@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{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}

}

@article{li2024chronic,

title = {Chronic disease and infection in China: lessons from the covid-19 pandemic},

author = {Yanming Li and Xunliang Tong and Mingyue Jiang and Zeni Wu and Yuping Duan and Li Zhang and Shengjie Lai and Weizhong Yang and Luzhao Feng},

url = {https://doi.org/10.1136/bmj.q2000},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {bmj},

volume = {387},

publisher = {British Medical Journal Publishing Group},

abstract = {The harms from covid-19 during the pandemic extended beyond the disease itself. Older people with a chronic or non-communicable disease were among the most vulnerable to SARS-CoV-2 and at the highest risk of death. The interplay between chronic disease and infection can exacerbate each condition, highlighting the importance of preventing infection in people with chronic disease and increasing the challenges of caring for patients with comorbidity. For instance, during the omicron outbreak in Hong Kong excess deaths occurred not only from covid-19 infections but from chronic diseases, including cerebrovascular disease and kidney disease. This may be because the healthcare system was heavily overwhelmed, leading to low quality healthcare services and longer waits for some interventions for non-covid illness. Moreover, patients with chronic diseases may face increased risks of complications and severe outcomes after covid-19 infection.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Spatiotemporal variations of “triple-demic” outbreaks of respiratory infections in the United States in the post-COVID-19 era},

author = {Wei Luo and Qianhuang Liu and Yuxuan Zhou and Yiding Ran and Zhaoyin Liu and Weitao Hou and Sen Pei and Shengjie Lai},

url = {https://doi.org/10.1186/s12889-023-17406-9},

doi = {10.1186/s12889-023-17406-9},

year  = {2023},

date = {2023-12-07},

journal = {BMC Public Health},

volume = {23},

number = {2452},

abstract = {The US confronted a “triple-demic” of influenza, respiratory syncytial virus (RSV), and COVID-19 in the winter of 2022, leading to increased respiratory infections and a higher demand for medical supplies. It is urgent to analyze these epidemics and their spatial-temporal co-occurrence, identifying hotspots and informing public health strategies.



We employed retrospective and prospective space-time scan statistics to assess the situations of COVID-19, influenza, and RSV in 51 US states from October 2021 to February 2022, and from October 2022 to February 2023, respectively. This enabled monitoring of spatiotemporal variations for each epidemic individually and collectively.



Compared to winter 2021, COVID-19 cases decreased while influenza and RSV infections significantly increased in winter 2022. We found a high-risk cluster of influenza and COVID-19 (not all three) in winter 2021. In late November 2022, a large high-risk cluster of triple-demic emerged in the central US. The number of states at high risk for multiple epidemics increased from 15 in October 2022 to 21 in January 2023.



Our study offers a novel spatiotemporal approach that combines both univariate and multivariate surveillance, as well as retrospective and prospective analyses. This approach offers a more comprehensive and timely understanding of how the co-occurrence of COVID-19, influenza, and RSV impacts various regions within the United States. Our findings assist in tailor-made strategies to mitigate the effects of these respiratory infections.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Prior water availability modifies the effect of heavy rainfall on dengue transmission: a time series analysis of passive surveillance data from southern China},

author = {Qu Cheng and Qinlong Jing and Philip A. Collender and Jennifer R. Head and Qi Li and Hailan Yu and Zhichao Li and Yang Ju and Tianmu Chen and Peng Wang and Eimear Cleary and Shengjie Lai},

url = {https://doi.org/10.3389/fpubh.2023.1287678},

doi = {10.3389/fpubh.2023.1287678},

year  = {2023},

date = {2023-12-01},

journal = {Frontiers in Public Health},

abstract = {Given the rapid geographic spread of dengue and the growing frequency and intensity of heavy rainfall events, it is imperative to understand the relationship between these phenomena in order to propose effective interventions. However, studies exploring the association between heavy rainfall and dengue infection risk have reached conflicting conclusions, potentially due to the neglect of prior water availability in mosquito breeding sites as an effect modifier.



In this study, we addressed this research gap by considering the impact of prior water availability for the first time. We measured prior water availability as the cumulative precipitation over the preceding 8 weeks and utilized a distributed lag non-linear model stratified by the level of prior water availability to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China.



Our findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24–55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 [95% credible interval (CI): 1.02–1.83] occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7–121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43–0.79), occurring at a lag of 45 days.



These findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Exploring the Relationship between Temporal Fluctuations in Satellite Nightlight Imagery and Human Mobility across Africa},

author = {Grant Rogers and Patrycja Koper and Cori Ruktanonchai and and Nick Ruktanonchai and Edson Utazi and Dorothea Woods and Alexander Cunningham and Andrew J. Tatem and Jessica Steele and Shengjie Lai and Alessandro Sorichetta},

url = {https://doi.org/10.3390/rs15174252},

doi = {10.3390/rs15174252},

year  = {2023},

date = {2023-09-30},

journal = {Remote Sensing},

volume = {15},

number = {4252;},

issue = {17},

abstract = {Mobile phone data have been increasingly used over the past decade or more as a pretty reliable indicator of human mobility to measure population movements and the associated changes in terms of population presence and density at multiple spatial and temporal scales. However, given the fact mobile phone data are not available everywhere and are generally difficult to access and share, mostly because of commercial restrictions and privacy concerns, more readily available data with global coverage, such as night-time light (NTL) imagery, have been alternatively used as a proxy for population density changes due to population movements. This study further explores the potential to use NTL brightness as a short-term mobility metric by analysing the relationship between NTL and smartphone-based Google Aggregated Mobility Research Dataset (GAMRD) data across twelve African countries over two periods: 2018–2019 and 2020. The data were stratified by a measure of the degree of urbanisation, whereby the administrative units of each country were assigned to one of eight classes ranging from low-density rural to high-density urban. Results from the correlation analysis, between the NTL Sum of Lights (SoL) radiance values and three different GAMRD-based flow metrics calculated at the administrative unit level, showed significant differences in NTL-GAMRD correlation values across the eight rural/urban classes. The highest correlations were typically found in predominantly rural areas, suggesting that the use of NTL data as a mobility metric may be less reliable in predominantly urban settings. This is likely due to the brightness saturation and higher brightness stability within the latter, showing less of an effect than in rural or peri-urban areas of changes in brightness due to people leaving or arriving. Human mobility in 2020 (during COVID-19-related restrictions) was observed to be significantly different than in 2018–2019, resulting in a reduced NTL-GAMRD correlation strength, especially in urban settings, most probably because of the monthly NTL SoL radiance values remaining relatively similar in 2018–2019 and 2020 and the human mobility, especially in urban settings, significantly decreasing in 2020 with respect to the previous considered period. The use of NTL data on its own to assess monthly mobility and the associated fluctuations in population density was therefore shown to be promising in rural and peri-urban areas but problematic in urban settings.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Effects of public-health measures for zeroing out different SARS-CoV-2 variants},

author = {Yong Ge and Xilin Wu and Wenbin Zhang and Xiaoli Wang and Die Zhang and Jianghao Wang and Haiyan Liu and Zhoupeng Ren and Nick W. Ruktanonchai and Corrine W. Ruktanonchai and Eimear Cleary and Yongcheng Yao and Amy Wesolowski and Derek A. T. Cummings and Zhongjie Li and Andrew J. Tatem and Shengjie La},

url = {https://doi.org/10.1038/s41467-023-40940-4

},

doi = {10.1038/s41467-023-40940-4},

year  = {2023},

date = {2023-08-29},

urldate = {2023-08-29},

journal = {Nature Communications},

volume = {14},

number = {5270},

abstract = {Targeted public health interventions for an emerging epidemic are essential for preventing pandemics. During 2020-2022, China invested significant efforts in strict zero-COVID measures to contain outbreaks of varying scales caused by different SARS-CoV-2 variants. Based on a multi-year empirical dataset containing 131 outbreaks observed in China from April 2020 to May 2022 and simulated scenarios, we ranked the relative intervention effectiveness by their reduction in instantaneous reproduction number. We found that, overall, social distancing measures (38% reduction, 95% prediction interval 31-45%), face masks (30%, 17-42%) and close contact tracing (28%, 24-31%) were most effective. Contact tracing was crucial in containing outbreaks during the initial phases, while social distancing measures became increasingly prominent as the spread persisted. In addition, infections with higher transmissibility and a shorter latent period posed more challenges for these measures. Our findings provide quantitative evidence on the effects of public-health measures for zeroing out emerging contagions in different contexts.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Combined and delayed impacts of epidemics and extreme weather on urban mobility recovery},

author = {Haiyan Liu and Jianghao Wang and Jian Liu and Yong Ge and Xiaoli Wang and Chi Zhang and Eimear Cleary and Nick W. Ruktanonchai and Corrine W. Ruktanonchai and Yongcheng Yao and Amy Wesolowski and Xin Lu and Andrew J. Tatem and Xuemei Bai and Shengjie Lai},

url = {https://doi.org/10.1016/j.scs.2023.104872

},

doi = {10.1016/j.scs.2023.104872},

year  = {2023},

date = {2023-08-22},

journal = {Sustainable Cities and Society},

volume = {99},

number = {104872},

abstract = {The ever-increasing pandemic and natural disasters might spatial-temporal overlap to trigger compound disasters that disrupt urban life, including human movements. In this study, we proposed a framework for data-driven analyses on mobility resilience to uncover the compound effects of COVID-19 and extreme weather events on mobility recovery across cities with varied socioeconomic contexts. The concept of suppression risk (SR) is introduced to quantify the relative risk of mobility being reduced below the pre-pandemic baseline when certain variables deviate from their normal values. By analysing daily mobility data within and between 313 Chinese cities, we consistently observed that the highest SR under outbreaks occurred at high temperatures and abnormal precipitation levels, regardless of the type of travel, incidences, and time. Specifically, extremely high temperatures (at 35 °C) increased SR during outbreaks by 12.5%-120% but shortened the time for mobility recovery. Increased rainfall (at 20 mm/day) added SRs by 12.5%-300%, with delayed effects reflected in cross-city movements. These compound impacts, with varying lagged responses, were aggravated in cities with high population density and low GDP levels. Our findings provide quantitative evidence to inform the design of preparedness and response strategies for enhancing urban resilience in the face of future pandemics and compound disasters.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Data-Driven Models Informed by Spatiotemporal Mobility Patterns for Understanding Infectious Disease Dynamics},

author = {Die Zhang and Yong Ge and Xilin Wu and Haiyan Liu and Wenbin Zhang and Shengjie Lai},

url = {https://doi.org/10.3390/ijgi12070266},

doi = {10.3390/ijgi12070266},

year  = {2023},

date = {2023-07-03},

journal = {International Journal of Geo-Information},

volume = {12},

number = {266},

issue = {7},

abstract = {Data-driven approaches predict infectious disease dynamics by considering various factors that influence severity and transmission rates. However, these factors may not fully capture the dynamic nature of disease transmission, limiting prediction accuracy and consistency. Our proposed data-driven approach integrates spatiotemporal human mobility patterns from detailed point-of-interest clustering and population flow data. These patterns inform the creation of mobility-informed risk indices, which serve as auxiliary factors in data-driven models for detecting outbreaks and predicting prevalence trends. We evaluated our approach using real-world COVID-19 outbreaks in Beijing and Guangzhou, China. Incorporating the risk indices, our models successfully identified 87% (95% Confidence Interval: 83–90%) of affected subdistricts in Beijing and Guangzhou. These findings highlight the effectiveness of our approach in identifying high-risk areas for targeted disease containment. Our approach was also tested with COVID-19 prevalence data in the United States, which showed that including the risk indices reduced the mean absolute error and improved the R-squared value for predicting weekly case increases at the county level. It demonstrates applicability for spatiotemporal forecasting of widespread diseases, contributing to routine transmission surveillance. By leveraging comprehensive mobility data, we provide valuable insights to optimize control strategies for emerging infectious diseases and facilitate proactive measures against long-standing diseases.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{,

title = {Assessing spread risk of COVID-19 within and beyond China in early 2020},

author = {Lai, Shengjie and Bogoch, Isaac I and Ruktanonchai, Nick W and Watts, Alexander and Lu, Xin and Yang, Weizhong and Yu, Hongjie and Khan, Kamran and Tatem, Andrew J},

doi = {10.1016/j.dsm.2022.08.004},

year  = {2022},

date = {2022-08-26},

urldate = {2022-08-26},

journal = {Data Science and Management},

abstract = {A novel coronavirus emerged in Wuhan in late 2019 and has caused the COVID-19 pandemic announced by the World Health Organization on March 12, 2020. This study was originally conducted in January 2020 to estimate the potential risk and geographic range of COVID-19 spread within and beyond China at the early stage of the pandemic. A series of connectivity and risk analyses based on domestic and international travel networks were conducted using historical aggregated mobile phone data and air passenger itinerary data. We found that the cordon sanitaire of Wuhan was likely to have occurred during the latter stages of peak population numbers leaving the city, with travellers departing into neighbouring cities and other megacities in China. We estimated that 59,912 air passengers, of which 834 (95% uncertainty interval: 478–1349) had COVID-19 infection, travelled from Wuhan to 382 cities outside of mainland China during the two weeks prior to the city’s lockdown. Most of these destinations were located in Asia, but major hubs in Europe, the US and Australia were also prominent, with a strong correlation seen between the predicted risks of importation and the number of imported cases found. Given the limited understanding of emerging infectious diseases in the very early stages of outbreaks, our approaches and findings in assessing travel patterns and risk of transmission can help guide public health preparedness and intervention design for new COVID-19 waves caused by variants of concern and future pandemics to effectively limit transmission beyond its initial extent.},

keywords = {China, covid-19, Mobility},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Exploring methods for mapping seasonal population changes using mobile phone data},

author = {D. Woods and A. Cunningham and C. E. Utazi and M. Bondarenko and L. Shengjie and G. E. Rogers and P. Koper and C. W. Ruktanonchai and E. zu Erbach-Schoenberg and A. J. Tatem and J. Steele and A. Sorichetta},

doi = {10.1057/s41599-022-01256-8},

year  = {2022},

date = {2022-07-28},

urldate = {2022-07-28},

journal = {Humanities and Social Sciences Communications},

number = {247},

abstract = {Data accurately representing the population distribution at the subnational level within countries is critical to policy and decision makers for many applications. Call data records (CDRs) have shown great promise for this, providing much higher temporal and spatial resolutions compared to traditional data sources. For CDRs to be integrated with other data and in order to effectively inform and support policy and decision making, mobile phone user must be distributed from the cell tower level into administrative units. This can be done in different ways and it is often not considered which method produces the best representation of the underlying population distribution. Using anonymised CDRs in Namibia between 2011 and 2013, four distribution methods were assessed at multiple administrative unit levels. Estimates of user density per administrative unit were ranked for each method and compared against the corresponding census-derived population densities, using Kendall’s tau-b rank tests. Seasonal and trend decomposition using Loess (STL) and multivariate clustering was subsequently used to identify patterns of seasonal user variation and investigate how different distribution methods can impact these. Results show that the accuracy of the results of each distribution method is influenced by the considered administrative unit level. While marginal differences between methods are displayed at “coarser” level 1, the use of mobile phone tower ranges provided the most accurate results for Namibia at finer levels 2 and 3. The use of STL is helpful to recognise the impact of the underlying distribution methods on further analysis, with the degree of consensus between methods decreasing as spatial scale increases. Multivariate clustering delivers valuable insights into which units share a similar seasonal user behaviour. The higher the number of prescribed clusters, the more the results obtained using different distribution methods differ. However, two major seasonal patterns were identified across all distribution methods, levels and most cluster numbers: (a) units with a 15% user decrease in August and (b) units with a 20–30% user increase in December. Both patterns are likely to be partially linked to school holidays and people going on vacation and/or visiting relatives and friends. This study highlights the need and importance of investigating CDRs in detail before conducting subsequent analysis like seasonal and trend decomposition. In particular, CDRs need to be investigated both in terms of their area and population coverage, as well as in relation to the appropriate distribution method to use based on the spatial scale of the specific application. The use of inappropriate methods can change observed seasonal patterns and impact the derived conclusions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Untangling the changing impact of non-pharmaceutical interventions and vaccination on European COVID-19 trajectories},

author = {Ge, Yong and Zhang, Wen-Bin and Wu, Xilin and Ruktanonchai, Corrine W and Liu, Haiyan and Wang, Jianghao and Song, Yongze and Liu, Mengxiao and Yan, Wei and Yang, Juan and Cleary, Eimear and Qader, Sarchil H. and Atuhaire, Fatumah and Ruktanonchai, Nick W. and Tatem, Andrew J. and Lai Shengjie },

doi = {10.1038/s41467-022-30897-1},

year  = {2022},

date = {2022-06-03},

urldate = {2022-06-03},

journal = {Nature Communications},

volume = {13},

issue = {3106},

abstract = {Non-pharmaceutical interventions (NPIs) and vaccination are two fundamental approaches for mitigating the coronavirus disease 2019 (COVID-19) pandemic. However, the real-world impact of NPIs versus vaccination, or a combination of both, on COVID-19 remains uncertain. To address this, we built a Bayesian inference model to assess the changing effect of NPIs and vaccination on reducing COVID-19 transmission, based on a large-scale dataset including epidemiological parameters, virus variants, vaccines, and climate factors in Europe from August 2020 to October 2021. We found that (1) the combined effect of NPIs and vaccination resulted in a 53% (95% confidence interval: 42–62%) reduction in reproduction number by October 2021, whereas NPIs and vaccination reduced the transmission by 35% and 38%, respectively; (2) compared with vaccination, the change of NPI effect was less sensitive to emerging variants; (3) the relative effect of NPIs declined 12% from May 2021 due to a lower stringency and the introduction of vaccination strategies. Our results demonstrate that NPIs were complementary to vaccination in an effort to reduce COVID-19 transmission, and the relaxation of NPIs might depend on vaccination rates, control targets, and vaccine effectiveness concerning extant and emerging variants.},

keywords = {covid-19, Europe, NPIs, vaccination},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Who and which regions are at high risk of returning to poverty during the COVID-19 pandemic?},

author = {Ge, Yong and Liu, Mengxiao and Hu, Shan and Wang, Daoping and Wang, Jinfeng and Wang, Xiaolin and Qader, Sarchil and Cleary, Eimear and Tatem, Andrew J. and Lai, Shengjie},

doi = {https://doi.org/10.1057/s41599-022-01205-5},

year  = {2022},

date = {2022-05-25},

urldate = {2022-05-25},

journal = {Humanities and Social Sciences Communications},

volume = {9},

number = {183},

abstract = {Pandemics such as COVID-19 and their induced lockdowns/travel restrictions have a significant impact on people’s lives, especially for lower-income groups who lack savings and rely heavily on mobility to fulfill their daily needs. Taking the COVID-19 pandemic as an example, this study analysed the risk of returning to poverty for low-income households in Hubei Province in China as a result of the COVID-19 lockdown. Employing a dataset including information on 78,931 government-identified poor households, three scenarios were analysed in an attempt to identify who is at high risk of returning to poverty, where they are located, and how the various risk factors influence their potential return to poverty. The results showed that the percentage of households at high risk of returning to poverty (falling below the poverty line) increased from 5.6% to 22% due to a 3-month lockdown. This vulnerable group tended to have a single source of income, shorter working hours, and more family members. Towns at high risk (more than 2% of households returning to poverty) doubled (from 27.3% to 46.9%) and were mainly located near railway stations; an average decrease of 10–50 km in the distance to the nearest railway station increased the risk from 1.8% to 9%. These findings, which were supported by the representativeness of the sample and a variety of robustness tests, provide new information for policymakers tasked with protecting vulnerable groups at high risk of returning to poverty and alleviating the significant socio-economic consequences of future pandemics.},

keywords = {covid-19},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Influenza’s plummeting during the COVID-19 pandemic: The roles of mask-wearing, mobility change, and SARS-CoV-2 interference},

author = {Shasha Han and Ting Zhang and Yan Lyu and Shengjie Lai and Peixi Dai and Jiandong Zheng and Weizhong Yang and Xiao-Hua Zhou and Luzhao Feng},

doi = {https://doi.org/10.1016/j.eng.2021.12.011},

issn = {2095-8099},

year  = {2022},

date = {2022-02-02},

urldate = {2022-02-02},

journal = {Engineering},

abstract = {Seasonal influenza activity typically peaks in the winter months but plummeted globally during the current coronavirus disease 2019 (COVID-19) pandemic. Unraveling lessons from influenza’s unprecedented low profile is critical in informing preparedness for incoming influenza seasons. Here, we explored a country-specific inference model to estimate the effects of mask-wearing, mobility changes (international and domestic), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) interference in China, England, and the United States. We found that a one-week increase in mask-wearing intervention had a percent reduction of 11.3%–35.2% in influenza activity in these areas. The one-week mobility mitigation had smaller effects for the international (1.7%–6.5%) and the domestic community (1.6%–2.8%). In 2020–2021, the mask-wearing intervention alone could decline percent positivity by 13.3–19.8. The mobility change alone could reduce percent positivity by 5.2–14.0, of which 79.8%–98.2% were attributed to the deflected international travel. Only in 2019–2020, SARS-CoV-2 interference had statistically significant effects. There was a reduction in percent positivity of 7.6 (2.4–14.4) and 10.2 (7.2–13.6) in northern China and England, respectively. Our results have implications for understanding how influenza evolves under non-pharmaceutical interventions and other respiratory diseases and will inform health policy and the design of tailored public health measures.},

keywords = {covid-19, influenza, NPIs},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Global holiday datasets for understanding seasonal human mobility and population dynamics},

author = {Lai, Shengjie and Sorichetta, Alessandro and Steele, Jessica and Ruktanonchai, Corrine W and Cunningham, Alexander D and Rogers, Grant and Koper, Patrycja and Woods, Dorothea and Bondarenko, Maksym and Ruktanonchai, Nick W and Shi, Weifeng and and Tatem, Andrew J.},

doi = {https://doi.org/10.1038/s41597-022-01120-z},

year  = {2022},

date = {2022-01-20},

urldate = {2022-01-20},

journal = {Scientific Data},

volume = {9},

number = {17},

abstract = {Public and school holidays have important impacts on population mobility and dynamics across multiple spatial and temporal scales, subsequently affecting the transmission dynamics of infectious diseases and many socioeconomic activities. However, worldwide data on public and school holidays for understanding their changes across regions and years have not been assembled into a single, open-source and multitemporal dataset. To address this gap, an open access archive of data on public and school holidays in 2010–2019 across the globe at daily, weekly, and monthly timescales was constructed. Airline passenger volumes across 90 countries from 2010 to 2018 were also assembled to illustrate the usage of the holiday data for understanding the changing spatiotemporal patterns of population movements.},

keywords = {holidays, Mobility, Population},

pubstate = {published},

tppubtype = {article}

}