@article{nokey,

title = {Domestic and international mobility trends in the United Kingdom during the COVID-19 pandemic: an analysis of facebook data},

author = {Shepherd, Harry ER and Atherden, Florence S and Chan, Ho Man Theophilus and Loveridge, Alexandra and Tatem, Andrew J},

doi = {10.1186/s12942-021-00299-5},

year  = {2021},

date = {2021-12-04},

urldate = {2021-12-04},

journal = {International Journal of Health Geographics},

volume = {20},

number = {46},

abstract = {Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which resulted in changes to mobility across different regions. An understanding of how these policies impacted travel patterns over time and at different spatial scales is important for designing effective strategies, future pandemic planning and in providing broader insights on the population geography of the country. Crowd level data on mobile phone usage can be used as a proxy for population mobility patterns and provide a way of quantifying in near-real time the impact of social distancing measures on changes in mobility.



Methods

Here we explore patterns of change in densities, domestic and international flows and co-location of Facebook users in the UK from March 2020 to March 2021.



Results

We find substantial heterogeneities across time and region, with large changes observed compared to pre-pademic patterns. The impacts of periods of lockdown on distances travelled and flow volumes are evident, with each showing variations, but some significant reductions in co-location rates. Clear differences in multiple metrics of mobility are seen in central London compared to the rest of the UK, with each of Scotland, Wales and Northern Ireland showing significant deviations from England at times. Moreover, the impacts of rapid changes in rules on international travel to and from the UK are seen in substantial fluctuations in traveller volumes by destination.



Conclusions

While questions remain about the representativeness of the Facebook data, previous studies have shown strong correspondence with census-based data and alternative mobility measures, suggesting that findings here are valuable for guiding strategies.},

keywords = {covid-19, Europe, Mobility, NPIs, United Kingdom},

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 = {Mobility and phone call behavior explain patterns in poverty at high-resolution across multiple settings},

author = {Steele, Jessica E and Pezzulo, Carla and Albert, Maximilian and Brooks, Christopher J and zu Erbach-Schoenberg, Elisabeth and O’Connor, Siobh{'a}n B and Sunds{o}y, P{aa}l R and Eng{o}-Monsen, Kenth and Nilsen, Kristine and Graupe, Bonita and Nyachhyon, Rajesh Lal and Silpakar, Pradeep and and Tatem, Andrew J },

doi = {https://doi.org/10.1057/s41599-021-00953-0},

year  = {2021},

date = {2021-11-22},

journal = {Humanities and Social Sciences Communications},

volume = {8},

number = {1},

abstract = {Call detail records (CDRs) from mobile phone metadata are a promising data source for mapping poverty indicators in low- and middle-income countries. These data provide information on social networks, call behavior, and mobility patterns in a population, which are correlated with measures of socioeconomic status. CDRs are passively collected and provide information with high spatial and temporal resolution. Identifying features from these data that are generalizable and able to predict poverty and wealth beyond a single context could promote broader usage of mobile data, contribute to a reduction in the cost of socioeconomic data collection and processing, as well as complement existing census and survey-based methods of poverty estimation with improved temporal resolution. This is especially important within the context of the sustainable development goals (SDGs), where poverty and related health indicators are to be reduced significantly across subnational geographies by 2030. Here we utilize measures of cell phone user behavior derived from three CDR datasets within a Bayesian modeling framework to map poverty and wealth patterns across Namibia, Nepal, and Bangladesh. We demonstrate five metrics of user mobility and call behavior that are able to explain between 50% and 65% of the variance in socioeconomic status nationally for these three countries. These key metrics prove useful in very different contexts and can be readily provided as part of an existing CDR platform or software package. This paper provides a key contribution in this regard by identifying such metrics relevant to estimating poverty. We highlight the inclusion of ancillary data and local context as an important factor in understanding model outputs when targeting poverty alleviation strategies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Key opportunities and challenges for the use of big data in migration research and policy},

author = {Franklinos, Lydia H V and Parrish, Rebecca and Burns, Rachel J and Caflisch, Andrea and Mallick, Bishawjit and Rahman, Taifur and Routsis, Vasileios and López, , Ana Sebastián and Tatem, Andrew J and and Trigwell, Robert},

doi = {10.14324/111.444/ucloe.000027},

year  = {2021},

date = {2021-10-27},

journal = {UCL Open Environment},

volume = {3},

issue = {1},

abstract = {Migration is one of the defining issues of the 21st century. Better data is required to improve understanding about how and why people are moving, target interventions and support evidence-based migration policy. Big data, defined as large, complex data from diverse sources, is regularly proposed as a solution to help address current gaps in knowledge. The authors participated in a workshop held in London, UK, in July 2019, that brought together experts from the United Nations (UN), humanitarian non-governmental organisations (NGOs), policy and academia to develop a better understanding of how big data could be used for migration research and policy. We identified six key areas regarding the application of big data in migration research and policy: accessing and utilising data; integrating data sources and knowledge; understanding environmental drivers of migration; improving healthcare access for migrant populations; ethical and security concerns around the use of big data; and addressing political narratives. We advocate the need for careful consideration of the challenges faced by the use of big data, as well as increased cross-disciplinary collaborations to advance the use of big data in migration research whilst safeguarding vulnerable migrant communities.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Infectious disease in an era of global change},

author = {Baker, Rachel E and Mahmud, Ayesha S and Miller, Ian F and Rajeev, Malavika and Rasambainarivo, Fidisoa and Rice, Benjamin L and Takahashi, Saki and Tatem, Andrew J and Wagner, Caroline E and Wang, Lin-Fa and Wesolowski, Amy and and Metcalf, C. Jessica E.},

doi = {https://doi.org/10.1038/s41579-021-00639-z},

year  = {2021},

date = {2021-10-13},

journal = {Nature Reviews Microbiology},

volume = {20},

pages = {193–205},

abstract = {The twenty-first century has witnessed a wave of severe infectious disease outbreaks, not least the COVID-19 pandemic, which has had a devastating impact on lives and livelihoods around the globe. The 2003 severe acute respiratory syndrome coronavirus outbreak, the 2009 swine flu pandemic, the 2012 Middle East respiratory syndrome coronavirus outbreak, the 2013–2016 Ebola virus disease epidemic in West Africa and the 2015 Zika virus disease epidemic all resulted in substantial morbidity and mortality while spreading across borders to infect people in multiple countries. At the same time, the past few decades have ushered in an unprecedented era of technological, demographic and climatic change: airline flights have doubled since 2000, since 2007 more people live in urban areas than rural areas, population numbers continue to climb and climate change presents an escalating threat to society. In this Review, we consider the extent to which these recent global changes have increased the risk of infectious disease outbreaks, even as improved sanitation and access to health care have resulted in considerable progress worldwide.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Characterizing human mobility patterns in rural settings of sub-Saharan Africa},

author = {Meredith, Hannah R and Giles, John R and Perez-Saez, Javier and Mande, Théophile and Rinaldo, Andrea and Mutembo, Simon and Kabalo, Elliot N and Makungo, Kabondo and Buckee, Caroline O and Tatem, Andrew J and Metcalf, C Jessica E and and Wesolowski, Amy},

editor = {Flegg, Jennifer and Walczak, Aleksandra M and Flegg, Jennifer and and Rerolle, Francois},

doi = {https://doi.org/10.7554/eLife.68441},

issn = {2050-084X},

year  = {2021},

date = {2021-10-01},

journal = {eLife},

volume = {10},

abstract = {Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.},

keywords = {Human mobility, spatial models, mobile phone data, gravity model, low and middle income countries},

keywords = {},

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 = {Mapping out-of-school adolescents and youths in low- and middle-income countries},

author = {Alegana, VA and Pezzulo, Carla and Tatem, AJ and Omar, B and and Christensen, Andrew},

doi = {https://doi.org/10.1057/s41599-021-00892-w},

year  = {2021},

date = {2021-09-15},

journal = {Humanities and Social Sciences Communications},

volume = {8},

number = {213},

abstract = {Education is a human right and a driver of development, but, is still not accessible for a vast number of adolescents and school-age-youths. Out-of-school adolescents and youth rates (SDG 4.3.1) in lower and middle-income countries have been at a virtual halt for almost a decade. Thus, there is an increasing need to understand geographic variation on accessibility and school attendance to aid in reducing inequalities in education. Here, the aim was to estimate physical accessibility and secondary school non-attendance amongst adolescents and school-age youths in Tanzania, Cambodia, and the Dominican Republic. Community cluster survey data were triangulated with the spatial location of secondary schools, non-proprietary geospatial data and fine-scale population maps to estimate accessibility to all levels of secondary school education and the number of out-of-school. School attendance rates for the three countries were derived from nationally representative household survey data, and a Bayesian model-based geostatistical framework was used to estimate school attendance at high resolution. Results show a sub-national variation in accessibility and secondary school attendance rates for the three countries considered. Attendance was associated with distance to the nearest school (R2 > 70%). These findings suggest increasing the number of secondary schools could reduce the long-distance commuted to school in low-income and middle-income countries. Future work could extend these findings to fine-scale optimisation models for school location, intervention planning, and understanding barriers associated with secondary school non-attendance at the household level.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A review of geospatial methods for population estimation and their use in constructing reproductive, maternal, newborn, child and adolescent health service indicators},

author = {Nilsen, Kristine and Tejedor-Garavito, Natalia and Leasure, Douglas R and Utazi, C Edson and Ruktanonchai, Corrine W and Wigley, Adelle S and Dooley, Claire A and Matthews, Zoe and and Tatem, Andrew J},

doi = {https://doi.org/10.1186/s12913-021-06370-y},

year  = {2021},

date = {2021-09-13},

urldate = {2021-09-13},

journal = {BMC Health Services Research},

volume = {21},

number = {1},

abstract = {Background

Household survey data are frequently used to measure reproductive, maternal, newborn, child and adolescent health (RMNCAH) service utilisation in low and middle income countries. However, these surveys are typically only undertaken every 5 years and tend to be representative of larger geographical administrative units. Investments in district health management information systems (DHMIS) have increased the capability of countries to collect continuous information on the provision of RMNCAH services at health facilities. However, reliable and recent data on population distributions and demographics at subnational levels necessary to construct RMNCAH coverage indicators are often missing. One solution is to use spatially disaggregated gridded datasets containing modelled estimates of population counts. Here, we provide an overview of various approaches to the production of gridded demographic datasets and outline their potential and their limitations. Further, we show how gridded population estimates can be used as alternative denominators to produce RMNCAH coverage metrics in combination with data from DHMIS, using childhood vaccination as examples.



Methods

We constructed indicators on the percentage of children one year old for diphtheria, pertussis and tetanus vaccine dose 3 (DTP3) and measles vaccine dose (MCV1) in Zambia and Nigeria at district levels. For the numerators, information on vaccines doses was obtained from each country’s respective DHMIS. For the denominators, the number of children was obtained from 3 different sources including national population projections and aggregated gridded estimates derived using top-down and bottom-up geospatial methods.



Results

In Zambia, vaccination estimates utilising the bottom-up approach to population estimation substantially reduced the number of districts with > 100% coverage of DTP3 and MCV1 compared to estimates using population projection and the top-down method. In Nigeria, results were mixed with bottom-up estimates having a higher number of districts > 100% and estimates using population projections performing better particularly in the South.



Conclusions

Gridded demographic data utilising traditional and novel data sources obtained from remote sensing offer new potential in the absence of up to date census information in the estimation of RMNCAH indicators. However, the usefulness of gridded demographic data is dependent on several factors including the availability and detail of input data.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Mapping access to basic hygiene services in low- and middle-income countries: A cross-sectional case study of geospatial disparities},

author = {Weiyu Yu and Robert E.S. Bain and Jie Yu and Victor Alegana and Winfred Dotse-Gborgbortsi and Yi Lin and and Jim A. Wright},

doi = {https://doi.org/10.1016/j.apgeog.2021.102549},

issn = {0143-6228},

year  = {2021},

date = {2021-09-07},

journal = {Applied Geography},

volume = {135},

abstract = {Handwashing with water and soap is among the most a cost-effective interventions to improve public health. Yet billions of people globally lacking handwashing facilities with water and soap on premises, with gaps particularly found in low- and middle-income countries. Targeted efforts to expand access to basic hygiene services require data at geospatially explicit scales. Drawing on country-specific cross-sectional Demographic and Health Surveys with georeferenced hygiene data, we developed an ensemble machine learning model to predict the prevalence of basic hygiene facilities in Malawi, Nepal, Nigeria, Pakistan and Uganda. The ensemble model was based on a multiple-level stacking structure, where four predictive modelling algorithms were used to produce sub-models, and a random forest model was used to generalise the final predictions. An inverse distance weighted interpolation was incorporated in the random forest model to account for spatial autocorrelation. Local coverage and a local dissimilarity index were calculated to examine the geographic disparities in access. Our methodology produced robust outputs, as evidenced by performance evaluations (all R2 were above 0.8). Among the five study countries, Pakistan had the highest overall coverage, whilst Malawi had the poorest coverage. Apparent disparities in basic hygiene services measured by local coverage were found across geographic locations and between urban and rural settings. Nigeria had the highest level of inequalities in basic hygiene services measured by a dissimilarity index, whilst Malawi showed the least segregation between populations with and without basic hygiene services. Both educational attainment and wealth were important predictors of the geospatial distribution of basic hygiene services. By producing geospatially explicit estimates of the prevalence of handwashing facilities with water and soap, this study provides a means of identifying geographical disparities in basic hygiene services. The method and outputs can be useful tools to identify areas of low coverage and to support efficient and precise targeting of efforts to scale up access to handwashing facilities and shift social and cultural norms on handwashing.}},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {The Role of Earth Observation in Achieving Sustainable Agricultural Production in Arid and Semi-Arid Regions of the World},

author = {Qader, Sarchil Hama and Dash, Jadu and Alegana, Victor A. and Khwarahm, Nabaz R. and Tatem, Andrew J. and and Atkinson, Peter M.},

doi = {https://doi.org/10.3390/rs13173382},

year  = {2021},

date = {2021-08-28},

urldate = {2021-08-28},

journal = {Remote Sensing},

volume = {13},

number = {17},

abstract = {Crop production is a major source of food and livelihood for many people in arid and semi-arid (ASA) regions across the world. However, due to irregular climatic events, ASA regions are affected commonly by frequent droughts that can impact food production. In addition, ASA regions in the Middle East and Africa are often characterised by political instability, which can increase population vulnerability to hunger and ill health. Remote sensing (RS) provides a platform to improve the spatial prediction of crop production and food availability, with the potential to positively impact populations. This paper, firstly, describes some of the important characteristics of agriculture in ASA regions that require monitoring to improve their management. Secondly, it demonstrates how freely available RS data can support decision-making through a cost-effective monitoring system that complements traditional approaches for collecting agricultural data. Thirdly, it illustrates the challenges of employing freely available RS data for mapping and monitoring crop area, crop status and forecasting crop yield in these regions. Finally, existing approaches used in these applications are evaluated, and the challenges associated with their use and possible future improvements are discussed. We demonstrate that agricultural activities can be monitored effectively and both crop area and crop yield can be predicted in advance using RS data. We also discuss the future challenges associated with maintaining food security in ASA regions and explore some recent advances in RS that can be used to monitor cropland and forecast crop production and yield.},

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 = {Etiological and epidemiological features of acute respiratory infections in China},

author = {Li, Zhong-Jie and Zhang, Hai-Yang and Ren, Li-Li and Lu, Qing-Bin and Ren, Xiang and Zhang, Cui-Hong and Wang, Yi-Fei and Lin, Sheng-Hong and Zhang, Xiao-Ai and Li, Jun and Zhao, Shi-Wen and Yi, Zhi-Gang and Chen, Xiao and Yang, Zuo-Sen and Meng, Lei and Wang, Xin-Hua and Liu, Ying-Le and Wang, Xin and Cui, Ai-Li and Lai, Sheng-Jie and and others },

doi = {https://doi.org/10.1038/s41467-021-25120-6},

year  = {2021},

date = {2021-08-18},

urldate = {2021-08-18},

journal = {Nature Communications},

volume = {12},

number = {5026},

abstract = {Nationwide prospective surveillance of all-age patients with acute respiratory infections was conducted in China between 2009‒2019. Here we report the etiological and epidemiological features of the 231,107 eligible patients enrolled in this analysis. Children <5 years old and school-age children have the highest viral positivity rate (46.9%) and bacterial positivity rate (30.9%). Influenza virus, respiratory syncytial virus and human rhinovirus are the three leading viral pathogens with proportions of 28.5%, 16.8% and 16.7%, and Streptococcus pneumoniae, Mycoplasma pneumoniae and Klebsiella pneumoniae are the three leading bacterial pathogens (29.9%, 18.6% and 15.8%). Negative interactions between viruses and positive interactions between viral and bacterial pathogens are common. A Join-Point analysis reveals the age-specific positivity rate and how this varied for individual pathogens. These data indicate that differential priorities for diagnosis, prevention and control should be highlighted in terms of acute respiratory tract infection patients’ demography, geographic locations and season of illness in China.},

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 = {Trip duration drives shift in travel network structure with implications for the predictability of spatial disease spread},

author = {Giles, John R and Cummings, Derek AT and Grenfell, Bryan T and Tatem, Andrew J and Erbach-Schoenberg, Elisabeth zu and Metcalf, C Jessica E and Wesolowski, Amy},

doi = {https://doi.org/10.1371/journal.pcbi.1009127},

year  = {2021},

date = {2021-08-10},

journal = {PLoS computational biology},

volume = {17},

number = {8},

abstract = {Human travel is one of the primary drivers of infectious disease spread. Models of travel are often used that assume the amount of travel to a specific destination decreases as cost of travel increases with higher travel volumes to more populated destinations. Trip duration, the length of time spent in a destination, can also impact travel patterns. We investigated the spatial patterns of travel conditioned on trip duration and find distinct differences between short and long duration trips. In short-trip duration travel networks, trips are skewed towards urban destinations, compared with long-trip duration networks where travel is more evenly spread among locations. Using gravity models to inform connectivity patterns in simulations of disease transmission, we show that pathogens with shorter generation times exhibit initial patterns of spatial propagation that are more predictable among urban locations. Further, pathogens with a longer generation time have more diffusive patterns of spatial spread reflecting more unpredictable disease dynamics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Implications for Tracking SDG Indicator Metrics with Gridded Population Data},

author = {Tuholske, Cascade and Gaughan, Andrea E. and Sorichetta, Alessandro and de Sherbinin, Alex and Bucherie, Agathe and Hultquist, Carolynne and Stevens, Forrest and Kruczkiewicz, Andrew and Huyck, Charles and Yetman, Greg},

doi = {https://doi.org/10.3390/su13137329},

issn = {2071-1050},

year  = {2021},

date = {2021-07-30},

journal = {Sustainability},

volume = {13},

number = {13},

abstract = {Achieving the seventeen United Nations Sustainable Development Goals (SDGs) requires accurate, consistent, and accessible population data. Yet many low- and middle-income countries lack reliable or recent census data at the sufficiently fine spatial scales needed to monitor SDG progress. While the increasing abundance of Earth observation-derived gridded population products provides analysis-ready population estimates, end users lack clear use criteria to track SDGs indicators. In fact, recent comparisons of gridded population products identify wide variation across gridded population products. Here we present three case studies to illuminate how gridded population datasets compare in measuring and monitoring SDGs to advance the “fitness for use” guidance. Our focus is on SDG 11.5, which aims to reduce the number of people impacted by disasters. We use five gridded population datasets to measure and map hazard exposure for three case studies: the 2015 earthquake in Nepal; Cyclone Idai in Mozambique, Malawi, and Zimbabwe (MMZ) in 2019; and flash flood susceptibility in Ecuador. First, we map and quantify geographic patterns of agreement/disagreement across gridded population products for Nepal, MMZ, and Ecuador, including delineating urban and rural populations estimates. Second, we quantify the populations exposed to each hazard. Across hazards and geographic contexts, there were marked differences in population estimates across the gridded population datasets. As such, it is key that researchers, practitioners, and end users utilize multiple gridded population datasets—an ensemble approach—to capture uncertainty and/or provide range estimates when using gridded population products to track SDG indicators. To this end, we made available code and globally comprehensive datasets that allows for the intercomparison of gridded population products.},

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 = {Geographical distribution of fertility rates in 70 low-income, lower-middle-income, and upper-middle-income countries, 2010–16: a subnational analysis of cross-sectional surveys},

author = {Carla Pezzulo and Kristine Nilsen and Alessandra Carioli and Natalia Tejedor-Garavito and Sophie E Hanspal and Theodor Hilber and William H M James and Corrine W Ruktanonchai and Victor Alegana and Alessandro Sorichetta and Adelle S Wigley and Graeme M Hornby and Zoe Matthews and Andrew J Tatem},

doi = {https://doi.org/10.1016/S2214-109X(21)00082-6},

issn = {2214-109X},

year  = {2021},

date = {2021-06-01},

journal = {The Lancet Global Health},

volume = {9},

number = {6},

pages = {e802-e812},

abstract = {Understanding subnational variation in age-specific fertility rates (ASFRs) and total fertility rates (TFRs), and geographical clustering of high fertility and its determinants in low-income and middle-income countries, is increasingly needed for geographical targeting and prioritising of policy. We aimed to identify variation in fertility rates, to describe patterns of key selected fertility determinants in areas of high fertility. We did a subnational analysis of ASFRs and TFRs from the most recent publicly available and nationally representative cross-sectional Demographic and Health Surveys and Multiple Indicator Cluster Surveys collected between 2010 and 2016 for 70 low-income, lower-middle-income, and upper-middle-income countries, across 932 administrative units. We assessed the degree of global spatial autocorrelation by using Moran's I statistic and did a spatial cluster analysis using the Getis-Ord Gi* local statistic to examine the geographical clustering of fertility and key selected fertility determinants. Descriptive analysis was used to investigate the distribution of ASFRs and of selected determinants in each cluster. TFR varied from below replacement (2·1 children per women) in 36 of the 932 subnational regions (mainly located in India, Myanmar, Colombia, and Armenia), to rates of 8 and higher in 14 subnational regions, located in sub-Saharan Africa and Afghanistan. Areas with high-fertility clusters were mostly associated with areas of low prevalence of women with secondary or higher education, low use of contraception, and high unmet needs for family planning, although exceptions existed. Substantial within-country variation in the distribution of fertility rates highlights the need for tailored programmes and strategies in high-fertility cluster areas to increase the use of contraception and access to secondary education, and to reduce unmet need for family planning.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}