Data-driven decision making for modelling COVID-19 and its implications: A cross-country study

Sariyer, Gorkem, Mangla, Sachin Kumar, Kazancoglu, Yigit, Jain, Vranda and Ataman, Mustafa Gokalp (2023) Data-driven decision making for modelling COVID-19 and its implications: A cross-country study. Technological Forecasting and Social Change, 197: 122886. ISSN 0040-1625

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Grounded in big data analytics capabilities, this study aims to model the COVID-19 spread globally by considering various factors such as demographic, cultural, health system, economic, technological, and policy-based. Classified values on each country's case, death, and recovery numbers (per 1000,000 population) were used to represent COVID-19 spread. Data sets also included 29 input variables for the corresponding six factors, containing data from 159 countries. The proposed model used a Multilayer Perceptron algorithm. The results show that each of the pre-mentioned factors significantly affects disease spread. Urban population, median age, life expectancy, numbers of medical doctors and nursing personnel, current health expenditure as a % of GDP, international health regulations capacity score, continent, literacy rate, governmental response stringency index, testing policy, internet usage %, human development index and GDP per capita were identified as significant. Taking early measures and adopting open public testing policies were recommended to policymakers in fighting pandemic diseases since the created scenarios on policy-based factors revealed their importance.

Item Type: Article
Keywords: Big Data Analytics | COVID-19 | Number of Cases | Number of Deaths | Policy-Based Factors
Subjects: Social Sciences and humanities > Business, Management and Accounting > General Management
JGU School/Centre: Jindal Global Business School
Depositing User: Amees Mohammad
Date Deposited: 12 Oct 2023 06:14
Last Modified: 12 Oct 2023 06:14
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