Background:The negative impacts of COVID-19 are commonly assessed using the cumulative numbers of confirmed cases. However, whether different mathematical models yield disparate results based on varying time frames remains unclear. The angle index was proposed in this study to measure the negative impacts of the COVID-19 pandemic.
Methods:Data used in this work were downloaded from the GitHub website. Three mathematical models were examined in two time-frame scenarios during the COVID-19 pandemic (early 20-day stage and the entire year of 2020). The angle index was determined by the ratio of a cumulative number of confirmed cases (CNCCs) divided by the inflection point. To evaluate the model’s accuracy and the prediction power in the two time-frame scenarios, both the R2 model and mean absolute percentage error (MAPE) were used. The following findings were obtained: (1) the exponential growth (EXPO) and item response theory (IRT) models are superior to the quadratic equation (QE) model at the earlier outbreak stage; (2) the IRT model had a higher model R2 and smaller MAPE than the EXPO model in 2020; (3) Hubei Province in China had the highest angle index at the early stage; and (4) India, California (US), and the UK had the highest angle indexes in 2020.
Results:The following findings were obtained: (1) the exponential growth (EXPO) and item response theory (IRT) models are superior to the quadratic equation (QE) model at the earlier outbreak stage; (2) the IRT model had a higher model R2 and smaller MAPE than the EXPO model in 2020; (3) Hubei Province in China had the highest angle index at the early stage; and (4) India, California (US), and the UK had the highest angle indexes in 2020.
Conclusion:The three proposed models are available for measuring the negative impacts of COVID-19. Thus, the IRT model (superior in the long term) and the EXPO model (better at the early stage) are recommended to epidemiologists and policymakers for the effective management of disease outbreak.