@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}

}

@article{nokey,

title = {Practical geospatial and sociodemographic predictors of human mobility},

author = {Ruktanonchai, Corrine W and Lai, Shengjie and Utazi, Chigozie E and Cunningham, Alex D and Koper, Patrycja and Rogers, Grant E and Ruktanonchai, Nick W and Sadilek, Adam and Woods, Dorothea and Tatem, Andrew J and Steele, Jessica E. and Sorichetta, Alessandro},

doi = {https://doi.org/10.1038/s41598-021-94683-7},

year  = {2021},

date = {2021-07-28},

journal = {Scientific Reports},

volume = {11},

number = {15389},

abstract = {Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Untangling introductions and persistence in COVID-19 resurgence in Europe},

author = {Lemey, Philippe and Ruktanonchai, Nick and Hong, Samuel L and Colizza, Vittoria and Poletto, Chiara and Van den Broeck, Frederik and Gill, Mandev S and Ji, Xiang and Levasseur, Anthony and Oude Munnink, Bas B and Koopmans, Marion and Sadilek, Adam and Lai, Shengjie and Tatem, Andrew J. and Baele, Guy and Suchard, Marc A. and Dellicour, Simon},

doi = {https://doi.org/10.1038/s41586-021-03754-2},

year  = {2021},

date = {2021-06-30},

journal = {Nature},

volume = {595},

number = {713–717},

abstract = {After the first wave of SARS-CoV-2 infections in spring 2020, Europe experienced a resurgence of the virus starting in late summer 2020 that was deadlier and more difficult to contain1. Relaxed intervention measures and summer travel have been implicated as drivers of the second wave2. Here we build a phylogeographical model to evaluate how newly introduced lineages, as opposed to the rekindling of persistent lineages, contributed to the resurgence of COVID-19 in Europe. We inform this model using genomic, mobility and epidemiological data from 10 European countries and estimate that in many countries more than half of the lineages circulating in late summer resulted from new introductions since 15 June 2020. The success in onward transmission of newly introduced lineages was negatively associated with the local incidence of COVID-19 during this period. The pervasive spread of variants in summer 2020 highlights the threat of viral dissemination when restrictions are lifted, and this needs to be carefully considered in strategies to control the current spread of variants that are more transmissible and/or evade immunity. Our findings indicate that more effective and coordinated measures are required to contain the spread through cross-border travel even as vaccination is reducing disease burden.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Impact of COVID-19 outbreaks and interventions on influenza in China and the United States},

author = {Feng, Luzhao and Zhang, Ting and Wang, Qing and Xie, Yiran and Peng, Zhibin and Zheng, Jiandong and Qin, Ying and Zhang, Muli and Lai, Shengjie and Wang, Dayan and Feng, Zijian and Li, Zhongjie and and Gao, George F.},

doi = {https://doi.org/10.1038/s41467-021-23440-1},

year  = {2021},

date = {2021-05-31},

journal = {Nature Communications},

volume = {12},

number = {3249},

issue = {1},

abstract = {Coronavirus disease 2019 (COVID-19) was detected in China during the 2019–2020 seasonal influenza epidemic. Non-pharmaceutical interventions (NPIs) and behavioral changes to mitigate COVID-19 could have affected transmission dynamics of influenza and other respiratory diseases. By comparing 2019–2020 seasonal influenza activity through March 29, 2020 with the 2011–2019 seasons, we found that COVID-19 outbreaks and related NPIs may have reduced influenza in Southern and Northern China and the United States by 79.2% (lower and upper bounds: 48.8%–87.2%), 79.4% (44.9%–87.4%) and 67.2% (11.5%–80.5%). Decreases in influenza virus infection were also associated with the timing of NPIs. Without COVID-19 NPIs, influenza activity in China and the United States would likely have remained high during the 2019–2020 season. Our findings provide evidence that NPIs can partially mitigate seasonal and, potentially, pandemic influenza.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Global COVID-19 lockdown highlights humans as both threats and custodians of the environment},

author = {Amanda E. Bates and Richard B. Primack and Brandy S. Biggar and Tomas J. Bird and Mary E. Clinton and Rylan J. Command and Cerren Richards and Marc Shellard and Nathan R. Geraldi and ... Shengjie Lai and and others.},

doi = {https://doi.org/10.1016/j.biocon.2021.109175},

issn = {0006-3207},

year  = {2021},

date = {2021-05-21},

urldate = {2021-05-21},

journal = {Biological Conservation},

volume = {263},

abstract = {The global lockdown to mitigate COVID-19 pandemic health risks has altered human interactions with nature. Here, we report immediate impacts of changes in human activities on wildlife and environmental threats during the early lockdown months of 2020, based on 877 qualitative reports and 332 quantitative assessments from 89 different studies. Hundreds of reports of unusual species observations from around the world suggest that animals quickly responded to the reductions in human presence. However, negative effects of lockdown on conservation also emerged, as confinement resulted in some park officials being unable to perform conservation, restoration and enforcement tasks, resulting in local increases in illegal activities such as hunting. Overall, there is a complex mixture of positive and negative effects of the pandemic lockdown on nature, all of which have the potential to lead to cascading responses which in turn impact wildlife and nature conservation. While the net effect of the lockdown will need to be assessed over years as data becomes available and persistent effects emerge, immediate responses were detected across the world. Thus, initial qualitative and quantitative data arising from this serendipitous global quasi-experimental perturbation highlights the dual role that humans play in threatening and protecting species and ecosystems. Pathways to favorably tilt this delicate balance include reducing impacts and increasing conservation effectiveness.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Assessing the Effect of Global Travel and Contact Restrictions on Mitigating the COVID-19 Pandemic},

author = {Shengjie Lai and Nick W. Ruktanonchai and Alessandra Carioli and Corrine W. Ruktanonchai and Jessica R. Floyd and Olivia Prosper and Chi Zhang and Xiangjun Du and Weizhong Yang and Andrew J. Tatem},

doi = {https://doi.org/10.1016/j.eng.2021.03.017},

year  = {2021},

date = {2021-05-07},

journal = {Engineering},

volume = {7},

number = {7},

pages = {914-923},

abstract = {Travel restrictions and physical distancing have been implemented across the world to mitigate the coronavirus disease 2019 (COVID-19) pandemic, but studies are needed to understand their effectiveness across regions and time. Based on the population mobility metrics derived from mobile phone geolocation data across 135 countries or territories during the first wave of the pandemic in 2020, we built a metapopulation epidemiological model to measure the effect of travel and contact restrictions on containing COVID-19 outbreaks across regions. We found that if these interventions had not been deployed, the cumulative number of cases could have shown a 97-fold (interquartile range 79–116) increase, as of May 31, 2020. However, their effectiveness depended upon the timing, duration, and intensity of the interventions, with variations in case severity seen across populations, regions, and seasons. Additionally, before effective vaccines are widely available and herd immunity is achieved, our results emphasize that a certain degree of physical distancing at the relaxation of the intervention stage will likely be needed to avoid rapid resurgences and subsequent lockdowns.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}