Which National Factors Are Most Influential in the Spread of COVID-19?

Hakyong Kim, Catherine Apio, Yeonghyeon Ko, Kyulhee Han, Taewan Goo, Gyujin Heo, Taehyun Kim, Hye Won Chung, Doeun Lee, Jisun Lim and Taesung Park
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Hakyong Kim: Department of Industrial Engineering, Seoul National University, Seoul 08826, Korea
Catherine Apio: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
Yeonghyeon Ko: Department of Statistics, Seoul National University, Seoul 08826, Korea
Kyulhee Han: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
Taewan Goo: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
Gyujin Heo: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
Taehyun Kim: Department of Statistics, Seoul National University, Seoul 08826, Korea
Hye Won Chung: Department of Chemistry, Seoul National University, Seoul 08826, Korea
Doeun Lee: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
Jisun Lim: The Research Institute of Basic Sciences, Seoul National University, Seoul 08826, Korea
Taesung Park: Department of Statistics, Seoul National University, Seoul 08826, Korea

IJERPH, 2021, vol. 18, issue 14, 1-22

Abstract: The outbreak of the novel COVID-19, declared a global pandemic by WHO, is the most serious public health threat seen in terms of respiratory viruses since the 1918 H1N1 influenza pandemic. It is surprising that the total number of COVID-19 confirmed cases and the number of deaths has varied greatly across countries. Such great variations are caused by age population, health conditions, travel, economy, and environmental factors. Here, we investigated which national factors (life expectancy, aging index, human development index, percentage of malnourished people in the population, extreme poverty, economic ability, health policy, population, age distributions, etc.) influenced the spread of COVID-19 through systematic statistical analysis. First, we employed segmented growth curve models (GCMs) to model the cumulative confirmed cases for 134 countries from 1 January to 31 August 2020 (logistic and Gompertz). Thus, each country’s COVID-19 spread pattern was summarized into three growth-curve model parameters. Secondly, we investigated the relationship of selected 31 national factors (from KOSIS and Our World in Data ) to these GCM parameters. Our analysis showed that with time, the parameters were influenced by different factors; for example, the parameter related to the maximum number of predicted cumulative confirmed cases was greatly influenced by the total population size, as expected. The other parameter related to the rate of spread of COVID-19 was influenced by aging index, cardiovascular death rate, extreme poverty, median age, percentage of population aged 65 or 70 and older, and so forth. We hope that with their consideration of a country’s resources and population dynamics that our results will help in making informed decisions with the most impact against similar infectious diseases.

Keywords: COVID-19; SARS-CoV-2; growth curve models; pandemic (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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