Non-Compulsory Measures Sufficiently Reduced Human Mobility in Tokyo during the COVID-19 Epidemic
arXiv:2005.09423
Abstract
While large scale mobility data has become a popular tool to monitor the mobility patterns during the COVID-19 pandemic, the impacts of non-compulsory measures in Tokyo, Japan on human mobility patterns has been under-studied. Here, we analyze the temporal changes in human mobility behavior, social contact rates, and their correlations with the transmissibility of COVID-19, using mobility data collected from more than 200K anonymized mobile phone users in Tokyo. The analysis concludes that by April 15th (1 week into state of emergency), human mobility behavior decreased by around 50%, resulting in a 70% reduction of social contacts in Tokyo, showing the effectiveness of non-compulsory measures. Furthermore, the reduction in data-driven human mobility metrics showed correlation with the decrease in estimated effective reproduction number of COVID-19 in Tokyo. Such empirical insights could inform policy makers on deciding sufficient levels of mobility reduction to contain the disease.
References in corpus (4)
- Understanding individual human mobility patterns
- Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in European countries: technical description update
- Effects of the COVID-19 Pandemic on Population Mobility under Mild Policies: Causal Evidence from Sweden
- Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown