National seroepidemiological study of COVID-19 after the initial rollout of vaccines: Before and at the peak of the Omicron-dominant period in Japan.

Background: Based on routine surveillance data, Japan has been affected much less by COVID-19 compared with other countries. To validate this, we aimed to estimate SARS-CoV-2 seroprevalence and examine sociodemographic factors associated with cumulative infection in Japan. Methods: A population-based serial cross-sectional seroepidemiological investigation was conducted in five prefectures in December 2021 (pre-Omicron) and February-March 2022 (Omicron [BA.1/BA.2]-peak). Anti-nucleocapsid and anti-spike antibodies were measured to detect infection-induced and vaccine/infection-induced antibodies, respectively. Logistic regression was used to identify associations between various factors and past infection. Results: Among 16 296 participants (median age: 53 [43-64] years), overall prevalence of infection-induced antibodies was 2.2% (95% CI: 1.9-2.5%) in December 2021 and 3.5% (95% CI: 3.1-3.9%) in February-March 2022. Factors associated with past infection included those residing in urban prefectures (Tokyo: aOR 3.37 [95% CI: 2.31-4.91], Osaka: aOR 3.23 [95% CI: 2.17-4.80]), older age groups (60s: aOR 0.47 [95% CI 0.29-0.74], 70s: aOR 0.41 [95% CI 0.24-0.70]), being vaccinated (twice: aOR 0.41 [95% CI: 0.28-0.61], three times: aOR 0.21 [95% CI: 0.12-0.36]), individuals engaged in occupations such as long-term care workers (aOR: 3.13 [95% CI: 1.47-6.66]), childcare workers (aOR: 3.63 [95% CI: 1.60-8.24]), food service workers (aOR: 3.09 [95% CI: 1.50-6.35]), and history of household contact (aOR: 26.4 [95% CI: 20.0-34.8]) or non-household contact (aOR: 5.21 [95% CI:3.80-7.14]) in February-March 2022. Almost all vaccinated individuals (15 670/15 681) acquired binding antibodies with higher titers among booster dose recipients. Conclusions: Before Omicron, the cumulative burden was >10 times lower in Japan (2.2%) compared with the US (33%), the UK (25%), or global estimates (45%), but most developed antibodies owing to vaccination.

National seroepidemiological study of COVID‐19 after the initial rollout of vaccines: Before and at the peak of the Omicron‐dominant period in Japan

Background Based on routine surveillance data, Japan has been affected much less by COVID‐19 compared with other countries. To validate this, we aimed to estimate SARS‐CoV‐2 seroprevalence and examine sociodemographic factors associated with cumulative infection in Japan. Methods A population‐based serial cross‐sectional seroepidemiological investigation was conducted in five prefectures in December 2021 (pre‐Omicron) and February–March 2022 (Omicron [BA.1/BA.2]‐peak). Anti‐nucleocapsid and anti‐spike antibodies were measured to detect infection‐induced and vaccine/infection‐induced antibodies, respectively. Logistic regression was used to identify associations between various factors and past infection. Results Among 16 296 participants (median age: 53 [43–64] years), overall prevalence of infection‐induced antibodies was 2.2% (95% CI: 1.9–2.5%) in December 2021 and 3.5% (95% CI: 3.1–3.9%) in February–March 2022. Factors associated with past infection included those residing in urban prefectures (Tokyo: aOR 3.37 [95% CI: 2.31–4.91], Osaka: aOR 3.23 [95% CI: 2.17–4.80]), older age groups (60s: aOR 0.47 [95% CI 0.29–0.74], 70s: aOR 0.41 [95% CI 0.24–0.70]), being vaccinated (twice: aOR 0.41 [95% CI: 0.28–0.61], three times: aOR 0.21 [95% CI: 0.12–0.36]), individuals engaged in occupations such as long‐term care workers (aOR: 3.13 [95% CI: 1.47–6.66]), childcare workers (aOR: 3.63 [95% CI: 1.60–8.24]), food service workers (aOR: 3.09 [95% CI: 1.50–6.35]), and history of household contact (aOR: 26.4 [95% CI: 20.0–34.8]) or non‐household contact (aOR: 5.21 [95% CI:3.80–7.14]) in February–March 2022. Almost all vaccinated individuals (15 670/15 681) acquired binding antibodies with higher titers among booster dose recipients. Conclusions Before Omicron, the cumulative burden was >10 times lower in Japan (2.2%) compared with the US (33%), the UK (25%), or global estimates (45%), but most developed antibodies owing to vaccination.

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.

Age-Dependent Effects of COVID-19 Vaccine and of Healthcare Burden on COVID-19 Deaths, Tokyo, Japan.

COVID-19 vaccine effectiveness against death in Japan remains unknown. Furthermore, although evidence indicates that healthcare capacity influences case-fatality risk (CFR), it remains unknown whether this relationship is mediated by age. With a modeling study, we analyzed daily COVID-19 cases and deaths during January-August 2021 by using Tokyo surveillance data to jointly estimate COVID-19 vaccine effectiveness against death and age-specific CFR. We also examined daily healthcare operations to determine the association between healthcare burden and age-specific CFR. Among fully vaccinated patients, vaccine effectiveness against death was 88.6% among patients 60-69 years of age, 83.9% among patients 70-79 years of age, 83.5% among patients 80-89 years of age, and 77.7% among patients >90 years of age. A positive association of several indicators of healthcare burden with CFR among patients >70 years of age suggested an age-dependent effect of healthcare burden on CFR in Japan.

Epidemiology of coronavirus disease 2019 (COVID-19) in Japan during the first and second waves.

Following the emergence and worldwide spread of coronavirus disease 2019 (COVID-19), each country has attempted to control the disease in different ways. The first patient with COVID-19 in Japan was diagnosed on 15 January 2020, and until 31 October 2020, the epidemic was characterized by two large waves. To prevent the first wave, the Japanese government imposed several control measures such as advising the public to avoid the 3Cs (closed spaces with poor ventilation, crowded places with many people nearby, and close-contact settings such as close-range conversations) and implementation of "cluster buster" strategies. After a major epidemic occurred in April 2020 (the first wave), Japan asked its citizens to limit their numbers of physical contacts and announced a non-legally binding state of emergency. Following a drop in the number of diagnosed cases, the state of emergency was gradually relaxed and then lifted in all prefectures of Japan by 25 May 2020. However, the development of another major epidemic (the second wave) could not be prevented because of continued chains of transmission, especially in urban locations. The present study aimed to descriptively examine propagation of the COVID-19 epidemic in Japan with respect to time, age, space, and interventions implemented during the first and second waves. Using publicly available data, we calculated the effective reproduction number and its associations with the timing of measures imposed to suppress transmission. Finally, we crudely calculated the proportions of severe and fatal COVID-19 cases during the first and second waves. Our analysis identified key characteristics of COVID-19, including density dependence and also the age dependence in the risk of severe outcomes. We also identified that the effective reproduction number during the state of emergency was maintained below the value of 1 during the first wave.

Phenomenological and mechanistic models for predicting early transmission data of COVID-19.

Forecasting future epidemics helps inform policy decisions regarding interventions. During the early coronavirus disease 2019 epidemic period in January-February 2020, limited information was available, and it was too challenging to build detailed mechanistic models reflecting population behavior. This study compared the performance of phenomenological and mechanistic models for forecasting epidemics. For the former, we employed the Richards model and the approximate solution of the susceptible-infected-recovered (SIR) model. For the latter, we examined the exponential growth (with lockdown) model and SIR model with lockdown. The phenomenological models yielded higher root mean square error (RMSE) values than the mechanistic models. When using the numbers from reported data for February 1 and 5, the Richards model had the highest RMSE, whereas when using the February 9 data, the SIR approximation model was the highest. The exponential model with a lockdown effect had the lowest RMSE, except when using the February 9 data. Once interventions or other factors that influence transmission patterns are identified, they should be additionally taken into account to improve forecasting.

Projecting a second wave of COVID-19 in Japan with variable interventions in high-risk settings.

An initial set of interventions, including the closure of host and hostess clubs and voluntary limitation of non-household contact, probably greatly contributed to reducing the disease incidence of coronavirus disease (COVID-19) in Japan, but this approach must eventually be replaced by a more sustainable strategy. To characterize such a possible exit strategy from the restrictive guidelines, we quantified the next-generation matrix, accounting for high- and low-risk transmission settings. This matrix was used to project the future incidence in Tokyo and Osaka after the state of emergency is lifted, presenting multiple 'post-emergency' scenarios with different levels of restriction. The effective reproduction numbers (R) for the increasing phase, the transition phase and the state-of-emergency phase in the first wave of the disease were estimated as 1.78 (95% credible interval (CrI): 1.73-1.82), 0.74 (95% CrI: 0.71-0.78) and 0.63 (95% CrI: 0.61-0.65), respectively, in Tokyo and as 1.58 (95% CrI: 1.51-1.64), 1.20 (95% CrI: 1.15-1.25) and 0.48 (95% CrI: 0.44-0.51), respectively, in Osaka. Projections showed that a 50% decrease in the high-risk transmission is required to keep R less than 1 in both locations-a level necessary to maintain control of the epidemic and minimize the risk of resurgence.

Containment, Contact Tracing and Asymptomatic Transmission of Novel Coronavirus Disease (COVID-19): A Modelling Study.

When a novel infectious disease emerges, enhanced contact tracing and isolation are implemented to prevent a major epidemic, and indeed, they have been successful for the control of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which have been greatly reduced without causing a global pandemic. Considering that asymptomatic and pre-symptomatic infections are substantial for the novel coronavirus disease (COVID-19), the feasibility of preventing the major epidemic has been questioned. Using a two-type branching process model, the present study assesses the feasibility of containing COVID-19 by computing the probability of a major epidemic. We show that if there is a substantial number of asymptomatic transmissions, cutting chains of transmission by means of contact tracing and case isolation would be very challenging without additional interventions, and in particular, untraced cases contribute to lowering the feasibility of containment. Even if isolation of symptomatic cases is conducted swiftly after symptom onset, only secondary transmissions after the symptom onset can be prevented.

Communicating the Risk of Death from Novel Coronavirus Disease (COVID-19).

To understand the severity of infection for a given disease, it is common epidemiological practice to estimate the case fatality risk, defined as the risk of death among cases. However, there are three technical obstacles that should be addressed to appropriately measure this risk. First, division of the cumulative number of deaths by that of cases tends to underestimate the actual risk because deaths that will occur have not yet observed, and so the delay in time from illness onset to death must be addressed. Second, the observed dataset of reported cases represents only a proportion of all infected individuals and there can be a substantial number of asymptomatic and mildly infected individuals who are never diagnosed. Third, ascertainment bias and risk of death among all those infected would be smaller when estimated using shorter virus detection windows and less sensitive diagnostic laboratory tests. In the ongoing COVID-19 epidemic, health authorities must cope with the uncertainty in the risk of death from COVID-19, and high-risk individuals should be identified using approaches that can address the abovementioned three problems. Although COVID-19 involves mostly mild infections among the majority of the general population, the risk of death among young adults is higher than that of seasonal influenza, and elderly with underlying comorbidities require additional care.

Epidemiological Identification of A Novel Pathogen in Real Time: Analysis of the Atypical Pneumonia Outbreak in Wuhan, China, 2019-2020.

Virological tests have now shown conclusively that a novel coronavirus is causing the 2019-2020 atypical pneumonia outbreak in Wuhan, China. We demonstrate that non-virological descriptive characteristics could have determined that the outbreak is caused by a novel pathogen in advance of virological testing. Characteristics of the ongoing outbreak were collected in real time from two medical social media sites. These were compared against characteristics of eleven pathogens that have previously caused cases of atypical pneumonia. The probability that the current outbreak is due to "Disease X" (i.e., previously unknown etiology) as opposed to one of the known pathogens was inferred, and this estimate was updated as the outbreak continued. The probability (expressed as a percentage) that Disease X is driving the outbreak was assessed as over 29% on 31 December 2019, one week before virus identification. After some specific pathogens were ruled out by laboratory tests on 5 January 2020, the inferred probability of Disease X was over 49%. We showed quantitatively that the emerging outbreak of atypical pneumonia cases is consistent with causation by a novel pathogen. The proposed approach, which uses only routinely observed non-virological data, can aid ongoing risk assessments in advance of virological test results becoming available.

Assessing the Impact of Reduced Travel on Exportation Dynamics of Novel Coronavirus Infection (COVID-19).

The impact of the drastic reduction in travel volume within mainland China in January and February 2020 was quantified with respect to reports of novel coronavirus (COVID-19) infections outside China. Data on confirmed cases diagnosed outside China were analyzed using statistical models to estimate the impact of travel reduction on three epidemiological outcome measures: (i) the number of exported cases, (ii) the probability of a major epidemic, and (iii) the time delay to a major epidemic. From 28 January to 7 February 2020, we estimated that 226 exported cases (95% confidence interval: 86,449) were prevented, corresponding to a 70.4% reduction in incidence compared to the counterfactual scenario. The reduced probability of a major epidemic ranged from 7% to 20% in Japan, which resulted in a median time delay to a major epidemic of two days. Depending on the scenario, the estimated delay may be less than one day. As the delay is small, the decision to control travel volume through restrictions on freedom of movement should be balanced between the resulting estimated epidemiological impact and predicted economic fallout.

The Rate of Underascertainment of Novel Coronavirus (2019-nCoV) Infection: Estimation Using Japanese Passengers Data on Evacuation Flights.

From 29 to 31 January 2020, a total of 565 Japanese citizens were evacuated from Wuhan, China on three chartered flights. All passengers were screened upon arrival in Japan for symptoms consistent with novel coronavirus (2019-nCoV) infection and tested for presence of the virus. Assuming that the mean detection window of the virus can be informed by the mean serial interval (estimated at 7.5 days), the ascertainment rate of infection was estimated at 9.2% (95% confidence interval: 5.0, 20.0). This indicates that the incidence of infection in Wuhan can be estimated at 20,767 infected individuals, including those with asymptomatic and mildly symptomatic infections. The infection fatality risk (IFR)-the actual risk of death among all infected individuals-is therefore 0.3% to 0.6%, which may be comparable to Asian influenza pandemic of 1957-1958.

The Extent of Transmission of Novel Coronavirus in Wuhan, China, 2020.

A cluster of pneumonia cases linked to a novel coronavirus (2019-nCoV) was reported by China in late December 2019. Reported case incidence has now reached the hundreds, but this is likely an underestimate. As of 24 January 2020, with reports of thirteen exportation events, we estimate the cumulative incidence in China at 5502 cases (95% confidence interval: 3027, 9057). The most plausible number of infections is in the order of thousands, rather than hundreds, and there is a strong indication that untraced exposures other than the one in the epidemiologically linked seafood market in Wuhan have occurred.

Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data.

The geographic spread of 2019 novel coronavirus (COVID-19) infections from the epicenter of Wuhan, China, has provided an opportunity to study the natural history of the recently emerged virus. Using publicly available event-date data from the ongoing epidemic, the present study investigated the incubation period and other time intervals that govern the epidemiological dynamics of COVID-19 infections. Our results show that the incubation period falls within the range of 2-14 days with 95% confidence and has a mean of around 5 days when approximated using the best-fit lognormal distribution. The mean time from illness onset to hospital admission (for treatment and/or isolation) was estimated at 3-4 days without truncation and at 5-9 days when right truncated. Based on the 95th percentile estimate of the incubation period, we recommend that the length of quarantine should be at least 14 days. The median time delay of 13 days from illness onset to death (17 days with right truncation) should be considered when estimating the COVID-19 case fatality risk.

Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases.

The exported cases of 2019 novel coronavirus (COVID-19) infection that were confirmed outside China provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in mainland China. Knowledge of the cCFR is critical to characterize the severity and understand the pandemic potential of COVID-19 in the early stage of the epidemic. Using the exponential growth rate of the incidence, the present study statistically estimated the cCFR and the basic reproduction number-the average number of secondary cases generated by a single primary case in a naïve population. We modeled epidemic growth either from a single index case with illness onset on 8 December, 2019 (Scenario 1), or using the growth rate fitted along with the other parameters (Scenario 2) based on data from 20 exported cases reported by 24 January 2020. The cumulative incidence in China by 24 January was estimated at 6924 cases (95% confidence interval [CI]: 4885, 9211) and 19,289 cases (95% CI: 10,901, 30,158), respectively. The latest estimated values of the cCFR were 5.3% (95% CI: 3.5%, 7.5%) for Scenario 1 and 8.4% (95% CI: 5.3%, 12.3%) for Scenario 2. The basic reproduction number was estimated to be 2.1 (95% CI: 2.0, 2.2) and 3.2 (95% CI: 2.7, 3.7) for Scenarios 1 and 2, respectively. Based on these results, we argued that the current COVID-19 epidemic has a substantial potential for causing a pandemic. The proposed approach provides insights in early risk assessment using publicly available data.