Clinical Time Delay Distributions of COVID-19 in 2020-2022 in the Republic of Korea: Inferences from a Nationwide Database Analysis

Combining wastewater surveillance and case data in estimating the time-varying effective reproduction number

Wastewater surveillance has been increasingly acknowledged as a useful tool for monitoring transmission dynamics of infections of public health concern, including the coronavirus disease (COVID-19). While a range of models have been proposed to estimate the time-varying effective reproduction number (Rt) utilizing clinical data, few have harnessed the viral concentration in wastewater samples to do so, leaving uncertainties about the potential precision gains with its use. In this study, we developed a Bayesian hierarchical model which simultaneously reconstructed the latent infection trajectory and estimated Rt. Focusing on the 2022 and early 2023 COVID-19 transmission trends in Singapore, where mass community wastewater surveillance has become routine, we performed estimations using a spectrum of data sources, including reported case counts, hospital admissions, deaths, and wastewater viral loads. We further explored the performance of our wastewater model across various scenarios with different sampling strategies. The results showed consistent estimates derived from models employing diverse data streams, while models incorporating more wastewater samples exhibited greater uncertainty and variation in the inferred Rts. Additionally, our analysis revealed prominent day-of-the-week effect in reported case counts and substantial temporal variations in ascertainment rates. In response to these findings, we advocate for a hybrid approach leveraging both clinical and wastewater surveillance data to account for changes in case-ascertainment rates. Furthermore, our study demonstrates the possibility of reducing sampling frequency or sample size without compromising estimation accuracy for Rt, highlighting the potential for optimizing resource allocation in surveillance efforts while maintaining robust insights into the transmission dynamics of infectious diseases.

Effectiveness of COVID-19 vaccines against severe outcomes in cancer patients: Real-world evidence from self-controlled risk interval and retrospective cohort studies

BACKGROUND

The effectiveness of COVID-19 vaccines is generally reduced in cancer patients compared to the general population. However, there are only a few studies that compare the relative risk of breakthrough infections and severe COVID-19 outcomes in fully vaccinated cancer patients versus their unvaccinated counterparts.

METHODS

To assess the effectiveness of COVID-19 vaccines in cancer patients, we employed (1) a self-controlled risk interval (SCRI) design, and (2) a retrospective matched cohort design. A SCRI design was used to compare the risk of breakthrough infection in vaccinated cancer patients during the period immediately following vaccination ("control window") and the period in which immunity is achieved ("exposure windows"). The retrospective matched cohort design was used to compare the risk of severe COVID-19 outcomes between vaccinated and unvaccinated cancer patients. For both studies, data were extracted from the Korea Disease Control and Prevention Agency-COVID-19-National Health Insurance Service cohort, including demographics, medical history, and vaccination records of all individuals confirmed with COVID-19. We used conditional Poisson regression to calculate the incidence rate ratio (IRR) for breakthrough infection and Cox regression to estimate the hazard ratio (HR) for severe outcomes.

RESULTS

Of 14,448 cancer patients diagnosed with COVID-19 between October 2020 and December 2021, a total of 217 and 3996 cancer patients were included in the SCRI and cohort study respectively. While the risk of breakthrough infections, measured by the incidence rate in the control and exposure windows, did not show statistically significant difference in vaccinated cancer patients (IRR=0.88, 95% CI: 0.64-1.22), the risk of severe COVID-19 outcomes was significantly lower in vaccinated cancer patients compared to those unvaccinated (HR=0.27, 95% CI: 0.22-0.34).

CONCLUSION

COVID-19 vaccines significantly reduce the risk of severe outcomes in cancer patients, though their efficacy against breakthrough infections is less evident.

Variability in the serial interval of COVID-19 in South Korea: a comprehensive analysis of age and regional influences

Introduction

Quantifying the transmissibility over time, particularly by region and age, using parameters such as serial interval and time-varying reproduction number, helps in formulating targeted interventions. Moreover, considering the impact of geographical factors on transmission provides valuable insights into the effectiveness of control measures.

Methods

Drawing on a comprehensive dataset of COVID-19 cases in South Korea, we analyzed transmission dynamics with a focus on age and regional variations. The dataset, compiled through the efforts of dedicated epidemiologists, includes information on symptom onset dates, enabling detailed investigations. The pandemic was divided into distinct phases, aligning with changes in policies, emergence of variants, and vaccination efforts. We analyzed various interventions such as social distancing, vaccination rates, school closures, and population density. Key parameters like serial interval, heatmaps, and time-varying reproduction numbers were used to quantify age and region-specific transmission trends.

Results

Analysis of transmission pairs within age groups highlighted the significant impact of school closure policies on the spread among individuals aged 0-19. This analysis also shed light on transmission dynamics within familial and educational settings. Changes in confirmed cases over time revealed a decrease in spread among individuals aged 65 and older, attributed to higher vaccination rates. Conversely, densely populated metropolitan areas experienced an increase in confirmed cases. Examination of time-varying reproduction numbers by region uncovered heterogeneity in transmission patterns, with regions implementing strict social distancing measures showing both increased confirmed cases and delayed spread, indicating the effectiveness of these policies.

Discussion

Our findings underscore the importance of evaluating and tailoring epidemic control policies based on key COVID-19 parameters. The analysis of social distancing measures, school closures, and vaccine impact provides valuable insights into controlling transmission. By quantifying the impact of these interventions on different age groups and regions, we contribute to the ongoing efforts to combat the COVID-19 pandemic effectively.

Patient Characteristics and Public Health Office Factors Associated With Long Reporting Delay of COVID-19 Cases in Sapporo City, Japan

BACKGROUND

For therapeutic efficacy, molnupiravir and nirmatrelvir-ritonavir must be started to treat patients within 5 days of disease onset to treat patients with novel coronavirus disease 2019 (COVID-19). However, some patients spend more than 5 days from disease onset before reporting to the Public Health Office. This study aimed to clarify the characteristics of patients with reporting delay.

METHODS

This study included data from 12,399 patients with COVID-19 who reported to the Public Health Office from March 3rd, 2021 to June 30th, 2021. Patients were stratified into "linked" (n = 7,814) and "unlinked" (n = 4,585) cases depending on whether they were linked to other patients. A long reporting delay was defined as the difference between the onset and reporting dates of 5 days or more. Univariate and multivariate analyses were performed using log-binomial regression to identify factors related to long reporting delay, and prevalence ratios with corresponding 95% confidence intervals were calculated.

RESULTS

The proportion of long reporting delay was 24.4% (1,904/7,814) and 29.3% (1,344/4,585) in linked and unlinked cases, respectively. Risks of long reporting delay among linked cases were living alone and onset on the day with a higher 7-day daily average confirmed cases or onset on weekends; whereas, risks for unlinked cases were age over 65 years, without occupation, and living alone.

CONCLUSION

Our results suggest the necessity to establish a Public Health Office system that is less susceptible to the rapid increase in the number of patients, promotes educational activities for people with fewer social connections, and improves access to health care.

Empirical distributions of time intervals between COVID-19 cases and more severe outcomes in Scotland

A critical factor in infectious disease control is the risk of an outbreak overwhelming local healthcare capacity. The overall demand on healthcare services will depend on disease severity, but the precise timing and size of peak demand also depends on the time interval (or clinical time delay) between initial infection, and development of severe disease. A broader distribution of intervals may draw that demand out over a longer period, but have a lower peak demand. These interval distributions are therefore important in modelling trajectories of e.g. hospital admissions, given a trajectory of incidence. Conversely, as testing rates decline, an incidence trajectory may need to be inferred through the delayed, but relatively unbiased signal of hospital admissions. Healthcare demand has been extensively modelled during the COVID-19 pandemic, where localised waves of infection have imposed severe stresses on healthcare services. While the initial acute threat posed by this disease has since subsided with immunity buildup from vaccination and prior infection, prevalence remains high and waning immunity may lead to substantial pressures for years to come. In this work, then, we present a set of interval distributions, for COVID-19 cases and subsequent severe outcomes; hospital admission, ICU admission, and death. These may be used to model more realistic scenarios of hospital admissions and occupancy, given a trajectory of infections or cases. We present a method for obtaining empirical distributions using COVID-19 outcomes data from Scotland between September 2020 and January 2022 (N = 31724 hospital admissions, N = 3514 ICU admissions, N = 8306 mortalities). We present separate distributions for individual age, sex, and deprivation of residing community. While the risk of severe disease following COVID-19 infection is substantially higher for the elderly and those residing in areas of high deprivation, the length of stay shows no strong dependence, suggesting that severe outcomes are equally severe across risk groups. As Scotland and other countries move into a phase where testing is no longer abundant, these intervals may be of use for retrospective modelling of patterns of infection, given data on severe outcomes.

COVID-19, post-acute COVID-19 syndrome (PACS, "long COVID") and post-COVID-19 vaccination syndrome (PCVS, "post-COVIDvac-syndrome"): Similarities and differences

Worldwide there have been over 760 million confirmed coronavirus disease 2019 (COVID-19) cases, and over 13 billion COVID-19 vaccine doses have been administered as of April 2023, according to the World Health Organization. An infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can lead to an acute disease, i.e. COVID-19, but also to a post-acute COVID-19 syndrome (PACS, "long COVID"). Currently, the side effects of COVID-19 vaccines are increasingly being noted and studied. Here, we summarise the currently available indications and discuss our conclusions that (i) these side effects have specific similarities and differences to acute COVID-19 and PACS, that (ii) a new term should be used to refer to these side effects (post-COVID-19 vaccination syndrome, PCVS, colloquially "post-COVIDvac-syndrome"), and that (iii) there is a need to distinguish between acute COVID-19 vaccination syndrome (ACVS) and post-acute COVID-19 vaccination syndrome (PACVS) - in analogy to acute COVID-19 and PACS ("long COVID"). Moreover, we address mixed forms of disease caused by natural SARS-CoV-2 infection and COVID-19 vaccination. We explain why it is important for medical diagnosis, care and research to use the new terms (PCVS, ACVS and PACVS) in order to avoid confusion and misinterpretation of the underlying causes of disease and to enable optimal medical therapy. We do not recommend to use the term "Post-Vac-Syndrome" as it is imprecise. The article also serves to address the current problem of "medical gaslighting" in relation to PACS and PCVS by raising awareness among the medical professionals and supplying appropriate terminology for disease.

The impact of earlier reopening to travel in the Western Pacific on SARS-CoV-2 transmission

Background

The COVID-19 pandemic has led to a fall of over 70% in international travel, resulting in substantial economic damages. The impact is especially pronounced in the Asia-Pacific region, where governments have been slow to relax border restrictions.

Methods

A retrospective approach was used to construct notional epidemic trajectories for eight Asia-Pacific countries or regions, from June to November 2021, under hypothetical scenarios of earlier resumption of international travel and selective border reopening. The numbers of local infections and deaths over the prediction window were calculated accordingly.

Results

Had quarantine-free entry been permitted for all travellers from all the regions investigated, and travel volumes recovered to the 2019 levels, Australia, New Zealand, and Singapore would have been the three most severely affected regions, with at least doubled number of deaths, while infections would have increased marginally (< 5%) for Japan, Malaysia, and Thailand.

Conclusions

Earlier resumption of travel in Asia-Pacific, while maintaining a controlled degree of importation risk, could have been implemented through selective border-reopening strategies and on-arrival testing. Once countries had experienced large, localized COVID-19 outbreaks, earlier relaxation of border containment measures would not have resulted in a great increase in morbidity and mortality.

Estimation of monkeypox spread in a nonendemic country considering contact tracing and self-reporting: A stochastic modeling study

In May 2022, monkeypox started to spread in nonendemic countries. To investigate contact tracing and self-reporting of the primary case in the local community, a stochastic model is developed. An algorithm based on Gillespie's stochastic chemical kinetics is used to quantify the number of infections, contacts, and duration from the arrival of the primary case to the detection of the index case (or until there are no more local infections). Different scenarios were set considering the delay in contact tracing and behavior of infectors. We found that the self-reporting behavior of a primary case is the most significant factor affecting outbreak size and duration. Scenarios with a self-reporting primary case have an 86% reduction in infections (average: 5-7, in a population of 10 000) and contacts (average: 27-72) compared with scenarios with a non-self-reporting primary case (average number of infections and contacts: 27-72 and 197-537, respectively). Doubling the number of close contacts per day is less impactful compared with the self-reporting behavior of the primary case as it could only increase the number of infections by 45%. Our study emphasizes the importance of the prompt detection of the primary case.