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BackgroundHong 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. MethodsWe 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. FindingsAlthough 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 Februarys average level, the estimated cumulative incidence could increase double to about 80% of total population. InterpretationTemperature 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).
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BackgroundDuring the COVID-19 outbreak in Taiwan between May 11 and June 20, 2021, the observed fatality rate (FR) was 5.3%, higher than the global average at 2.1%. The high number of reported deaths suggests that hospital capacity was insufficient. However, many unexplained deaths were subsequently identified as cases, indicating that there were a few undetected cases, hence resulting in a higher estimate of FR. Estimating the number of total infected cases or knowing how to reduce the undetected cases can allow an accurate estimation of the fatality rate (FR) and effective reproduction number (Rt). MethodsAfter adjusting for reporting delays, we estimated the number of undetected cases using reported deaths that were and were not previously detected. The daily FR and Rt were calculated using the number of total cases (i.e. including undetected cases). A logistic regression model was developed to predict the detection ratio among deaths using selected predictors from daily testing and tracing data. ResultsThe estimated true daily case number at the peak of the outbreak on May 22 was 897, which was 24.3% higher than the reported number, but the difference became less than 4% on June 9 and afterward. After taking account of undetected cases, our estimated mean FR (4.7%) was still high but the daily rate showed a large decrease from 6.5% on May 19 to 2.8% on June 6. Rt reached a maximum value of 6.4 on May 11, compared to 6.0 estimated using the reported case number. The decreasing proportion of undetected cases was associated with the increases in the ratio of the number of tests conducted to reported cases, and the proportion of cases that are contact-traced before symptom onset. ConclusionsIncreasing testing capacity and tracing efficiency can lead to a reduction of hidden cases and hence improvement in epidemiological parameter estimation.
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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 (approximately 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. HighlightsO_LITemperature and humidity conditions that patients were exposed to during their early infection stages were associated with COVID-19 case fatality rate. C_LIO_LIWarmer temperatures (> 20{degrees}C) at infection time or after symptom onset, but not during the incubation period, were associated with lower death risk. Low relative humidity (< 50%) during the early stages and high relative humidity (> 85%) after symptom onset were related to higher death risk. C_LIO_LICreating optimal indoor conditions for cases who are under quarantine/isolation may reduce their risk of death. C_LI
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With the COVID-19 vaccination widely implemented in most countries, propelled by the need to revive the tourism economy, there is a growing prospect for relieving the social distancing regulation and reopening borders in tourism-oriented countries and regions. The need incentivizes stakeholders to develop border control strategies that fully evaluate health risks if mandatory quarantines are lifted. In this study, we have employed a computational approach to investigate the contact tracing integrated policy in different border reopening scenarios in Hong Kong, China. Built on a modified SEIR epidemic model with a 30% vaccination coverage, the results suggest that scenarios with digital contact tracing and quick isolation intervention can reduce the infectious population by 92.11% compared to those without contact tracing. By further restricting the inbound population with a 10,000 daily quota and applying moderate-to-strong community non-pharmacological interventions (NPIs), the average daily confirmed cases in the forecast period of 60 days can be well controlled at around 9 per day (95% CI: 7-12). Two main policy recommendations are drawn from the study. First, digital contact tracing would be an effective countermeasure for reducing local virus spread, especially when it is applied along with a moderate level of vaccination coverage. Second, implementing a daily quota on inbound travelers and restrictive community NPIs would further keep the local infection under control. This study offers scientific evidence and prospective guidance for developing and instituting plans to lift mandatory border control policies in preparing for the global economic recovery.
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Sharp increases in COVID-19 cases occurred after reopening in the United States. We show that the post-intervention effective reproduction number is a strong predictor of the surge in late June. Lax interventions in the early stages coupled with elevated virus spread are primarily responsible for surges in most affected states.
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BackgroundThe rapid expansion of the current COVID-19 outbreak has caused a global pandemic but how quarantine-based measures can prevent or suppress an outbreak without other more intrusive interventions has not yet been determined. Hong Kong had a massive influx of travellers from mainland China, where the outbreak began, during the early expansion period coinciding with the Lunar New Year festival; however, the spread of the virus has been relatively limited even without imposing severe control measures, such as a full city lockdown. Understanding how quarantine measures in Hong Kong were effective in limiting community spread can provide us with valuable insights into how to suppress an outbreak. However, challenges exist in evaluating the effects of quarantine on COVID-19 transmission dynamics in Hong Kong due to the fact that the effects of border control have to be also taken into account. MethodsWe have developed a two-layered susceptible-exposed-infectious-quarantined-recovered (SEIQR) meta-population model which can estimate the effects of quarantine on virus transmissibility after stratifying infections into imported and subsequent community infections, in a region closely connected to the outbreaks source. We fitted the model to both imported and local confirmed case data with symptom onset from 18 January to 29 February 2020 in Hong Kong, together with daily transportation data and the transmission dynamics of COVID-19 from Wuhan and mainland China. After model fitting, epidemiological parameters and the timing of the start of quarantine for infected cases were estimated. ResultsThe model estimated that the reproduction number of COVID-19 in Hong Kong was 0.76 (95% CI, 0.66 to 0.86), achieved through quarantining infected cases -0.57 days (95% CI, -4.21 - 3.88) relative to symptom onset, with an estimated incubation time of 5.43 days (95% CI, 1.30 - 9.47). However, if delaying the quarantine start by more than 1.43 days, the reproduction number would be greater than one, making community spread more likely. The model also determined the timing of the start of quarantine necessary in order to suppress an outbreak in the presence of population immunity. ConclusionThe results suggest that the early quarantine for infected cases before symptom onset is a key factor to prevent COVID-19 outbreak.
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The rapid expansion of COVID-19 has caused a global pandemic. Although quarantine measures have been used widely, the critical steps among them to suppress the outbreak without a huge social-economic loss remain unknown. Hong Kong, unlike other regions in the world, had a massive number of travellers from Mainland China during the early expansion period, and yet the spread of virus has been relatively limited. Understanding the effect of control measures to reduce the transmission in Hong Kong can improve the control of the virus spreading. We have developed a susceptible-exposed-infectious-quarantined-recovered (SEIQR) meta-population model that can stratify the infections into imported and subsequent local infections, and therefore to obtain the control effects on transmissibility in a region with many imported cases. We fitted the model to both imported and local confirmed cases with symptom onset from 18 January to 29 February 2020 in Hong Kong with daily transportation data and the transmission dynamics from Wuhan and Mainland China. The model estimated that the reproductive number was dropped from 2.32 to 0.76 (95% CI, 0.66 to 0.86) after an infected case was estimated to be quarantined half day before the symptom onset, corresponding to the incubation time of 5.43 days (95% CI, 1.30-9.47). If the quarantine happened about one day after the onset, community spread would be likely to occur, indicated by the reproductive number larger than one. The results suggest that the early quarantine for a suspected case before the symptom onset is a key factor to suppress COVID-19.
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BackgroundAlthough 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. MethodsTo 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. ResultsOur 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 Re 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%)).
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The rapid expansion of coronavirus (COVID-19) has been observed in many parts of the world. Many newly reported cases of this new coronavirus during early outbreak phases have been associated with travel history from an epidemic region (identified as imported cases). For those cases without travel history, the risk of wider spreads through community contact is even higher. However, most population models assume a homogeneous infected population without considering that the imported and secondary cases contracted by the imported cases can pose a different risk to community spread. We have developed an "easy-to-use" mathematical framework extending from a meta-population model embedding city-to-city connections to stratify the dynamics of transmission waves caused by imported, secondary, and others from an outbreak source region when control measures are considered. Using the dynamics of the secondary cases, we are able to determine the probability of community spread. Using the top 10 visiting cities from Wuhan in China as an example, we first demonstrated that the arrival time and the dynamics of the outbreaks at these cities can be successfully predicted under the reproductive number R0 = 2.92 and latent period{tau} = 5.2 days. Next, we showed that although control measures can gain extra 32.5 and 44.0 days in arrival time through a high intensive border control measure and a shorter time to quarantine under a low R0 (1.4), if the R0 is higher (2.92), only 10 extra days can be gained for each of the same measures. This suggests the importance of lowering the incidence at source regions together with infectious disease control measures in susceptible regions. The study allows us to assess the effects of border control and quarantine measures on the emergence and the global spread in a fully connected world using the dynamics of the secondary cases.
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A novel corona virus (2019-nCoV) was identified in Wuhan, China and has been causing an unprecedented outbreak in China. The spread of this novel virus can eventually become an international emergency. During the early outbreak phase in Wuhan, one of the most important public health tasks is to prevent the spread of the virus to other cities. Therefore, full-scale border control measures to prevent the spread of virus have been discussed in many nearby countries. At the same time, lockdown in Wuhan cityu (border control from leaving out) has been imposed. The challenge is that many people have traveled from Wuhan to other cities before the border control. Thus, it is difficult to forecast the number of imported cases at different cities and estimate their risk on outbreak emergence. Here, we have developed a mathematical framework incorporating city-to-city connections to calculate the number of imported cases of the novel virus from an outbreak source, and the cumulative number of secondary cases generated by the imported cases. We used this number to estimate the arrival time of outbreak emergence using air travel frequency data from Wuhan to other cities, collected from the International Air Transport Association database. In addition, a meta-population compartmental model was built based on a classical SIR approach to simulate outbreaks at different cities. We consider the scenarios under three basic reproductive number (R0) settings using the best knowledge of the current findings, from high (2.92), mild (1.68), to a much lower numbers (1.4). The mean arrival time of outbreak spreading has been determined. Under the high R0, the critical time is 17.9 days after December 31, 2019 for outbreak spreading. Under the low R0, the critical time is between day 26.2 to day 35 after December 31, 2019. To make an extra 30 days gain, under the low R0 (1.4), the control measures have to reduce 87% of the connections between the source and target cities. Under the higher R0 (2.92), the effect on reducing the chance of outbreak emergence is generally low until the border control measure was enhanced to reduce more than 95% of the connections.
