Impact of meteorological and demographic factors on the influenza epidemic in Japan: a large observational database study.

Factors affecting the start date of the influenza epidemic season and total number of infected persons per 1,000,000 population in 47 prefectures of Japan were evaluated. This retrospective observational study (September 2014-August 2019; N = 472,740-883,804) evaluated data from a Japanese health insurance claims database. Single and multiple regression analyses evaluated the time to start of the epidemic or total infected persons per 1,000,000 population with time to absolute humidity (AH) or number of days with AH (≤ 5.5, ≤ 6.0, ≤ 6.5, and ≤ 7.0), total visitors (first epidemic month or per day), and total population. For the 2014/15, 2015/16, and 2016/17 seasons, a weak-to-moderate positive correlation (R2: 0.042-0.417) was observed between time to start of the epidemic and time to first day with AH below the cutoff values. Except in the 2016/17 season (R2: 0.089), a moderate correlation was reported between time to start of the epidemic and the total population (R2: 0.212-0.401). For all seasons, multiple regression analysis showed negative R2 for time to start of the epidemic and total visitors and population density (positive for time to AH ≤ 7.0). The earlier the climate becomes suitable for virus transmission and the higher the human mobility (more visitors and higher population density), the earlier the epidemic season tends to begin.

Determination of optimal prevention strategy for COVID-19 based on multi-agent simulation.

This study proposes a direction for the utilization of multi-agent simulation (MAS) to consider an optimal prevention strategy for the spread of the coronavirus disease of 2019 (COVID-19) through a pandemic modeling example in Japan. MAS can flexibly express macroscopic phenomena formed through the interaction of micro-agents modeled to act autonomously. The use of MAS can provide a variety of recommendations for bringing a pandemic under control, even in the case of the COVID-19 pandemic, which has become more intense as of 2021. However, models that do not consider individual heterogeneity, such as analytical Susceptible-Exposed-Infectious-Recovered (SEIR) models, are often used as predictive models for infectious diseases and the main reference for decision-making. In this study, we show that by constructing a MAS that simulates a metropolitan city in Japan in a simple manner while considering the heterogeneity of age and other background information, we can capture the effects of various measures such as vaccinations on the spread of infections in a more realistic setting. Moreover, it is possible to offer various recommendations for optimal strategies to suppress a pandemic by combining reinforcement learning with MAS. This study explicates the potential of MAS in the development of strategies to prevent the spread of infection.

Comparison of Intra-Familial Transmission of Influenza Virus From Index Patients Treated With Baloxavir Marboxil or Oseltamivir Using an Influenza Transmission Model and a Health Insurance Claims Database.

BACKGROUND: Influenza affects approximately a billion people globally, including > 10 million Japanese individuals every year. Baloxavir marboxil (baloxavir [BXM]; a selective cap-dependent endonuclease inhibitor) is approved for influenza treatment in Japan. We compared the incidence of intra-familial transmission of influenza between BXM and oseltamivir (OTV) treatments using a simulation model. METHODS: Using the JMDC Claims Database, we identified index case (IC) as the first family member diagnosed with influenza during the 2018-19 influenza season, and classified the families into BXM or OTV group per the drug dispensed to ICs. Using a novel influenza intra-familial infection model, we simulated the duration of influenza infection in ICs based on agent-specific virus shedding periods. Intra-familial infections were defined as non-IC family members infected during the agent-specific viral shedding period in ICs. The virus incubation periods in the non-IC family members were considered to exclude secondary infections from potentially external exposure. The primary endpoint was proportion of families with intra-familial infections. For between-group comparisons, we used a multivariate logistic regression model. RESULTS: The median proportion of families with intra-familial transmission was 9.57% and 19.35% in the BXM (N = 84 672) and OTV (N = 62 004) groups, respectively. The multivariate odds ratio of 1.73 (2.5th-97.5th percentiles, 1.68-1.77) indicated a substantially higher incidence of intra-familial infections in the OTV group versus the BXM group. Subgroup analyses by ICs' age category, virus type, and month of onset revealed similar trends favoring BXM. CONCLUSIONS: BXM treatment of ICs may contribute to a greater reduction in intra-familial influenza transmission than OTV treatment.