Decrease in overdispersed secondary transmission of COVID-19 over time in Japan.

Coronavirus disease 2019 (COVID-19) has been described as having an overdispersed offspring distribution, i.e. high variation in the number of secondary transmissions of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) per single primary COVID-19 case. Accordingly, countermeasures focused on high-risk settings and contact tracing could efficiently reduce secondary transmissions. However, as variants of concern with elevated transmissibility continue to emerge, controlling COVID-19 with such focused approaches has become difficult. It is vital to quantify temporal variations in the offspring distribution dispersibility. Here, we investigated offspring distributions for periods when the ancestral variant was still dominant (summer, 2020; wave 2) and when Alpha variant (B.1.1.7) was prevailing (spring, 2021; wave 4). The dispersion parameter (k) was estimated by analysing contact tracing data and fitting a negative binomial distribution to empirically observed offspring distributions from Nagano, Japan. The offspring distribution was less dispersed in wave 4 (k = 0.32; 95% confidence interval (CI) 0.24-0.43) than in wave 2 (k = 0.21 (95% CI 0.13-0.36)). A high proportion of household transmission was observed in wave 4, although the proportion of secondary transmissions generating more than five secondary cases did not vary over time. With this decreased variation, the effectiveness of risk group-focused interventions may be diminished.

Model-informed COVID-19 exit strategy with projections of SARS-CoV-2 infections generated by variants in the Republic of Korea.

BACKGROUND: With the prompt administration of coronavirus disease 2019 (COVID-19) vaccines, highly vaccinated countries have begun to lift their stringent control measures. However, considering the spread of highly transmissible new variants, resuming socio-economic activities may lead to the resurgence of incidence, particularly in nations with a low proportion of individuals who have natural immunity. Here, we aimed to quantitatively assess an optimal COVID-19 exit strategy in the Republic of Korea, where only a small number of cumulative incidences have been recorded as of September 2021, comparing epidemiological outcomes via scenario analysis. METHODS: A discrete-time deterministic compartmental model structured by age group was used, accounting for the variant-specific transmission dynamics and the currently planned nationwide vaccination. All parameters were calibrated using comprehensive empirical data obtained from the Korea Disease Control and Prevention Agency. RESULTS: Our projection suggests that tapering the level of social distancing countermeasures to the minimum level from November 2021 can efficiently suppress a resurgence of incidence given the currently planned nationwide vaccine roll-out. In addition, considering the spread of the Delta variant, our model suggested that gradual easing of countermeasures for more than 4 months can efficiently withstand the prevalence of severe COVID-19 cases until the end of 2022. CONCLUSIONS: Our model-based projections provide evidence-based guidance for an exit strategy that allows society to resume normal life while sustaining the suppression of the COVID-19 epidemic in countries where the spread of COVID-19 has been well controlled.

Go To Travel campaign and the geographic spread of COVID-19 in Japan.

BACKGROUND: In 2020, the Japanese government implemented first of two Go To Travel campaigns to promote the tourism sector as well as eating and drinking establishments, especially in remote areas. The present study aimed to explore the relationship between enhanced travel and geographic propagation of COVID-19 across Japan, focusing on the second campaign with nationwide large-scale economic boost in 2020. METHODS: We carried out an interrupted time-series analysis to identify the possible cause-outcome relationship between the Go To Travel campaign and the spread of infection to nonurban areas in Japan. Specifically, we counted the number of prefectures that experienced a weekly incidence of three, five, and seven COVID-19 cases or more per 100,000 population, and we compared the rate of change before and after the campaign. RESULTS: Three threshold values and three different models identified an increasing number of prefectures above the threshold, indicating that the inter-prefectural spread intensified following the launch of the second Go To Travel campaign from October 1st, 2020. The simplest model that accounted for an increase in the rate of change only provided the best fit. We estimated that 0.24 (95% confidence interval 0.15 to 0.34) additional prefectures newly exceeded five COVID-19 cases per 100,000 population per week during the second campaign. CONCLUSIONS: The enhanced movement resulting from the Go To Travel campaign facilitated spatial spread of COVID-19 from urban to nonurban locations, where health-care capacity may have been limited.

The Possible Impact of Nationwide Vaccination on Outcomes of the COVID-19 Epidemic in North Korea: A Modelling Study.

BACKGROUND: The Democratic People's Republic of Korea (North Korea) had successfully suppressed the coronavirus disease 2019 (COVID-19) epidemic via border closures. However, a rapid surge in incidence was reported due to the spread of the omicron variant (B.1.1.529), leading to a national emergency declaration in May 2022. Moreover, with the lack of vaccine accessibility and medical facilities, it is unclear how the disease burden may be exacerbated. Despite the limited epidemiological data, we aimed to project the COVID-19 transmissions in North Korea and quantify the potential impact of nationwide vaccination, comparing epidemiological outcomes via scenario analysis. METHODS: A discrete-time deterministic compartmental model was used. The parameters were calibrated using empirical data. Numerical simulations incorporated nationwide COVID-19 vaccination into the proposed model with various asymptomatic proportions. RESULTS: Our model suggested that the stringent public health and social measures (PHSMs) reduced the severe acute respiratory syndrome coronavirus 2 transmissibility by more than 80% in North Korea. Projections that explicitly incorporated vaccination indicated that nationwide vaccination would be necessary to suppress a huge resurgence in both COVID-19 cases and hospitalizations after the stringent PHSMs are eased. Moreover, vaccinating more than 80% of the population with two doses may keep the peak prevalence of hospitalizations below 1,500, averting more than 40,000 hospitalizations across all scenarios. CONCLUSION: Nationwide vaccination would be essential to suppress the prevalence of COVID-19 hospitalizations in North Korea after the stringent PHSMs are lifted, especially in the case of a small asymptomatic proportion.

Response to COVID-19 during the Tokyo Olympic Games: Did we properly assess the risk?

BACKGROUND: The number of coronavirus disease 2019 (COVID-19) cases was expected to increase during the Tokyo Olympic Games because of the increased physical contact within and between the domestic population and international participants of the Games. The rapid rise of the Delta variant (B.1.617) in Japan meant that hosting the Olympic Games without any restrictions was likely to lead to an increase in cases. We aimed to quantitatively assess possible COVID-19 response strategies for the Olympic Games, comparing the prevalence of severe cases and the cumulative number of COVID-19 deaths via scenario analysis. METHODS: We used a discrete-time deterministic compartmental model structured by age group. Parameters were calibrated using the age-stratified COVID-19 incidence data in Osaka. Numerical simulations incorporated the planned Olympics Games and nationwide COVID-19 vaccination into the proposed model, alongside various subjects and types of countermeasures. RESULTS: Our model-informed approach suggested that having spectators at the Tokyo Olympic Games could lead to a surge in both cases and hospitalization. Projections for the scenario that explicitly incorporated the spread of the Delta variant (i.e., time-dependent increase in the relative transmissibility) showed that imposing stringent social distancing measures (Rt=0.7) for more than 8 weeks from the end of the Olympic Games might be required to suppress the prevalence of severe cases of COVID-19 to avoid overwhelming the intensive care unit capacity in Tokyo. CONCLUSIONS: Our modeling analyses guided an optimal choice of COVID-19 response during and after the Tokyo Olympic Games, allowing the epidemic to be brought under control despite such a large mass gathering.

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.

Not all fun and games: Potential incidence of SARS-CoV-2 infections during the Tokyo 2020 Olympic Games.

The Tokyo 2020 Olympic and Paralympic Games represent the most diverse international mass gathering event held since the start of the coronavirus disease 2019 (COVID-19) pandemic. Postponed to summer 2021, the rescheduled Games were set to be held amidst what would become the highest-ever levels of COVID-19 transmission in the host city of Tokyo. At the same time, the Delta variant of concern was gaining traction as the dominant viral strain and Japan had yet to exceed fifteen percent of its population fully vaccinated against COVID-19. To quantify the potential number of secondary cases that might arise during the Olympic Games, we performed a scenario analysis using a multitype branching process model. We considered the different contributions to transmission of Games accredited individuals, the general Tokyo population, and domestic spectators. In doing so, we demonstrate how transmission might evolve in these different groups over time, cautioning against any loosening of infection prevention protocols and supporting the decision to ban all spectators. If prevention measures were well observed, we estimated that the number of new cases among Games accredited individuals would approach zero by the end of the Games. However, if transmission was not controlled our model indicated hundreds of Games accredited individuals would become infected and daily incidence in Tokyo would reach upwards of 4,000 cases. Had domestic spectators been allowed (at 50% venue capacity), we estimated that over 250 spectators might have arrived infected to Tokyo venues, potentially generating more than 300 additional secondary infections while in Tokyo/at the Games. We also found the number of cases with infection directly attributable to hypothetical exposure during the Games was highly sensitive to the local epidemic dynamics. Therefore, reducing and maintaining transmission levels below epidemic levels using public health measures would be necessary to prevent cross-group transmission.

Predicting the effective reproduction number of COVID-19: inference using human mobility, temperature, and risk awareness.

OBJECTIVES: The effective reproduction number (Rt) has been critical for assessing the effectiveness of countermeasures during the coronavirus disease 2019 (COVID-19) pandemic. Conventional methods using reported incidences are unable to provide timely Rt data due to the delay from infection to reporting. Our study aimed to develop a framework for predicting Rt in real time, using timely accessible data - i.e. human mobility, temperature, and risk awareness. METHODS: A linear regression model to predict Rt was designed and embedded in the renewal process. Four prefectures of Japan with high incidences in the first wave were selected for model fitting and validation. Predictive performance was assessed by comparing the observed and predicted incidences using cross-validation, and by testing on a separate dataset in two other prefectures with distinct geographical settings from the four studied prefectures. RESULTS: The predicted mean values of Rt and 95% uncertainty intervals followed the overall trends for incidence, while predictive performance was diminished when Rt changed abruptly, potentially due to superspreading events or when stringent countermeasures were implemented. CONCLUSIONS: The described model can potentially be used for monitoring the transmission dynamics of COVID-19 ahead of the formal estimates, subject to delay, providing essential information for timely planning and assessment of countermeasures.

A hospital-related outbreak of SARS-CoV-2 associated with variant Epsilon (B.1.429) in Taiwan: transmission potential and outbreak containment under intensified contact tracing, January-February 2021.

OBJECTIVES: A hospital-related cluster of 22 cases of coronavirus disease 2019 (COVID-19) occurred in Taiwan in January-February 2021. Rigorous control measures were introduced and could only be relaxed once the outbreak was declared over. Each day after the apparent outbreak end, we estimated the risk of future cases occurring in order to inform decision-making. METHODS: Probabilistic transmission networks were reconstructed, and transmission parameters (the reproduction number R and overdispersion parameter k) were estimated. The reporting delay during the outbreak was estimated (Scenario 1). In addition, a counterfactual scenario with less effective interventions characterized by a longer reporting delay was considered (Scenario 2). Each day, the risk of future cases was estimated under both scenarios. RESULTS: The values of R and k were estimated to be 1.30 ((95% credible interval (CI) 0.57-3.80) and 0.38 (95% CI 0.12-1.20), respectively. The mean reporting delays considered were 2.5 days (Scenario 1) and 7.8 days (Scenario 2). Following the final case, ttthe inferred probability of future cases occurring declined more quickly in Scenario 1 than Scenario 2. CONCLUSIONS: Rigorous control measures allowed the outbreak to be declared over quickly following outbreak containment. This highlights the need for effective interventions, not only to reduce cases during outbreaks but also to allow outbreaks to be declared over with confidence.

Estimation of the Actual Incidence of Coronavirus Disease (COVID-19) in Emergent Hotspots: The Example of Hokkaido, Japan during February-March 2020.

Following the first report of the coronavirus disease 2019 (COVID-19) in Sapporo city, Hokkaido Prefecture, Japan, on 14 February 2020, a surge of cases was observed in Hokkaido during February and March. As of 6 March, 90 cases were diagnosed in Hokkaido. Unfortunately, many infected persons may not have been recognized due to having mild or no symptoms during the initial months of the outbreak. We therefore aimed to predict the actual number of COVID-19 cases in (i) Hokkaido Prefecture and (ii) Sapporo city using data on cases diagnosed outside these areas. Two statistical frameworks involving a balance equation and an extrapolated linear regression model with a negative binomial link were used for deriving both estimates, respectively. The estimated cumulative incidence in Hokkaido as of 27 February was 2,297 cases (95% confidence interval (CI): 382-7091) based on data on travelers outbound from Hokkaido. The cumulative incidence in Sapporo city as of 28 February was estimated at 2233 cases (95% CI: 0-4893) based on the count of confirmed cases within Hokkaido. Both approaches resulted in similar estimates, indicating a higher incidence of infections in Hokkaido than were detected by the surveillance system. This quantification of the gap between detected and estimated cases helped to inform the public health response at the beginning of the pandemic and provided insight into the possible scope of undetected transmission for future assessments.

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.