COVID-19 forecasting using new viral variants and vaccination effectiveness models (2201.10356v2)

Abstract: Background: Recently, a high number of daily positive COVID-19 cases have been reported in regions with relatively high vaccination rates; hence, booster vaccination has become necessary. In addition, infections caused by the different variants and correlated factors have not been discussed in depth. With large variabilities and different co-factors, it is difficult to use conventional mathematical models to forecast the incidence of COVID-19. Methods: Machine learning based on long short-term memory was applied to forecasting the time series of new daily positive cases (DPC), serious cases, hospitalized cases, and deaths. Data acquired from regions with high rates of vaccination, such as Israel, were blended with the current data of other regions in Japan to factor in the potential effects of vaccination. The protection provided by symptomatic infection was also considered in terms of the population effectiveness of vaccination as well as the waning protection and ratio and infectivity of viral variants. To represent changes in public behavior, public mobility and interactions through social media were also included in the analysis. Findings: Comparing the observed and estimated new DPC in Tel Aviv, Israel, the parameters characterizing vaccination effectiveness and the waning protection from infection were well estimated; the vaccination effectiveness of the second dose after 5 months and the third dose after two weeks from infection by the delta variant were 0.24 and 0.95, respectively. Using the extracted parameters regarding vaccination effectiveness, new cases in three prefectures of Japan were replicated.