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 cat

SUBMITTER: Guan G 

PROVIDER: S-EPMC8291683 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature