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Global-scale modeling of early factors and country-specific trajectories of COVID-19 incidence: a cross-sectional study of the first 6 months of the pandemic

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NIAID Data Ecosystem2026-03-14 收录
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http://datadryad.org/dataset/doi%253A10.5061%252Fdryad.612jm6465
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Studies examining factors responsible for COVID-19 incidence are mostly focused at the national or sub-national level. A global-level characterization of contributing factors and temporal trajectories of disease incidence is lacking. Here we conducted a global-scale analysis of COVID-19 infections to identify key factors associated with early disease incidence. Additionally, we compared longitudinal trends of COVID-19 incidence at a per-country level and classified countries based on COVID-19 incidence trajectories and effects of lockdown responses. Univariate analysis identified eleven variables as independently associated with COVID-19 infections at a global level (p<1e-05). Multivariable analysis identified a 4-variable model as optimal for explaining global variations in COVID-19 (p<0.01). COVID-19 case trajectories for most countries were best captured by a log-logistic model, as determined by AIC estimates. Six predominant country clusters were identified when characterizing the effects of lockdown intervals on variations in COVID-19 new cases per country. Globally, economic and meteorological factors are important determinants of early COVID-19 incidence. Analysis of longitudinal trends and lockdown effects on COVID-19 highlights important nuances in country-specific responses to infections. These results provide valuable insights into disease incidence at a per-country level, possibly allowing for more informed decision making by individual governments in future disease outbreaks. Methods Data for COVID-19 confirmed cases was obtained from https://ourworldindata.org/coronavirus-source-data, which is updated daily and based on data on confirmed cases and deaths from Johns Hopkins University. Data on additional demographic, meteorological, health or economic variables were downloaded from a variety of sources online. For each variable, values from the most recent year for which data on the greatest number of countries were available were utilized (varied between 2016-2019). Variables were categorized as Demographic, Meterological, Health or Economic domains. Please see the README document ("README_data_COVID19_112322.txt") and the accompanying published article: Ghosh, S., Roy, S.S. Global-scale modeling of early factors and country-specific trajectories of COVID-19 incidence: a cross-sectional study of the first 6 months of the pandemic. BMC Public Health 22, 1919 (2022). https://doi.org/10.1186/s12889-022-14336-w
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2022-11-29
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