Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Application Of Statistical Modeling To Interpret A Health System Crisis In Sri Lanka Due To COVID- 19

Version 1 : Received: 27 December 2021 / Approved: 28 December 2021 / Online: 28 December 2021 (16:11:44 CET)

How to cite: Karunathilake, I.; Amarasiri, M.; Hamdani, A. Application Of Statistical Modeling To Interpret A Health System Crisis In Sri Lanka Due To COVID- 19. Preprints 2021, 2021120455 (doi: 10.20944/preprints202112.0455.v1). Karunathilake, I.; Amarasiri, M.; Hamdani, A. Application Of Statistical Modeling To Interpret A Health System Crisis In Sri Lanka Due To COVID- 19. Preprints 2021, 2021120455 (doi: 10.20944/preprints202112.0455.v1).

Abstract

This paper will discuss the application of statistic modeling to interpret a health system crisis in Sri Lanka due to COVID- 19.A strong focus on the preventive approach and the contact tracing with the utilization of available resources in a rational manner describes Sri Lanka’s response towards COVID- 19 prevention and mitigation. The early contact tracing, preemptive quarantining, isolation, and treatment were implemented as a concerted effort. This approach, proven efficient during the early phase of the pandemic, was sustainable when there was a rapid increase in the COVID- 19 patients since July 2021, exceeding the health system capacity.The country’s COVID- 19 situation during the period from 01st of August 2021 to 31st of October 2021 was taken into consideration. Variables used for analysis were; total number of cases, recovered cases, comorbid and O2 dependent patients, ICU patients, and deaths. The regression model was applied to analyze the data by using the EViews 12 (x64) software application.The correlation coefficients of all the independent variables under consideration implies that they have a strong positive relationship with the number of deaths occurred during the said period. According to the computed multiple linear regression model, the number of positive cases and O2 dependents have a positive relationship with the dependent variable. Further, the Durbin- Watson stat value of the model and multicollinearity test reflect that it is free from serial correlation thereby the model is fit. From the perspective of epidemiological control, these findings highlight the importance of keeping the number of cases within the limits of health system capacity.

Keywords

COVID- 19; Durbin-Watson statistic; Multiple Linear Regression; Multicollinearity

Subject

MEDICINE & PHARMACOLOGY, Other

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