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BackgroundPublic health and social measures (PHSM) against COVID-19 in Japan involve requesting the public to voluntarily reduce social contact; these measures are not legally binding. The effectiveness of such PHSM has been questioned with emergence of the SARS-CoV-2 Alpha variant (B.1.1.7), which exhibited elevated transmissibility.Materials and MethodsWe investigated the epidemic dynamics during the fourth epidemic wave in Japan from March to June 2021 involving pre-emergency measures and declaration of a state of emergency (SoE). We estimated the effective reproduction number (Rt) before and after these interventions, and then analyzed the relationship between lower Rt values and each PHSM.ResultsWith implementation of pre-emergency measures (PEM) in 16 prefectures, the Rt was estimated to be < 1 in six prefectures; its average relative reduction ranged from 2 to 19%. During the SoE, 8 of 10 prefectures had an estimated Rt < 1, and the average relative reduction was 26%–39%. No single intervention was identified that uniquely resulted in an Rt value < 1.ConclusionAn SoE can substantially reduce the Rt and may be required to curb a surge in cases caused by future SARS-CoV-2 variants of concern with elevated transmissibility. More customized interventions did not reduce the Rt value to < 1 in this study, but that may be partly attributable to the greater transmissibility of the Alpha variant.

Background Predictive scenarios of heatstroke over the long-term future have yet to be formulated. The purpose of the present study was to generate baseline scenarios of heat-related ambulance transportations using climate change scenario datasets in Tokyo, Japan. Methods Data on the number of heat-related ambulance transportations in Tokyo from 2015 to 2019 were examined, and the relationship between the risk of heat-related ambulance transportations and the daily maximum wet-bulb globe temperature (WBGT) was modeled using three simple dose–response models. To quantify the risk of heatstroke, future climatological variables were then retrieved to compute the WBGT up to the year 2100 from climate change scenarios (i.e., RCP2.6, RCP4.5, and RCP8.5) using two scenario models. The predicted risk of heat-related ambulance transportations was embedded onto the future age-specific projected population. Results The proportion of the number of days with a WBGT above 28°C is predicted to increase every five years by 0.16% for RCP2.6, 0.31% for RCP4.5, and 0.68% for RCP8.5. In 2100, compared with 2000, the number of heat-related ambulance transportations is predicted to be more than three times greater among people aged 0–64 years and six times greater among people aged 65 years or older. The variance of the heatstroke risk becomes greater as the WBGT increases. Conclusions The increased risk of heatstroke for the long-term future was demonstrated using a simple statistical approach. Even with the RCP2.6 scenario, with the mildest impact of global warming, the risk of heatstroke is expected to increase. The future course of heatstroke predicted by our approach acts as a baseline for future studies.

BackgroundIn metropolitan Tokyo in 2014, Japan experienced its first domestic dengue outbreak since 1945. The objective of the present study was to quantitatively assess the future risk of dengue in Japan using climate change scenarios in a high-resolution geospatial environment by building on a solid theory as a baseline in consideration of future adaptation strategies.MethodsUsing climate change scenarios of the Model for Interdisciplinary Research on Climate version 6 (MIROC6), representative concentration pathway (RCP) 2.6, 4.5, and 8.5, we computed the daily average temperature and embedded this in the effective reproduction number of dengue, R(T), to calculate the extinction probability and interepidemic period across Japan.ResultsIn June and October, the R(T) with daily average temperature T, was <1 as in 2022; however, an elevation in temperature increased the number of days with R(T) >1 during these months under RCP8.5. The time period with a risk of dengue transmission gradually extended to late spring (April–May) and autumn (October–November). Under the RCP8.5 scenario in 2100, the possibility of no dengue-free months was revealed in part of southernmost Okinawa Prefecture, and the epidemic risk extended to the entire part of northernmost Hokkaido Prefecture.ConclusionEach locality in Japan must formulate action plans in response to the presented scenarios. Our geographic analysis can help local governments to develop adaptation policies that include mosquito breeding site elimination, distribution of adulticides and larvicides, and elevated situation awareness to prevent transmission via bites from Aedes vectors.

Background: The quantitative measurement of the anticipated number of disaster deaths is very important shortly after the mainshock because the forecasted fatalities could help determine the size of the health and medical services team to be deployed. This study aimed to devise a simple method to predict the cumulative number of deaths during the immediate or early stage of a large earthquake. Methods:We analyzed six earthquakes in Japan that involved at least 20 deaths, 1990-2018. Analyzing statistical patterns in the cumulative number of deaths, we used three models-the exponential model, the Weibull model, and the percentile-based model-to predict the likely number of deaths during the early stage of earthquakes. Results:The median time required to reach the median number of deaths was 2.2 (interquartile range: 1.5, 3.8) days from the mainshock. By only multiplying the cumulative number of deaths as on day 2 by a factor of two, the likely number of deaths was calculated using the percentile-based method. The validity of this simple method was better than the results from day 4 using the parametric models. The Great East Japan earthquake was exceptionally large and difficult to predict in real time, and it involved a large number of fatalities following a tsunami.Conclusions: For all other earthquakes, the median number of deaths was reached on day 2. Even in a setting with poor technical resources, the predicted number of deaths can be obtained by multiplying the reported cumulative number on day 2 by a factor of two.