanisation in Thailand appears to be transitory, with rural users beginning to decline during both day and night as COVID-19 restrictions were lifted. By contrast, in Belgium, at the country level, there is as yet no evidence of a return to urban residences, though daytime numbers in rural areas are decreasing and in urban centres are increasing, suggesting an increase in commuting post-pandemic. These variation characteristics observed both between Belgium and Thailand and between day and night, extend the current understanding of counter-urbanisation. The use of novel social media data provides an effective quantitative perspective to comprehend counter-urbanisation in different settings.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}