Simulation of future COVID-19 epidemic by vaccination coverage scenarios in Japan

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

BACKGROUND

The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19).

METHODOLOGY

PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542.

FINDINGS

In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias.

INTERPRETATION

The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.

Forecasting and what-if analysis of new positive COVID-19 cases during the first three waves in Italy

The joint exploitation of data related to epidemiological, mobility, and restriction aspects of COVID-19 with machine learning algorithms can support the development of predictive models that can be used to forecast new positive cases and study the impact of more or less severe restrictions. In this work, we integrate heterogeneous data from several sources and solve a multivariate time series forecasting task, specifically targeting the Italian case at both national and regional levels, during the first three waves of the pandemic. The goal is to build a robust predictive model to predict the number of new cases over a given time horizon so that any restrictive actions can be better planned. In addition, we perform a what-if analysis based on the best-identified predictive models to evaluate the impact of specific restrictions on the trend of positive cases. Our focus on the first three waves is motivated by the fact that it represents a typical emergency scenario (when no stable cure or vaccine is available) that may occur when a new pandemic spreads. Our experimental results prove that exploiting the considered heterogeneous data leads to accurate predictive models, reaching a WAPE of 5.75% at the national level. Furthermore, in the subsequent what-if analysis, we observed that strong all-in-one initiatives, such as total lockdowns, may not be adequate, while more specific and targeted solutions should be adopted. The developed models can help policy and decision-makers better plan intervention strategies and retrospectively analyze the effects of the decisions made at different scales. Joint exploitation of data on epidemiological, mobility, and restriction aspects of COVID-19 with machine learning algorithms to learn predictive models to forecast new positive cases.

Effect of vaccination patterns and vaccination rates on the spread and mortality of the COVID-19 pandemic

Objectives

Acquiring herd immunity through vaccination is the best way to curb the COVID-19 infection. Many countries have attempted to reach the herd immunity threshold as early as possible since the commencement of vaccination at the end of 2020. The purpose of this study is to (1) examine whether the pattern of vaccination rates affects the spread of COVID-19 and the consequent mortality and (2) investigate the level of cumulative vaccination rates that can begin to have an impact on reducing the spread and mortality of the pandemic.

Methods

This study selected 33 countries with higher vaccination rates as its sample set, classifying them into three groups as per vaccination patterns.

Results

The results showed that vaccination patterns have a significant impact on reducing spread and mortality. The full-speed vaccination pattern showed greater improvement in the spread of the COVID-19 pandemic than the other two patterns, while the striving vaccination pattern improved the most in terms of mortality. Secondly, the spread and mortality of the COVID pandemic started to significantly decline when the average cumulative vaccination rate reached 29.06 doses per 100 people and 7.88 doses per 100 people, respectively.

Conclusion

The study highlights the important role of vaccination patterns and the VTMR in reducing the epidemic spread and mortality.

Age-Stratified Seroprevalence of SARS-CoV-2 Antibodies before and during the Vaccination Era, Japan, February 2020–March 2022

Japan has reported a relatively small number of COVID-19 cases. Because not all infected persons receive diagnostic tests for COVID-19, the reported number must be lower than the actual number of infections. We assessed SARS-CoV-2 seroprevalence by analyzing >60,000 samples collected in Japan (Tokyo Metropolitan Area and Hokkaido Prefecture) during February 2020–March 2022. The results showed that ≈3.8% of the population had become seropositive by January 2021. The seroprevalence increased with the administration of vaccinations; however, among the elderly, seroprevalence was not as high as the vaccination rate. Among children, who were not eligible for vaccination, infection was spread during the epidemic waves caused by the SARS-CoV-2 Delta and Omicron variants. Nevertheless, seroprevalence for unvaccinated children <5 years of age was as low as 10% as of March 2022. Our study underscores the low incidence of SARS-CoV-2 infection in Japan and the effects of vaccination on immunity at the population level.

Properties of the Omicron Variant of SARS-CoV-2 Affect Public Health Measure Effectiveness in the COVID-19 Epidemic

Nonpharmaceutical and pharmaceutical public health interventions are important to mitigate the coronavirus disease 2019 (COVID-19) epidemic. However, it is still unclear how the effectiveness of these interventions changes with the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) novel variants. This simulation study utilized data from Japan and investigated how the characteristic properties of the Omicron variant, which emerged in late 2021, influence the effectiveness of public health interventions, including vaccination, the reduction of interpersonal contact, and the early isolation of infectious people. Although the short generation time of the Omicron variant increases the effectiveness of vaccination and the reduction of interpersonal contact, it decreases the effectiveness of early isolation. The latter feature may make the containment of case clusters difficult. The increase of infected children during the Omicron-dominant epidemic diminishes the effects of previously adult-targeted interventions. These findings underscore the importance of monitoring viral evolution and consequent changes in epidemiological characteristics. An assessment and adaptation of public health measures against COVID-19 are required as SARS-CoV-2 novel variants continue to emerge.

The allocation of COVID-19 vaccines and antivirals against emerging SARS-CoV-2 variants of concern in East Asia and Pacific region: A modelling study

BACKGROUND

In view of emerging variants of concern (VOCs), we aimed to evaluate the impact of various allocation strategies of COVID-19 vaccines and antiviral such that the pandemic exit strategy could be tailored to risks and preferences of jurisdictions in the East Asia and Pacific region (EAP) to improve its efficiency and effectiveness.

METHODS

Vaccine efficacies were estimated from the titre distributions of 50% plaque reduction neutralization test (PRNT50), assuming that PRNT50 titres of primary vaccination decreased by 2-10 folds due to antibody waning and emergence of VOCs, and an additional dose of vaccine would increase PRNT50 titres by 3- or 9-fold. We then used an existing SARS-CoV-2 transmission model to assess the outcomes of vaccine allocation strategies with and without the use of antivirals for symptomatic patients in Japan, Hong Kong, and Vietnam.

FINDINGS

Increasing primary vaccination coverage was the most important contributing factor in reducing the total and peak number of COVID-19 hospitalisations, especially when population vaccine coverage or vaccine uptake among older adults was low. Providing antivirals to 50% of symptomatic infections only further reduced total and peak hospitalisations by 10-13%. The effectiveness of an additional dose of vaccine was highly dependent on the immune escape potential of VOCs and antibody waning, but less dependent on the boosting efficacy of the additional dose.

INTERPRETATION

Increasing primary vaccination coverage should be prioritised in the design of allocation strategies of COVID-19 vaccines and antivirals against emerging VOCs, such as Omicron, in the EAP region. Heterologous vaccination with any available vaccine as the additional dose could be considered when planning pandemic exit strategies tailored to the circumstances of EAP jurisdictions.

FUNDING

Health and Medical Research Fund, General Research Fund, AIR@InnoHK.

Number of public health nurses and COVID-19 incidence rate by variant type: an ecological study of 47 prefectures in Japan

Background

Community health activities by public health nurses (PHNs) are known to improve lifestyle habits of local residents, and may encourage the practice of infectious disease prevention behaviors during the COVID-19 pandemic. We investigated the association between prefecture-level COVID-19 incidence rate and the number of PHNs per population in Japan, by the COVID-19 variant type.

Methods

Our data were based on government surveys where prefectural-level data are accessible to the public. The outcome variable was the COVID-19 incidence rate (i.e., the cumulative number of COVID-19 cases per 100,000 population for each variant type in 47 prefectures). The explanatory variable was the number of PHNs per 100,000 population by prefecture. Covariates included socioeconomic factors, regional characteristics, healthcare resources, and health behaviors. The generalized estimating equations of the multivariable Poisson regression models were used to estimate adjusted incidence rate ratio (IRR) and 95% confidence interval (CI) for the COVID-19 cases. We performed stratified analyses by variant type (i.e., wild type, alpha variant, and delta variant).

Results

A total of 1,705,224 confirmed COVID-19 cases (1351.6 per 100,000 population) in Japan were reported as of September 30, 2021. The number of PHNs per 100,000 population in Japan was 41.9. Multivariable Poisson regression models showed that a lower number of PHNs per population was associated with higher IRR of COVID-19. Among all COVID-19 cases, compared to the highest quintile group of the number of PHNs per population, the adjusted IRR of the lowest quintile group was consistently significant in the models adjusting for socioeconomic factors (IRR: 3.76, 95% CI: 2.55–5.54), regional characteristics (1.73, 1.28–2.34), healthcare resources (3.88, 2.45–6.16), and health behaviors (2.17, 1.39–3.37). These significant associations were unaffected by the variant type of COVID-19.

Conclusion

We found that the COVID-19 incidence rate was higher in prefectures with fewer PHNs per population, regardless of the COVID-19 variant type. By increasing the number of PHNs, it may be possible to contain the spread of COVID-19 in Japan and provide an effective human resource to combat emerging infectious diseases in the future.

Supplementary information

The online version contains supplementary material available at https://doi.org/10.1265/ehpm.22-00013.

[Comprehensive understanding of viral diseases by field, molecular, and theoretical studies]

Viral diseases are responsible for substantial morbidity and mortality and continue to be of great concern. To ensure better control of viral infections, I have been tackling the issue as a medical doctor, an academic researcher, and a public health officer. Especially, I have studied respiratory viruses, such as the influenza virus, from the perspectives of molecular virology, theoretical modeling, and field epidemiology. RNA biology and its involvement with viral life-cycle and pathogenicity are central topics of molecular study, while mathematical models of transmission dynamics and phylogenetics are major components of theoretical research. As a field epidemiologist, I work with public health authorities during viral disease outbreaks. I was deployed to West Africa for viral hemorrhagic fever outbreak responses as a WHO consultant, and I have served the Japanese Government as an advisor for COVID-19 countermeasures. I would like to integrate various approaches from clinical medicine to epidemiology, theoretical modeling, evolutionary biology, genetics, and molecular biology in my research. In that way, we could gain a more comprehensive understanding of viral diseases. I hope these findings will help ease the disease burden of viral infections around the world.

Simulation study reveals factors that affect the predominance of SARS-CoV-2 novel variant

The novel variants of the SARS-CoV-2 are a great global concern for the ongoing COVID-19 pandemic. However, how the novel variants predominate and replace existing strains remains elusive. In this study, I simulated the infection spread to investigate what kinds of viral, immunological, and epidemiological factors affect the predominance of SARS-CoV-2 novel variants. The results showed that the increase of the transmissibility of the novel variant substantially enhanced the predominance probability. In addition, the increasing trend of the infection spread, the large case number of the epidemic, and the ability of immune escape of the novel variant increased the predominance probability. A small number of cases and a decreasing trend of an entire epidemic, including not only the novel variant but also earlier strains, are especially important to reduce the chance of the predominance of the novel variant and delay the process. Good control of the COVID-19 epidemic could make the disease burden small and sequester the spread of the SARS-CoV-2 novel variants.