BACKGROUND: Contact mixing plays a key role in the spread of COVID-19. Thus, mobility restrictions of varying degrees up to and including nationwide lockdowns have been implemented in over 200 countries. To appropriately target the timing, location, and severity of measures intended to encourage social distancing at a country level, it is essential to predict when and where outbreaks will occur, and how widespread they will be. METHODS: We analyze aggregated, anonymized health data and cell phone mobility data from Israel. We develop predictive models for daily new cases and the test positivity rate over the next 7 days for different geographic regions in Israel. We evaluate model goodness of fit using root mean squared error (RMSE). We use these predictions in a five-tier categorization scheme to predict the severity of COVID-19 in each region over the next week. We measure magnitude accuracy (MA), the extent to which the correct severity tier is predicted. RESULTS: Models using mobility data outperformed models that did not use mobility data, reducing RMSE by 17.3% when predicting new cases and by 10.2% when predicting the test positivity rate. The best set of predictors for new cases consisted of 1-day lag of past 7-day average new cases, along with a measure of internal movement within a region. The best set of predictors for the test positivity rate consisted of 3-days lag of past 7-day average test positivity rate, along with the same measure of internal movement. Using these predictors, RMSE was 4.812 cases per 100,000 people when predicting new cases and 0.79% when predicting the test positivity rate. MA in predicting new cases was 0.775, and accuracy of prediction to within one tier was 1.0. MA in predicting the test positivity rate was 0.820, and accuracy to within one tier was 0.998. CONCLUSIONS: Using anonymized, macro-level data human mobility data along with health data aids predictions of when and where COVID-19 outbreaks are likely to occur. Our method provides a useful tool for government decision makers, particularly in the post-vaccination era, when focused interventions are needed to contain COVID-19 outbreaks while mitigating the collateral damage from more global restrictions.
BACKGROUND: Contact mixing plays a key role in the spread of COVID-19. Thus, mobility restrictions of varying degrees up to and including nationwide lockdowns have been implemented in over 200 countries. To appropriately target the timing, location, and severity of measures intended to encourage social distancing at a country level, it is essential to predict when and where outbreaks will occur, and how widespread they will be. METHODS: We analyze aggregated, anonymized health data and cell phone mobility data from Israel. We develop predictive models for daily new cases and the test positivity rate over the next 7 days for different geographic regions in Israel. We evaluate model goodness of fit using root mean squared error (RMSE). We use these predictions in a five-tier categorization scheme to predict the severity of COVID-19 in each region over the next week. We measure magnitude accuracy (MA), the extent to which the correct severity tier is predicted. RESULTS: Models using mobility data outperformed models that did not use mobility data, reducing RMSE by 17.3% when predicting new cases and by 10.2% when predicting the test positivity rate. The best set of predictors for new cases consisted of 1-day lag of past 7-day average new cases, along with a measure of internal movement within a region. The best set of predictors for the test positivity rate consisted of 3-days lag of past 7-day average test positivity rate, along with the same measure of internal movement. Using these predictors, RMSE was 4.812 cases per 100,000 people when predicting new cases and 0.79% when predicting the test positivity rate. MA in predicting new cases was 0.775, and accuracy of prediction to within one tier was 1.0. MA in predicting the test positivity rate was 0.820, and accuracy to within one tier was 0.998. CONCLUSIONS: Using anonymized, macro-level data human mobility data along with health data aids predictions of when and where COVID-19 outbreaks are likely to occur. Our method provides a useful tool for government decision makers, particularly in the post-vaccination era, when focused interventions are needed to contain COVID-19 outbreaks while mitigating the collateral damage from more global restrictions.
Authors: Seth Flaxman; Swapnil Mishra; Axel Gandy; H Juliette T Unwin; Thomas A Mellan; Helen Coupland; Charles Whittaker; Harrison Zhu; Tresnia Berah; Jeffrey W Eaton; Mélodie Monod; Azra C Ghani; Christl A Donnelly; Steven Riley; Michaela A C Vollmer; Neil M Ferguson; Lucy C Okell; Samir Bhatt Journal: Nature Date: 2020-06-08 Impact factor: 49.962
Authors: Nuria Oliver; Bruno Lepri; Harald Sterly; Renaud Lambiotte; Sébastien Deletaille; Marco De Nadai; Emmanuel Letouzé; Albert Ali Salah; Richard Benjamins; Ciro Cattuto; Vittoria Colizza; Nicolas de Cordes; Samuel P Fraiberger; Till Koebe; Sune Lehmann; Juan Murillo; Alex Pentland; Phuong N Pham; Frédéric Pivetta; Jari Saramäki; Samuel V Scarpino; Michele Tizzoni; Stefaan Verhulst; Patrick Vinck Journal: Sci Adv Date: 2020-06-05 Impact factor: 14.136
Authors: Song Gao; Jinmeng Rao; Yuhao Kang; Yunlei Liang; Jake Kruse; Dorte Dopfer; Ajay K Sethi; Juan Francisco Mandujano Reyes; Brian S Yandell; Jonathan A Patz Journal: JAMA Netw Open Date: 2020-09-01
Authors: Raffaele Palladino; Jordy Bollon; Luca Ragazzoni; Francesco Barone-Adesi Journal: Int J Environ Res Public Health Date: 2020-08-05 Impact factor: 3.390
Authors: Nishant Kishore; Aimee R Taylor; Pierre E Jacob; Navin Vembar; Ted Cohen; Caroline O Buckee; Nicolas A Menzies Journal: Lancet Digit Health Date: 2021-11-02