Projection of COVID-19 Positive Cases Considering Hybrid Immunity: Case Study in Tokyo

Abstract

Since the emergence of COVID-19, the forecasting of new daily positive cases and deaths has been one of the essential elements in policy setting and medical resource management worldwide. An essential factor in forecasting is the modeling of susceptible populations and vaccination effectiveness (VE) at the population level. Owing to the widespread viral transmission and wide vaccination campaign coverage, it becomes challenging to model the VE in an efficient and realistic manner, while also including hybrid immunity which is acquired through full vaccination combined with infection. Here, the VE model of hybrid immunity was developed based on an in vitro study and publicly available data. Computational replication of daily positive cases demonstrates a high consistency between the replicated and observed values when considering the effect of hybrid immunity. The estimated positive cases were relatively larger than the observed value without considering hybrid immunity. Replication of the daily positive cases and its comparison would provide useful information of immunity at the population level and thus serve as useful guidance for nationwide policy setting and vaccination strategies.

Population-Level Immunity for Transient Suppression of COVID-19 Waves in Japan from April 2021 to September 2022

“…Figure 1 b depicts the time course of the ratio of estimated infected cases to confirmed positive cases based on PCR tests. From the figure, the ratio varies depending on the time window that also holds true for other variants [ 32 ]. This ratio becomes relatively unstable when the number of DPCs is small.…”

“…This comparison suggested that the heuristic method based on the numerical derivation of immunity was appropriate. Note that our vaccination parameters and the prediction model have been verified to be acceptable using machine learning under various periods during the COVID-19 pandemic [ 18 , 23 , 28 , 32 , 43 ]. Using machine learning, we have provided an interpolation of what would have happened if the vaccination was not administrated in Tokyo after 11 July 2021, during the Olympic Games ( Figure 6 ).…”

“…The function e inf ( i ) denotes the natural immunity of the individual on day i after infection that wanes as day i progresses from the peak a inf . The parameters a t for each variant, a inf , and s were used the same as in Table 1 of [ 32 ]. The model of VE was validated in our previous studies [ 18 , 23 , 28 , 32 ].…”

“…The parameters a t for each variant, a inf , and s were used the same as in Table 1 of [ 32 ]. The model of VE was validated in our previous studies [ 18 , 23 , 28 , 32 ].…”