Explaining the effective reproduction number of COVID-19 through mobility and enterprise statistics: Evidence from the first wave in Japan

Abstract

This study uses mobility statistics combined with business census data for the eight Japanese prefectures with the highest coronavirus disease-2019 (COVID-19) infection rates to study the effect of mobility reductions on the effective reproduction number (i.e., the average number of secondary cases caused by one infected person). Mobility statistics are a relatively new data source created by compiling smartphone location data; they can be effectively used for understanding pandemics if integrated with epidemiological findings and other economic data sets. Based on data for the first wave of infections in Japan, we found that reductions targeting the hospitality industry were slightly more effective than restrictions on general business activities. Specifically, we found that to hold back the pandemic (that is, to reduce the effective reproduction number to one or less for all days), a 20%–35% reduction in weekly mobility is required, depending on the region. A lesser goal, 80% of days with one or less observed transmission, can be achieved with a 6%–30% reduction in weekly mobility. These are the results if other potential causes of spread are ignored; for a fuller picture, more careful observations, expanded data sets, and advanced statistical modeling are needed.

Mobility Patterns in Different Age Groups in Japan during the COVID-19 Pandemic: a Small Area Time Series Analysis through March 2021

“…Nagata et al, who tracked human mobility in Tokyo, Osaka, and Aichi Prefectures using smartphone data, pointed out that the reduction in human mobility started before the first declaration of the state of emergency, and that the degree of reduction varied from place to place [ 21 ]. Furthermore, Kajitani et al found that a 20–35% reduction in mobility may have been necessary to hold back the pandemic (i.e., to reduce the effective reproduction number to one or less) in the situation during the first wave of the pandemic in the business and commercial districts of nine prefectures including Tokyo [ 22 ]. These studies did not identify age-specific human mobility patterns as an analytical limitation, but we were able to show that the degrees of mobility reduction as well as the trend patterns after the emergency declarations varied among different age groups, and further varied at the small area level.…”

Modelling digital and manual contact tracing for COVID-19 Are low uptakes and missed contacts deal-breakers?

“…Since the inception of the COVID-19 pandemic, much of the literature on epidemiological modeling and public-health messages alike have scrutinized different nonpharmaceutical interventions in relation with their impact on R , the effective reproduction number (Di Domenico et al, 2020; Kajitani and Hatayama, 2020). We estimate our simulations’ R value after t = 7 days (since t 0 ) from the corresponding exponential growth rate λ by applying Eq 1, following the Wallinga and Lipsitch (2007) methodology.…”

Modelling digital and manual contact tracing for COVID-19. Are low uptakes and missed contacts deal-breakers?

“…Since the inception of the COVID-19 pandemic, the majority of the literature on epidemiological modelling and public-health messages alike have scrutinized different nonpharmaceutical interventions in relation with their impact on R, the effective reproduction number [24,67]. For the latter more realistic scenarios (i.e.…”