Does Oxford Government Response Tracker Explain the SARS-CoV-2 Morbidity and Mortality: An Ecological Time Series Study.

Abstract

Many nations swiftly designed and executed government policies to contain the rapid rise of SARS-CoV-2 cases. The government actions can be broadly segmented as movement and mass gathering restrictions (such as travel and lockdown), public awareness (such as facial covering and hand washing), emergency healthcare investment and social welfare provisions (such as poor welfare schemes to distribute food and shelter). The Blavatnik School of Government-Oxford university tracked various policy initiatives by governments across the globe and released them as composite indices. We assessed the overall government response using Oxford Comprehensive Health Index (CHI) and Stringency Index (SI) to combat the SARS-CoV-2 pandemic. This study aims to demonstrate the utility of CHI and SI to gauge and evaluate the government responses for containing the spread of SARS-CoV-2. We expect a significant inverse relationship between policy indices (CHI and SI) and SARS-CoV-2 severity indices (morbidity and mortality). In this ecological study, we analysed data from two publicly available data sources released between March 2020, to October 2021: Oxford Covid-19 Government Response Tracker (OxCGRT) and World Health Organization (WHO). We applied Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA) to model the data. The performance of different models was assessed using a combination of evaluation criteria: Adj-R2, Root Mean Square of Error (RMSE) and Bayesian Information Criteria (BIC). The strict implementation of policies by the government to contain the crises of SARS-CoV-2 resulted in higher CHI and SI in the beginning. Although the value of CHI and SI gradually fell--the same was consistently higher at values of more than 80% points. During the initial investigation, we found that Cases Per Million (CPM) and Deaths Per Million (DPM) followed the same trend. However, the final CPM and DPM model were SARIMA (3,2,1)(1,0,1) and ARIMA (1,1,1), respectively. The current study does not support the hypotheses that SARS-CoV-2 severity (CPM and DPM) is associated with stringent policy measures (CHI and SI). Our study concludes that the policy measures (CHI and SI) do not explain the change in epidemiological indicators (CPM and DPM). The study reiterates our understanding that strict policies do not necessarily lead to better compliance but may overwhelm the overstretched physical health systems. The 21st-century problems, thus, demand 21st-century solutions. The digital ecosystem was instrumental in the timely collection, curation, cloud storage and data communication. Thus, digital epidemiology can--and--should be successfully integrated into existing surveillance systems for better disease monitoring, management and evaluation.