Hakyong Kim1†, Catherine Apio2†, Yeonghyeon Ko3,4†, Kyulhee Han2, Taewan Goo2, Gyujin Heo2, Taehyun Kim3, Hyewon Chung5, Doeun Lee2, Jisun Lim6, Taesung Park3*
1 Department of Industrial Engineering, Seoul National University, Seoul 08826, Republic of Korea; [email protected] (H.K.)
2 Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Republic of Korea; [email protected] (G.H.); [email protected] (C.A.); [email protected] (K.H.); [email protected] (T.G.); [email protected] (D.L.)
3 Department of Statistics, Seoul National University, Seoul 08826, Republic of Korea; [email protected] (Y.K.); [email protected] (T.K.); [email protected] (T.P.)
4 Department of Archeology and Art History, Seoul National University, Seoul 08826, Republic of Korea; [email protected] (Y.K.)
5 Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea; [email protected] (H.C.)
6 The Research Institute of Basic Sciences, Seoul National University, Seoul 08826, Republic of Korea; [email protected] (J.L.)
†These authors contributed equally to this work as co-first authors.
*Corresponding author, Tel: 82-2-880-8924, Fax: 82-2-883-6144, Email: [email protected] (T.P)
The outbreak of the novel COVID-19 declared a global pandemic by WHO, is the most serious public health threat seen in respiratory virus since the 1918 H1N1 influenza pandemic. It is surprising the total number of COVID-19 confirmed cases and the number of deaths vary 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, average annual temperature, aging index, human development index, percentage of malnourished people in the population, extreme poverty, economic ability, health policy, population, age distributions, etc.) influence the spread of COVID-19 through systematic statistical analysis. First, we employed segmented growth-curve models (Logistic and Gompertz) to model the cumulative confirmed cases for 134 countries from January 1, 2020 to August 31, 2020. Thus, each country’s COVID-19 spread pattern was summarized into three growth-curve model parameters. Second, we investigated the relationship of selected 31 national factors (from KOSIS and Our World in Data) to these growth curve model parameters. Our analysis showed that 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 is influenced by aging index, cardiovascular death rate, extreme poverty, median age, percentage of population aged 65, 70 and older and so forth. We hope with consideration of a country’s resources and population dynamics; our results will help in making informed decisions that make the most impact against similar infectious diseases.