Toward a Country-Based Prediction Model of COVID-19 Infections and Deaths Between Disease Apex and End: Evidence From Countries With Contained Numbers of COVID-19 - Gu, Tianshu; Wang, Lishi; Xie, Ning; Meng, Xia; Li, Zhijun; Postlethwaite, Arnold; Aleya, Lotfi; Howard, Scott C.; Gu, Weikuan; Wang, Yongjun (Frontiers in Medicine, 2021)
The complexity of COVID-19 and variation in control measures and containment efforts of different countries have caused difficulties in the prediction and modelling of the COVID-19 pandemic. We attempted to predict the scale of the latter half of the pandemic based on real data using the ratio between the early and latter halves from countries where the pandemic is largely over. We collected daily pandemic data from China, South Korea and Switzerland and subtracted ratio of pandemic days before and after the disease apex day of COVID-19. We obtained the ratio of pandemic data and created multiple regression models for the relationship between before and after the apex day. We then tested our models using data from the first wave of the disease from 14 countries in Europe and the US. We then tested the models using data of these countries from entire pandemics up to March 30, 2021. Results indicates that the actual number of cases of these countries during the first wave mostly fall in the predicted ranges of liner regression excepting Spain and Russia. Similarly, the actual death of these countries mostly falls into the range of predicted data. Using the accumulated data up to the day of apex and total accumulated data up to March 30, 2021, the data of case numbers in these countries are falling into the range of predicted data, except for data from Brazil. The actual number of deaths in all the countries are at or below the predicted data. In conclusion, liner regression model built with real data from countries or regions of early pandemics can predict pandemic scales of the countries which the pandemics occur late. Such a prediction with high degree of accuracy provides valuable information for governments and public.