Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Mar 30, 2022
Open Peer Review Period: Mar 30, 2022 - May 30, 2022
Date Accepted: Jan 18, 2023
Date Submitted to PubMed: Nov 17, 2022
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Does Oxford Government Response Tracker Explain the SARS-CoV-2 Morbidity and Mortality
ABSTRACT
Background:
The significance of Non-pharmaceutical Interventions by governments across the globe in containing the spread of COVID-19 is well established. The Oxford tracker keeps track of various policy initiatives to mitigate SARS-CoV-2 and release them as Comprehensive Health Index (CHI) and Stringency Index (SI).
Objective:
This study aims to demonstrate the utility of CHI and SI–innovative measures of Oxford university to gauge and evaluate the government responses for containing the spread of SARS-CoV-2.
Methods:
In this ecological study, we analyzed data from two publicly available data sources: Oxford Covid-19 Government Response Tracker (OxCGRT) and World Health Organization (WHO). The current study used data from March 4, 2020 till October 24, 2021. 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).
Results:
The strict implementation of policies by the government to contain the crises of SARS-CoV-2 led to a very high value of CHI and SI in the beginning. Although the value of CHI and SI gradually fell over subsequent lockdowns–the same was consistently higher at values of more than 80% points. During the initial investigation, we found that both Cases Per Million (CPM) and Deaths Per Million (DPM) were following the same trend. However, the final CPM and DPM model were SARIMA (3,2,1)(1,0,1) and ARIMA (1,1,1), respectively.
Conclusions:
Digital epidemiology can–and–should be successfully integrated into existing surveillance systems for better disease monitoring, management, and evaluation. The wider adoption to integrate, collect and disseminate epidemiological data digitally will also facilitate individual-specific models rather than population-specific models. Therefore, the policymakers, public health experts, and programmers must come on board to design a hybrid health system that can borrow from the strengths of the existing physical surveillance system and the ever-expanding digital ecosystem.
Citation
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Copyright
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