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.
Authors
Gorkem Sariyer, Yasar University, Department of Business Administration, Izmir, Turkey.
Sachin Kumar Mangla, Research Centre – Digital Circular Economy for Sustainable Development Goals (DCE-SDG) Jindal Global Business School, O P Jindal Global University, Haryana, India.
Yigit Kazancoglu, Yasar University, Department of International Logistics Management, Izmir, Turkey.
Vranda Jain, Jaipuria Institute of Management Noida, India.
Mustafa Gokalp Ataman, Izmir Bakircay University, Cigli Training and Research Hospital, Department of Emergency Medicine, Izmir, Turkey.
Summary
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.
Published in: Technological Forecasting and Social Change
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