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Background: While many investigations examined the association between environmental covariates and COVID-19 incidence, none have examined their relationship with superspreading, a characteristic describing very few individuals disproportionally infecting a large number of people. Methods: Contact tracing data of all the laboratory-confirmed COVID-19 cases in Hong Kong from February 16, 2020 to April 30, 2021 were used to form the infection clusters for estimating the time-varying dispersion parameter (kt), a measure of superspreading potential. Generalized additive models with identity link function were used to examine the association between negative-log kt (larger means higher superspreading potential) and the environmental covariates, adjusted with mobility metrics that account for the effect of social distancing measures. Results: A total of 6,645 clusters covering 11,717 cases were reported over the study period. After centering at the median temperature, a lower ambient temperature at 10th percentile (18.2 °C) was significantly associated with a lower estimate of negative-log kt (adjusted expected change: −0.239 [95 % CI: −0.431 to −0.048]). While a U-shaped relationship between relative humidity and negative-log kt was observed, an inverted U-shaped relationship with actual vapour pressure was found. A higher total rainfall was significantly associated with lower estimates of negative-log kt. Conclusions: This study demonstrated a link between meteorological factors and the superspreading potential of COVID-19. We speculated that cold weather and rainy days reduced the social activities of individuals minimizing the interaction with others and the risk of spreading the diseases in high-risk facilities or large clusters, while the extremities of relative humidity may favor the stability and survival of the SARS-CoV-2 virus.

The rapid spread of dengue fever (DF) infection has posed severe threats to global health. Environmental factors, such as weather conditions, are believed to regulate DF spread. While previous research reported inconsistent change of DF risk with varying weather conditions, few of them evaluated the impact of extreme weather conditions on DF infection risk. This study aims to examine the short-term associations between extreme temperatures, extreme rainfall, and DF infection risk in South and Southeast Asia. A total of 35 locations in Singapore, Malaysia, Sri Lanka, and Thailand were included, and weekly DF data, as well as the daily meteorological data from 2012 to 2020 were collected. A two-stage meta-analysis was used to estimate the overall effect of extreme weather conditions on the DF infection risk. Location-specific associations were obtained by the distributed lag nonlinear models. The DF infection risk appeared to increase within 1–3 weeks after extremely high temperature (e.g. lag week 2: RR = 1.074, 95 % CI: 1.022–1.129, p = 0.005). Compared with no rainfall, extreme rainfall was associated with a declined DF risk (RR = 0.748, 95 % CI: 0.620–0.903, p = 0.003), and most of the impact was across 0–3 weeks lag. In addition, the DF risk was found to be associated with more intensive extreme weathers (e.g. seven extreme rainfall days per week: RR = 0.338, 95 % CI: 0.120–0.947, p = 0.039). This study provides more evidence in support of the impact of extreme weather conditions on DF infection and suggests better preparation of DF control measures according to climate change.

Despite high incidence of acute kidney injury (AKI) among patients hospitalised for influenza, no previous work has attempted to analyse and quantify the association between the two. Herein, we made use of Hong Kong's surveillance data to evaluate the time-varying relationship between seasonal influenza and risk of AKI with adjustment for potential environmental covariates. Generalized additive model was used in conjunction with distributed-lag non-linear model to estimate the association of interest with daily AKI admissions as outcome and daily influenza admissions as predictor, while controlling for environmental variables (i.e. temperature, relative humidity, total rainfall, nitrogen dioxide, and ozone). Results suggested a positive association between risk of AKI admission and number of influenza hospitalisation cases, with relative risk reaching 1.12 (95% confidence interval, 1.10–1.15) at the 95th percentile. Using median as reference, an almost U-shaped association between risk of AKI admission and temperature was observed; the risk increased significantly when the temperature was low. While ozone was not shown to be a risk factor for AKI, moderate-to-high levels of nitrogen dioxide (50–95th percentile) were significantly associated with increased risk of AKI admission. This study mentioned the possibility that AKI hospitalisations are subject to environmental influences and offered support for a positive association between seasonal influenza and AKI occurrence in Hong Kong. Authorities are urged to extend the influenza vaccination program to individuals with pre-existing renal conditions to safeguard the health of the vulnerable. Given that adverse health effects are evident at current ambient levels of nitrogen dioxide, the government is recommended to adopt clean-air policies at the earliest opportunity to protect the health of the community.