Forecasting of influenza activity and associated hospital admission burden and estimating the impact of COVID-19 pandemic on 2019/20 winter season in Hong Kong.

Like other tropical and subtropical regions, influenza viruses can circulate year-round in Hong Kong. However, during the COVID-19 pandemic, there was a significant decrease in influenza activity. The objective of this study was to retrospectively forecast influenza activity during the year 2020 and assess the impact of COVID-19 public health social measures (PHSMs) on influenza activity and hospital admissions in Hong Kong. Using weekly surveillance data on influenza virus activity in Hong Kong from 2010 to 2019, we developed a statistical modeling framework to forecast influenza virus activity and associated hospital admissions. We conducted short-term forecasts (1-4 weeks ahead) and medium-term forecasts (1-13 weeks ahead) for the year 2020, assuming no PHSMs were implemented against COVID-19. We estimated the reduction in transmissibility, peak magnitude, attack rates, and influenza-associated hospitalization rate resulting from these PHSMs. For short-term forecasts, mean ambient ozone concentration and school holidays were found to contribute to better prediction performance, while absolute humidity and ozone concentration improved the accuracy of medium-term forecasts. We observed a maximum reduction of 44.6% (95% CI: 38.6% - 51.9%) in transmissibility, 75.5% (95% CI: 73.0% - 77.6%) in attack rate, 41.5% (95% CI: 13.9% - 55.7%) in peak magnitude, and 63.1% (95% CI: 59.3% - 66.3%) in cumulative influenza-associated hospitalizations during the winter-spring period of the 2019/2020 season in Hong Kong. The implementation of PHSMs to control COVID-19 had a substantial impact on influenza transmission and associated burden in Hong Kong. Incorporating information on factors influencing influenza transmission improved the accuracy of our predictions.

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.

Insights into COVID-19 epidemiology and control from temporal changes in serial interval distributions in Hong Kong.

The serial interval distribution is used to approximate the generation time distribution, an essential parameter to infer the transmissibility (${R}_t$) of an epidemic. However, serial interval distributions may change as an epidemic progresses. We examined detailed contact tracing data on laboratory-confirmed cases of COVID-19 in Hong Kong during the five waves from January 2020 to July 2022. We reconstructed the transmission pairs and estimated time-varying effective serial interval distributions and factors associated with longer or shorter intervals. Finally, we assessed the biases in estimating transmissibility using constant serial interval distributions. We found clear temporal changes in mean serial interval estimates within each epidemic wave studied and across waves, with mean serial intervals ranged from 5.5 days (95% CrI: 4.4, 6.6) to 2.7 (95% CrI: 2.2, 3.2) days. The mean serial intervals shortened or lengthened over time, which were found to be closely associated with the temporal variation in COVID-19 case profiles and public health and social measures and could lead to the biases in predicting ${R}_t$. Accounting for the impact of these factors, the time-varying quantification of serial interval distributions could lead to improved estimation of ${R}_t$, and provide additional insights into the impact of public health measures on transmission.

Serial Intervals and Case Isolation Delays for Coronavirus Disease 2019: A Systematic Review and Meta-Analysis.

BACKGROUND: Estimates of the serial interval distribution contribute to our understanding of the transmission dynamics of coronavirus disease 2019 (COVID-19). Here, we aimed to summarize the existing evidence on serial interval distributions and delays in case isolation for COVID-19. METHODS: We conducted a systematic review of the published literature and preprints in PubMed on 2 epidemiological parameters, namely, serial intervals and delay intervals relating to isolation of cases for COVID-19 from 1 January 2020 to 22 October 2020 following predefined eligibility criteria. We assessed the variation in these parameter estimates using correlation and regression analysis. RESULTS: Of 103 unique studies on serial intervals of COVID-19, 56 were included, providing 129 estimates. Of 451 unique studies on isolation delays, 18 were included, providing 74 estimates. Serial interval estimates from 56 included studies varied from 1.0 to 9.9 days, while case isolation delays from 18 included studies varied from 1.0 to 12.5 days, which were associated with spatial, methodological, and temporal factors. In mainland China, the pooled mean serial interval was 6.2 days (range, 5.1-7.8) before the epidemic peak and reduced to 4.9 days (range, 1.9-6.5) after the epidemic peak. Similarly, the pooled mean isolation delay related intervals were 6.0 days (range, 2.9-12.5) and 2.4 days (range, 2.0-2.7) before and after the epidemic peak, respectively. There was a positive association between serial interval and case isolation delay. CONCLUSIONS: Temporal factors, such as different control measures and case isolation in particular, led to shorter serial interval estimates over time. Correcting transmissibility estimates for these time-varying distributions could aid mitigation efforts.

Serial Interval and Transmission Dynamics during SARS-CoV-2 Delta Variant Predominance, South Korea.

We estimated mean serial interval and superspreading potential for the Delta variant of severe acute respiratory syndrome coronavirus 2 in South Korea. Intervals were similar for the first (3.7 days) and second (3.5 days) study periods. Risk for superspreading events was also similar; 23% and 25% of cases, respectively, seeded 80% of transmissions.

Evaluation of Effectiveness of Global COVID-19 Vaccination Campaign.

To model estimated deaths averted by COVID-19 vaccines, we used state-of-the-art mathematical modeling, likelihood-based inference, and reported COVID-19 death and vaccination data. We estimated that >1.5 million deaths were averted in 12 countries. Our model can help assess effectiveness of the vaccination program, which is crucial for curbing the COVID-19 pandemic.

Epidemiology of Infections with SARS-CoV-2 Omicron BA.2 Variant, Hong Kong, January-March 2022.

Our analysis of data collected from multiple epidemics in Hong Kong indicated a shorter serial interval and generation time of infections with the SARS-CoV-2 Omicron variant. The age-specific case-fatality risk for Omicron BA.2.2 case-patients without complete primary vaccination was comparable to that of persons infected with ancestral strains in earlier waves.

Transmission dynamics and epidemiological characteristics of SARS-CoV-2 Delta variant infections in Guangdong, China, May to June 2021.

BackgroundThe Delta variant of SARS-CoV-2 had become predominant globally by November 2021.AimWe evaluated transmission dynamics and epidemiological characteristics of the Delta variant in an outbreak in southern China.MethodsData on confirmed COVID-19 cases and their close contacts were retrospectively collected from the outbreak that occurred in Guangdong, China in May and June 2021. Key epidemiological parameters, temporal trend of viral loads and secondary attack rates were estimated. We also evaluated the association of vaccination with viral load and transmission.ResultsWe identified 167 patients infected with the Delta variant in the Guangdong outbreak. Mean estimates of latent and incubation period were 3.9 days and 5.8 days, respectively. Relatively higher viral load was observed in infections with Delta than in infections with wild-type SARS-CoV-2. Secondary attack rate among close contacts of cases with Delta was 1.4%, and 73.1% (95% credible interval (CrI): 32.9-91.4) of the transmissions occurred before onset. Index cases without vaccination (adjusted odds ratio (aOR): 2.84; 95% CI: 1.19-8.45) or with an incomplete vaccination series (aOR: 6.02; 95% CI: 2.45-18.16) were more likely to transmit infection to their contacts than those who had received the complete primary vaccination series.DiscussionPatients infected with the Delta variant had more rapid symptom onset compared with the wild type. The time-varying serial interval should be accounted for in estimation of reproduction numbers. The higher viral load and higher risk of pre-symptomatic transmission indicated the challenges in control of infections with the Delta variant.

Decreased Use of Broad-Spectrum Antibiotics During the Coronavirus Disease 2019 Epidemic in South Korea.

BACKGROUND: Early in the coronavirus disease 2019 (COVID-19) pandemic, there was a concern over possible increase in antibiotic use due to coinfections among COVID-19 patients in the community. Here, we evaluate the changes in nationwide use of broad-spectrum antibiotics during the COVID-19 epidemic in South Korea. METHODS: We obtained national reimbursement data on the prescription of antibiotics, including penicillin with ß-lactamase inhibitors, cephalosporins, fluoroquinolones, and macrolides. We examined the number of antibiotic prescriptions compared with the previous 3 years in the same period from August to July. To quantify the impact of the COVID-19 epidemic on antibiotic use, we developed a regression model adjusting for changes of viral acute respiratory tract infections (ARTIs), which are an important factor driving antibiotic use. RESULTS: During the COVID-19 epidemic in South Korea, the broad-spectrum antibiotic use dropped by 15%-55% compared to the previous 3 years. Overall reduction in antibiotic use adjusting for ARTIs was estimated to be 14%-30%, with a larger impact in children. CONCLUSIONS: Our study found that broad-spectrum antibiotic use was substantially reduced during the COVID-19 epidemic in South Korea. This reduction can be in part due to reduced ARTIs as a result of stringent public health interventions including social distancing measures.

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.

Risk for International Importations of Variant SARS-CoV-2 Originating in the United Kingdom.

A fast-spreading severe acute respiratory syndrome coronavirus 2 variant identified in the United Kingdom in December 2020 has raised international alarm. We analyzed data from 15 countries and estimated that the chance that this variant was imported into these countries by travelers from the United Kingdom by December 7 is >50%.

Influenza Virus: Tracking, Predicting, and Forecasting.

Influenza is a common respiratory infection that causes considerable morbidity and mortality worldwide each year. In recent years, along with the improvement in computational resources, there have been a number of important developments in the science of influenza surveillance and forecasting. Influenza surveillance systems have been improved by synthesizing multiple sources of information. Influenza forecasting has developed into an active field, with annual challenges in the United States that have stimulated improved methodologies. Work continues on the optimal approaches to assimilating surveillance data and information on relevant driving factors to improve estimates of the current situation (nowcasting) and to forecast future dynamics.

Transmission dynamics and control of two epidemic waves of SARS-CoV-2 in South Korea.

BACKGROUND: After relaxing social distancing measures, South Korea experienced a resurgent second epidemic wave of coronavirus disease 2019 (COVID-19). In this study, we aimed to identify the transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and assess the impact of COVID-19 case finding and contact tracing in each epidemic wave. METHODS: We collected data on COVID-19 cases published by local public health authorities in South Korea and divided the study into two epidemic periods (19 January-19 April 2020 for the first epidemic wave and 20 April-11 August 2020 for the second epidemic wave). To identify changes in the transmissibility of SARS-CoV-2, the daily effective reproductive number (Rt) was estimated using the illness onset of the cases. Furthermore, to identify the characteristics of each epidemic wave, frequencies of cluster types were measured, and age-specific transmission probability matrices and serial intervals were estimated. The proportion of asymptomatic cases and cases with unknown sources of infection were also estimated to assess the changes of infections identified as cases in each wave. RESULTS: In early May 2020, within 2-weeks of a relaxation in strict social distancing measures, Rt increased rapidly from 0.2 to 1.8 within a week and was around 1 until early July 2020. In both epidemic waves, the most frequent cluster types were religious-related activities and transmissions among the same age were more common. Furthermore, children were rarely infectors or infectees, and the mean serial intervals were similar (~ 3 days) in both waves. The proportion of asymptomatic cases at presentation increased from 22% (in the first wave) to 27% (in the second wave), while the cases with unknown sources of infection were similar in both waves (22 and 24%, respectively). CONCLUSIONS: Our study shows that relaxing social distancing measures was associated with increased SARS-CoV-2 transmission despite rigorous case findings in South Korea. Along with social distancing measures, the enhanced contact tracing including asymptomatic cases could be an efficient approach to control further epidemic waves.

Effects of School Holidays on Seasonal Influenza in South Korea, 2014-2016.

School closures are considered as a potential nonpharmaceutical intervention to mitigate severe influenza epidemics and pandemics. In this study, we assessed the effects of scheduled school closure on influenza transmission using influenza surveillance data before, during, and after spring breaks in South Korea, 2014-2016. During the spring breaks, influenza transmission was reduced by 27%-39%, while the overall reduction in transmissibility was estimated to be 6%-23%, with greater effects observed among school-aged children.

Reconstruction of Transmission Pairs for Novel Coronavirus Disease 2019 (COVID-19) in Mainland China: Estimation of Superspreading Events, Serial Interval, and Hazard of Infection.

BACKGROUND: Knowledge on the epidemiological features and transmission patterns of novel coronavirus disease (COVID-19) is accumulating. Detailed line-list data with household settings can advance the understanding of COVID-19 transmission dynamics. METHODS: A unique database with detailed demographic characteristics, travel history, social relationships, and epidemiological timelines for 1407 transmission pairs that formed 643 transmission clusters in mainland China was reconstructed from 9120 COVID-19 confirmed cases reported during 15 January-29 February 2020. Statistical model fittings were used to identify the superspreading events and estimate serial interval distributions. Age- and sex-stratified hazards of infection were estimated for household vs nonhousehold transmissions. RESULTS: There were 34 primary cases identified as superspreaders, with 5 superspreading events occurred within households. Mean and standard deviation of serial intervals were estimated as 5.0 (95% credible interval [CrI], 4.4-5.5) days and 5.2 (95% CrI, 4.9-5.7) days for household transmissions and 5.2 (95% CrI, 4.6-5.8) and 5.3 (95% CrI, 4.9-5.7) days for nonhousehold transmissions, respectively. The hazard of being infected outside of households is higher for people aged 18-64 years, whereas hazard of being infected within households is higher for young and old people. CONCLUSIONS: Nonnegligible frequency of superspreading events, short serial intervals, and a higher risk of being infected outside of households for male people of working age indicate a significant barrier to the identification and management of COVID-19 cases, which requires enhanced nonpharmaceutical interventions to mitigate this pandemic.

Mitigation of Influenza B Epidemic with School Closures, Hong Kong, 2018.

In winter 2018, schools in Hong Kong were closed 1 week before the scheduled Chinese New Year holiday to mitigate an influenza B virus epidemic. The intervention occurred after the epidemic peak and reduced overall incidence by ≈4.2%. School-based vaccination programs should be implemented to more effectively reduce influenza illnesses.

Ozone as an environmental driver of influenza.

Under long-standing threat of seasonal influenza outbreaks, it remains imperative to understand the drivers of influenza dynamics which can guide mitigation measures. While the role of absolute humidity and temperature is extensively studied, the possibility of ambient ozone (O3) as an environmental driver of influenza has received scant attention. Here, using state-level data in the USA during 2010-2015, we examined such research hypothesis. For rigorous causal inference by evidence triangulation, we applied 3 distinct methods for data analysis: Convergent Cross Mapping from state-space reconstruction theory, Peter-Clark-momentary-conditional-independence plus as graphical modeling algorithms, and regression-based Generalised Linear Model. The negative impact of ambient O3 on influenza activity at 1-week lag is consistently demonstrated by those 3 methods. With O3 commonly known as air pollutant, the novel findings here on the inhibition effect of O3 on influenza activity warrant further investigations to inform environmental management and public health protection.

Rapid global spread of variants of concern of SARS-CoV-2.

Objectives: Variants of concern (VOCs) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), such as the Delta variant and the Omicron variant, have reached all countries/regions of the world and have had a tremendous impact. This study analyses the global spread of VOCs of SARS-CoV-2. Methods: Biweekly aggregated numbers of several VOCs were retrieved for 58 locations. The time interval for the proportion of VOC samples to exceed 60% (indicating dominance) among all samples sequenced in each location was calculated. The times taken for a VOC to become dominant in 12 (or 36) locations was defined in order to quantify the speed of spread. Results: It took 63, 56 and 28 days for the Alpha, Delta and Omicron variants to become dominant in 12 locations, respectively, and 133, 70 and 28 days for the Alpha, Delta and Omicron variants to become dominant in 36 locations. Conclusions: The Omicron variant has much higher transmission potential compared with the Delta variant, and the Delta variant has higher transmission potential compared with the pre-Delta VOCs.