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 of more global restrictions.

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 (Chang et al 2021;Guan et al 2021;Leung et al 2021). 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.…”

A Deep Learning Approach to Forecast Short-Term COVID-19 Cases and Deaths in the US

“…Previous studies have shown that aggregate human mobility patterns can be used to evaluate the impact of certain non-pharmaceutical interventions on the spread of COVID-19. 9–12 However, the role of such aggregate mobility data in predicting COVID-19 transmission patterns is complex, highly variable over time and space, and notably diminishing since Spring 2020. 13,14 Therefore, we conduct extensive data analysis and modeling, to generate novel mobility related variables that explicitly consider trip purpose in addition to broader mobility patterns and incorporate these new mobility-derived metrics into our modeling framework.…”

Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study

“…4 , 5 , 6 These metrics have also been used extensively to identify spatiotemporal and individual transmission dynamics of the pandemic. 7 , 8 , 9 It remains unclear, however, whether these metrics are reliable proxies of the face-to-face contact patterns that underlie SARS-CoV-2 transmission. 10 , 11 , 12 …”