Inferring super-spreading from transmission clusters of COVID-19 in Hong Kong, Japan, and Singapore

DDE: Deep Dynamic Epidemiological Modeling for Infectious Illness Development Forecasting in Multi-level Geographic Entities

Understanding and addressing the dynamics of infectious diseases, such as coronavirus disease 2019, are essential for effectively managing the current situation and developing intervention strategies. Epidemiologists commonly use mathematical models, known as epidemiological equations (EE), to simulate disease spread. However, accurately estimating the parameters of these models can be challenging due to factors like variations in social distancing policies and intervention strategies. In this study, we propose a novel method called deep dynamic epidemiological modeling (DDE) to address these challenges. The DDE method combines the strengths of EE with the capabilities of deep neural networks to improve the accuracy of fitting real-world data. In DDE, we apply neural ordinary differential equations to solve variant-specific equations, ensuring a more precise fit for disease progression in different geographic regions. In the experiment, we tested the performance of the DDE method and other state-of-the-art methods using real-world data from five diverse geographic entities: the USA, Colombia, South Africa, Wuhan in China, and Piedmont in Italy. Compared to the state-of-the-art method, DDE significantly improved accuracy, with an average fitting Pearson coefficient exceeding 0.97 across the five geographic entities. In summary, the DDE method enhances the accuracy of parameter fitting in epidemiological models and provides a foundation for constructing simpler models adaptable to different geographic areas.

Social Mixing and Network Characteristics of COVID-19 Patients Before and After Widespread Interventions: A Population-based Study

SARS-CoV-2 rapidly spreads among humans via social networks, with social mixing and network characteristics potentially facilitating transmission. However, limited data on topological structural features has hindered in-depth studies. Existing research is based on snapshot analyses, preventing temporal investigations of network changes. Comparing network characteristics over time offers additional insights into transmission dynamics. We examined confirmed COVID-19 patients from an eastern Chinese province, analyzing social mixing and network characteristics using transmission network topology before and after widespread interventions. Between the two time periods, the percentage of singleton networks increased from 38.9 to 62.8 ; the average shortest path length decreased from 1.53 to 1.14 ; the average betweenness reduced from 0.65 to 0.11 ; the average cluster size dropped from 4.05 to 2.72 ; and the out-degree had a slight but nonsignificant decline from 0.75 to 0.63 Results show that nonpharmaceutical interventions effectively disrupted transmission networks, preventing further disease spread. Additionally, we found that the networks’ dynamic structure provided more information than solely examining infection curves after applying descriptive and agent-based modeling approaches. In summary, we investigated social mixing and network characteristics of COVID-19 patients during different pandemic stages, revealing transmission network heterogeneities.

Superspreading, overdispersion and their implications in the SARS-CoV-2 (COVID-19) pandemic: a systematic review and meta-analysis of the literature

BACKGROUND

A recurrent feature of infectious diseases is the observation that different individuals show different levels of secondary transmission. This inter-individual variation in transmission potential is often quantified by the dispersion parameter k. Low values of k indicate a high degree of variability and a greater probability of superspreading events. Understanding k for COVID-19 across contexts can assist policy makers prepare for future pandemics.

METHODS

A literature search following a systematic approach was carried out in PubMed, Embase, Web of Science, Cochrane Library, medRxiv, bioRxiv and arXiv to identify publications containing epidemiological findings on superspreading in COVID-19. Study characteristics, epidemiological data, including estimates for k and R0, and public health recommendations were extracted from relevant records.

RESULTS

The literature search yielded 28 peer-reviewed studies. The mean k estimates ranged from 0.04 to 2.97. Among the 28 studies, 93% reported mean k estimates lower than one, which is considered as marked heterogeneity in inter-individual transmission potential. Recommended control measures were specifically aimed at preventing superspreading events. The combination of forward and backward contact tracing, timely confirmation of cases, rapid case isolation, vaccination and preventive measures were suggested as important components to suppress superspreading.

CONCLUSIONS

Superspreading events were a major feature in the pandemic of SARS-CoV-2. On the one hand, this made outbreaks potentially more explosive but on the other hand also more responsive to public health interventions. Going forward, understanding k is critical for tailoring public health measures to high-risk groups and settings where superspreading events occur.

The Nexus Between COVID-19 Factors and Air Pollution

Background and Objective

There have been significant effects of the current coronavirus-19 (COVID-19) infection outbreak on many facets of everyday life, particularly the environment. Despite the fact that a number of studies have already been published on the topic, an analysis of those studies' findings on COVID-19's effects on environmental pollution is still lacking. The goal of the research is to look into greenhouse gas emissions and air pollution in Bangladesh when COVID-19 is under rigorous lockdown. The specific drivers of the asymmetric relationship between air pollution and COVID-19 are being investigated.

Methods

The nonlinear relationship between carbon dioxide ( C O 2 ) emissions, fine particulate matter ( P M 2 . 5 ) , and COVID-19, as well as its precise components, are also being investigated. To examine the asymmetric link between COVID-19 factors on C O 2 emissions and P M 2 . 5 , we employed the nonlinear autoregressive distributed lag (NARDL) model. Daily positive cases and daily confirmed death by COVID-19 are considered the factors of COVID-19, with lockdown as a dummy variable.

Results

The bound test confirmed the existence of long-run and short-run relationships between variables. Bangladesh's strict lockdown, enforced in reaction to a surge of COVID-19 cases, reduced air pollution and dangerous gas emissions, mainly C O 2 , according to the dynamic multipliers graph.

Identifying High-Risk Events for COVID-19 Transmission: Estimating the Risk of Clustering Using Nationwide Data

The transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is known to be overdispersed, meaning that only a fraction of infected cases contributes to super-spreading. While cluster interventions are an effective measure for controlling pandemics due to the viruses' overdispersed nature, a quantitative assessment of the risk of clustering has yet to be sufficiently presented. Using systematically collected cluster surveillance data for coronavirus disease 2019 (COVID-19) from June 2020 to June 2021 in Japan, we estimated the activity-dependent risk of clustering in 23 establishment types. The analysis indicated that elderly care facilities, welfare facilities for people with disabilities, and hospitals had the highest risk of clustering, with 4.65 (95% confidence interval [CI]: 4.43-4.87), 2.99 (2.59-3.46), and 2.00 (1.88-2.12) cluster reports per million event users, respectively. Risks in educational settings were higher overall among older age groups, potentially being affected by activities with close and uncontrollable contact during extracurricular hours. In dining settings, drinking and singing increased the risk by 10- to 70-fold compared with regular eating settings. The comprehensive analysis of the COVID-19 cluster records provides an additional scientific basis for the design of customized interventions.

COVID-19 outbreaks analysis in the Valencian Region of Spain in the prelude of the third wave

Introduction

The COVID-19 pandemic has led to unprecedented social and mobility restrictions on a global scale. Since its start in the spring of 2020, numerous scientific papers have been published on the characteristics of the virus, and the healthcare, economic and social consequences of the pandemic. However, in-depth analyses of the evolution of single coronavirus outbreaks have been rarely reported.

Methods

In this paper, we analyze the main properties of all the tracked COVID-19 outbreaks in the Valencian Region between September and December of 2020. Our analysis includes the evaluation of the origin, dynamic evolution, duration, and spatial distribution of the outbreaks.

Results

We find that the duration of the outbreaks follows a power-law distribution: most outbreaks are controlled within 2 weeks of their onset, and only a few last more than 2 months. We do not identify any significant differences in the outbreak properties with respect to the geographical location across the entire region. Finally, we also determine the cluster size distribution of each infection origin through a Bayesian statistical model.

Discussion

We hope that our work will assist in optimizing and planning the resource assignment for future pandemic tracking efforts.

Systematic review and meta-analyses of superspreading of SARS-CoV-2 infections

Superspreading, or overdispersion in transmission, is a feature of SARS-CoV-2 transmission which results in surging epidemics and large clusters of infection. The dispersion parameter is a statistical parameter used to characterize and quantify heterogeneity. In the context of measuring transmissibility, it is analogous to measures of superspreading potential among populations by assuming that collective offspring distribution follows a negative-binomial distribution. We conducted a systematic review and meta-analysis on globally reported dispersion parameters of SARS-CoV-2 infection. All searches were carried out on 10 September 2021 in PubMed for articles published from 1 January 2020 to 10 September 2021. Multiple estimates of the dispersion parameter have been published for 17 studies, which could be related to where and when the data were obtained, in 8 countries (e.g. China, the United States, India, Indonesia, Israel, Japan, New Zealand and Singapore). High heterogeneity was reported among the included studies. The mean estimates of dispersion parameters range from 0.06 to 2.97 over eight countries, the pooled estimate was 0.55 (95% CI: 0.30, 0.79), with changing means over countries and decreasing slightly with the increasing reproduction number. The expected proportion of cases accounting for 80% of all transmissions is 19% (95% CrI: 7, 34) globally. The study location and method were found to be important drivers for diversity in estimates of dispersion parameters. While under high potential of superspreading, larger outbreaks could still occur with the import of the COVID-19 virus by traveling even when an epidemic seems to be under control.

Analysis of Superspreading Potential from Transmission Clusters of COVID-19 in South Korea

The COVID-19 pandemic has been spreading worldwide with more than 246 million confirmed cases and 5 million deaths across more than 200 countries as of October 2021. There have been multiple disease clusters, and transmission in South Korea continues. We aim to analyze COVID-19 clusters in Seoul from 4 March to 4 December 2020. A branching process model is employed to investigate the strength and heterogeneity of cluster-induced transmissions. We estimate the cluster-specific effective reproduction number Reff and the dispersion parameter κ using a maximum likelihood method. We also compute Rm as the mean secondary daily cases during the infection period with a cluster size m. As a result, a total of 61 clusters with 3088 cases are elucidated. The clusters are categorized into six groups, including religious groups, convalescent homes, and hospitals. The values of Reff and κ of all clusters are estimated to be 2.26 (95% CI: 2.02-2.53) and 0.20 (95% CI: 0.14-0.28), respectively. This indicates strong evidence for the occurrence of superspreading events in Seoul. The religious groups cluster has the largest value of Reff among all clusters, followed by workplaces, schools, and convalescent home clusters. Our results allow us to infer the presence or absence of superspreading events and to understand the cluster-specific characteristics of COVID-19 outbreaks. Therefore, more effective suppression strategies can be implemented to halt the ongoing or future cluster transmissions caused by small and sporadic clusters as well as large superspreading events.

Epidemiology of hospital-based COVID- 19 cluster in a tertiary care cancer hospital, Chennai, India 2020

Objectives

To identify risk factors associated with Coronavirus disease 2019 (COVID-19) in a Tertiary care cancer hospital-based cluster and recommend control measures.

Methods

We conducted tracing and confirmation among hospital and community contacts. We telephonically interviewed and abstracted information from hospital records and registers. We described the cluster by time, place and person. We conducted unmatched case-control study to compare risk factors and computed Odds Ratio (OR) and 95% confidence interval.

Results

We confirmed COVID-19 in 21 of 1478 tested (1.4%). Secondary attack (%) of COVID-19 among 824 contacts was higher among in-patients of block A (18), household contacts (3.4), housekeeping staff (3.3) and nurses (1.7). The cluster started on April 22 with two successive peaks five days apart and lasted until May 8. Being male, patients aged >33 years [OR = 30·7; 95% CI = 3·6 to 264], having hypertension [OR = 4·3; 95% CI = 1·1 to 16·7] or diabetes [OR = 3·8; 95% CI = 1·0 to 14·1] were associated with COVID-19. Mask compliance was poor (20%) among hospital workers.

Discussion

We recommended screening of all patients for diabetes and hypertension and isolation/testing of anyone with influenza-like illness for preventing COVID-19 clusters in hospital settings.

Superspreading and heterogeneity in transmission of SARS, MERS, and COVID-19: A systematic review

BACKGROUND

Severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and coronavirus disease 2019 (COVID-19) have caused substantial public health burdens and global health threats. Understanding the superspreading potentials of these viruses are important for characterizing transmission patterns and informing strategic decision-making in disease control. This systematic review aimed to summarize the existing evidence on superspreading features and to compare the heterogeneity in transmission within and among various betacoronavirus epidemics of SARS, MERS and COVID-19.

METHODS

PubMed, MEDLINE, and Embase databases were extensively searched for original studies on the transmission heterogeneity of SARS, MERS, and COVID-19 published in English between January 1, 2003, and February 10, 2021. After screening the articles, we extracted data pertaining to the estimated dispersion parameter (k) which has been a commonly-used measurement for superspreading potential.

FINDINGS

We included a total of 60 estimates of transmission heterogeneity from 26 studies on outbreaks in 22 regions. The majority (90%) of the k estimates were small, with values less than 1, indicating an over-dispersed transmission pattern. The point estimates of k for SARS and MERS ranged from 0.12 to 0.20 and from 0.06 to 2.94, respectively. Among 45 estimates of individual-level transmission heterogeneity for COVID-19 from 17 articles, 91% were derived from Asian regions. The point estimates of k for COVID-19 ranged between 0.1 and 5.0.

CONCLUSIONS

We detected a substantial over-dispersed transmission pattern in all three coronaviruses, while the k estimates varied by differences in study design and public health capacity. Our findings suggested that even with a reduced R value, the epidemic still has a high resurgence potential due to transmission heterogeneity.

Convergence of climate-driven hurricanes and COVID-19: The impact of 2020 hurricanes Eta and Iota on Nicaragua

The 2020 Atlantic hurricane season was notable for a record-setting 30 named storms while, contemporaneously, the COVID-19 pandemic was circumnavigating the globe. The active spread of COVID-19 complicated disaster preparedness and response actions to safeguard coastal and island populations from hurricane hazards. Major hurricanes Eta and Iota, the most powerful storms of the 2020 Atlantic season, made November landfalls just two weeks apart, both coming ashore along the Miskito Coast in Nicaragua's North Caribbean Coast Autonomous Region. Eta and Iota bore the hallmarks of climate-driven storms, including rapid intensification, high peak wind speeds, and decelerating forward motion prior to landfall. Hurricane warning systems, combined with timely evacuation and sheltering procedures, minimized loss of life during hurricane impact. Yet these protective actions potentially elevated risks for COVID-19 transmission for citizens sharing congregate shelters during the storms and for survivors who were displaced post-impact due to severe damage to their homes and communities. International border closures and travel restrictions that were in force to slow the spread of COVID-19 diminished the scope, timeliness, and effectiveness of the humanitarian response for survivors of Eta and Iota. Taken together, the extreme impacts from hurricanes Eta and Iota, compounded by the ubiquitous threat of COVID-19 transmission, and the impediments to international humanitarian response associated with movement restrictions during the pandemic, acted to exacerbate harms to population health for the citizens of Nicaragua.

COVID-19 versus SARS: A comparative review

The two genetically similar severe acute respiratory syndrome coronaviruses, SARS-CoV-1 and SARS-CoV-2, have each been responsible for global epidemics of vastly different scales. Although both viruses arose from similar origins, they quickly diverged due to differences in their transmission dynamics and spectrum of clinical presentations. The potential involvement of multiple organs systems, including the respiratory, cardiac, gastrointestinal and neurological, during infection necessitates a comprehensive understanding of the clinical pathogenesis of each virus. The management of COVID-19, initially modelled after SARS and other respiratory illnesses, has continued to evolve as we accumulate more knowledge and experience during the pandemic, as well as develop new therapeutics and vaccines. The impact of these two coronaviruses has been profound for our health care and public health systems, and we hope that the lessons learned will not only bring the current pandemic under control, but also prevent and reduce the impact of future pandemics.

Epidemiology, clinical spectrum, viral kinetics and impact of COVID-19 in the Asia-Pacific region

COVID-19 has hit the world by surprise, causing substantial mortality and morbidity since 2020. This narrative review aims to provide an overview of the epidemiology, induced impact, viral kinetics and clinical spectrum of COVID-19 in the Asia-Pacific Region, focusing on regions previously exposed to outbreaks of coronavirus. COVID-19 progressed differently by regions, with some (such as China and Taiwan) featured by one to two epidemic waves and some (such as Hong Kong and South Korea) featured by multiple waves. There has been no consensus on the estimates of important epidemiological time intervals or proportions, such that using them for making inferences should be done with caution. Viral loads of patients with COVID-19 peak in the first week of illness around days 2 to 4 and hence there is very high transmission potential causing community outbreaks. Various strategies such as government-guided and suppress-and-lift strategies, trigger-based/suppression approaches and alert systems have been employed to guide the adoption and easing of control measures. Asymptomatic and pre-symptomatic transmission is a hallmark of COVID-19. Identification and isolation of symptomatic patients alone is not effective in controlling the ongoing outbreaks. However, early, prompt and coordinated enactment predisposed regions to successful disease containment. Mass COVID-19 vaccinations are likely to be the light at the end of the tunnel. There is a need to review what we have learnt in this pandemic and examine how to transfer and improve existing knowledge for ongoing and future epidemics.

Identification of superspreading environment under COVID-19 through human mobility data

COVID-19 reaffirms the vital role of superspreaders in a pandemic. We propose to broaden the research on superspreaders through integrating human mobility data and geographical factors to identify superspreading environment. Six types of popular public facilities were selected: bars, shopping centres, karaoke/cinemas, mega shopping malls, public libraries, and sports centres. A historical dataset on mobility was used to calculate the generalized activity space and space-time prism of individuals during a pre-pandemic period. Analysis of geographic interconnections of public facilities yielded locations by different classes of potential spatial risk. These risk surfaces were weighed and integrated into a "risk map of superspreading environment" (SE-risk map) at the city level. Overall, the proposed method can estimate empirical hot spots of superspreading environment with statistical accuracy. The SE-risk map of Hong Kong can pre-identify areas that overlap with the actual disease clusters of bar-related transmission. Our study presents first-of-its-kind research that combines data on facility location and human mobility to identify superspreading environment. The resultant SE-risk map steers the investigation away from pure human focus to include geographic environment, thereby enabling more differentiated non-pharmaceutical interventions and exit strategies to target some places more than others when complete city lockdown is not practicable.

SARS-CoV-2 outbreak investigation in a German meat processing plant

We describe a multifactorial investigation of a SARS-CoV-2 outbreak in a large meat processing complex in Germany. Infection event timing, spatial, climate and ventilation conditions in the processing plant, sharing of living quarters and transport, and viral genome sequences were analyzed. Our results suggest that a single index case transmitted SARS-CoV-2 to co-workers over distances of more than 8 m, within a confined work area in which air is constantly recirculated and cooled. Viral genome sequencing shows that all cases share a set of mutations representing a novel sub-branch in the SARS-CoV-2 C20 clade. We identified the same set of mutations in samples collected in the time period between this initial infection cluster and a subsequent outbreak within the same factory, with the largest number of confirmed SARS-CoV-2 cases in a German meat processing facility reported so far. Our results indicate climate conditions, fresh air exchange rates, and airflow as factors that can promote efficient spread of SARS-CoV-2 via long distances and provide insights into possible requirements for pandemic mitigation strategies in industrial workplace settings.

SARS-CoV-2 outbreak investigation in a German meat processing plant

We describe a multifactorial investigation of a SARS-CoV-2 outbreak in a large meat processing complex in Germany. Infection event timing, spatial, climate and ventilation conditions in the processing plant, sharing of living quarters and transport, and viral genome sequences were analyzed. Our results suggest that a single index case transmitted SARS-CoV-2 to co-workers over distances of more than 8 m, within a confined work area in which air is constantly recirculated and cooled. Viral genome sequencing shows that all cases share a set of mutations representing a novel sub-branch in the SARS-CoV-2 C20 clade. We identified the same set of mutations in samples collected in the time period between this initial infection cluster and a subsequent outbreak within the same factory, with the largest number of confirmed SARS-CoV-2 cases in a German meat processing facility reported so far. Our results indicate climate conditions, fresh air exchange rates, and airflow as factors that can promote efficient spread of SARS-CoV-2 via long distances and provide insights into possible requirements for pandemic mitigation strategies in industrial workplace settings.

Work at inpatient care units is associated with an increased risk of SARS-CoV-2 infection; a cross-sectional study of 8679 healthcare workers in Sweden

BACKGROUND

During the Covid-19 pandemic, the protection of healthcare workers has been in focus throughout the world, but the availability and quality of personal protective equipment has at times and in some settings been suboptimal.

MATERIALS AND METHODS

A total of 8679 healthcare workers and healthcare support staff in the county of Uppsala, north of Stockholm, were included in this cross-sectional study. All subjects were analysed for IgG anti-SARS-CoV-2, and predictors for positive serostatus were analysed in a logistic regression model including demographic parameters and self-reported employment characteristics.

RESULTS

Overall, 577 (6.6%) were classified as seropositive, with no statistically significant differences between healthcare workers and support staff. Among healthcare workers, age (OR 0.987 per year, 95% CI 0.980-0.995), time to sampling (OR 1.019 per day, 95% CI 1.004-1.035), and employment at an outpatient care unit (OR 0.620, 95% CI 0.487-0.788) were statistically significantly associated with risk of infection. Covid-19 specific units were not at particular risk, compared to other units with comparable characteristics and staff demography.

CONCLUSION

Our findings indicate that SARS-CoV-2 transmission is related to inpatient healthcare work, and illustrate the need for a high standard of basic hygiene routines in all inpatient care settings.

Settings of virus exposure and their implications in the propagation of transmission networks in a COVID-19 outbreak

BACKGROUND

Transmission dynamics of SARS-CoV-2 varied by the settings of virus exposure. Understanding the inter-relationship between exposure setting and transmission networks would provide a basis for informing public health control strategies.

METHODS

Surveillance and clinical data from the first wave of COVID-19 outbreaks in Hong Kong were accessed. Twelve exposure setting types were differentiated - household, neighbourhood, eateries, entertainment, parties, shopping, personalised service, workplace, education, worship, healthcare, transport. Clustering was investigated followed by reconstructing the transmission cascades of clustered cases using social networking approach. Linked and unlinked cases were compared in statistical analyses.

FINDINGS

Between 23 January and 19 June 2020, 1128 cases were reported. Among 324 cases related to local transmission, 123 clusters comprising two or more epidemiologically linked cases were identified. Linked cases had lower Ct value (p < 0·001) than unlinked cases. Households accounted for 63% of all clusters with half as primary setting, while entertainment accounted for the highest number of primary setting transmission cases. There were altogether 19 cascades involving >1 exposure setting, with a median reproduction number of 3(IQR: 2-4), versus 1(IQR:1-2) for cascades involving a single setting (n = 36 cascades). The longest cascade featured a bar (entertainment) as primary setting, with propagation through 30 non-primary exposure settings from seven setting types, reflecting, propensity for widespread dispersion and difficulty in containment.

INTERPRETATION

There was marked heterogeneity in the characteristics of SARS-CoV-2 transmission cascades which differed by exposure setting. Network epidemiological analyses of transmission cascades can be applied as a risk assessment tool in decision-making for calibrating social distancing measures.

FUNDING

Health and Medical Research Fund.

The Potential Role of Super Spread Events in SARS-COV-2 Pandemic; a Narrative Review

Coronaviruses, members of Coronaviridae family, cause extensive epidemics of vast diseases like severe acute respiratory syndrome (SARS) and Coronavirus Disease-19 (COVID-19) in animals and humans. Super spread events (SSEs) potentiate early outbreak of the disease and its constant spread in later stages. Viral recombination events within species and across hosts lead to natural selection based on advanced infectivity and resistance. In this review, the importance of containment of SSEs was investigated with emphasis on stopping COVID-19 spread and its socio-economic consequences. A comprehensive search was conducted among literature available in multiple electronic sources to find articles that addressed the "potential role of SSEs on severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) pandemic" and were published before 20^th of August 2020. Overall, ninety-eight articles were found eligible and reviewed. Specific screening strategies within potential super spreading host groups can also help to efficiently manage severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) epidemics, in contrast to the partially effective general restriction measures. The effect of SSEs on previous SARS epidemics has been documented in detail. However, the respective potential impact of SSEs on SARS-COV-2 outbreak is composed and presented in the current review, thereby implying the warranted effort required for effective SSE preventive strategies, which may lead to overt global community health benefits. This is crucial for SARS-COV-2 pandemic containment as the vaccine(s) development process will take considerable time to safely establish its potential usefulness for future clinical usage.