Resurgence of Omicron BA.2 in SARS-CoV-2 infection-naive Hong Kong.

Hong Kong experienced a surge of Omicron BA.2 infections in early 2022, resulting in one of the highest per-capita death rates of COVID-19. The outbreak occurred in a dense population with low immunity towards natural SARS-CoV-2 infection, high vaccine hesitancy in vulnerable populations, comprehensive disease surveillance and the capacity for stringent public health and social measures (PHSMs). By analyzing genome sequences and epidemiological data, we reconstructed the epidemic trajectory of BA.2 wave and found that the initial BA.2 community transmission emerged from cross-infection within hotel quarantine. The rapid implementation of PHSMs suppressed early epidemic growth but the effective reproduction number (Re) increased again during the Spring festival in early February and remained around 1 until early April. Independent estimates of point prevalence and incidence using phylodynamics also showed extensive superspreading at this time, which likely contributed to the rapid expansion of the epidemic. Discordant inferences based on genomic and epidemiological data underscore the need for research to improve near real-time epidemic growth estimates by combining multiple disparate data sources to better inform outbreak response policy.

The effect of variation of individual infectiousness on SARS-CoV-2 transmission in households.

Quantifying variation of individual infectiousness is critical to inform disease control. Previous studies reported substantial heterogeneity in transmission of many infectious diseases including SARS-CoV-2. However, those results are difficult to interpret since the number of contacts is rarely considered in such approaches. Here, we analyze data from 17 SARS-CoV-2 household transmission studies conducted in periods dominated by ancestral strains, in which the number of contacts was known. By fitting individual-based household transmission models to these data, accounting for number of contacts and baseline transmission probabilities, the pooled estimate suggests that the 20% most infectious cases have 3.1-fold (95% confidence interval: 2.2- to 4.2-fold) higher infectiousness than average cases, which is consistent with the observed heterogeneity in viral shedding. Household data can inform the estimation of transmission heterogeneity, which is important for epidemic management.

Pandemic fatigue impedes mitigation of COVID-19 in Hong Kong.

Hong Kong has implemented stringent public health and social measures (PHSMs) to curb each of the four COVID-19 epidemic waves since January 2020. The third wave between July and September 2020 was brought under control within 2 m, while the fourth wave starting from the end of October 2020 has taken longer to bring under control and lasted at least 5 mo. Here, we report the pandemic fatigue as one of the potential reasons for the reduced impact of PHSMs on transmission in the fourth wave. We contacted either 500 or 1,000 local residents through weekly random-digit dialing of landlines and mobile telephones from May 2020 to February 2021. We analyze the epidemiological impact of pandemic fatigue by using the large and detailed cross-sectional telephone surveys to quantify risk perception and self-reported protective behaviors and mathematical models to incorporate population protective behaviors. Our retrospective prediction suggests that an increase of 100 daily new reported cases would lead to 6.60% (95% CI: 4.03, 9.17) more people worrying about being infected, increase 3.77% (95% CI: 2.46, 5.09) more people to avoid social gatherings, and reduce the weekly mean reproduction number by 0.32 (95% CI: 0.20, 0.44). Accordingly, the fourth wave would have been 14% (95% CI%: -53%, 81%) smaller if not for pandemic fatigue. This indicates the important role of mitigating pandemic fatigue in maintaining population protective behaviors for controlling COVID-19.

Characterizing Human Collective Behaviors During COVID-19 - Hong Kong SAR, China, 2020.

What is already known about this topic?: People are likely to engage in collective behaviors online during extreme events, such as the coronavirus disease 2019 (COVID-19) crisis, to express awareness, take action, and work through concerns. What is added by this report?: This study offers a framework for evaluating interactions among individuals' emotions, perceptions, and online behaviors in Hong Kong Special Administrative Region (SAR) during the first two waves of COVID-19 (February to June 2020). Its results indicate a strong correlation between online behaviors, such as Google searches, and the real-time reproduction numbers. To validate the model's output of risk perception, this investigation conducted 10 rounds of cross-sectional telephone surveys on 8,593 local adult residents from February 1 through June 20 in 2020 to quantify risk perception levels over time. What are the implications for public health practice?: Compared to the survey results, the estimates of the risk perception of individuals using our network-based mechanistic model capture 80% of the trend of people's risk perception (individuals who are worried about being infected) during the studied period. We may need to reinvigorate the public by involving people as part of the solution that reduced the risk to their lives.

Epidemiology of SARS-CoV-2 Omicron BA.5 Infections, Macau, June-July 2022.

A SARS-CoV-2 Omicron BA.5 outbreak occurred in Macau from mid-June through July 2022. Out of >1,800 laboratory-confirmed cases, most were mild or asymptomatic; only 6 deaths were recorded. The outbreak was controlled through stringent public health and social measures, such as repeated universal testing and a stay-at-home order lasting 2 weeks.

Accounting for the Potential of Overdispersion in Estimation of the Time-varying Reproduction Number.

BACKGROUND: The time-varying reproduction number, Rt, is commonly used to monitor the transmissibility of an infectious disease during an epidemic, but standard methods for estimating Rt seldom account for the impact of overdispersion on transmission. METHODS: We developed a negative binomial framework to estimate Rt and a time-varying dispersion parameter (kt). We applied the framework to COVID-19 incidence data in Hong Kong in 2020 and 2021. We conducted a simulation study to compare the performance of our model with the conventional Poisson-based approach. RESULTS: Our framework estimated an Rt peaking around 4 (95% credible interval = 3.13, 4.30), similar to that from the Poisson approach but with a better model fit. Our approach further estimated kt <0.5 at the start of both waves, indicating appreciable heterogeneity in transmission. We also found that kt decreased sharply to around 0.4 when a large cluster of infections occurred. CONCLUSIONS: Our proposed approach can contribute to the estimation of Rt and monitoring of the time-varying dispersion parameters to quantify the role of superspreading.

Association Between Population-Level Factors and Household Secondary Attack Rate of SARS-CoV-2: A Systematic Review and Meta-analysis.

Background: Accurate estimation of household secondary attack rate (SAR) is crucial to understand the transmissibility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The impact of population-level factors, such as transmission intensity in the community, on SAR estimates is rarely explored. Methods: In this study, we included articles with original data to compute the household SAR. To determine the impact of transmission intensity in the community on household SAR estimates, we explored the association between SAR estimates and the incidence rate of cases by country during the study period. Results: We identified 163 studies to extract data on SARs from 326 031 cases and 2 009 859 household contacts. The correlation between the incidence rate of cases during the study period and SAR estimates was 0.37 (95% CI, 0.24-0.49). We found that doubling the incidence rate of cases during the study period was associated with a 1.2% (95% CI, 0.5%-1.8%) higher household SAR. Conclusions: Our findings suggest that the incidence rate of cases during the study period is associated with higher SAR. Ignoring this factor may overestimate SARs, especially for regions with high incidences, which further impacts control policies and epidemiological characterization of emerging variants.

Inferring time-varying generation time, serial interval, and incubation period distributions for COVID-19.

The generation time distribution, reflecting the time between successive infections in transmission chains, is a key epidemiological parameter for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true. We estimated incubation period and serial interval distributions using 629 transmission pairs reconstructed by investigating 2989 confirmed cases in China in January-February 2020, and developed an inferential framework to estimate the generation time distribution that accounts for variation over time due to changes in epidemiology, sampling biases and public health and social measures. We identified substantial reductions over time in the serial interval and generation time distributions. Our proposed method provides more reliable estimation of the temporal variation in the generation time distribution, improving assessment of transmission dynamics.

Immunogenicity, efficacy, and safety of SARS-CoV-2 vaccine dose fractionation: a systematic review and meta-analysis.

BACKGROUND: Dose fractionation of a coronavirus disease 2019 (COVID-19) vaccine could effectively accelerate global vaccine coverage, while supporting evidence of efficacy, immunogenicity, and safety are unavailable, especially with emerging variants. METHODS: We systematically reviewed clinical trials that reported dose-finding results and estimated the dose-response relationship of neutralizing antibodies (nAbs) of COVID-19 vaccines using a generalized additive model. We predicted the vaccine efficacy against both ancestral and variants, using previously reported correlates of protection and cross-reactivity. We also reviewed and compared seroconversion to nAbs, T cell responses, and safety profiles between fractional and standard dose groups. RESULTS: We found that dose fractionation of mRNA and protein subunit vaccines could induce SARS-CoV-2-specific nAbs and T cells that confer a reasonable level of protection (i.e., vaccine efficacy > 50%) against ancestral strains and variants up to Omicron. Safety profiles of fractional doses were non-inferior to the standard dose. CONCLUSIONS: Dose fractionation of mRNA and protein subunit vaccines may be safe and effective, which would also vary depending on the characteristics of emerging variants and updated vaccine formulations.

Effectiveness of CoronaVac and BNT162b2 Vaccines Against Severe Acute Respiratory Syndrome Coronavirus 2 Omicron BA.2 Infections in Hong Kong.

There is limited evidence on vaccine effectiveness against asymptomatic or mild Omicron infections. We estimated that recent third doses of messenger RNA or inactivated vaccines reduced the risk of self-reported infection by 52% (95% confidence interval, 17%-73%) among randomly sampled adults during the Omicron BA.2-dominated surge in Hong Kong.

Prediction of upcoming global infection burden of influenza seasons after relaxation of public health and social measures during the COVID-19 pandemic: a modelling study.

BACKGROUND: The transmission dynamics of influenza were affected by public health and social measures (PHSMs) implemented globally since early 2020 to mitigate the COVID-19 pandemic. We aimed to assess the effect of COVID-19 PHSMs on the transmissibility of influenza viruses and to predict upcoming influenza epidemics. METHODS: For this modelling study, we used surveillance data on influenza virus activity for 11 different locations and countries in 2017-22. We implemented a data-driven mechanistic predictive modelling framework to predict future influenza seasons on the basis of pre-COVID-19 dynamics and the effect of PHSMs during the COVID-19 pandemic. We simulated the potential excess burden of upcoming influenza epidemics in terms of fold rise in peak magnitude and epidemic size compared with pre-COVID-19 levels. We also examined how a proactive influenza vaccination programme could mitigate this effect. FINDINGS: We estimated that COVID-19 PHSMs reduced influenza transmissibility by a maximum of 17·3% (95% CI 13·3-21·4) to 40·6% (35·2-45·9) and attack rate by 5·1% (1·5-7·2) to 24·8% (20·8-27·5) in the 2019-20 influenza season. We estimated a 10-60% increase in the population susceptibility for influenza, which might lead to a maximum of 1-5-fold rise in peak magnitude and 1-4-fold rise in epidemic size for the upcoming 2022-23 influenza season across locations, with a significantly higher fold rise in Singapore and Taiwan. The infection burden could be mitigated by additional proactive one-off influenza vaccination programmes. INTERPRETATION: Our results suggest the potential for substantial increases in infection burden in upcoming influenza seasons across the globe. Strengthening influenza vaccination programmes is the best preventive measure to reduce the effect of influenza virus infections in the community. FUNDING: Health and Medical Research Fund, Hong Kong.

Universal Community Nucleic Acid Testing for Coronavirus Disease 2019 (COVID-19) in Hong Kong Reveals Insights Into Transmission Dynamics: A Cross-Sectional and Modeling Study.

BACKGROUND: Testing of an entire community has been used as an approach to control coronavirus disease 2019 (COVID-19). In Hong Kong, a universal community testing program (UCTP) was implemented at the fadeout phase of a community epidemic in July to September 2020. We described the utility of the UCTP in finding unrecognized infections and analyzed data from the UCTP and other sources to characterize transmission dynamics. METHODS: We described the characteristics of people participating in the UCTP and compared the clinical and epidemiological characteristics of COVID-19 cases detected by the UCTP versus those detected by clinical diagnosis and public health surveillance (CDPHS). We developed a Bayesian model to estimate the age-specific incidence of infection and the proportion of cases detected by CDPHS. RESULTS: In total, 1.77 million people, 24% of the Hong Kong population, participated in the UCTP from 1 to 14 September 2020. The UCTP identified 32 new infections (1.8 per 100000 samples tested), consisting of 29% of all local cases reported during the two-week UCTP period. Compared with the CDPHS, the UCTP detected a higher proportion of sporadic cases (62% vs 27%, P<.01) and identified 6 (out of 18) additional clusters during that period. We estimated that 27% (95% credible interval: 22%, 34%) of all infections were detected by the CDPHS in the third wave. CONCLUSIONS: We reported empirical evidence of the utility of population-wide COVID-19 testing in detecting unrecognized infections and clusters. Around three quarters of infections have not been identified through existing surveillance approaches including contact tracing.

Incorporating temporal distribution of population-level viral load enables real-time estimation of COVID-19 transmission.

Many locations around the world have used real-time estimates of the time-varying effective reproductive number ([Formula: see text]) of COVID-19 to provide evidence of transmission intensity to inform control strategies. Estimates of [Formula: see text] are typically based on statistical models applied to case counts and typically suffer lags of more than a week because of the latent period and reporting delays. Noting that viral loads tend to decline over time since illness onset, analysis of the distribution of viral loads among confirmed cases can provide insights into epidemic trajectory. Here, we analyzed viral load data on confirmed cases during two local epidemics in Hong Kong, identifying a strong correlation between temporal changes in the distribution of viral loads (measured by RT-qPCR cycle threshold values) and estimates of [Formula: see text] based on case counts. We demonstrate that cycle threshold values could be used to improve real-time [Formula: see text] estimation, enabling more timely tracking of epidemic dynamics.

Variability in transmission risk of SARS-CoV-2 in close contact settings: A contact tracing study in Shandong Province, China.

BACKGROUND: Understanding the relative transmissibility of SARS-CoV-2 virus across different contact settings and the possibility of superspreading events is important for prioritizing disease control. Such assessment requires proper consideration of individual level exposure history, which is made possible by contact tracing. METHODS: The case-ascertained study in Shandong, China including 97 laboratory-confirmed index cases and 3158 close contacts. All close contacts were quarantined after their last exposure of index cases. Contacts were tested for COVID-19 regularly by PCR to identify both symptomatic and asymptomatic infections. We developed a Bayesian transmission model to the contact tracing data to account for different duration of exposure among individuals to transmission risk in different settings, and the heterogeneity of infectivity of cases. RESULTS: We estimate secondary attack rates (SAR) to be 39% (95% credible interval (CrI): 20-64%) in households, 30% (95% CrI: 11-67%) in healthcare facilities, 23% (95% CrI: 7-51%) at workplaces, and 4% (95% CrI: 1-17%) during air travel. Models allowing heterogeneity of infectivity of cases provided a better goodness-of-fit. We estimated that 64% (95% CrI: 55-72%) of cases did not generate secondary transmissions, and 20% (95% CrI: 15-26%) cases explained 80% of secondary transmissions. CONCLUSIONS: Household, healthcare facilities and workplaces are efficient setting for transmission. Timely identification of potential superspreaders in most transmissible settings remains crucial for containing the pandemic.

Pandemic fatigue and attenuated impact of avoidance behaviours against COVID-19 transmission in Hong Kong by cross-sectional telephone surveys.

OBJECTIVES: This study aims to explore the attenuated impact of reported avoidance behaviours adherence on the transmission of COVID-19 through cross-sectional surveys in Hong Kong, in order to make up for the lack of research on avoidance behaviours fatigue. DESIGN: 40 cross-sectional telephone surveys. SETTING: All districts in Hong Kong. PARTICIPANTS: 31 332 Cantonese or English-speaking participants at age of 18 years or above. METHODS: We collected data on behaviours and estimated the average effective reproduction number ([Formula: see text]) among the Hong Kong adult population during the COVID-19 epidemic wave in November-December 2020 and compared with the preceding epidemic in June-July 2020. RESULTS: We observed a reduction in adherence to voluntary avoidance behaviours due to pandemic fatigue, but continued adherence to regulated avoidance behaviours. The average [Formula: see text] during the post-work from home period was higher in November-December wave with estimated [Formula: see text] of 0.81 (95% CI: 0.75 to 0.87) compared with the June-July wave with an [Formula: see text] of 0.67 (95% CI: 0.60 to 0.75). CONCLUSIONS: The declined effectiveness of social distancing interventions in reducing COVID-19 transmission was associated with fatigue with voluntary avoidance behaviours in Hong Kong population, implying a need for the government to reinvigorate the public to maintain effective pandemic control.

Restaurant-Based Measures to Control Community Transmission of COVID-19, Hong Kong.

Controlling transmission in restaurants is an important component of public health and social measures for coronavirus disease. We examined the effects of restaurant measures in Hong Kong. Our findings indicate that shortening operating hours did not have an effect on time-varying effective reproduction number when capacity was already reduced.

Joint Estimation of Generation Time and Incubation Period for Coronavirus Disease 2019.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has caused a heavy disease burden globally. The impact of process and timing of data collection on the accuracy of estimation of key epidemiological distributions are unclear. Because infection times are typically unobserved, there are relatively few estimates of generation time distribution. METHODS: We developed a statistical framework to jointly estimate generation time and incubation period from human-to-human transmission pairs, accounting for sampling biases. We applied the framework on 80 laboratory-confirmed human-to-human transmission pairs in China. We further inferred the infectiousness profile, serial interval distribution, proportions of presymptomatic transmission, and basic reproduction number (R0) for COVID-19. RESULTS: The estimated mean incubation period was 4.8 days (95% confidence interval [CI], 4.1-5.6), and mean generation time was 5.7 days (95% CI, 4.8-6.5). The estimated R0 based on the estimated generation time was 2.2 (95% CI, 1.9-2.4). A simulation study suggested that our approach could provide unbiased estimates, insensitive to the width of exposure windows. CONCLUSIONS: Properly accounting for the timing and process of data collection is critical to have correct estimates of generation time and incubation period. R0 can be biased when it is derived based on serial interval as the proxy of generation time.

Changing Disparities in Coronavirus Disease 2019 (COVID-19) Burden in the Ethnically Homogeneous Population of Hong Kong Through Pandemic Waves: An Observational Study.

BACKGROUND: Disparities were marked in previous pandemics, usually with higher attack rates reported for those in lower socioeconomic positions and for ethnic minorities. METHODS: We examined characteristics of laboratory-confirmed coronavirus disease 2019 (COVID-19) cases in Hong Kong, assessed associations between incidence and population-level characteristics at the level of small geographic areas, and evaluated relations between socioeconomics and work-from-home (WFH) arrangements. RESULTS: The largest source of COVID-19 importations switched from students studying overseas in the second wave to foreign domestic helpers in the third. The local cases were mostly individuals not in formal employment (retirees and homemakers) and production workers who were unable to WFH. For every 10% increase in the proportion of population employed as executives or professionals in a given geographic region, there was an 84% (95% confidence interval [CI], 1-97%) reduction in the incidence of COVID-19 during the third wave. In contrast, in the first 2 waves, the same was associated with 3.69 times (95% CI, 1.02-13.33) higher incidence. Executives and professionals were more likely to implement WFH and experienced frequent changes in WFH practice compared with production workers. CONCLUSIONS: Consistent findings on the reversed socioeconomic patterning of COVID-19 burden between infection waves in Hong Kong in both individual- and population-level analyses indicated that risks of infections may be related to occupations involving high exposure frequency and WFH flexibility. Contextual determinants should be taken into account in policy planning aiming at mitigating such disparities.