The Impact of Mobility Patterns on the Spread of the COVID-19 in Indonesia

Abstract

Background: The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly across the world and infected millions of people, many of whom died. As part of the response plans, many countries have been attempting to restrict people’s mobility by launching social distancing protocol, including in Indonesia. It is then necessary to identify the campaign’s impact and analyze the influence of mobility patterns on the pandemic’s transmission rate. Objective: Using mobility data from Google and Apple, this research discovers that COVID-19 daily new cases in Indonesia are mostly related to the mobility trends in the previous eight days. Methods: We generate ten-day predictions of COVID-19 daily new cases and Indonesians’ mobility by using Long-Short Term Memory (LSTM) algorithm to provide insight for future implementation of social distancing policies. Results: We found that all eight-mobility categories result in the highest accumulation correlation values between COVID-19 daily new cases and the mobility eight days before. We forecast of the pandemic daily new cases in Indonesia, DKI Jakarta and worldwide (with error on MAPE 6.2% - 9.4%) as well as the mobility trends in Indonesia and DKI Jakarta (with error on MAPE 6.4 - 287.3%). Conclusion: We discover that the driver behind the rapid transmission in Indonesia is the number of visits to retail and recreation, groceries and pharmacies, and parks. In contrast, the mobility to the workplaces negatively correlates with the pandemic spread rate.