An analysis to identify the structural breaks of COVID-19 in Turkey

TL;DR

This paper uses breakpoint linear regression to analyze the impact of COVID-19 measures on Turkey's epidemic trends and compares these patterns with eight other countries.

Contribution

It introduces a breakpoint regression approach to identify structural changes in COVID-19 data and compares Turkey's epidemic trajectory with other affected nations.

Findings

Lockdown and precautions significantly impacted epidemic trends.

Structural breaks in case trajectories correspond to implemented measures.

Comparison reveals differing epidemic responses across countries.

Abstract

In countries with a severe outbreak of COVID-19, most governments are considering whether anti-transmission measures are worth social and economic costs. The seriousness of economic costs such as the closure of some workplaces, unemployment, reduction in production, and social costs such as school closures, disruptions in education could be observable. However, the effect of the measures taken on the spread of the epidemic, such as the number of delayed or prevented cases, could not be observed. For this reason, the direct effects of the measures taken on health, that is, the effects on the course of the epidemic, are important research subjects. For this purpose, in this study, the breakpoint linear regression analysis is performed to analyze the trends of daily active cases, recovered, and deaths in Turkey. The analysis reveals that there has been a remarkable impact on lockdown and other precautions. Using the breakpoint regression model, we also analyze the active cases' trajectory for eight affected countries and compare the patterns in these countries with Turkey.