Comparative Effectiveness of Baloxavir Marboxil and Oseltamivir Treatment in Reducing Household Transmission of Influenza: A Post Hoc Analysis of the BLOCKSTONE Trial.

BACKGROUND: The transmission of influenza virus in households, especially by children, is a major route of infection. Prior studies suggest that timely antiviral treatment of ill cases may reduce infection in household contacts. The aim of the study was to compare the effects of oseltamivir (OTV) and baloxavir marboxil (BXM) treatment of index cases on the secondary attack rate (SAR) of influenza within household. METHODS: A post hoc analysis was done in BLOCKSTONE trial-a placebo-controlled, double-blinded post-exposure prophylaxis of BXM. Data were derived from the laboratory-confirmed index cases' household contacts who received placebo in the trial and also from household members who did not participate in the trial but completed illness questionnaires. To assess the SAR of household members, multivariate analyses adjusted for factors including age, vaccination status, and household size were performed and compared between contacts of index cases treated with BXM or OTV. RESULTS: In total, 185 index cases (116 treated with BXM and 69 treated with OTV) and 410 household contacts (201 from trial, 209 by questionnaire) were included. The Poisson regression modeling showed that the SAR in household contacts of index cases treated with BXM and OTV was 10.8% and 18.5%, respectively; the adjusted relative reduction in SAR was 41.8% (95% confidence interval: 1.0%-65.7%, p = 0.0456) greater with BXM than OTV. Similar reductions were found in contacts from the trial and those included by questionnaire. CONCLUSION: BXM treatment of index cases appeared to result in a greater reduction in secondary household transmission than OTV treatment.

COVID-19 risk assessment at the opening ceremony of the Tokyo 2020 Olympic Games.

The 2020 Olympic/Paralympic Games have been postponed to 2021, due to the COVID-19 pandemic. We developed a model that integrated source-environment-receptor pathways to evaluate how preventive efforts can reduce the infection risk among spectators at the opening ceremony of Tokyo Olympic Games. We simulated viral loads of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emitted from infectors through talking/coughing/sneezing and modeled temporal environmental behaviors, including virus inactivation and transfer. We performed Monte Carlo simulations to estimate the expected number of newly infected individuals with and without preventive measures, yielding the crude probability of a spectator being an infector among the 60,000 people expected to attend the opening ceremony. Two indicators, i.e., the expected number of newly infected individuals and the newly infected individuals per infector entry, were proposed to demonstrate the extent of achievable infection risk reduction levels by implementing possible preventive measures. A no-prevention scenario produced 1.5-1.7 newly infected individuals per infector entry, whereas a combination of cooperative preventive measures by organizers and the spectators achieved a 99% risk reduction, corresponding to 0.009-0.012 newly infected individuals per infector entry. The expected number of newly infected individuals was calculated as 0.005 for the combination of cooperative preventive scenarios with the crude probability of a spectator being an infector of 1 × 10-5. Based on our estimates, a combination of cooperative preventions between organizers and spectators is required to prevent a viral spread at the Tokyo Olympic/Paralympic Games. Further, under the assumption that society accepts < 10 newly infected persons traced to events held during the entire Olympic/Paralympic Games, we propose a crude probability of infectors of < 5 × 10-5 as a benchmark for the suppression of the infection. This is the first study to develop a model that can assess the infection risk among spectators due to exposure pathways at a mass gathering event.

Reconstructing the household transmission of influenza in the suburbs of Tokyo based on clinical cases.

BACKGROUND: Influenza is a public health issue that needs to be addressed strategically. The assessment of detailed infectious profiles is an important part of this effort. Household transmission data play a key role in estimating such profiles. We used diagnostic and questionnaire-based data on influenza patients at a Japanese clinic to estimate the detailed infectious period (as well as incubation period, symptomatic and infectious periods, and extended infectious period after recovery) and the secondary attack ratio (SAR) of influenza for households of various sizes based on a modified Cauchemez-type model. RESULTS: The data were from enrolled patients with confirmed influenza who were treated at the Hirotsu Clinic (Kawasaki, Japan) with a neuraminidase inhibitor (NAI) during six northern hemisphere influenza seasons between 2010 and 2016. A total of 2342 outpatients, representing 1807 households, were included. For influenza type A, the average incubation period was 1.43 days (95% probability interval, 0.03-5.32 days). The estimated average symptomatic and infective period was 1.76 days (0.33-4.62 days); the extended infective period after recovery was 0.25 days. The estimated SAR rose from 20 to 32% as household size increased from 3 to 5. For influenza type B, the average incubation period, average symptomatic and infective period, and extended infective period were estimated as 1.66 days (0.21-4.61), 2.62 days (0.54-5.75) and 1.00 days, respectively. The SAR increased from 12 to 21% as household size increased from 3 to 5. CONCLUSION: All estimated periods of influenza type B were longer than the corresponding periods for type A. However, the SAR for type B was less than that for type A. These results may reflect Japanese demographics and treatment policy. Understanding the infectious profiles of influenza is necessary for assessing public health measures.

Reconstructing the transmission dynamics of rubella in Japan, 2012-2013.

BACKGROUND: Japan experienced a nationwide rubella epidemic from 2012 to 2013, mostly in urban prefectures with large population sizes. The present study aimed to capture the spatiotemporal patterns of rubella using a parsimonious metapopulation epidemic model and examine the potential usefulness of spatial vaccination. METHODOLOGY/PRINCIPAL FINDINGS: A metapopulation epidemic model in discrete time and space was devised and applied to rubella notification data from 2012 to 2013. Employing a piecewise constant model for the linear growth rate in six different time periods, and using the particle Markov chain Monte Carlo method, the effective reproduction numbers were estimated at 1.37 (95% CrI: 1.12, 1.77) and 1.37 (95% CrI: 1.24, 1.48) in Tokyo and Osaka groups, respectively, during the growing phase of the epidemic in 2013. The rubella epidemic in 2012 involved substantial uncertainties in its parameter estimates and forecasts. We examined multiple scenarios of spatial vaccination with coverages of 1%, 3% and 5% for all of Japan to be distributed in different combinations of prefectures. Scenarios indicated that vaccinating the top six populous urban prefectures (i.e., Tokyo, Kanagawa, Osaka, Aichi, Saitama and Chiba) could potentially be more effective than random allocation. However, greater uncertainty was introduced by stochasticity and initial conditions such as the number of infectious individuals and the fraction of susceptibles. CONCLUSIONS: While the forecast in 2012 was accompanied by broad uncertainties, a narrower uncertainty bound of parameters and reliable forecast were achieved during the greater rubella epidemic in 2013. By better capturing the underlying epidemic dynamics, spatial vaccination could substantially outperform the random vaccination.

Assessing the Effectiveness and Cost-Benefit of Test-and-Vaccinate Policy for Supplementary Vaccination against Rubella with Limited Doses.

Elevating herd immunity level against rubella is essential to prevent congenital rubella syndrome (CRS). Insufficient vaccination coverage left susceptible pockets among adults in Japan, and the outbreak of rubella from 2012 to 2013 resulted in 45 observed CRS cases. Given a limited stock of rubella-containing vaccine (RCV) available, the Japanese government recommended healthcare providers to prioritize vaccination to those confirmed with low level of immunity, or to those likely to transmit to pregnant women. Although a test-and-vaccinate policy could potentially help reduce the use of the limited stockpile of vaccines, by selectively elevating herd immunity, the cost of serological testing is generally high and comparable to the vaccine itself. Here, we aimed to examine whether random vaccination would be more cost-beneficial than the test-and-vaccinate strategy. A mathematical model was employed to evaluate the vaccination policy implemented in 2012-2013, quantifying the benefit-to-cost ratio to achieve herd immunity. The modelling exercise demonstrated that, while the test-and-vaccinate strategy can efficiently achieve herd immunity when stockpiles of RCV are limited, random vaccination would be a more cost-beneficial strategy. As long as the herd immunity acts as the goal of vaccination, our findings apply to future supplementary immunization strategy.

Cost-effective length and timing of school closure during an influenza pandemic depend on the severity.

BACKGROUND: There has been a variation in published opinions toward the effectiveness of school closure which is implemented reactively when substantial influenza transmissions are seen at schools. Parameterizing an age-structured epidemic model using published estimates of the pandemic H1N1-2009 and accounting for the cost effectiveness, we examined if the timing and length of school closure could be optimized. METHODS: Age-structured renewal equation was employed to describe the epidemic dynamics of an influenza pandemic. School closure was assumed to take place only once during the course of the pandemic, abruptly reducing child-to-child transmission for a fixed length of time and also influencing the transmission between children and adults. Public health effectiveness was measured by reduction in the cumulative incidence, and cost effectiveness was also examined by calculating the incremental cost effectiveness ratio and adopting a threshold of 1.0 × 107 Japanese Yen/life-year. RESULTS: School closure at the epidemic peak appeared to yield the largest reduction in the final size, while the time of epidemic peak was shown to depend on the transmissibility. As the length of school closure was extended, we observed larger reduction in the cumulative incidence. Nevertheless, the cost effectiveness analysis showed that the cost of our school closure scenario with the parameters derived from H1N1-2009 was not justifiable. If the risk of death is three times or greater than that of H1N1-2009, the school closure could be regarded as cost effective. CONCLUSIONS: There is no fixed timing and duration of school closure that can be recommended as universal guideline for different types of influenza viruses. The effectiveness of school closure depends on the transmission dynamics of a particular influenza virus strain, especially the virulence (i.e. the infection fatality risk).

Extension and verification of the SEIR model on the 2009 influenza A (H1N1) pandemic in Japan.

In order to understand the evolution of the 2009 influenza A (H1N1) pandemic within local regions of Japan, we studied the significance of regional migration between these regions. For this purpose, we have employed an extended SEIR model to describe the immigration of infected people and the stochastic variation of the infectious efficiency. We then applied a data assimilation technique in order to study how the agreement of the simulation results with the observed data depends on the presence/absence of immigration and the degree of variation of the infectious efficiency. Reproducibility is evaluated by log-likelihood values. The log-likelihood does not indicate the significance of immigration. Although there are multiple waves in the time course of the number of reported infected individuals, these waves could be explained by the stochastic nature of infectious events.

Enhancement of collective immunity in Tokyo metropolitan area by selective vaccination against an emerging influenza pandemic.

Vaccination is a preventive measure against influenza that does not require placing restrictions on social activities. However, since the stockpile of vaccine that can be prepared before the arrival of an emerging pandemic strain is generally quite limited, one has to select priority target groups to which the first stockpile is distributed. In this paper, we study a simulation-based priority target selection method with the goal of enhancing the collective immunity of the whole population. To model the region in which the disease spreads, we consider an urban area composed of suburbs and central areas connected by a single commuter train line. Human activity is modelled following an agent-based approach. The degree to which collective immunity is enhanced is judged by the attack rate in unvaccinated people. The simulation results show that if students and office workers are given exclusive priority in the first three months, the attack rate can be reduced from [Formula: see text] in the baseline case down to 1-2%. In contrast, random vaccination only slightly reduces the attack rate. It should be noted that giving preference to active social groups does not mean sacrificing those at high risk, which corresponds to the elderly in our simulation model. Compared with the random administration of vaccine to all social groups, this design successfully reduces the attack rate across all age groups.

Bayesian experts in exploring reaction kinetics of transcription circuits.

MOTIVATION: Biochemical reactions in cells are made of several types of biological circuits. In current systems biology, making differential equation (DE) models simulatable in silico has been an appealing, general approach to uncover a complex world of biochemical reaction dynamics. Despite of a need for simulation-aided studies, our research field has yet provided no clear answers: how to specify kinetic values in models that are difficult to measure from experimental/theoretical analyses on biochemical kinetics. RESULTS: We present a novel non-parametric Bayesian approach to this problem. The key idea lies in the development of a Dirichlet process (DP) prior distribution, called Bayesian experts, which reflects substantive knowledge on reaction mechanisms inherent in given models and experimentally observable kinetic evidences to the subsequent parameter search. The DP prior identifies significant local regions of unknown parameter space before proceeding to the posterior analyses. This article reports that a Bayesian expert-inducing stochastic search can effectively explore unknown parameters of in silico transcription circuits such that solutions of DEs reproduce transcriptomic time course profiles. AVAILABILITY: A sample source code is available at the URL http://daweb.ism.ac.jp/~yoshidar/lisdas/.