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

Bibliographic Data

ID15464640
AuthorsHakyong Kim (0000-0002-6021-5121, Seoul National University), Catherine Apio (0000-0003-0240-794X, Seoul National University), Yeonghyeon Ko (0000-0002-1523-8922, Seoul National University), Kyulhee Han (0000-0002-7243-5711, Seoul National University), Taewan Goo (0000-0001-9427-2290, Seoul National University), Gyujin Heo (0000-0003-3499-2848, Seoul National University), Taehyun Kim (0000-0001-8665-1821, Seoul National University), Hye Won Chung (0000-0002-6162-9158, Seoul National University), Doeun Lee (0000-0002-2791-8431, Seoul National University), Jisun Lim (0000-0002-7341-4584, Seoul National University), Taesung Park (0000-0002-8294-590X, Seoul National University, corresponding author)
Year2021
Volume18
Issue14
Pages7592-7592
Publication date2021-07-16
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph18147592
PMID34300044
OpenAlexW3183892302
LanguageEN
References cited39

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

Coronavirus disease 2019 (COVID-19 · Economic growth · Economics · Environmental health · Geography · Gompertz function · Index (typography · Life expectancy · Logistic regression · Outbreak · Pandemic · Population · Poverty · Public health · Socioeconomics · Sociology · Statistics · COVID-19 epidemiological studies · COVID-19 impact on air quality · COVID-19 Pandemic Impacts · Demography · Mathematics · Medicine · Virology

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