Evolving Epidemiological Characteristics of COVID-19 in Hong Kong From January to August 2020: Retrospective Study.

BACKGROUND: COVID-19 has plagued the globe, with multiple SARS-CoV-2 clusters hinting at its evolving epidemiology. Since the disease course is governed by important epidemiological parameters, including containment delays (time between symptom onset and mandatory isolation) and serial intervals (time between symptom onsets of infector-infectee pairs), understanding their temporal changes helps to guide interventions. OBJECTIVE: This study aims to characterize the epidemiology of the first two epidemic waves of COVID-19 in Hong Kong by doing the following: (1) estimating the containment delays, serial intervals, effective reproductive number (Rt), and proportion of asymptomatic cases; (2) identifying factors associated with the temporal changes of the containment delays and serial intervals; and (3) depicting COVID-19 transmission by age assortativity and types of social settings. METHODS: We retrieved the official case series and the Apple mobility data of Hong Kong from January-August 2020. The empirical containment delays and serial intervals were fitted to theoretical distributions, and factors associated with their temporal changes were quantified in terms of percentage contribution (the percentage change in the predicted outcome from multivariable regression models relative to a predefined comparator). Rt was estimated with the best fitted distribution for serial intervals. RESULTS: The two epidemic waves were characterized by imported cases and clusters of local cases, respectively. Rt peaked at 2.39 (wave 1) and 3.04 (wave 2). The proportion of asymptomatic cases decreased from 34.9% (0-9 years) to 12.9% (≥80 years). Log-normal distribution best fitted the 1574 containment delays (mean 5.18 [SD 3.04] days) and the 558 serial intervals (17 negative; mean 4.74 [SD 4.24] days). Containment delays decreased with involvement in a cluster (percentage contribution: 10.08%-20.73%) and case detection in the public health care sector (percentage contribution: 27.56%, 95% CI 22.52%-32.33%). Serial intervals decreased over time (6.70 days in wave 1 versus 4.35 days in wave 2) and with tertiary transmission or beyond (percentage contribution: -50.75% to -17.31%), but were lengthened by mobility (percentage contribution: 0.83%). Transmission within the same age band was high (18.1%). Households (69.9%) and social settings (20.3%) were where transmission commonly occurred. CONCLUSIONS: First, the factors associated with reduced containment delays suggested government-enacted interventions were useful for achieving outbreak control and should be further encouraged. Second, the shorter serial intervals associated with the composite mobility index calls for empirical surveys to disentangle the role of different contact dimensions in disease transmission. Third, the presymptomatic transmission and asymptomatic cases underscore the importance of remaining vigilant about COVID-19. Fourth, the time-varying epidemiological parameters suggest the need to incorporate their temporal variations when depicting the epidemic trajectory. Fifth, the high proportion of transmission events occurring within the same age group supports the ban on gatherings outside of households, and underscores the need for residence-centered preventive measures.

Epidemiological characteristics of the first 53 laboratory-confirmed cases of COVID-19 epidemic in Hong Kong, 13 February 2020.

BackgroundCOVID-19, caused by SARS-CoV-2, first appeared in China and subsequently developed into an ongoing epidemic. Understanding epidemiological factors characterising the transmission dynamics of this disease is of fundamental importance.AimsThis study aimed to describe key epidemiological parameters of COVID-19 in Hong Kong.MethodsWe extracted data of confirmed COVID-19 cases and their close contacts from the publicly available information released by the Hong Kong Centre for Health Protection. We used doubly interval censored likelihood to estimate containment delay and serial interval, by fitting gamma, lognormal and Weibull distributions to respective empirical values using Bayesian framework with right truncation. A generalised linear regression model was employed to identify factors associated with containment delay. Secondary attack rate was also estimated.ResultsThe empirical containment delay was 6.39 days; whereas after adjusting for right truncation with the best-fit Weibull distribution, it was 10.4 days (95% CrI: 7.15 to 19.81). Containment delay increased significantly over time. Local source of infection and number of doctor consultations before isolation were associated with longer containment delay. The empirical serial interval was 4.58-6.06 days; whereas the best-fit lognormal distribution to 26 certain-and-probable infector-infectee paired data gave an estimate of 4.77 days (95% CrI: 3.47 to 6.90) with right-truncation. The secondary attack rate among close contacts was 11.7%.ConclusionWith a considerable containment delay and short serial interval, contact-tracing effectiveness may not be optimised to halt the transmission with rapid generations replacement. Our study highlights the transmission risk of social interaction and pivotal role of physical distancing in suppressing the epidemic.

Association of public health interventions and COVID-19 incidence in Vietnam, January to December 2020.

BACKGROUND: Vietnam implemented various public health interventions such as contact tracing and testing, mandatory quarantine, and lockdowns in response to coronavirus disease 2019 (COVID-19). However, the effects of these measures on the epidemic remain unclear. METHODS: This article describes the public health interventions in relation to COVID-19 incidence. Maximum likelihood estimations were used to assess containment delays (time between symptom onset and start of isolation) and multivariable regression was employed to identify associated factors between interventions and COVID-19 incidence. The effective reproductive numbers (Rt) were calculated based on transmission pairs. RESULTS: Interventions were introduced periodically in response to the epidemic. Overall, 817 (55.4%) among 1474 COVID-19 cases were imported. Based on a serial interval of 8.72 ± 5.65 days, it was estimated that Rt decreased to below 1 (lowest at 0.02, 95% CI 0-0.12) during periods of strict border control and contact tracing, and increased ahead of new clusters. The main method to detect cases shifted over time from passive notification to active case-finding at immigration or in lockdown areas, with containment delays showing significant differences between modes of case detection. CONCLUSIONS: A combination of early, strict, and consistently implemented interventions is crucial to control COVID-19. Low-middle income countries with limited capacity can contain COVID-19 successfully using non-pharmaceutical interventions.