Use of interrupted time series design in COVID-19 data of Nepal

Abstract

Introduction: Nepal was hit hard by COVID-19 pandemic and is still struggling with its implications for individuals, societies, and health systems. We applied Interrupted Time Series (ITS) analysis to see if the government's decision to end two nationwide lockdowns had an impact on the average daily new cases of COVID-19 reported in Nepal.

Method: ITS analysis on daily new cases of COVID-19 was performed for first and second nationwide lockdown on data of Nepal from data depository for 2019 Novel Coronavirus Visual Dashboard operated by Johns Hopkins University Center for Systems Science and Engineering. Impact of ''ending nationwide lockdown'' and possible associated factors were investigated.

Result: The impact of removing first nationwide lockdown contributed to statistically significant rise of an average difference of 899 daily new cases due to community transmission of COVID-19. During second lockdown, even after imposing lockdown, cases overwhelmed existing healthcare system due to high infectivity of new Delta variant followed by a lack of adherence to public health measures and delayed decision to impose lockdown due to economic reasons. Whereas significant decline of cases was observed with an average difference of 2970 daily new cases after ending second nationwide lockdown mostly due to vaccine acceptance.

Conclusion: First lockdown was imposed early due to uncertainty about progression of disease, curative measures and unavailability of vaccines. Ending of first lockdown was done untimely due to economic reasons and festive seasons.  The unusual decline in cases after ending second lockdown could be contributed to increased vaccination in the country.