Respiratory Syncytial Virus Outbreaks Are Predicted after the COVID-19 Pandemic in Tokyo, Japan.

Nonpharmaceutical interventions (NPIs) for COVID-19 can affect the current and future dynamics of respiratory syncytial virus infections (RSV). In Tokyo, RSV activity declined by 97.9% (95% CI: 94.8%-99.2%) during NPIs. A long period of NPIs could increase susceptible populations, thus enhancing the potential for large RSV outbreaks after the end of NPIs.

COVID-19 pandemic modifies temperature and heat-related illness ambulance transport association in Japan: a nationwide observational study.

BACKGROUND: During the COVID-19 pandemic, several illnesses were reduced. In Japan, heat-related illnesses were reduced by 22% compared to pre-pandemic period. However, it is uncertain as to what has led to this reduction. Here, we model the association of maximum temperature and heat-related illnesses in the 47 Japanese prefectures. We specifically examined how the exposure and lag associations varied before and during the pandemic. METHODS: We obtained the summer-specific, daily heat-related illness ambulance transport (HIAT), exposure variable (maximum temperature) and covariate data from relevant data sources. We utilized a stratified (pre-pandemic and pandemic), two-stage approach. In each stratified group, we estimated the 1) prefecture-level association using a quasi-Poisson regression coupled with a distributed lag non-linear model, which was 2) pooled using a random-effects meta-analysis. The difference between pooled pre-pandemic and pandemic associations was examined across the exposure and the lag dimensions. RESULTS: A total of 321,655 HIAT cases was recorded in Japan from 2016 to 2020. We found an overall reduction of heat-related risks for HIAT during the pandemic, with a wide range of reduction (10.85 to 57.47%) in the HIAT risk, across exposure levels ranging from 21.69 °C to 36.31 °C. On the contrary, we found an increment in the delayed heat-related risks during the pandemic at Lag 2 (16.33%; 95% CI: 1.00, 33.98%). CONCLUSION: This study provides evidence of the impact of COVID-19, particularly on the possible roles of physical interventions and behavioral changes, in modifying the temperature-health association. These findings would have implications on subsequent policies or heat-related warning strategies in light of ongoing or future pandemics.

Immediate and Delayed Meteorological Effects on COVID-19 Time-Varying Infectiousness in Tropical Cities

The novel coronavirus, which was first reported in Wuhan, China in December 2019, has been spreading globally at an unprecedented rate, leading to the virus being declared a global pandemic by the WHO on 12 March 2020. The clinical disease, COVID-19, associated with the pandemic is caused by the pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Aside from the inherent transmission dynamics, environmental factors were found to be associated with COVID-19. However, most of the evidence documenting the association was from temperate locations. In this study, we examined the association between meteorological factors and the time-varying infectiousness of COVID-19 in the Philippines. We obtained the daily time series from 3 April 2020 to 2 September 2020 of COVID-19 confirmed cases from three major cities in the Philippines, namely Manila, Quezon, and Cebu. Same period city-specific daily average temperature (degrees Celsius;°C), dew point (degrees Celsius;°C), relative humidity (percent;%), air pressure (kilopascal;kPa), windspeed (meters per second;m/s) and visibility (kilometer;km) data were obtained from the National Oceanic and Atmospheric Administration—National Climatic Data Center. City-specific COVID-19-related detection and intervention measures such as reverse transcriptase polymerase chain reaction (RT-PCR) testing and community quarantine measures were extracted from online public resources. We estimated the time-varying reproduction number (Rt) using the serial interval information sourced from the literature. The estimated Rt was used as an outcome variable for model fitting via a generalized additive model, while adjusting for relevant covariates. Results indicated that a same-day and the prior week’s air pressure was positively associated with an increase in Rt by 2.59 (95% CI: 1.25 to 3.94) and 2.26 (95% CI: 1.02 to 3.50), respectively. Same-day RT-PCR was associated with an increase in Rt, while the imposition of community quarantine measures resulted in a decrease in Rt. Our findings suggest that air pressure plays a role in the infectiousness of COVID-19. The determination of the association of air pressure on infectiousness, aside from the testing frequency and community quarantine measures, may aide the current health systems in controlling the COVID-19 infectiousness by integrating such information into an early warning platform.

Can SARS-CoV-2 Global Seasonality be Determined After One Year of Pandemic?

Supplemental Digital Content is available in the text.

Guidelines for Modeling and Reporting Health Effects of Climate Change Mitigation Actions.

BACKGROUND: Modeling suggests that climate change mitigation actions can have substantial human health benefits that accrue quickly and locally. Documenting the benefits can help drive more ambitious and health-protective climate change mitigation actions; however, documenting the adverse health effects can help to avoid them. Estimating the health effects of mitigation (HEM) actions can help policy makers prioritize investments based not only on mitigation potential but also on expected health benefits. To date, however, the wide range of incompatible approaches taken to developing and reporting HEM estimates has limited their comparability and usefulness to policymakers. OBJECTIVE: The objective of this effort was to generate guidance for modeling studies on scoping, estimating, and reporting population health effects from climate change mitigation actions. METHODS: An expert panel of HEM researchers was recruited to participate in developing guidance for conducting HEM studies. The primary literature and a synthesis of HEM studies were provided to the panel. Panel members then participated in a modified Delphi exercise to identify areas of consensus regarding HEM estimation. Finally, the panel met to review and discuss consensus findings, resolve remaining differences, and generate guidance regarding conducting HEM studies. RESULTS: The panel generated a checklist of recommendations regarding stakeholder engagement: HEM modeling, including model structure, scope and scale, demographics, time horizons, counterfactuals, health response functions, and metrics; parameterization and reporting; approaches to uncertainty and sensitivity analysis; accounting for policy uptake; and discounting. DISCUSSION: This checklist provides guidance for conducting and reporting HEM estimates to make them more comparable and useful for policymakers. Harmonization of HEM estimates has the potential to lead to advances in and improved synthesis of policy-relevant research that can inform evidence-based decision making and practice. https://doi.org/10.1289/EHP6745.

Characteristics of COVID-19 epidemic and control measures to curb transmission in Malaysia.

The first wave of COVID-19 epidemic began in late January in Malaysia and ended with a very small final size. The second wave of infections broke out in late February and grew rapidly in the first 3 weeks. Authorities in the country responded quickly with a series of control strategies collectively known as the Movement Control Order (MCO) with different levels of intensity matching the progression of the epidemic. We examined the characteristics of the second wave and discussed the key control strategies implemented in the country. In the second wave, the epidemic doubled in size every 3.8 days (95% confidence interval [CI]: 3.3, 4.5) in the first month and decayed slowly after that with a halving time of approximately 3 weeks. The time-varying reproduction number Rt peaked at 3.1 (95% credible interval: 2.7, 3.5) in the 3rd week, declined sharply thereafter and stayed below 1 in the last 3 weeks of April, indicating low transmissibility approximately 3 weeks after the MCO. Experience of the country suggests that adaptive triggering of distancing policies combined with a population-wide movement control measure can be effective in suppressing transmission and preventing a rebound.