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Browsing by Author "Sadilek, Adam"

Now showing 1 - 4 of 4
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    Assessing the impact of coordinated COVID-19 exit strategies across Europe
    Ruktanonchai, Nick W.; Floyd, J. R.; Lai, S.; Ruktanonchai, Corrine W.; Sadilek, Adam; Rente-Lourenco, P.; Ben, X.; Carioli, A.; Gwinn, J.; Steele, J. E.; Prosper, Olivia F.; Schneider, A.; Oplinger, A.; Eastham, Paul; Tatem, A. J. (2020-09-18)
    As rates of new coronavirus disease 2019 (COVID-19) cases decline across Europe owing to nonpharmaceutical interventions such as social distancing policies and lockdown measures, countries require guidance on how to ease restrictions while minimizing the risk of resurgent outbreaks. We use mobility and case data to quantify how coordinated exit strategies could delay continental resurgence and limit community transmission of COVID-19. We find that a resurgent continental epidemic could occur as many as 5 weeks earlier when well-connected countries with stringent existing interventions end their interventions prematurely. Further, we find that appropriate coordination can greatly improve the likelihood of eliminating community transmission throughout Europe. In particular, synchronizing intermittent lockdowns across Europe means that half as many lockdown periods would be required to end continent-wide community transmission.
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    Forecasting influenza activity using machine-learned mobility map
    Venkatramanan, Srinivasan; Sadilek, Adam; Fadikar, Arindam; Barrett, Christopher L.; Biggerstaff, Matthew; Chen, Jiangzhuo; Dotiwalla, Xerxes; Eastham, Paul; Gipson, Bryant; Higdon, Dave; Kucuktunc, Onur; Lieber, Allison; Lewis, Bryan L.; Reynolds, Zane; Vullikanti, Anil Kumar S.; Wang, Lijing; Marathe, Madhav V. (2021-02-09)
    Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials and private citizens alike. In this work, we focus on a machine-learned anonymized mobility map (hereon referred to as AMM) aggregated over hundreds of millions of smartphones and evaluate its utility in forecasting epidemics. We factor AMM into a metapopulation model to retrospectively forecast influenza in the USA and Australia. We show that the AMM model performs on-par with those based on commuter surveys, which are sparsely available and expensive. We also compare it with gravity and radiation based models of mobility, and find that the radiation model's performance is quite similar to AMM and commuter flows. Additionally, we demonstrate our model's ability to predict disease spread even across state boundaries. Our work contributes towards developing timely infectious disease forecasting at a global scale using human mobility datasets expanding their applications in the area of infectious disease epidemiology. Human mobility plays a central role in the spread of infectious diseases and can help in forecasting incidence. Here the authors show a comparison of multiple mobility benchmarks in forecasting influenza, and demonstrate the value of a machine-learned mobility map with global coverage at multiple spatial scales.
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    Practical geospatial and sociodemographic predictors of human mobility
    Ruktanonchai, Corrine W.; Lai, Shengjie; Utazi, Chigozie E.; Cunningham, Alex D.; Koper, Patrycja; Rogers, Grant E.; Ruktanonchai, Nick W.; Sadilek, Adam; Woods, Dorothea; Tatem, Andrew J.; Steele, Jessica E.; Sorichetta, Alessandro (2021-07-28)
    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.
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    SARS-CoV-2 European resurgence foretold: interplay of introductions and persistence by leveraging genomic and mobility data
    Lemey, Philippe; Ruktanonchai, Nick W.; Hong, Samuel; Colizza, Vittoria; Poletto, Chiara; Van den Broeck, Frederik; Gill, Mandev; Ji, Xiang; Levasseur, Anthony; Sadilek, Adam; Lai, Shengjie; Tatem, Andrew; Baele, Guy; Suchard, Marc; Dellicour, Simon (Springer, 2021-02-10)
    Following the first wave of SARS-CoV-2 infections in spring 2020, Europe experienced a resurgence of the virus starting late summer that was deadlier and more difficult to contain. Relaxed intervention measures and summer travel have been implicated as drivers of the second wave. Here, we build a phylogeographic model to evaluate how newly introduced lineages, as opposed to the rekindling of persistent lineages, contributed to the COVID-19 resurgence in Europe. We inform this model using genomic, mobility and epidemiological data from 10 West European countries and estimate that in many countries more than 50% of the lineages circulating in late summer resulted from new introductions since June 15th. The success in onwards transmission of these lineages is predicted by SARS-CoV-2 incidence during this period. Relatively early introductions from Spain into the United Kingdom contributed to the successful spread of the 20A.EU1/B.1.177 variant. The pervasive spread of variants that have not been associated with an advantage in transmissibility highlights the threat of novel variants of concern that emerged more recently and have been disseminated by holiday travel. Our findings indicate that more effective and coordinated measures are required to contain spread through cross-border travel.