@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}

}

@article{nokey,

title = {Impacts of worldwide individual non-pharmaceutical interventions on COVID-19 transmission across waves and space},

author = {Yong Ge and Wen-Bin Zhang and Haiyan Liu and Corrine W Ruktanonchai and Maogui Hu and Xilin Wu and Yongze Song and Nick W Ruktanonchai and Wei Yan and Eimear Cleary and Luzhao Feng and Zhongjie Li and Weizhong Yang and Mengxiao Liu and Andrew J Tatem and Jin-Feng Wang and and Shengjie Lai},

doi = {https://doi.org/10.1016/j.jag.2021.102649},

year  = {2021},

date = {2021-12-13},

urldate = {2021-12-13},

journal = {International Journal of Applied Earth Observation and Geoinformation},

volume = {106},

abstract = {Governments worldwide have rapidly deployed non-pharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic. However, the effect of these individual NPI measures across space and time has yet to be sufficiently assessed, especially with the increase of policy fatigue and the urge for NPI relaxation in the vaccination era. Using the decay ratio in the suppression of COVID-19 infections and multi-source big data, we investigated the changing performance of different NPIs across waves from global and regional levels (in 133 countries) to national and subnational (in the United States of America [USA]) scales before the implementation of mass vaccination. The synergistic effectiveness of all NPIs for reducing COVID-19 infections declined along waves, from 95.4% in the first wave to 56.0% in the third wave recently at the global level and similarly from 83.3% to 58.7% at the USA national level, while it had fluctuating performance across waves on regional and subnational scales. Regardless of geographical scale, gathering restrictions and facial coverings played significant roles in epidemic mitigation before the vaccine rollout. Our findings have important implications for continued tailoring and implementation of NPI strategies, together with vaccination, to mitigate future COVID-19 waves, caused by new variants, and other emerging respiratory infectious diseases.},

keywords = {covid-19, NPIs},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {The emergence, genomic diversity and global spread of SARS-CoV-2},

author = {Li, Juan and Lai, Shengjie and Gao, George F and and Shi, Weifeng},

doi = {https://doi.org/10.1038/s41586-021-04188-6},

year  = {2021},

date = {2021-12-08},

urldate = {2021-12-08},

journal = {Nature},

volume = {600},

pages = {408–418},

abstract = {Since the first cases of COVID-19 were documented in Wuhan, China in 2019, the world has witnessed a devastating global pandemic, with more than 238 million cases, nearly 5 million fatalities and the daily number of people infected increasing rapidly. Here we describe the currently available data on the emergence of the SARS-CoV-2 virus, the causative agent of COVID-19, outline the early viral spread in Wuhan and its transmission patterns in China and across the rest of the world, and highlight how genomic surveillance, together with other data such as those on human mobility, has helped to trace the spread and genetic variation of the virus and has also comprised a key element for the control of the pandemic. We pay particular attention to characterizing and describing the international spread of the major variants of concern of SARS-CoV-2 that were first identified in late 2020 and demonstrate that virus evolution has entered a new phase. More broadly, we highlight our currently limited understanding of coronavirus diversity in nature, the rapid spread of the virus and its variants in such an increasingly connected world, the reduced protection of vaccines, and the urgent need for coordinated global surveillance using genomic techniques. In summary, we provide important information for the prevention and control of both the ongoing COVID-19 pandemic and any new diseases that will inevitably emerge in the human population in future generations.},

keywords = {covid-19, Mobility},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Methods and Applications: The Incoming Influenza Season — China, the United Kingdom, and the United States, 2021–2022},

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

doi = {10.46234/ccdcw2021.253},

year  = {2021},

date = {2021-12-03},

urldate = {2021-12-03},

journal = {China CDC Weekly},

volume = {3},

number = {49},

pages = {1039},

abstract = {Seasonal influenza activity has declined globally since the widespread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. There has been scarce information to understand the future dynamics of influenza — and under different hypothesis on relaxation of non-pharmaceutical interventions (NPIs) in particular — after the disruptions to seasonal patterns.



Methods

We collected data from public sources in China, the United Kingdom, and the United States, and forecasted the influenza dynamics in the incoming 2021–2022 season under different NPIs. We considered Northern China and Southern China separately, due to the sharp difference in the patterns of seasonal influenza. For the United Kingdom, data were collected for England only.



Results

Compared to the epidemics in 2017–2019, longer and blunter influenza outbreaks could occur should NPIs be fully lifted, with percent positivity varying from 10.5 to 18.6 in the studying regions. The rebounds would be smaller if the mask-wearing intervention continued or the international mobility stayed low, but sharper if the mask-wearing intervention was lifted in the middle of influenza season. Further, influenza activity could stay low under a much less stringent mask-wearing intervention coordinated with influenza vaccination.



Conclusions

The results added to our understandings of future influenza dynamics after the global decline during the coronavirus disease 2019 (COVID-19) pandemic. In light of the uncertainty on the incoming circulation strains and the relatively low negative impacts of mask wearing on society, our findings suggested that wearing mask could be considered as an accompanying mitigation measure in influenza prevention and control, especially for seasons after long periods of low-exposure to influenza viruses.},

keywords = {influenza, Mobility, NPIs, Predictive clustering},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Rapid and sustained containment of covid-19 is achievable and worthwhile: implications for pandemic response},

author = {Chen, Qiulan and Rodewald, Lance and Lai, Shengjie and Gao, George F},

doi = {https://doi.org/10.1136/BMJ-2021-066169},

year  = {2021},

date = {2021-12-02},

urldate = {2021-12-02},

journal = {BMJ},

volume = {375},

number = {e066169},

abstract = {In the 20 months since the World Health Organization declared covid-19 to be a pandemic, more than 250 million cases have been confirmed. More than 5 million deathshave occurred disproportionately by country, with 10% of countries reporting 80% of cases.1 In a new pandemic, with no vaccines prepared, non-pharmaceutical interventions are the only response. In the first year of the covid-19 pandemic, several countries that aimed to completely interrupt community transmission—China, Singapore, Australia, and New Zealand—achieved and maintained containment.23 Here, we analyse China’s containment strategy before vaccine roll-out and describe measures for initial and sustained containment and benefits for the country.



Containment of covid-19 means stopping transmission of SARS-CoV-2.4 Core measures of containment include actively finding infected people and treatment in isolation, close contact tracing and quarantine, and restrictions of travel from areas with community transmission. Additional measures that reduce transmission include stay at home orders, cancellation of mass gatherings, closing schools, and use of personal protection measures, such as hand hygiene, respiratory etiquette, keeping at least 1 m distance, and mask wearing.



Sustained containment is maintaining elimination of SARS-CoV-2 by completely stopping all externally introduced outbreaks—a policy of “zero tolerance for local transmission (zero covid).” Responses to an outbreak are based on an assessment of the epidemic risk and use the same measures as those used for the initial containment. These measures are augmented by strict border protection to minimise the number of imported outbreaks and by surveillance for infection using highly sensitive, routine polymerase chain reaction (PCR). Table 1 shows the initial and sustained containment measures based on assessment of the risk of epidemic.



},

keywords = {covid-19, NPIs},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Risk of SARS-CoV-2 Transmission among Air Passengers in China},

author = {Hu, Maogui and Wang, Jinfeng and Lin, Hui and Ruktanonchai, Corrine W and Xu, Chengdong and Meng, Bin and Zhang, Xin and Carioli, Alessandra and Feng, Yuqing and Yin, Qian and Floyd, Jessica R and Ruktanonchai, Nick W and Li, Zhongjie and Yang, Weizhong and Tatem, Andrew J and and Lai, Shengjie},

doi = {https://doi.org/10.1093/cid/ciab836},

issn = {1058-4838},

year  = {2021},

date = {2021-09-21},

journal = {Clinical Infectious Diseases},

abstract = {Background

Modern transportation plays a key role in the spread of SARS-CoV-2 and new variants. However, little is known about the exact transmission risk of the virus on airplanes.



Methods

Using the itinerary and epidemiological data of COVID-19 cases and close contacts on domestic airplanes departing from Wuhan city in China before the lockdown on January 23, 2020, we estimated the upper and lower bounds of overall transmission risk of COVID-19 among travellers.



Results

175 index cases were identified among 5797 passengers on 177 airplanes. The upper and lower attack rates (ARs) of a seat were 0.60% (34/5622, 95%CI 0.43%-0.84%) and 0.33% (18/5400, 95%CI 0.21%-0.53%), respectively. In the upper- and lower-bound risk estimates, each index case infected 0.19 (SD 0.45) and 0.10 (SD 0.32) cases respectively. The seats immediately adjacent to the index cases had an AR of 9.2% (95%CI 5.7%-14.4%), with a relative risk 27.8 (95%CI 14.4-53.7) compared to other seats in the upper limit estimation. The middle seat had the highest AR (0.7%, 95%CI 0.4%-1.2%). The upper-bound AR increased from 0.7% (95%CI 0.5%-1.0%) to 1.2% (95%CI 0.4%-3.3%) when the co-travel time increased from 2.0 hours to 3.3 hours.



Conclusions

The ARs among travellers varied by seat distance from the index case and joint travel time, but the variation was not significant between the types of aircraft. The overall risk of SARS-CoV-2 transmission during domestic travel on planes was relatively low. These findings can improve our understanding of COVID-19 spread during travel and inform response efforts in the pandemic.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A data driven agent-based model that recommends non-pharmaceutical interventions to suppress Coronavirus disease 2019 resurgence in megacities},

author = {Yin, Ling and Zhang, Hao and Li, Yuan and Liu, Kang and Chen, Tianmu and Luo, Wei and Lai, Shengjie and Li, Ye and Tang, Xiujuan and Ning, Li and Feng, Shengzhong and Wei, Yanjie and Zhao, Zhiyuan and Wen, Ying and Mao, Liang and and Mei, Shujiang},

doi = {https://doi.org/10.1098/rsif.2021.0112},

year  = {2021},

date = {2021-08-25},

journal = {Journal of The Royal Society Interface},

volume = {18},

number = {181},

abstract = {Before herd immunity against Coronavirus disease 2019 (COVID-19) is achieved by mass vaccination, science-based guidelines for non-pharmaceutical interventions are urgently needed to reopen megacities. This study integrated massive mobile phone tracking records, census data and building characteristics into a spatially explicit agent-based model to simulate COVID-19 spread among 11.2 million individuals living in Shenzhen City, China. After validation by local epidemiological observations, the model was used to assess the probability of COVID-19 resurgence if sporadic cases occurred in a fully reopened city. Combined scenarios of three critical non-pharmaceutical interventions (contact tracing, mask wearing and prompt testing) were assessed at various levels of public compliance. Our results show a greater than 50% chance of disease resurgence if the city reopened without contact tracing. However, tracing household contacts, in combination with mandatory mask use and prompt testing, could suppress the probability of resurgence under 5% within four weeks. If household contact tracing could be expanded to work/class group members, the COVID resurgence could be avoided if 80% of the population wear facemasks and 40% comply with prompt testing. Our assessment, including modelling for different scenarios, helps public health practitioners tailor interventions within Shenzhen City and other world megacities under a variety of suppression timelines, risk tolerance, healthcare capacity and public compliance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Mobility in China, 2020: a tale of four phases},

author = {Tan, Suoyi and Lai, Shengjie and Fang, Fan and Cao, Ziqiang and Sai, Bin and Song, Bing and Dai, Bitao and Guo, Shuhui and Liu, Chuchu and Cai, Mengsi and Wang, Tong and Wang, Mengning and Li, Jiaxu and Chen, Saran and Qin, Shuo and Floyd, Jessica R and Cao, Zhidong and Tan, Jing and Sun, Xin and Zhou, Tao and Zhang, Wei and Tatem, Andrew J and Holme, Petter and Chen, Xiaohong and Lu, Xin},

doi = {https://doi.org/10.1093/nsr/nwab148},

year  = {2021},

date = {2021-08-16},

journal = {National Science Review},

volume = {8},

number = {11},

abstract = {2020 was an unprecedented year, with rapid and drastic changes in human mobility due to the COVID-19 pandemic. To understand the variation in commuting patterns among the Chinese population across stable and unstable periods, we used nationwide mobility data from 318 million mobile phone users in China to examine the extreme fluctuations of population movements in 2020, ranging from the Lunar New Year travel season (chunyun), to the exceptional calm of COVID-19 lockdown, and then to the recovery period. We observed that cross-city movements, which increased substantially in chunyun and then dropped sharply during the lockdown, are primarily dependent on travel distance and the socio-economic development of cities. Following the Lunar New Year holiday, national mobility remained low until mid-February, and COVID-19 interventions delayed more than 72.89 million people returning to large cities. Mobility network analysis revealed clusters of highly connected cities, conforming to the social-economic division of urban agglomerations in China. While the mass migration back to large cities was delayed, smaller cities connected more densely to form new clusters. During the recovery period after travel restrictions were lifted, the netflows of over 55% city pairs reversed in direction compared to before the lockdown. These findings offer the most comprehensive picture of Chinese mobility at fine resolution across various scenarios in China and are of critical importance for decision making regarding future public-health-emergency response, transportation planning and regional economic development, among others.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}