A Trendline and Predictive Analysis of the First-Wave COVID-19 Infections in Malta

Area of Science:

• Epidemiology
• Public Health
• Infectious Disease Modeling

Background:

• Malta implemented stringent COVID-19 lockdown measures, including travel restrictions and social distancing, in response to the initial outbreak.
• Understanding the impact of these interventions and their subsequent relaxation on the infection rate is crucial for public health strategies.

Purpose of the Study:

• To analyze the effect of social distancing interventions and their relaxation on the COVID-19 infection rate in Malta.
• To develop a predictive model for forecasting SARS-CoV-2 infection rates using historical data and swab testing rates.

Main Methods:

• Trendline analysis of daily COVID-19 case data to identify infection patterns.
• Fitting historical SARS-CoV-2 case data to a two-parameter Weibull distribution, incorporating swab testing rates, to create a predictive model.

Main Results:

• Infection rate analysis revealed a tri-phasic pattern, with the initial phase corresponding to social measure interventions.
• Subsequent phases, following relaxation of measures, showed two peaks that resolved without further escalation of national interventions.
• The developed forecasting model accurately predicted daily infected cases with a high goodness-of-fit.

Conclusions:

• Social distancing measures in Malta effectively managed the initial COVID-19 wave, demonstrating a tri-phasic infection pattern.
• The Weibull distribution model, incorporating swab testing data, provides an accurate and computationally efficient method for forecasting COVID-19 infections.
• The findings suggest that phased relaxation of measures can be managed effectively, supported by robust predictive modeling.