Secondary transmission of SARS-CoV-2 during the first two waves in Japan: Demographic characteristics and overdispersion

Changes in the spatiotemporal patterns of COVID-19 in Japan from 2020 through 2023

BACKGROUND

The spread of coronavirus disease 2019 (COVID-19) varied among countries. The spatiotemporal trends of COVID-19 in Japan remain understudied. Therefore, this study aimed to conduct a detailed investigation of the spatiotemporal evolution of infections/deaths across prefectures in Japan, to analyze the changing patterns of COVID-19 circulation in metropolitan and nonmetropolitan areas.

METHODS

We extracted data from nationally represented open-source data from January 15, 2020, to May 9, 2023, and we calculated the incidence rate of infection and the mortality. Further the ratios were obtained by dividing those rates in prefectural level by those in national level to make them comparable across country. Then, the spatiotemporal trends of COVID-19 were depicted via heatmaps. A Poisson regression model was used to compare the incidence rate ratios (IRRs) of infection and death between nonmetropolitan and metropolitan prefectures.

RESULTS

During the study period, Japan experienced eight waves of COVID-19 resulting in 33,738,398 confirmed infections and 74,688 deaths. Both infections and deaths increased significantly overtime. Transmission was initially concentrated in metropolitan prefectures. Nonmetropolitan prefectures were protected and had lower numbers of infections and deaths through June 2022. Thereafter, COVID-19 became more widespread, with more localized surges in nonmetropolitan prefectures. Eventually, during the eighth wave (October 16, 2022-May 9, 2023), there was a marked increase in the IRR in nonmetropolitan prefectures reaching 1.25 (95 % confidence interval (CI), 1.15-1.34) for infection and 1.38 (95 % CI, 1.16-1.65) for death.

CONCLUSIONS

In Japan, COVID-19 transmission was suppressed for the first 2 years of the pandemic, especially in nonmetropolitan prefectures, but the trends changed over time, and more infections and deaths were observed from late 2022 in nonmetropolitan prefectures. These findings underscore the importance of addressing the geographical disparities that likely exist between metropolitan and nonmetropolitan prefectures Delaying large surges in nonmetropolitan prefectures may be an important takeaway that could aid in the future management of major infectious disease outbreaks.

modelSSE: An R Package for Characterizing Infectious Disease Superspreading from Contact Tracing Data

Infectious disease superspreading is a phenomenon where few primary cases generate unexpectedly large numbers of secondary cases. Superspreading, is frequently documented in epidemiology literature, and is considered a consequence of heterogeneity in transmission. Since understanding the risks of superspreading became a rising concern from both statistical modelling and public health aspects, the R package modelSSE provides comprehensive analytical tools to characterize transmission heterogeneity. The package modelSSE integrates recent advances in statistical methods, such as decomposition of reproduction number, for modelling infectious disease superspreading using various types and sources of contact tracing data that allow models to be grounded in real-world observations. This study provided an overview of the theoretical background and implementation of modelSSE, designed to facilitate learning infectious disease transmission, and explore novel research questions for transmission risks and superspreading potentials. Detailed examples of classic, historical infectious disease datasets are given for demonstration and model extensions.

Climatic determinants of monkeypox transmission: A multi-national analysis using generalized count mixed models

Monkeypox (mpox) is a rare viral disease that can cause severe illness in humans, with outbreaks occurring primarily in central and western Africa. Well-coordinated and synchronized efforts are necessary to understand the factors involved in disease transmission and develop effective health interventions. The aim of this study is to assess the relationship between climate factors and daily mpox cases, as well as to identify the most suitable predictive model for transmission. We analyzed confirmed mpox cases from May 5, 2022, to February 14, 2023, in the 33 most affected countries. We employed and compared the efficiency of four models: Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial. We found a significant correlation between climate factors and daily mpox cases across most of the studied countries. Specifically, for each 1°C increase in the heat index (HI), daily cases increased by 7.7 % (IRR = 1.077, p < 0.05). Conversely, higher relative humidity (RH) decreased daily cases by 2.4 %, and increased wind speed (WS) reduced them by 7.3 %. The HI positively influences mpox spread, while RH and WS act as protective factors. Public health officials should consider these climate influences when developing targeted interventions.

State-space modelling using wastewater virus and epidemiological data to estimate reported COVID-19 cases and the potential infection numbers

The current situation of COVID-19 measures makes it difficult to accurately assess the prevalence of SARS-CoV-2 due to a decrease in reporting rates, leading to missed initial transmission events and subsequent outbreaks. There is growing recognition that wastewater virus data assist in estimating potential infections, including asymptomatic and unreported infections. Understanding the COVID-19 situation hidden behind the reported cases is critical for decision-making when choosing appropriate social intervention measures. However, current models implicitly assume homogeneity in human behaviour, such as virus shedding patterns within the population, making it challenging to predict the emergence of new variants due to variant-specific transmission or shedding parameters. This can result in predictions with considerable uncertainty. In this study, we established a state-space model based on wastewater viral load to predict both reported cases and potential infection numbers. Our model using wastewater virus data showed high goodness-of-fit to COVID-19 case numbers despite the dataset including waves of two distinct variants. Furthermore, the model successfully provided estimates of potential infection, reflecting the superspreading nature of SARS-CoV-2 transmission. This study supports the notion that wastewater surveillance and state-space modelling have the potential to effectively predict both reported cases and potential infections.

Temporal patterns of asymptomatic SARS-CoV-2 infection among pediatric population in Japan: A 3-year analysis

To elucidate the seroprevalence and rate of asymptomatic infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Japanese children, serological analysis was performed using serum samples collected from March 2020 to February 2023. A total of 1493 serum samples were collected during the first study period (March 2020 to February 2021). None of the serum samples was positive for SARS-CoV-2 antibody. In the second period (March 2021 to February 2022), seven of the 1055 patients (0.7%) experienced SARS-CoV-2 infection. The third period (March 2022 to February 2023) was divided into three terms: from March to June 30, 2022; from July to October 2022; and from November 2022 to February 2023. The seroprevalence gradually increased throughout this period, with rates of 6.0%, 18.6%, and 30.4% in the three terms, respectively. Pediatric cases of asymptomatic SARS-CoV-2 infection occurred after the surge of Omicron variants. Since none of the SARS-CoV-2 antibody-positive patients had a previous history of coronavirus disease 2019, the seroprevalence rate in this study may represent the rate of asymptomatic infection.

COVID-19 cluster surveillance using exposure data collected from routine contact tracing: The genomic validation of a novel informatics-based approach to outbreak detection in England

Contact tracing was used globally to prevent onwards transmission of COVID-19. Tracing contacts alone is unlikely to be sufficient in controlling community transmission, due to the pre-symptomatic, overdispersed and airborne nature of COVID-19 transmission. We describe and demonstrate the validity of a national enhanced contact tracing programme for COVID-19 cluster surveillance in England. Data on cases occurring between October 2020 and September 2021 were extracted from the national contact tracing system. Exposure clusters were identified algorithmically by matching ≥2 cases attending the same event, identified by matching postcode and event category within a 7-day rolling window. Genetic validity was defined as exposure clusters with ≥2 cases from different households with identical viral sequences. Exposure clusters were fuzzy matched to the national incident management system (HPZone) by postcode and setting description. Multivariable logistic regression modelling was used to determine cluster characteristics associated with genetic validity. Over a quarter of a million (269,470) exposure clusters were identified. Of the eligible clusters, 25% (3,306/13,008) were genetically valid. 81% (2684/3306) of these were not recorded on HPZone and were identified on average of one day earlier than incidents recorded on HPZone. Multivariable analysis demonstrated that exposure clusters occurring in workplaces (aOR = 5·10, 95% CI 4·23-6·17) and education (aOR = 3·72, 95% CI 3·08-4·49) settings were those most strongly associated with genetic validity. Cluster surveillance using enhanced contact tracing in England was a timely, comprehensive and systematic approach to the detection of transmission events occurring in community settings. Cluster surveillance can provide intelligence to stakeholders to support the assessment and management of clusters of COVID-19 at a local, regional, and national level. Future systems should include predictive modelling and network analysis to support risk assessment of exposure clusters to improve the effectiveness of enhanced contract tracing for outbreak detection.

The value of manual backward contact tracing to control COVID-19 in practice, the Netherlands, February to March 2021: a pilot study

BackgroundContact tracing has been a key component of COVID-19 outbreak control. Backward contact tracing (BCT) aims to trace the source that infected the index case and, thereafter, the cases infected by the source. Modelling studies have suggested BCT will substantially reduce SARS-CoV-2 transmission in addition to forward contact tracing.AimTo assess the feasibility and impact of adding BCT in practice.MethodsWe identified COVID-19 cases who were already registered in the electronic database between 19 February and 10 March 2021 for routine contact tracing at the Public Health Service (PHS) of Rotterdam-Rijnmond, the Netherlands (pop. 1.3 million). We investigated if, through a structured questionnaire by dedicated contact tracers, we could trace additional sources and cases infected by these sources. Potential sources identified by the index were approached to trace the source's contacts. We evaluated the number of source contacts that could be additionally quarantined.ResultsOf 7,448 COVID-19 cases interviewed in the study period, 47% (n = 3,497) indicated a source that was already registered as a case in the PHS electronic database. A potential, not yet registered source was traced in 13% (n = 979). Backward contact tracing was possible in 62 of 979 cases, from whom an additional 133 potential sources were traced, and four were eligible for tracing of source contacts. Two additional contacts traced had to stay in quarantine for 1 day. No new COVID-19 cases were confirmed.ConclusionsThe addition of manual BCT to control the COVID-19 pandemic did not provide added value in our study setting.

Time-varying overdispersion of SARS-CoV-2 transmission during the periods when different variants of concern were circulating in Japan

Japan has implemented a cluster-based approach for coronavirus disease 2019 (COVID-19) from the pandemic's beginning based on the transmission heterogeneity (overdispersion) of severe acute respiratory coronavirus 2 (SARS-CoV-2). However, studies analyzing overdispersion of transmission among new variants of concerns (VOCs), especially for Omicron, were limited. Thus, we aimed to clarify how the transmission heterogeneity has changed with the emergence of VOCs (Alpha, Delta, and Omicron) using detailed contact tracing data in Yamagata Prefecture, Japan. We estimated the time-varying dispersion parameter ([Formula: see text]) by fitting a negative binomial distribution for each transmission generation. Our results showed that even after the emergence of VOCs, there was transmission heterogeneity of SARS-CoV-2, with changes in [Formula: see text] during each wave. Continuous monitoring of transmission dynamics is vital for implementing appropriate measures. However, a feasible and sustainable epidemiological analysis system should be established to make this possible.

Superspreading, overdispersion and their implications in the SARS-CoV-2 (COVID-19) pandemic: a systematic review and meta-analysis of the literature

BACKGROUND

A recurrent feature of infectious diseases is the observation that different individuals show different levels of secondary transmission. This inter-individual variation in transmission potential is often quantified by the dispersion parameter k. Low values of k indicate a high degree of variability and a greater probability of superspreading events. Understanding k for COVID-19 across contexts can assist policy makers prepare for future pandemics.

METHODS

A literature search following a systematic approach was carried out in PubMed, Embase, Web of Science, Cochrane Library, medRxiv, bioRxiv and arXiv to identify publications containing epidemiological findings on superspreading in COVID-19. Study characteristics, epidemiological data, including estimates for k and R0, and public health recommendations were extracted from relevant records.

RESULTS

The literature search yielded 28 peer-reviewed studies. The mean k estimates ranged from 0.04 to 2.97. Among the 28 studies, 93% reported mean k estimates lower than one, which is considered as marked heterogeneity in inter-individual transmission potential. Recommended control measures were specifically aimed at preventing superspreading events. The combination of forward and backward contact tracing, timely confirmation of cases, rapid case isolation, vaccination and preventive measures were suggested as important components to suppress superspreading.

CONCLUSIONS

Superspreading events were a major feature in the pandemic of SARS-CoV-2. On the one hand, this made outbreaks potentially more explosive but on the other hand also more responsive to public health interventions. Going forward, understanding k is critical for tailoring public health measures to high-risk groups and settings where superspreading events occur.

Differences in the superspreading potentials of COVID-19 across contact settings

BACKGROUND

Superspreading events (SSEs) played a critical role in fueling the COVID-19 outbreaks. Although it is well-known that COVID-19 epidemics exhibited substantial superspreading potential, little is known about the risk of observing SSEs in different contact settings. In this study, we aimed to assess the potential of superspreading in different contact settings in Japan.

METHOD

Transmission cluster data from Japan was collected between January and July 2020. Infector-infectee transmission pairs were constructed based on the contact tracing history. We fitted the data to negative binomial models to estimate the effective reproduction number (R) and dispersion parameter (k). Other epidemiological issues relating to the superspreading potential were also calculated.

RESULTS

The overall estimated R and k are 0.561 (95% CrI: 0.496, 0.640) and 0.221 (95% CrI: 0.186, 0.262), respectively. The transmission in community, healthcare facilities and school manifest relatively higher superspreading potentials, compared to other contact settings. We inferred that 13.14% (95% CrI: 11.55%, 14.87%) of the most infectious cases generated 80% of the total transmission events. The probabilities of observing superspreading events for entire population and community, household, health care facilities, school, workplace contact settings are 1.75% (95% CrI: 1.57%, 1.99%), 0.49% (95% CrI: 0.22%, 1.18%), 0.07% (95% CrI: 0.06%, 0.08%), 0.67% (95% CrI: 0.31%, 1.21%), 0.33% (95% CrI: 0.13%, 0.94%), 0.32% (95% CrI: 0.21%, 0.60%), respectively.

CONCLUSION

The different potentials of superspreading in contact settings highlighted the need to continuously monitoring the transmissibility accompanied with the dispersion parameter, to timely identify high risk settings favoring the occurrence of SSEs.

Updates on Coronavirus Disease 2019 in Children in Japan

This review provides updates on coronavirus disease 2019 (COVID-19) in children in Japan by summarizing published data. By the end of March 2022, Japan had experienced 6 waves of COVID-19 outbreaks. Over this time, the clinical features presented among children have changed in the context of the predominant variants. Although the COVID-19 pandemic affected children in terms of medical, physical and psychosocial aspects, the clinical outcomes have been favorable in Japan compared with those in some European countries and the United States, which may be partly due to a lower incidence of multisystem inflammatory syndromes in children and obesity. The COVID-19 vaccine has been available for children; however, the vaccination rate in children 5-11 years of age is lower than that in older children due to the government's lack of an active approach in this specific population. Further action is needed to improve the overall vaccination rates in children.

Impact of the Coming-of-Age Day and ceremony on the risk of SARS-CoV-2 transmission in Japan: A natural-experimental study based on national surveillance data

Background

Quantifying the impact on COVID‐19 transmission from a single event has been difficult due to the virus transmission dynamics, such as lag from exposure to reported infection, non‐linearity arising from the person‐to‐person transmission, and the modifying effects of non‐pharmaceutical interventions over time. To address these issues, we aimed to estimate the COVID‐19 transmission risk of social events focusing on the Japanese Coming‐of‐Age Day and Coming‐of‐Age ceremony in which “new adults” practice risky behavior on that particular day.

Methods

Using national surveillance data in Japan in 2021 and 2022, we conducted difference‐in‐differences regression against COVID‐19 incidences by setting “new adults” cases as the treatment group and the cases 1 year younger or older than these “new adults” as the control group. In addition, we employed a triple differences approach to estimate the risk of holding the Coming‐Age ceremony by using a binary variable regarding the presence or absence of the ceremony in each municipality.

Results

We estimated the relative risks (RRs) of the Coming‐of‐Age Day as 1.27 (95% confidence interval [CI] 1.02–1.57) in 2021 and 3.22 (95% CI 2.68–3.86) in 2022. The RR of the Coming‐of‐Age ceremony was also large, estimated as 2.83 (1.81–4.43) in 2022.

Conclusions

When planning large social events, it is important to be aware of the unique risks associated with these gatherings, along with effective public health messages to best communicate these risks.

Epidemiology of SARS-CoV-2 infection in nursing facilities and the impact of their clusters in a Japanese core city

Introduction

Nursing facilities are vulnerable to coronavirus disease 2019 (COVID-19) due to the congregate nature of their housing, the older age of the residents, and the variety of their geriatric chronic conditions. Little is known about the impact of nursing facility COVID-19 on the local health system.

Methods

We collected data of COVID-19 cases in Nagasaki city from April 15, 2020 to June 30, 2021. We performed universal screening of the healthcare workers (HCWs) and the users of nursing facilities, once the first case of COVID-19 was detected within that facility. The community-dwelling people received testing if they had symptoms or if they were suspected of having close contact with the positive cases. The epidemiological survey for each COVID-19 case was performed by the public health officers of the local public health center.

Results

Out of 111,773 community-dwelling older adults (age ≥ 65 years) and 20,668 nursing facility users in Nagasaki city, we identified 358 and 71 COVID-19 cases, and 33 and 12 COVID-19 deaths, respectively, during the study period. The incidence rate ratios (IRRs) for COVID-19 and its deaths among the nursing facility users were 1.07 (95% confidence interval (CI), 0.82–1.39) and 1.97 (95%CI, 0.92–3.91) compared with the community-dwelling older adults. Four clusters, which had more than 10 COVID-19 cases, accounted for 60% (65/109) of the overall cases by the HCWs and the users.

Conclusions

The prevention of COVID-19 clusters is important to reduce the number of COVID-19 cases and deaths among the nursing facility population.

[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.