COVID-19 forecasting using new viral variants and vaccination effectiveness models
Essam A. Rashed, Sachiko Kodera, Akimasa Hirata

TL;DR
This study develops a machine learning model incorporating viral variants, vaccination effects, and social behavior to improve COVID-19 case forecasting across different regions.
Contribution
It introduces a novel LSTM-based forecasting approach that integrates vaccination effectiveness, waning immunity, and social factors for more accurate predictions.
Findings
Vaccination effectiveness estimates matched observed data in Israel.
Model accurately replicated COVID-19 cases in Japanese regions.
Incorporating social mobility improved forecast accuracy.
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…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Data-Driven Disease Surveillance · COVID-19 diagnosis using AI
