Comparison of control and transmission of COVID-19 across epidemic waves in Hong Kong: an observational study (preprint)

Background: Hong Kong contained COVID-19 for two years, but experienced a large epidemic of Omicron BA.2 in early 2022 and endemic transmission of Omicron subvariants thereafter. Methods: We examined the use and impact of pandemic controls in Hong Kong by analysing data on more than 1.7 million confirmed COVID-19 cases and characterizing non-pharmaceutical and pharmaceutical interventions implemented from January 2020 through to 30 December 2022. We estimated the daily effective reproductive number (Rt) to track changes in transmissibility and effectiveness of community-based measures against infection over time. We examined the temporal changes of pharmaceutical interventions, mortality rate and case-fatality risks (CFRs), particularly among older adults. Findings: Hong Kong experienced four local epidemic waves predominated by the ancestral strain in 2020 and early 2021 and prevented multiple SARS-CoV-2 variants from spreading in the community before 2022. Strict travel-related, case-based, and community-based measures were increasingly tightened in Hong Kong over the first two years of the pandemic. However, even very stringent measures were unable to contain the spread of Omicron BA.2 in Hong Kong. Despite high overall vaccination uptake (>70% with at least two doses), high mortality was observed during the Omicron BA.2 wave due to lower vaccine coverage (42%) among adults [≥]65 years of age. Increases in antiviral usage and vaccination uptake over time through 2022 was associated with decreased case fatality risks. Interpretation: Integrated strict measures were able to reduce importation risks and interrupt local transmission to contain COVID-19 transmission and disease burden while awaiting vaccine development and rollout. Increasing coverage of pharmaceutical interventions among high-risk groups reduced infection-related mortality and mitigated the adverse health impact of the pandemic.

Revisiting assumptions in test-negative studies for estimating vaccine effectiveness: the need for a clinical case definition (preprint)

Test negative studies have been used extensively for the estimation of COVID-19 vaccine effectiveness (VE). Such studies are able to estimate VE against medically-attended illness under certain assumptions. Selection bias may be present if the probability of participation is associated with vaccination or COVID-19, but this can be mitigated through use of a clinical case definition to screen patients for eligibility, which increases the likelihood that cases and non-cases come from the same source population. We examined the extent to which this type of bias could harm COVID-19 VE through systematic review and simulation. A systematic review of test-negative studies was re-analysed to identify studies ignoring the need for clinical criteria. Studies using a clinical case definition had a lower pooled VE estimate compared with studies that did not. Simulations varied the probability of selection by case and vaccination status. Positive bias away from the null (i.e., inflated VE consistent with the systematic review) was observed when there was a higher proportion of healthy, vaccinated non-cases, which may occur if a dataset contains many results from asymptomatic screening in settings where vaccination coverage is high. We provide an html tool for researchers to explore site-specific sources of selection bias in their own studies. We recommend all group consider the potential for selection bias in their vaccine effectiveness studies, particularly when using administrative data.

Viral shedding among symptomatic COVID-19 cases infected with the ancestral strain and Omicron BA.2 (preprint)

Assessment of viral kinetics of SARS-CoV-2 may inform host immune responses to and transmission potential of the virus. We analyzed longitudinal data of RT-PCR tests conducted for symptomatic COVID-19 patients at confirmation and during hospital isolation in Hong Kong during three major epidemic waves, and modeled the temporal trajectories of viral burden in these patients. Our analysis demonstrated that a longer duration of viral shedding appeared in more severe and older cases. Vaccinated individuals with a breakthrough infection of Omicron BA.2 exhibited a lower viral burden than non-vaccinated Omicron cases in early days following onset and a shorter duration of viral shedding particularly in more recently vaccinated patients. The viral kinetics characterized in our study provided insight into further studies on host immune responses to SARS-CoV-2 infection, prognosis of COVID-19 patients as well as possible protective mechanisms of vaccination against infection and severe outcomes.

Prior infections and effectiveness of SARS-CoV-2 vaccine in test-negative study: A systematic review and meta-analysis (preprint)

Background: Prior infection with SARS-CoV-2 can provide protection against infection and severe COVID-19. In settings with high pre-existing immunity, vaccine effectiveness (VE) should decrease with higher levels of immunity among unvaccinated individuals. Here, we conducted a systematic review and meta-analysis to understand the influence of prior infection on VE. Methods: We included test-negative design (TND) studies that examined VE against infection or severe disease (hospitalization, ICU admission, or death) for primary vaccination series. To determine the impact of prior infections on VE estimates, we compared studies that excluded or included people with prior COVID-19 infection. We also compared VE estimates by the cumulative incidence of cases before the start of and incidence rates during each study in the study locations, as further measures of prior infections in the community. Findings: We identified 67 studies that met inclusion criteria. Pooled VE among studies that included people with prior COVID-19 infection was lower against infection (pooled VE: 77%; 95% confidence interval (CI): 72%, 81%) and severe disease (pooled VE: 86%; 95% CI: 83%, 89%), compared with studies that excluded people with prior COVID-19 infection (pooled VE against infection: 87%; 95% CI: 85%, 89%; pooled VE against severe disease: 93%; 95% CI: 91%, 95%). There was a negative correlation between the cumulative incidence of cases before the start of the study and VE estimates against infection (spearman correlation ({rho}) = -0.32; 95% CI: -0.45, -0.18) and severe disease ({rho} = -0.49; 95% CI: -0.64, -0.30). There was also a negative correlation between the incidence rates of cases during the study period and VE estimates against infection ({rho} = -0.48; 95% CI: -0.59, -0.34) and severe disease ({rho} = -0.42; 95% CI: -0.58, -0.23). Interpretation: Based on a review of published VE estimates we found clear empirical evidence that higher levels of pre-existing immunity in a population were associated with lower VE estimates. Excluding previously infected individuals from VE studies may result in higher VE estimates with limited generalisability to the wider population. Prior infections should be treated as confounder and effect modificatory when the policies were targeted to whole population or stratified by infection history, respectively.

The effect of variation of individual infectiousness on SARS-CoV-2 transmission in households (preprint)

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: 2.2-4.2 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.

Inferring time-varying generation time, serial interval and incubation period distributions for COVID-19 (preprint)

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 investigated incubation period and serial interval distributions in data on 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.

Inferring time-varying generation time, serial interval and incubation period distributions for COVID-19 (preprint)

Background The generation time distribution, reflecting the time between successive infections in transmission chains, is one of the fundamental epidemiological parameters for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution, reflecting the time between illness onsets of infector and infectee. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true. Methods We analyzed data on observed incubation period and serial interval distributions in China, during January and February 2020, under different sampling approaches, 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. Results We analyzed data on a total of 2989 confirmed cases for COVID-19 during January 1 to February 29, 2020 in Mainland China. During the study period, the empirical forward serial interval decreased from a mean of 8.90 days to 2.68 days. The estimated mean backward incubation period of infectors increased from 3.77 days to 9.61 days, and the mean forward incubation period of infectees also increased from 5.39 days to 7.21 days. The estimated mean forward generation time decreased from 7.27 days (95% confidence interval: 6.42, 8.07) to 4.21 days (95% confidence interval: 3.70, 4.74) days by January 29. We used simulations to examine the sensitivity of our modelling approach to a number of assumptions and alternative dynamics. Conclusions The proposed method can provide more reliable estimation of the temporal variation in the generation time distribution, enabling proper assessment of transmission dynamics.

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

Background: Dose fractionation of 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 reported dose-finding results and estimated the dose-response relationship of neutralizing antibodies (nAbs) of COVID-19 vaccines using 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.Conclusion: Dose fractionation of mRNA and protein subunit vaccines may be safe and effective.

Time-varying transmission heterogeneity of SARS and COVID-19 in Hong Kong (preprint)

Transmission heterogeneity is a notable feature of the severe acute respiratory syndrome (SARS) and coronavirus disease 2019 (COVID-19) epidemics, though previous efforts to estimate how heterogeneity changes over time are limited. Using contact tracing data, we compared the epidemiology of SARS and COVID-19 infection in Hong Kong in 2003 and 2020-21 and estimated time-varying transmission heterogeneity (kt) by fitting negative binomial models to offspring distributions generated across variable observation windows. kt fluctuated over time for both COVID-19 and SARS on a continuous scale though SARS exhibited significantly greater (p < 0.001) heterogeneity compared to COVID-19 overall and in-time. For COVID-19, kt declined over time and was significantly associated with increasingly stringent non-pharmaceutical interventions though similar evidence for SARS was inconclusive. Underdetection of sporadic COVID-19 cases led to a moderate overestimation of kt, indicating COVID-19 heterogeneity of could be greater than observed. Time-varying or real-time estimates of transmission heterogeneity could become a critical indicator for epidemic intelligence in the future.

Incorporating temporal distribution of population-level viral load enables real-time estimation of COVID-19 transmissibility (preprint)

Many locations around the world have used real-time estimates of the time-varying effective reproductive number (\({R}_{t}\)) of COVID-19 to provide evidence of transmission intensity to inform control strategies. Estimates of \({R}_{t}\) are typically based on statistical models applied to case counts and typically suffer lags of more than a week because of the incubation 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 cycle threshold values) and estimates of \({R}_{t}\) based on case counts. We demonstrate that cycle threshold values could be used to improve real-time \({R}_{t}\) estimation, enabling more timely tracking of epidemic dynamics.

Pandemic fatigue impedes mitigation of COVID-19 in Hong Kong (preprint)

Community-wide social distancing has been a cornerstone of pandemic control prior to mass vaccinations. The extent to which pandemic fatigue is undermining adherence to such measures and accelerating transmission remains unclear. Using large-scale weekly telephone surveys and mobility data, we characterize the evolution of risk perception and protective behaviours in Hong Kong. We estimate a 1.5% to 5.5% reduction in population compliance with protective policies for the fourth wave (October 2020 to January 2021) versus the third wave (July to August 2020), inducing prolonged disease circulation with increased infections. Mathematical models incorporating population protective behaviours estimates that the fourth wave would have been 14% smaller if not for pandemic fatigue. Mitigating pandemic fatigue is essential in maintaining population protective behaviours for controlling COVID-19.

Universal Community Nucleic Acid Testing for COVID-19 in Hong Kong Reveals Insights into Transmission Dynamics (preprint)

Background: Testing of an entire community has been used as an approach to control COVID-19. In Hong Kong, a universal community testing programme (UCTP) was implemented at the fadeout phase of a community epidemic in July to September 2020, to determine the prevalence of unrecognised cases and limit any remaining transmission chains. We described the utility of the UCTP in finding unrecognised cases, and analysed data from the UCTP and other sources to characterise 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. We developed a Bayesian model to estimate the age-specific incidence of infection and the proportion of cases detected by clinical diagnosis and public health surveillance.Findings: 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 100,000 samples tested), consisting of 29% of all local cases reported during the two-week UCTP period. Compared with the existing clinical diagnosis and public health surveillance, the UCTP detected a higher proportion of sporadic cases (62% versus 27%, p <0.01) and identified 6 (out of 18) additional transmission chains during that period. We estimated that 27% (95% credible interval: 22%, 34%) of all infections were detected by the existing clinical diagnosis and public health surveillance in the third wave.Interpretation: We reported empirical evidence of the utility of population-wide COVID-19 testing in detecting unrecognised infections and transmission chains. Around three quarters of infections have not been identified through existing surveillance approaches including contact tracing.Funding Statement: This project was supported by the Health and Medical Research Fund, Food and Health Bureau, Government of the Hong Kong Special Administrative Region (grant no. COVID190118).Declaration of Interests: BJC consults for Roche, Sanofi Pasteur, GSK and Moderna. The authors report no other potential conflicts of interest.Ethics Approval Statement: Our project was approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (HKU/HA HKW IRB).

Clustering and superspreading potential of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in Hong Kong (preprint)

Superspreading events have characterised previous epidemics of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) infections. Using contact tracing data, we identified and characterized SARS-CoV-2 clusters in Hong Kong. Given a superspreading threshold of 6-8 secondary cases, we identified 5-7 probable superspreading events and evidence of substantial overdispersion in transmissibility, and estimated that 20% of cases were responsible for 80% of local transmission. Among terminal cluster cases, 27% (45/167) ended in quarantine. Social exposures produced a greater number of secondary cases compared to family or work exposures (p<0.001) while delays between symptom onset and isolation did not reliably predict the number of individual secondary cases or resulting cluster sizes. Public health authorities should focus on rapid tracing and quarantine of contacts, along with physical distancing to prevent superspreading events in high-risk social environments.