Title: Clinical decision support system for the early diagnosis and management of COVID-19

Authors: Pravalika Nyalapatla; Rodly Gaillard; Dinesh P. Mital

Addresses: Department of Biomedical Informatics, Rutgers Biomedical and Health Sciences, Newark, New Jersey, USA ' Department of Biomedical Informatics, Rutgers Biomedical and Health Sciences, Newark, New Jersey, USA ' Department of Biomedical Informatics, Rutgers Biomedical and Health Sciences, Newark, New Jersey, USA

Abstract: Having a tool that can figure out the likelihood of contracted severe acute respiratory syndrome (SARS-CoV-2) COVID-19 to combat its spread will be a considerable step to slow the speed of the virus. Therefore, we describe a series of steps to analyse data from the New York State Government Health and New Jersey using SAS and Python. First, focusing on counties bordering each state using variables such as individual social-economic, gender, age group to determine the likelihood of getting the SARS-CoV-2 COVID-19. Furthermore, we created a clinical decision support system (CDSS) interface to collect data and analyse them better to understand this virus.

Keywords: severe acute respiratory syndrome coronavirus; SARS-CoV-2; COVID-19; clinical decision support system; CDSS; SAS; Python.

DOI: 10.1504/IJMEI.2025.147628

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.4, pp.346 - 359

Received: 18 Nov 2021
Accepted: 23 Apr 2022
Published online: 24 Jul 2025 *

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