Objectives: We analyzed global trends in the daily number of new cases during the first wave of COVID-19 and factors associated with these trends. Methods: Data from 151 countries were analyzed. The index date for each country was set with consideration for a 7-day moving average (MA7) of ≥100 people. Data were collected for 60 and 90 days from the index date. Time-series hierarchical clustering was used to analyze the trends in the number of new cases in each country on the basis of their MA7 values. Multinomial logistic regression was performed to identify factors associated with these trends. Results: The trends in the daily number of new cases in the early stage of COVID-19 were classified into growth, declines, and smooth declines. The number of cases in countries with ≥25.60% residents with obesity (odds ratio = 6.69; p = 0.004) was more likely to exhibit growth than were those with obesity of 9.60-20.79%. The number in countries with a GDP of ≥US$34,341 (odds ratio = 0.10; p = 0.001) was more likely to exhibit a decline than were those with a GDP of US$5,277–14,932. Conclusions: COVID-19 epidemic prevention policies should account for country-specific characteristics such as the proportion of residents with obesity and GDP.
- Daily new cases
- Multinomial logistic regression
- Time-series hierarchical clustering
- Trend type
ASJC Scopus subject areas
- Public Health, Environmental and Occupational Health