Public mobility data enables COVID-19 forecasting and management at local and global scales

Cornelia Ilin,Shikhar Mehra,Solomon Hsiang,Xiao Hui Tai,Joshua E Blumenstock,Sébastien Annan-Phan

doi:10.1038/s41598-021-92892-8

Cornelia Ilin, Shikhar Mehra + Show 4 more

Open Access

https://doi.org/10.1038/s41598-021-92892-8

Copy DOI

Abstract

Policymakers everywhere are working to determine the set of restrictions that will effectively contain the spread of COVID-19 without excessively stifling economic activity. We show that publicly available data on human mobility—collected by Google, Facebook, and other providers—can be used to evaluate the effectiveness of non-pharmaceutical interventions (NPIs) and forecast the spread of COVID-19. This approach uses simple and transparent statistical models to estimate the effect of NPIs on mobility, and basic machine learning methods to generate 10-day forecasts of COVID-19 cases. An advantage of the approach is that it involves minimal assumptions about disease dynamics, and requires only publicly-available data. We evaluate this approach using local and regional data from China, France, Italy, South Korea, and the United States, as well as national data from 80 countries around the world. We find that NPIs are associated with significant reductions in human mobility, and that changes in mobility can be used to forecast COVID-19 infections.

Highlights

Societies and decision-makers around the globe are deploying unprecedented non-pharmaceutical interventions (NPIs) to manage the COVID-19 pandemic
We estimate the reduction in human mobility associated with the deployment of NPIs by linking comprehensive data on policy interventions to mobility data from several different countries at multiple geographic scales
We find that the combined impact of all NPIs reduced mobility between administrative units (Facebook/Baidu) by 73% on average across the countries with sub-national policy data (Fig. 2a)

Summary

Introduction

Societies and decision-makers around the globe are deploying unprecedented non-pharmaceutical interventions (NPIs) to manage the COVID-19 pandemic. Different populations have adopted wildly different containment strategies[11], and local decision-makers face difficult decisions about when to impose or lift specific interventions in their community In some contexts, these decision-makers have access to state-of-the-art models, which simulate potential scenarios based on detailed epidemiological models and rich sources of data (for e xample[12,13]). We show that basic concepts from econometrics and machine learning can be used to construct these 10-day forecasts, effectively emulating the behavior of more sophisticated epidemiological models, including those which incorporate mobility data[27,28] This approach is not a substitute for more refined epidemiological models. Confirmed Cases to enable any individual with access to standard statistical software to produce forecasts of NPI impacts with a level of fidelity that is practical for decision-making in an ongoing crisis

Objectives

Methods

Results

Conclusion