Analyzing the Effectiveness of COVID-19 Mitigation Policies Using ARIMA Forecasting

Abstract

The SARS-CoV-2 virus has triggered a worldwide pandemic situation which countries are desperately trying to adapt to. In order to halt the transmission of this virus, these countries have implemented COVID-19 mitigation policies, which are designed to suppress the spread and deadliness of the virus. However, there has not been much research into the effectiveness of these COVID-19 mitigation policies. Using data from the Kaggle Platform as well as the European Centre for Disease Prevention and Control, we hope to use an ARIMA time series forecasting model in order to identify effective COVID-19 mitigation polices. This will be done by analyzing the cases time series before the mitigation policy was implemented in a certain country and generating a predicted forecast curve during the time range of the mitigation policy. By comparing this generated curve with the actual curve, deviations will be able to be identified, indicating the significance of the mitigation policy during its implementation. Although most forecasting was relatively inaccurate due to a shortage in training data, one social distancing mitigation policy in South Korea had a clear deviation between the forecast curve without the influence of the mitigation policy and the actual curve. Overall, the ARIMA model has its merits and may prove to be useful with the collection of more data. By analyzing the effectiveness of these policies, future research into this topic may lead to a greater understanding about the transmission of COVID-19 and ways to suppress it.