Multiple changepoint analysis of COVID-19 infection progression and related deaths in the small island state of Malta

Abstract

In December 2019, in the city of Wuhan (China), Severe Acute Respiratory Syndrome Coronavirus - 2 (SARS-CoV−2), a virus that causes what is known as Coronavirus Disease 2019 (better known as COVID-19), emerged. In a few months the virus spread around the world becoming a global pandemic that has shaken the world. On Malta (a nation consisting of an archipelago of islands of approximately 500000 people), which is the case study of this analysis, the first case was identified on 7/3/2020. In this paper, we shall fit a piecewise linear trend model to the log-scale of cumulative cases and deaths due to COVID-19 in Malta by implementing the SN-NOT changepoint model. This model combines the self-normalisation (SN) technique, which is used to test whether there is a single change-point in the linear trend of a time series, with the Narrowest Over Threshold algorithm (NOT) to achieve multiple change-point in the linear trend. Through analysis of news reports and other sources of information, estimated change-points are then compared to potential factors such as health restrictions, mass events, government policy and population behaviour that have affected these changes, in order to determine the efffect of these factors on the spread of the disease.