Early detection of COVID-19 outbreaks using human mobility data

Abstract

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.

COVID-19 lockdown introduces human mobility pattern changes for both Guangdong-Hong Kong-Macao greater bay area and the San Francisco bay area

“…Mobility data have been used extensively during the COVID-19 pandemic to better comprehend epidemic transmission between populations ( Oliver et al, 2020 , Xiong et al, 2020 ), identify transmission hotspots ( Chang et al, 2021 ), and guide policy interventions ( Lucchini et al, 2021 ). For instance, Guan et al (2021) combined mobility data and case data comprising travel histories to assess the influence of human mobility on the COVID-19 pandemic in Brazil. Beria and Lunkar (2021) used Facebook data and found that lockdowns in Italy resulted in a near-zero reduction in both local and national mobility.…”

Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection

“…Using smart devices and digital transactions is the most rapid and convenient way to collect human mobility data for forecasting the COVID-19 pandemic [27][28][29]. While the smart devices are GPS-tracked, the locations of the transaction data can be found at retail outlets, leisure facilities and other public amenities.…”

The role of the California tier system in controlling population mobility during the COVID- 19 pandemic

“…Several previous studies have focused on the effects of mobility restriction policies on COVID-19 transmission rates and have largely identi ed that mobility restrictions led to reductions in COVID-19 transmission (5,9,18,19,(10)(11)(12)(13)(14)(15)(16)(17). Fewer studies, however, have assessed the impacts of population mobility from COVID-19 policies.…”