ABSTRACT
Background Hong Kong, has operated under a zero-Covid policy in the past few years. As a result, population immunity from natural infections has been low. The 'fifth wave' in Hong Kong, caused by the Omicron variant, grew substantially in February 2022 during the transition from winter into spring. The daily number of reported cases began to decline quickly in a few days after social distancing regulations were tightened and rapid antigen test (RAT) kits were largely distributed. How the non-pharmaceutical interventions (NPIs) and seasonal factors (temperature and relative humidity) could affect the spread of Omicron remains unknown. Methods We developed a model with stratified immunity, to incorporate antibody responses, together with changes in mobility and seasonal factors. After taking into account the detection rates of PCR test and RAT, we fitted the model to the daily number of reported cases between 1 February and 31 March, and quantified the associated effects of individual NPIs and seasonal factors on infection dynamics. Findings Although NPIs and vaccine boosters were critical in reducing the number of infections, temperature was associated with a larger change in transmissibility. Cold days appeared to drive Re from about 2-3 sharply to 10.6 (95%CI: 9.9-11.4). But this number reduced quickly below one a week later when the temperature got warmer. The model projected that if weather in March maintained as February's average level, the estimated cumulative incidence could increase double to about 80% of total population. Interpretation Temperature should be taken into account when making public health decisions (e.g. a more relaxed (or tightened) social distancing during a warmer (or colder) season).
ABSTRACT
BackgroundAlthough associations between key weather indicators (i.e. temperature and humidity) and COVID-19 mortality has been reported, the relationship between these exposures among different timing in early infection stages (from virus exposure up to a few days after symptom onset) and the probability of death after infection (also called case fatality rate, CFR) has yet to be determined. MethodsWe estimated the instantaneous CFR of eight European countries using Bayesian inference in conjunction with stochastic transmission models, taking account of delays in reporting the number of newly confirmed cases and deaths. The exposure-lag-response associations between fatality rate and weather conditions to which patients were exposed at different timing were obtained using distributed lag nonlinear models coupled with mixed-effect models. ResultsOur results showed that the Odds Ratio (OR) of death is negatively associated with the temperature, with two maxima (OR=1.29 (95% CI: 1.23, 1.35) at -0.1{degrees}C; OR=1.12 (95% CI: 1.08, 1.16) at 0.1{degrees}C) occurred at the time of virus exposure and after symptom onset. Two minima (OR=0.81 (95% CI: 0.71, 0.92) at 23.2{degrees}C; OR=0.71 (95% CI: 0.63, 0.80) at 21.7{degrees}C) also occurred at these two distinct periods correspondingly. Low humidity (below 50%) during the early stages and high humidity (around 89%) after symptom onset were related to the lower fatality. ConclusionEnvironmental conditions may affect not only the initial viral load when exposure to viruses but also individuals immunity response around symptom onset. Warmer temperatures and higher humidity after symptom onset were related to the lower fatality. Key MessagesO_LIThe temperature and humidity conditions that patients were exposed to during early infection stages were associated with COVID-19 case fatality rate. C_LIO_LIWarmer temperatures (above 20{degrees}C) at infection time and after symptom onset, but not during the incubation period, were associated with the lower fatality. Low humidity (below 50%) during the early stages and high humidity (around 89%) after symptom onset were related to the lower fatality. C_LIO_LICreating an optimal indoor condition especially for those early-stage cases who are under quarantine or home-isolation would likely help to reduce the potential severity or death of infection. C_LI
Subject(s)
COVID-19 , DeathABSTRACT
Background: Although by late February 2020 the COVID-19 epidemic was effectively controlled in Wuhan, China, the virus has since spread around the world and been declared a pandemic on March 11. Estimating the effects of interventions, such as transportation restrictions and quarantine measures, on the early COVID-19 transmission dynamics in Wuhan is critical for guiding future virus containment strategies. Since the exact number of COVID-19 infected cases is unknown, the number of documented cases was used by many disease transmission models to infer epidemiological parameters. However, this means that it would not be possible to adequately estimate epidemiological parameters and the effects of intervention measures, because the percentage of all infected cases that were documented changed during the first 2 months of the epidemic as a consequence of a gradually increasing diagnostic capability. Methods: To overcome the limitations, we constructed a stochastic susceptible-exposed-infected-quarantined-recovered (SEIQR) model, accounting for intervention measures and temporal changes in the proportion of new documented infections out of total new infections, to characterize the transmission dynamics of COVID-19 in Wuhan across different stages of the outbreak. Pre-symptomatic transmission was taken into account in our model, and all epidemiological parameters were estimated using Particle Markov-chain Monte Carlo (PMCMC) method. Results: Our model captured the local Wuhan epidemic pattern as a two-peak transmission dynamics, with one peak on February 4 and the other on February 12, 2020. The impact of intervention measures determined the timing of the first peak, leading to an 86% drop in the R_e from 3.23 (95% CI, 2.22 to 4.20) to 0.45 (95% CI, 0.20 to 0.69). An improved diagnostic capability led to the second peak and a higher proportion of documented infections. Our estimated proportion of new documented infections out of the total new infections increased from 11% (95% CI 1% - 43%) to 28% (95% CI 4% - 62%) after January 26 when more detection kits were released. After the introduction of a new diagnostic criterion (case definition) on February 12, a higher proportion of daily infected cases were documented (49% (95% CI 7% - 79%)).