A trendline and predictive analysis of the first-wave COVID-19 infections in Malta

Abstract

Following the first COVID-19 infected cases, Malta rapidly imposed strict lockdown measures, including restrictions on international travel, together with national social distancing measures, such as prohibition of public gatherings and closure of workplaces. The study aimed to elucidate the effect of the intervention and relaxation of the social distancing measures upon the infection rate by means of a trendline analysis of the daily case data. In addition, the study derived a predictive model by fitting historical data of the SARS-CoV-2 positive cases within a two-parameter Weibull distribution, whilst incorporating swab-testing rates, to forecast the infection rate at minute computational expense. The trendline analysis portrayed the wave of infection to fit within a tri-phasic pattern, where the primary phase was imposed with social measure interventions. Following the relaxation of public measures, the two latter phases transpired, where the two peaks resolved without further escalation of national measures. The derived forecasting model attained accurate predictions of the daily infected cases, attaining a high goodness-of-fit, utilising uncensored government-official infection-rate and swabbing-rate data within the first COVID-19 wave in Malta.