A systematic review of using population-level human mobility data to understand SARS-CoV-2 transmission

Abstract

The emergence of SARS-CoV-2 into a highly susceptible global population was primarily driven by human mobility-induced introduction events. Especially in the early stages, understanding mobility was vital to mitigating the pandemic prior to widespread vaccine availability. We conducted a systematic review of studies published from January 1, 2020, to May 9, 2021, that used population-level human mobility data to understand SARS-CoV-2 transmission. Of the 5505 papers with abstracts screened, 232 were included in the analysis. These papers focused on a range of specific questions but were dominated by analyses focusing on the USA and China. The majority included mobile phone data, followed by Google Community Mobility Reports, and few included any adjustments to account for potential biases in population sampling processes. There was no clear relationship between methods used to integrate mobility and SARS-CoV-2 data and goals of analysis. When considering papers focused only on the estimation of the effective reproductive number within the US, there was no clear relationship identified between this measure and changes in mobility patterns. Our findings underscore the need for standardized, systematic ways to identify the source of mobility data, select an appropriate approach to using it in analysis, and reporting.

Fig. 1. Geographical and country focus, primary goal, and type of mobility data used in papers published between January 2020 and May 9, 2021, included in the systematic review (n=232).

A Geographical focus for papers published between Jabuary 2020 and May 9, 2021. National focus refers to analysis focused on one country at national or subnational level. B Distribution of countries of focus for papers published between January 2020 and May 9, 2021, for the eight countries with the most papers included in the analysis. C Primary goal of the study. Assoc mobility, trans: Quantifying association between mobility and transmission of SARS-CoV-2; Change mobility: Change in mobility during pandemic; Intro—case or variant: Introduction or importation of SARS-CoV-2 cases or variants; NPI: Effectiveness of non-pharmaceutical interventions; SES or race Ineq: Socio-economic or racial inequities. D Types of mobility data used, by date.

Fig. 2. Mobility metrics, type of mobility data, and spatial scale used in mobility datasets in the papers analyzed (n = 232).

A Distribution of mobility metrics used, by date of publication. B Mobility metric by country of paper focus, for the ten most commonly used countries. Width of the bars represents the relative number of mobility metrics presented for that country (Australia, Brazil, Canada, India, and Singapore with less than 8 metrics; Italy, Japan, and the UK with 8–12 metrics; and China and the USA with 62 and 91 metrics, respectively). C Type of mobility data, by mobility metric. D Spatial scale by mobility data type. Subnational scale includes city/village/metro area, province/state, county/district, and smaller than city/village/metro area.

Fig. 3. Ways in which mobility data and data on SARS-CoV-2 were integrated, and results presented in the papers (n = 232).

A Integration of mobility data and data on SARS-CoV-2/COVID-19, by date. B Results presented in papers assessing effectiveness of lockdown measures. Lockdown includes lockdown, social distancing, and stay-at-home orders. Mixed associations refer to associations that vary across periods, space, or groups of interest. C Results presented in papers assessing effectiveness of travel restriction measures. Mixed associations refer to associations that vary across periods, space, or groups of interest. D Results presented in papers assessing relationship between change in mobility and transmission of SARS-CoV-2/COVID-19.