Search

A Viral Infection Risk Indoor Simulator (VIRIS) has been developed to quickly assess and compare mitigations for airborne disease spread. This agent-based simulator combines people movement in an indoor space, viral transmission modelling and detailed architectural design, and it is powered by topologicpy, an open-source Python library. VIRIS generates very fast predictions of the viral concentration and the spatiotemporal infection risk for individuals as they move through a given space. The simulator is validated with data from a courtroom superspreader event. A sensitivity study for unknown parameter values is also performed. We compare several non-pharmaceutical interventions (NPIs) issued in UK government guidance, for two indoor settings: a care home and a supermarket. Additionally, we have developed the user-friendly VIRIS web app that allows quick exploration of diverse scenarios of interest and visualisation, allowing policymakers, architects and space managers to easily design or assess infection risk in an indoor space. [ABSTRACT FROM AUTHOR]

Introduction: COVID-19 pandemic has highlighted the role of aerosol transmission and the importance of superspreading events. We analyzed a choir rehearsal in November 2020, where all participants, except one who had recently earlier recovered from COVID-19, were infected. We explore the risk factors for severe disease in this event and model the aerosol dispersion in the rehearsal room. Materials and methods: Characteristics of participants were collected by interviews and supplemented with patient records. A computational simulation of aerosol distribution in the rehearsal room and the efficacy of potential safety measures was conducted using the Large-Eddy Simulation approach. Infection risk was studied by analyzing quanta emission and exposure with the Wells-Riley equation. Results: The simulation showed that airborne transmission likely explains this mass contagion event. Every singer was exposed to the virus in only 5 min from the beginning of the rehearsal, and maximum concentration levels were reached at 20 min the concentration levels started to approach a steady state after 20 min. Although concentration differences existed in the room, risk levels near (1 m) and far (5 m) from the aerosol source were similar for certain singers. Modeling indicated infection risk levels of 70–100% after one hour; the risk would have been considerably reduced by wearing high-filtration respirators. Age and pre-existing comorbidities predicted more severe disease. The high incidence of illness may be partly attributed to the relatively high median age of individuals. Additionally, those admitted to the hospital had multiple underlying health conditions that predispose them to more severe disease. Conclusions: Airborne transmission and indoor space can explain this mass exposure event. High-filtration respirators could have prevented some infections. The importance of safety distances diminishes the longer the indoor event. The concept of safety distance is challenging, as our study suggests that long range airborne transmission may occur in indoor events with extended duration. We encourage informing the public, especially persons at risk, of safety measures during epidemics. [ABSTRACT FROM AUTHOR]

As the coronavirus disease 2019 pandemic continues to rage, the unprecedented manifold increase in monkeypox (mpox) cases throughout the world is raising qualms about a possible pandemic. As of January 2024, the disease has been reported in around 116 countries, with nearly 92,500 confirmed cases and 170 deaths. In this mini-review, we have endeavored to cover multiple aspects of the mpox disease. Mpox virus is an enveloped double-stranded DNA virus that belongs to the Orthopoxvirus genus. The disease is endemic in certain African countries. Until recently, however, the disease was rarely reported in Europe and the United States. In contrast to the previous outbreaks outside Africa, reported cases in the 2022 outbreak did not travel to the endemic areas. Superspreading events at mass gatherings, multiple-partner sexual encounters, and international travel were major drivers of the recent global outbreak of mpox. The 2022 mpox virus may have undergone accelerated evolution. It diverges from the related 2018 to 2019 viruses by around 50 single-nucleotide polymorphisms, some of which brought about amino acid changes in immunogenic surface glycoprotein B21. Differential diagnosis for mpox could be quite challenging since it can masquerade as a wide variety of illnesses. Worse still, some patients may be asymptomatic or show subtle symptoms. The infection is confirmed by conventional or real-time polymerase chain reaction on lesion material. Although there is no specific therapy approved for mpox infections, two antivirals (tecovirimat and brincidofovir) and vaccinia immune globulin may be used. Vaccines also provide protection against mpox when properly administered prior to exposure. Finally, the implementation of preventive measures is of paramount importance, especially in regions where mpox transmission is widespread and among high-risk populations. [ABSTRACT FROM AUTHOR]

Modeling of airborne virus transmission and protection against it requires knowledge of the amount of biofluid emitted into the atmosphere and its viral load. Whereas viral concentrations in biofluids are readily measured by quantitative PCR, the total volume of fluids aerosolized during speaking, as measured by different researchers using various technologies, differs by several orders of magnitude. We compared collection methods in which the aerosols first enter into a low-humidity chamber either by direct injection or via commonly used funnel and tubing arrangements, followed by standard optical particle sizer measurement. This "collect first, measure later" approach sacrifices the recording of the temporal correlation between aerosol generation and sound types such as plosives and vowels. However, the direct-injection mode prevents inertia deposition associated with the funnel arrangements and reveals far more intermediate-size (5–20 μm in diameter) particles that can dominate the total mass of ejected respiratory aerosol. The larger aerosol mass observed with our method partially reconciles the large discrepancy between the SARS-CoV-2 infectious dose estimated from superspreader event analyses and that from human challenge data. Our results also impact measures to combat airborne virus transmission because they indicate that aerosols that settle faster than good room ventilation rates can dominate this process. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic profoundly affected all mass gatherings for sporting and religious events, causing cancellation, postponement, or downsizing. On March 24, 2020, the Japanese Government, the Tokyo Organising Committee of the Olympic and Paralympic Games, and the International Olympic Committee decided to postpone the Tokyo 2020 Olympic and Paralympic Games until the summer of 2021. With the emergence of SARS-CoV-2, the potential creation of a superspreading event that would overwhelm the Tokyo health system was perceived as a risk. Even with a delayed start date, an extensive scale of resources, planning, risk assessment, communication, and SARS-CoV-2 testing were required for the Games to be held during the COVID-19 pandemic. The effectiveness of various mitigation and control measures, including the availability of vaccines and the expansion of effective testing options, allowed event organisers and the Japanese Government to successfully host the rescheduled 2020 Tokyo Olympic Games from July 23 to Aug 8, 2021 with robust safety plans in place. In February and March, 2022, Beijing hosted the 2022 Winter Olympic Games as scheduled, built on the lessons learnt from the Tokyo Games, and developed specific COVID-19 countermeasure plans in the context of China's national framework for the plan called Zero COVID. Results from the testing programmes at both the Tokyo and Beijing Games show that the measures put in place were effective at preventing the spread of COVID-19 within the Games, and ensured that neither event became a COVID-19-spreading event. The extensive experience from the Tokyo and Beijing Olympic Games highlights that it is possible to organise mass gatherings during a pandemic, provided that appropriate risk assessment, risk mitigation, and risk communication arrangements are in place, leaving legacies for future mass gatherings, public health, epidemic preparedness, and wider pandemic response. [ABSTRACT FROM AUTHOR]

With the COVID-19 pandemic still ongoing, there is a need to ensure that people are not subjected to the risk of getting infected with the disease. Since COVID-19 is airborne, engineering controls must be provided to monitor and mitigate the spread of the disease in the air. One of the measures is to ensure proper ventilation within indoor spaces where superspreader events were previously documented in poorly ventilated spaces. CO2 levels reflect the rate at which the used air within is replaced and, therefore, can be used as a proxy for COVID-19 risk. This study developed economical CO2 monitors which are deployed across indoor spaces and public transportation, such as air-conditioned jeepneys, to communicate the risk. Using the least squares method on the first-order ODE of mass balance, a multivariate method is devised for deriving the occupancy and the ventilation rate from the recorded CO2 levels. Using the multivariate method, the ventilation rates of different indoor spaces are determined and used for the subsequent computations using the Wells-Riley model to derive the respective infection risk, particularly of COVID-19. [ABSTRACT FROM AUTHOR]

Background: The first local outbreak of Delta Variant B.1.617.2 COVID-19 of China occurred in Guangzhou city, south China, in May 2021. This study analyzed the transmission chains and local cluster characteristics of this outbreak, intended to provide information support for the development and adjustment of local prevention and control strategies. Methods: The transmission chains and local cluster characteristics of 161 local cases in the outbreak were described and analyzed. Incubation period, serial interval and generation time were calculated using the exact time of exposure and symptom onset date of the cases. The daily number of reported cases and the estimated generation time were used to estimate the effective reproduction number (Rt). Results: We identified 7 superspreading events who had more than 5 next generation cases and their infected cases infected 70.81%(114/161) of all the cases transmission. Dining and family exposure were the main transmission routes in the outbreak, with 29.19% exposed through dining and 32.30% exposed through family places. Through further analysis of the outbreak, the estimated mean incubation period was 4.22 (95%CI: 3.66–4.94) days, the estimated mean generation time was 2.60 (95%CI: 1.96–3.11) days, and the estimated Rt was 3.29 (95%CI: 2.25–5.07). Conclusions: Classification and dynamically adjusted prevention and control measures had been carried out according to analysis of transmission chains and epidemical risk levels, including promoting nucleic acid screening at different regions and different risk levels, dividing closed-off area, controlled area according to the risk of infection, raising the requirements of leaving Guangzhou. By the above control measures, Guangzhou effectively control the outbreak within 28 days without implementing a large-scale lockdown policy. [ABSTRACT FROM AUTHOR]

Public holidays have been associated with SARS-CoV-2 incidence surges, although a firm link remains to be established. This association is sometimes attributed to events where transmissions occur at a disproportionately high rate, known as superspreading events. Here, we describe a sudden surge in new cases with the Omicron BA.1 strain amongst higher education students in Belgium. Contact tracers classed most of these cases as likely or possibly infected on New Year's Eve, indicating a direct trigger by New Year celebrations. Using a combination of contact tracing and phylogenetic data, we show the limited role of superspreading events in this surge. Finally, the numerous simultaneous transmissions allowed a unique opportunity to determine the distribution of incubation periods of the Omicron strain. Overall, our results indicate that, even under social restrictions, a surge in transmissibility of SARS-CoV-2 can occur when holiday celebrations result in small social gatherings attended simultaneously and communitywide. [ABSTRACT FROM AUTHOR]

The spread of airborne infectious diseases has largely been driven by superspreading events, in which a single individual directly infects several contacts. Superspreading events that occurred at several construction sites around the world afflicted construction practitioners and forced the suspension of construction activities. To reduce the probability of superspreading events, this study developed a network-based computational framework based on a K -shell decomposition approach with the input of the topological interaction network of project participants to identify potential superspreaders in construction projects. The feasibility of the developed framework was evaluated with three numerical case studies: one sample case with a hierarchical structure with an average accuracy of 98.45%, one sample case with a matrix structure with an average accuracy of 92.25%, and an empirical case related to a COVID-19 outbreak in a construction project in Hong Kong with an accuracy of over 80.13%. This study recommends that all potential superspreaders, especially if they are employed by the main contractor, take rapid antigen tests (RATs) regularly. If all potential superspreaders are detected through regular RATs and all potential secondary cases are detected by contract tracing, up to 82.35% of infected cases can be prevented. [ABSTRACT FROM AUTHOR]

Superspreading has become a key mechanism of COVID-19 transmission which creates chaos. The classical approach of compartmental models may not sufficiently reflect the epidemiological situation amid superspreading events (SSEs). We perform a data-driven approach and recognise the deterministic chaos of confirmed cases. The first derivative (≈ difference of total confirmed cases) and the second derivative (≈ difference of the first derivative) are used upon SSEs to showcase the chaos. Varying solution trajectories, sensitivity and numerical unpredictability are the chaotic characteristics discussed here. [ABSTRACT FROM AUTHOR]

Most models of epidemic spread, including many designed specifically for COVID-19, implicitly assume mass-action contact patterns and undirected contact networks, meaning that the individuals most likely to spread the disease are also the most at risk of contracting it from others. Here, we review results from the theory of random directed graphs which show that many important quantities, including the reproduction number and the epidemic size, depend sensitively on the joint distribution of in- and out-degrees ("risk" and "spread"), including their heterogeneity and the correlation between them. By considering joint distributions of various kinds, we elucidate why some types of heterogeneity cause a deviation from the standard Kermack--McKendrick analysis of SIR models, i.e., so-called mass-action models where contacts are homogeneous and random, and why some do not. We also show that some structured SIR models informed by realistic complex contact patterns among types of individuals (age or activity) are simply mixtures of Poisson processes and tend not to deviate significantly from the simplest mass-action model. Finally, we point out some possible policy implications of this directed structure, both for contact tracing strategy and for interventions designed to prevent superspreading events. In particular, directed graphs have a forward and a backward version of the classic "friendship paradox"--forward edges tend to lead to individuals with high risk, while backward edges lead to individuals with high spread--such that a combination of both forward and backward contact tracing is necessary to find superspreading events and prevent future cascades of infection. [ABSTRACT FROM AUTHOR]

During infectious disease outbreaks, some infected individuals may spread the disease widely and amplify risks in the community. People whose daily activities bring them in close proximity to many others can unknowingly become superspreaders. The use of contact tracking based on social networks, GPS, or mobile tracking data can help to identify superspreaders and break the chain of transmission. We propose a model that aims at providing insight into risk factors of superspreading events. Here, we use a social force model to estimate the superspreading potential of individuals walking in a bidirectional corridor. First, we applied the model to identify parameters that favor exposure to an infectious person in scattered crowds. We find that low walking speed and high body mass both increase the expected number of close exposures. Panic events exacerbate the risks while social distancing reduces both the number and duration of close encounters. Further, in dense crowds, pedestrians interact more and cannot easily maintain the social distance between them. The number of exposures increases with the density of person in the corridor. The study of movements reveals that individuals walking toward the center of the corridor tend to rotate and zigzag more than those walking along the edges, and thus have higher risks of superspreading. The corridor model can be applied to designing risk reduction measures for specific high volume venues, including transit stations, stadiums, and schools.

Structural features and the heterogeneity of disease transmissions play an essential role in the dynamics of epidemic spread. But these aspects can not completely be assessed from aggregate data or macroscopic indicators such as the effective reproduction number. We propose in this paper an index of effective aggregate dispersion (EffDI) that indicates the significance of infection clusters and superspreading events in the progression of outbreaks by carefully measuring the level of relative stochasticity in time series of reported case numbers using a specially crafted statistical model for reproduction. This allows to detect potential transitions from predominantly clustered spreading to a diffusive regime with diminishing significance of singular clusters, which can be a decisive turning point in the progression of outbreaks and relevant in the planning of containment measures. We evaluate EffDI for SARS-CoV-2 case data in different countries and compare the results with a quantifier for the socio-demographic heterogeneity in disease transmissions in a case study to substantiate that EffDI qualifies as a measure for the heterogeneity in transmission dynamics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Two distinct strategies for controlling an emerging epidemic are physical distancing and regular testing with self-isolation. These strategies are especially important before effective vaccines or treatments become widely available. The testing strategy has been promoted frequently but used less often than physical distancing to mitigate COVID-19. We compared the performance of these strategies in an integrated epidemiological and economic model that includes a simple representation of transmission by "superspreading," wherein a relatively small fraction of infected individuals cause a large share of infections. We examined the economic benefits of distancing and testing over a wide range of conditions, including variations in the transmissibility and lethality of the disease meant to encompass the most prominent variants of COVID-19 encountered so far. In a head-to-head comparison using our primary parameter values, both with and without superspreading and a declining marginal value of mortality risk reductions, an optimized testing strategy outperformed an optimized distancing strategy. In a Monte Carlo uncertainty analysis, an optimized policy that combined the two strategies performed better than either one alone in more than 25% of random parameter draws. Insofar as diagnostic tests are sensitive to viral loads, and individuals with high viral loads are more likely to contribute to superspreading events, superspreading enhances the relative performance of testing over distancing in our model. Both strategies performed best at moderate levels of transmissibility, somewhat lower than the transmissibility of the ancestral strain of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

• Superspreading events are commonly observed since the individual variance of contact patterns is heterogeneous. • Heterogeneity of contact patterns should be incorporated as a key factor assessing the severity of the COVID-19 outbreaks. • An agent-based model is developed to propose an alternative measurement, the degree-specific basic reproduction number. • The degree-specific basic reproduction number can capture more accurate and realistic transmission probability. Assessing the transmission potential of emerging infectious diseases, such as COVID-19, is crucial for implementing prompt and effective intervention policies. The basic reproduction number is widely used to measure the severity of the early stages of disease outbreaks. The basic reproduction number of standard ordinary differential equation models is computed for homogeneous contact patterns; however, realistic contact patterns are far from homogeneous, specifically during the early stages of disease transmission. Heterogeneity of contact patterns can lead to superspreading events that show a significantly high level of heterogeneity in generating secondary infections. This is primarily due to the large variance in the contact patterns of complex human behaviours. Hence, in this work, we investigate the impacts of heterogeneity in contact patterns on the basic reproduction number by developing two distinct model frameworks: 1) an SEIR-Erlang ordinary differential equation model and 2) an SEIR stochastic agent-based model. Furthermore, we estimated the transmission probability of both models in the context of COVID-19 in South Korea. Our results highlighted the importance of heterogeneity in contact patterns and indicated that there should be more information than one quantity (the basic reproduction number as the mean quantity), such as a degree-specific basic reproduction number in the distributional sense when the contact pattern is highly heterogeneous. [ABSTRACT FROM AUTHOR]

Background information on viruses is first presented which include topics like evolution, ecology, history, identification, structure, and application. The novel SARS-CoV-2, which causes COVID-19, pandemic originated in Wuhan, China in December 2019. Millions of people were infected with the virus in a short time period causing urgent concern worldwide. The purpose of this review is to provide a brief and general understanding of the SARS-CoV-2 to increase awareness and actions toward preventive measures. COVID-19 is a (+) ssRNA spherical enveloped virion that causes primarily respiratory illnesses. The S protein interacts with ACE-2 receptors on the host cell to gain entry and uses the cell’s machinery to achieve replication and release. COVID-19 is transmitted by inhalation of infected droplets and aerosols. Asymptomatic individuals are still able to pass the infectious disease where careful evaluations are needed to prevent superspreading events. Face masks are necessary form of interventions to reduce direct human to human transmissions. The transition from local endemic to worldwide pandemic was rapid and profound. A better understanding and knowledge of viruses, and more specifically COVID-19, among the population will reduce transmissions and the prolonged pandemic. Global and community efforts are necessary for individuals of varying age, sex, and health since they are affected in different ways by the COVID-19 virus. In a world of infectious diseases and novel emergences, new treatments and medical interventions is an urgent initiative in combating pathogens like the SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Indoor superspreading events are significant drivers of transmission of respiratory diseases. In this work, we study the dynamics of airborne transmission in consecutive meetings of individuals in enclosed spaces. In contrast to the usual pairwise-interaction models of infection where effective contacts transmit the disease, we focus on group interactions where individuals with distinct health states meet simultaneously. Specifically, the disease is transmitted by infected individuals exhaling droplets (contributing to the viral load in the closed space) and susceptible ones inhaling the contaminated air. We propose a modeling framework that couples the fast dynamics of the viral load attained over meetings in enclosed spaces and the slow dynamics of disease progression at the population level. Our modeling framework incorporates the multiple time scales involved in different setups in which indoor events may happen, from single-time events to events hosting multiple meetings per day, over many days. We present theoretical and numerical results of trade-offs between the room characteristics (ventilation system efficiency and air mass) and the group’s behavioral and composition characteristics (group size, mask compliance, testing, meeting time, and break times), that inform indoor policies to achieve disease control in closed environments through different pathways. Our results emphasize the impact of break times, mask-wearing, and testing on facilitating the conditions to achieve disease control. We study scenarios of different break times, mask compliance, and testing. We also derive policy guidelines to contain the infection rate under a certain threshold. [ABSTRACT FROM AUTHOR]

There is an ongoing debate on the COVID-19 infection fatality rate (IFR) and the impact of COVID-19 on overall population mortality. Here, we addressed these issues in a community in Germany with a major superspreader event analyzing deaths over time and auditing death certificates in the community.18 deaths that occurred within the first six months of the pandemic had a positive test for SARS-CoV-2. Six out of 18 deaths had non-COVID-19 related causes of death (COD). Individuals with COVID-19 COD typically died of respiratory failure (75%) and tended to have fewer reported comorbidities (p = 0.029). Duration between first confirmed infection and death was negatively associated with COVID-19 being COD (p = 0.04). Repeated seroprevalence essays in a cross-sectional epidemiological study showed modest increases in seroprevalence over time, and substantial seroreversion (30%). IFR estimates accordingly varied depending on COVID-19 death attribution. Careful ascertainment of COVID-19 deaths is important in understanding the impact of the pandemic. [ABSTRACT FROM AUTHOR]

Proximity social interactions are crucial for infectious diseases transmission. Crowded agglomerations pose serious risk of triggering superspreading events. Locations like transportation hubs (airports and stations) are designed to optimize logistic efficiency, not to reduce crowding, and are characterized by a constant in and out flow of people. Here, we analyze the paradigmatic example of London Heathrow, one of the busiest European airports. Thanks to a dataset of anonymized individuals' trajectories, we can model the spreading of different diseases to localize the contagion hotspots and to propose a spatial immunization policy targeting them to reduce disease spreading risk. We also detect the most vulnerable destinations to contagions produced at the airport and quantify the benefits of the spatial immunization technique to prevent regional and global disease diffusion. This method is immediately generalizable to train, metro and bus stations and to other facilities such as commercial or convention centers. Efficient spatial targeting of interventions could reduce the spread of infections in transportation hubs. Here, the authors assess the optimal locations to target in Heathrow airport using disease transmission models informed by a contact network based on anonymised location data from 200,000 individuals. [ABSTRACT FROM AUTHOR]

Background: Superspreading events (SSEs) played a critical role in fueling the COVID-19 outbreaks. Although it is well-known that COVID-19 epidemics exhibited substantial superspreading potential, little is known about the risk of observing SSEs in different contact settings. In this study, we aimed to assess the potential of superspreading in different contact settings in Japan. Method: Transmission cluster data from Japan was collected between January and July 2020. Infector-infectee transmission pairs were constructed based on the contact tracing history. We fitted the data to negative binomial models to estimate the effective reproduction number (R) and dispersion parameter (k). Other epidemiological issues relating to the superspreading potential were also calculated. Results: The overall estimated R and k are 0.561 (95% CrI: 0.496, 0.640) and 0.221 (95% CrI: 0.186, 0.262), respectively. The transmission in community, healthcare facilities and school manifest relatively higher superspreading potentials, compared to other contact settings. We inferred that 13.14% (95% CrI: 11.55%, 14.87%) of the most infectious cases generated 80% of the total transmission events. The probabilities of observing superspreading events for entire population and community, household, health care facilities, school, workplace contact settings are 1.75% (95% CrI: 1.57%, 1.99%), 0.49% (95% CrI: 0.22%, 1.18%), 0.07% (95% CrI: 0.06%, 0.08%), 0.67% (95% CrI: 0.31%, 1.21%), 0.33% (95% CrI: 0.13%, 0.94%), 0.32% (95% CrI: 0.21%, 0.60%), respectively. Conclusion: The different potentials of superspreading in contact settings highlighted the need to continuously monitoring the transmissibility accompanied with the dispersion parameter, to timely identify high risk settings favoring the occurrence of SSEs. [ABSTRACT FROM AUTHOR]

The transmission heterogeneity of an epidemic is associated with a complex mixture of host, pathogen and environmental factors. And it may indicate superspreading events to reduce the efficiency of population-level control measures and to sustain the epidemic over a larger scale and a longer duration. Methods have been proposed to identify significant transmission heterogeneity in historic epidemics based on several data sources, such as contact history, viral genomes and spatial information, which may not be available, and more importantly ignore the temporal trend of transmission heterogeneity. Here we attempted to establish a convenient method to estimate real-time heterogeneity over an epidemic. Within the branching process framework, we introduced an instant-individualheterogenous infectiousness model to jointly characterize the variation in infectiousness both between individuals and among different times. With this model, we could simultaneously estimate the transmission heterogeneity and the reproduction number from incidence time series. We validated the model with data of both simulated and real outbreaks. Our estimates of the overall and real-time heterogeneities of the six epidemics were consistent with those presented in the literature. Additionally, our model is robust to the ubiquitous bias of under-reporting and misspecification of serial interval. By analyzing recent data from South Africa, we found evidence that the Omicron might be of more significant transmission heterogeneity than Delta. Our model based on incidence data was proved to be reliable in estimating the real-time transmission heterogeneity. Author Summary: The transmission of many infectious diseases is usually heterogeneous in time and space. Such transmission heterogeneity may indicate superspreading events (where some infected individuals transmit to disproportionately more susceptibles than others), reduce the efficiency of the population-level control measures, and sustain the epidemic over a larger scale and a longer duration. Classical methods of monitoring epidemic spread centered on the reproduction number which represent the average transmission potential of the epidemic at the population level, but failed to reflect the systematic variation in transmission. Several recent methods have been proposed to identify significant transmission heterogeneity in the epidemics such as Ebola, MERS, COVID-19. However, these methods are developed based on some sophisticated information such as contact history, viral genome and spatial information, of the confirmed cases, which are typically field-specific and not easy to generalize. In this study, we proposed a simple and generic method of estimating transmission heterogeneity from incidence time series, which provided consistent estimation of heterogeneity with those records with detailed data. It also helps in exploring the transmission heterogeneity of the newly emerging variant of Omicron. Our model enhances current understanding of epidemic dynamics, and highlight the potential importance of targeted control measures. [ABSTRACT FROM AUTHOR]

Background: Superspreading events are important drivers of the SARS-CoV-2 pandemic and long-range (LR) transmission is believed to play a major role. We investigated two choir outbreaks with different attack rates (AR) to analyze the contribution of LR transmission and highlight important measures for prevention. Methods: We conducted two retrospective cohort studies and obtained demographic, clinical, laboratory and contact data, performed SARS-CoV-2 serology, whole genome sequencing (WGS), calculated LR transmission probabilities, measured particle emissions of selected choir members, and calculated particle air concentrations and inhalation doses. Results: We included 65 (84%) and 42 (100%) members of choirs 1 and 2, respectively, of whom 58 (89%) and 10 (24%) became cases. WGS confirmed strain identity in both choirs. Both primary cases transmitted presymptomatically. Particle emission rate when singing was 7 times higher compared to talking. In choir 1, the median concentration of primary cases' emitted particles in the room was estimated to be 8 times higher, exposure at least 30 minutes longer and room volume smaller than in choir 2, resulting in markedly different estimated probabilities for LR transmission (mode: 90% vs. 16%, 95% CI: 80–95% vs. 6–36%). According to a risk model, the first transmission in choir 1 occurred likely after 8 minutes of singing. Conclusions: The attack rate of the two choirs differed significantly reflecting the differences in LR transmission risks. The pooled proportion of cases due to LR transmission was substantial (81%; 55/68 cases) and was facilitated by likely highly infectious primary cases, high particle emission rates, and indoor rehearsing for an extended time. Even in large rooms, singing of an infectious person may lead to secondary infections through LR exposure within minutes. In the context of indoor gatherings without mask-wearing and waning or insufficient immunity, these results highlight the ongoing importance of non-pharmaceutical interventions wherever aerosols can accumulate. [ABSTRACT FROM AUTHOR]

Thailand was the first country reporting the first Coronavirus disease 2019 (COVID-19) infected individual outside mainland China. Here we delineated the course of the COVID-19 outbreak together with the timeline of the control measures and public health policies employed by the Thai government during the first wave of the COVID-19 outbreak in Thailand. Based on the comprehensive epidemiological data, we reconstructed the dynamics of COVID-19 transmission in Thailand using a stochastic modeling approach. Our stochastic model incorporated the effects of individual heterogeneity in infectiousness on disease transmission, which allows us to capture relevant features of superspreading events. We found that our model could accurately capture the transmission dynamics of the first COVID-19 epidemic wave in Thailand. The model predicted that at the end of the first wave, the number of cumulative confirmed cases was 3091 (95%CI: 2782–3400). We also estimated the time-varying reproduction number (Rt) during the first epidemic wave. We found that after implementing the nationwide interventions, the Rt in Thailand decreased from the peak value of 5.67 to a value below one in less than one month, indicating that the control measures employed by the Thai government during the first COVID-19 epidemic wave were effective. Finally, the effects of transmission heterogeneity and control measures on the likelihood of outbreak extinction were also investigated. [ABSTRACT FROM AUTHOR]

Superspreading events play an important role in the spread of several pathogens, such as SARS-CoV-2. While the basic reproduction number of the original Wuhan SARS-CoV-2 is estimated to be about 3 for Belgium, there is substantial inter-individual variation in the number of secondary cases each infected individual causes—with most infectious individuals generating no or only a few secondary cases, while about 20% of infectious individuals is responsible for 80% of new infections. Multiple factors contribute to the occurrence of superspreading events: heterogeneity in infectiousness, individual variations in susceptibility, differences in contact behavior, and the environment in which transmission takes place. While superspreading has been included in several infectious disease transmission models, research into the effects of different forms of superspreading on the spread of pathogens remains limited. To disentangle the effects of infectiousness-related heterogeneity on the one hand and contact-related heterogeneity on the other, we implemented both forms of superspreading in an individual-based model describing the transmission and spread of SARS-CoV-2 in a synthetic Belgian population. We considered its impact on viral spread as well as on epidemic resurgence after a period of social distancing. We found that the effects of superspreading driven by heterogeneity in infectiousness are different from the effects of superspreading driven by heterogeneity in contact behavior. On the one hand, a higher level of infectiousness-related heterogeneity results in a lower risk of an outbreak persisting following the introduction of one infected individual into the population. Outbreaks that did persist led to fewer total cases and were slower, with a lower peak which occurred at a later point in time, and a lower herd immunity threshold. Finally, the risk of resurgence of an outbreak following a period of lockdown decreased. On the other hand, when contact-related heterogeneity was high, this also led to fewer cases in total during persistent outbreaks, but caused outbreaks to be more explosive in regard to other aspects (such as higher peaks which occurred earlier, and a higher herd immunity threshold). Finally, the risk of resurgence of an outbreak following a period of lockdown increased. We found that these effects were conserved when testing combinations of infectiousness-related and contact-related heterogeneity. Author summary: To investigate the effect of different sources of superspreading on disease dynamics, we implemented superspreading driven by heterogeneity in infectiousness and heterogeneity in contact behavior into an individual-based model for the transmission of SARS-CoV-2 in the Belgian population. We compared the impact of both forms of superspreading in a scenario without interventions as well as in a scenario in which a period of strict social distancing (i.e. a lockdown) is followed by a period of partial release. We found that both forms of superspreading have very different effects. On the one hand, increasing the level of infectiousness-related heterogeneity led to less outbreaks being observed following the introduction of one infected individual in the population. Furthermore, final outbreak sizes decreased, and outbreaks became slower, with lower and later peaks, and a lower herd immunity threshold. Finally, the risk for resurgence of an outbreak following a period of lockdown also decreased. On the other hand, when contact-related heterogeneity was high, this also led to smaller final sizes, but caused outbreaks to be more explosive regarding other aspects (such as higher peaks that occurred earlier). The herd immunity threshold also increased, as did the risk of resurgence of outbreaks. [ABSTRACT FROM AUTHOR]

Background: India has experienced the second largest outbreak of COVID-19 globally, yet there is a paucity of studies analysing contact tracing data in the region which can optimise public health interventions (PHI's). Methods: We analysed contact tracing data from Karnataka, India between 9 March and 21 July 2020. We estimated metrics of transmission including the reproduction number (R), overdispersion (k), secondary attack rate (SAR), and serial interval. R and k were jointly estimated using a Bayesian Markov Chain Monte Carlo approach. We studied determinants of risk of further transmission and risk of being symptomatic using Poisson regression models. Findings: Up to 21 July 2020, we found 111 index cases that crossed the super-spreading threshold of ≥8 secondary cases. Among 956 confirmed traced cases, 8.7% of index cases had 14.4% of contacts but caused 80% of all secondary cases. Among 16715 contacts, overall SAR was 3.6% [95% CI, 3.4–3.9] and symptomatic cases were more infectious than asymptomatic cases (SAR 7.7% vs 2.0%; aRR 3.63 [3.04–4.34]). As compared to infectors aged 19–44 years, children were less infectious (aRR 0.21 [0.07–0.66] for 0–5 years and 0.47 [0.32–0.68] for 6–18 years). Infectors who were confirmed ≥4 days after symptom onset were associated with higher infectiousness (aRR 3.01 [2.11–4.31]). As compared to asymptomatic cases, symptomatic cases were 8.16 [3.29–20.24] times more likely to cause symptomatic infection in their secondary cases. Serial interval had a mean of 5.4 [4.4–6.4] days, and case fatality rate was 2.5% [2.4–2.7] which increased with age. Conclusion: We found significant heterogeneity in the individual-level transmissibility of SARS-CoV-2 which could not be explained by the degree of heterogeneity in the underlying number of contacts. To strengthen contact tracing in over-dispersed outbreaks, testing and tracing delays should be minimised and retrospective contact tracing should be implemented. Targeted measures to reduce potential superspreading events should be implemented. Interventions aimed at children might have a relatively small impact on reducing transmission owing to their low symptomaticity and infectivity. We propose that symptomatic cases could cause a snowballing effect on clinical severity and infectiousness across transmission generations; further studies are needed to confirm this finding. [ABSTRACT FROM AUTHOR]

The long-term evolution of viruses is ultimately due to viral mutants that arise within infected individuals and transmit to other individuals. Here, we use deep sequencing to investigate the transmission of viral genetic variation among individuals during a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak that infected the vast majority of crew members on a fishing boat. We deep-sequenced nasal swabs to characterize the within-host viral population of infected crew members, using experimental duplicates and strict computational filters to ensure accurate variant calling. We find that within-host viral diversity is low in infected crew members. The mutations that did fix in some crew members during the outbreak are not observed at detectable frequencies in any of the sampled crew members in which they are not fixed, suggesting that viral evolution involves occasional fixation of low-frequency mutations during transmission rather than persistent maintenance of within-host viral diversity. Overall, our results show that strong transmission bottlenecks dominate viral evolution even during a superspreading event with a very high attack rate. [ABSTRACT FROM AUTHOR]

Detailed characterization of severe acute respiratory syndrome coronavirus 2 (SARSCoV- 2) transmission across different settings can help design less disruptive interventions. We used real-time, privacy-enhanced mobility data in the New York City, NY and Seattle, WA metropolitan areas to build a detailed agent-based model of SARS-CoV-2 infection to estimate the where, when, and magnitude of transmission events during the pandemic's first wave. We estimate that only 18% of individuals produce most infections (80%), with about 10% of events that can be considered superspreading events (SSEs). Although mass gatherings present an important risk for SSEs, we estimate that the bulk of transmission occurred in smaller events in settings like workplaces, grocery stores, or food venues. The places most important for transmission change during the pandemic and are different across cities, signaling the large underlying behavioral component underneath them. Our modeling complements case studies and epidemiological data and indicates that real-time tracking of transmission events could help evaluate and define targeted mitigation policies. [ABSTRACT FROM AUTHOR]

Historically, emerging and reemerging infectious diseases have caused large, deadly, and expensive multinational outbreaks. Often outbreak investigations aim to identify who infected whom by reconstructing the outbreak transmission tree, which visualizes transmission between individuals as a network with nodes representing individuals and branches representing transmission from person to person. We compiled a database, called OutbreakTrees, of 382 published, standardized transmission trees consisting of 16 directly transmitted diseases ranging in size from 2 to 286 cases. For each tree and disease, we calculated several key statistics, such as tree size, average number of secondary infections, the dispersion parameter, and the proportion of cases considered superspreaders, and examined how these statistics varied over the course of each outbreak and under different assumptions about the completeness of outbreak investigations. We demonstrated the potential utility of the database through 2 short analyses addressing questions about superspreader epidemiology for a variety of diseases, including Coronavirus Disease 2019 (COVID-19). First, we found that our transmission trees were consistent with theory predicting that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events. Additionally, we investigated patterns in how superspreaders are infected. Across trees with more than 1 superspreader, we found preliminary support for the theory that superspreaders generate other superspreaders. In sum, our findings put the role of superspreading in COVID-19 transmission in perspective with that of other diseases and suggest an approach to further research regarding the generation of superspreaders. These data have been made openly available to encourage reuse and further scientific inquiry. This study compiles and standardizes reported infectious disease transmission trees to analyze trends in superspreader frequency and generation; these data support theories that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events, and that superspreaders generate other superspreaders. [ABSTRACT FROM AUTHOR]

We probed the transmission of COVID-19 by applying an airborne transmission model to five well-documented case studies—a Washington state church choir, a Korean call center, a Korean exercise class, and two different Chinese bus trips. For all events the likely index patients were pre-symptomatic or mildly symptomatic, which is when infective patients are most likely to interact with large groups of people. Applying the model to those events yields results that suggest the following: (1) transmission was airborne; (2) superspreading events do not require an index patient with an unusually high viral load; (3) the viral loads for all of the index patients were of the same order of magnitude and consistent with experimentally measured values for patients at the onset of symptoms, even though viral loads across the population vary by a factor of >108. In particular we used a Wells-Riley exposure model to calculate q, the total average number of infectious quanta inhaled by a person at the event. Given the q value for each event, the simple airborne transmission model was used to determined Sq, the rate at which the index patient exhaled infectious quanta and N0, the characteristic number of COVID-19 virions needed to induce infection. Despite the uncertainties in the values of some parameters of the superspreading events, all five events yielded (N0∼300–2,000 virions), which is similar to published values for influenza. Finally, this work describes the conditions under which similar methods can provide actionable information on the transmission of other viruses. [ABSTRACT FROM AUTHOR]

Superspreading events and overdispersion are hallmarks of the COVID-19 pandemic. However, the specific roles and influence of established viral and physical factors related to the mechanisms of transmission, on overdispersion, remain unresolved. We, therefore, conducted mechanistic modeling of SARS-CoV-2 point-source transmission by infectious aerosols using real-world occupancy data from more than 100 000 social contact settings in ten US metropolises. We found that 80% of secondary infections are predicted to arise from approximately 4% of index cases, which show up as a stretched tail in the probability density function of secondary infections per infectious case. Individual-level variability in viral load emerges as the dominant driver of overdispersion, followed by occupancy. We then derived an analytical function, which replicates the simulated overdispersion, and with which we demonstrate the effectiveness of potential mitigation strategies. Our analysis, connecting the mechanistic understanding of SARS-CoV-2 transmission by aerosols with observed large-scale epidemiological characteristics of COVID-19 outbreaks, adds an important dimension to the mounting body of evidence with regard to airborne transmission of SARS-CoV-2 and thereby emerges as a powerful tool toward assessing the probability of outbreaks and the potential impact of mitigation strategies on large scale disease dynamics. [ABSTRACT FROM AUTHOR]

Between November 2020 and May 2021, Germany faced the largest highly pathogenic avian influenza (HPAI) epidemic recorded so far with 245 outbreaks in poultry and captive birds and more than 1000 diagnosed cases in wild birds. In March 2021, an HPAI outbreak of subtype H5N8 was confirmed in a holding rearing laying hens for sales. Disease introduction probably occurred via indirect contact with infected wild birds. Since the index farm sold chicken to customers including many smallholders, partly in travel trade, the primary outbreak triggered 105 known secondary outbreaks in five German federal states. An outbreak investigation was carried out with links between the involved farms retrieved from the German Animal Disease Notification System used for network analysis. In some cases, links were confirmed through sequence-based molecular analysis. Special emphasis was put on the estimation of the flock incubation period as a prerequisite of sound contact tracing. The unique circumstances of an outbreak farm with frequent direct trade contacts prior to disease suspicion enabled an assessment of the flock incubation period based on the consequences of contacts, further supported by molecular analysis and modeling of disease spread. In this case, the flock incubation period was at least 14 days. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 has now infected 15 million people and produced more than six hundred thousand deaths around the world. Due to high transmission levels, many governments implemented social distancing and confinement measures with different levels of required compliance to mitigate the COVID-19 epidemic. In several countries, these measures were effective, and it was possible to flatten the epidemic curve and control it. In others, this objective was not or has not been achieved. In far too many cities around the world, rebounds of the epidemic are occurring or, in others, plateaulike states have appeared, where high incidence rates remain constant for relatively long periods of time. Nonetheless, faced with the challenge of urgent social need to reactivate their economies, many countries have decided to lift mitigation measures at times of high incidence. In this paper, we use a mathematical model to characterize the impact of short duration transmission events within the confinement period previous but close to the epidemic peak. The model also describes the possible consequences on the disease dynamics after mitigation measures are lifted. We use Mexico City as a case study. The results show that events of high mobility may produce either a later higher peak, a long plateau with relatively constant but high incidence or the same peak as in the original baseline epidemic curve, but with a post-peak interval of slower decay. Finally, we also show the importance of carefully timing the lifting of mitigation measures. If this occurs during a period of high incidence, then the disease transmission will rapidly increase, unless the effective contact rate keeps decreasing, which will be very difficult to achieve once the population is released.

It appears inevitable that severe acute respiratory syndrome coronavirus 2 will continue to spread. Although we still have limited information on the epidemiology of this virus, there have been multiple reports of superspreading events (SSEs), which are associated with both explosive growth early in an outbreak and sustained transmission in later stages. Although SSEs appear to be difficult to predict and therefore difficult to prevent, core public health actions can prevent and reduce the number and impact of SSEs. To prevent and control of SSEs, speed is essential. Prevention and mitigation of SSEs depends, first and foremost, on quickly recognizing and understanding these events, particularly within healthcare settings. Better understanding transmission dynamics associated with SSEs, identifying and mitigating high-risk settings, strict adherence to healthcare infection prevention and control measures, and timely implementation of nonpharmaceutical interventions can help prevent and control severe acute respiratory syndrome coronavirus 2, as well as future infectious disease outbreaks.

Background: We consider cluster size data of SARS-CoV-2 transmissions for a number of different settings from recently published data. The statistical characteristics of superspreading events are commonly described by fitting a negative binomial distribution to secondary infection and cluster size data as an alternative to the Poisson distribution as it is a longer tailed distribution, with emphasis given to the value of the extra parameter which allows the variance to be greater than the mean. Here we investigate whether other long tailed distributions from more general extended Poisson process modelling can better describe the distribution of cluster sizes for SARS-CoV-2 transmissions.Methods: We use the extended Poisson process modelling (EPPM) approach with nested sets of models that include the Poisson and negative binomial distributions to assess the adequacy of models based on these standard distributions for the data considered.Results: We confirm the inadequacy of the Poisson distribution in most cases, and demonstrate the inadequacy of the negative binomial distribution in some cases.Conclusions: The probability of a superspreading event may be underestimated by use of the negative binomial distribution as much larger tail probabilities are indicated by EPPM distributions than negative binomial alternatives. We show that the large shared accommodation, meal and work settings, of the settings considered, have the potential for more severe superspreading events than would be predicted by a negative binomial distribution. Therefore public health efforts to prevent transmission in such settings should be prioritised. [ABSTRACT FROM AUTHOR]

Contagion phenomena are often the results of multibody interactions—such as superspreading events or social reinforcement—describable as hypergraphs. We develop an approximate master equation framework to study contagions on hypergraphs with a heterogeneous structure in terms of group size (hyperedge cardinality) and of node membership (hyperdegree). By mapping multibody interactions to nonlinear infection rates, we demonstrate the influence of large groups in two ways. First, we characterize the phase transition, which can be continuous or discontinuous with a bistable regime. Our analytical expressions for the critical and tricritical points highlight the influence of the first three moments of the membership distribution. We also show that heterogeneous group sizes and nonlinear contagion promote a mesoscopic localization regime where contagion is sustained by the largest groups, thereby inhibiting bistability. Second, we formulate an optimal seeding problem for hypergraph contagion and compare two strategies: allocating seeds according to node or group properties. We find that, when the contagion is sufficiently nonlinear, groups are more effective seeds than individual hubs. Group interactions can dramatically alter social contagion dynamics and lead to the emergence of new phenomena like abrupt transitions and critical mass effects. The authors develop an approximate master equation framework to analytically describe contagion in heterogeneous hypergraphs and study the impact of large influential groups in seeding and sustaining epidemics. [ABSTRACT FROM AUTHOR]

Background: A large cluster of 59 cases were linked to a single flight with 146 passengers from New Delhi to Hong Kong in April 2021. This outbreak coincided with early reports of exponential pandemic growth in New Delhi, which reached a peak of > 400 000 newly confirmed cases on 7 May 2021.Methods: Epidemiological information including date of symptom onset, date of positive-sample detection and travel and contact history for individual cases from this flight were collected. Whole genome sequencing was performed, and sequences were classified based on the dynamic Pango nomenclature system. Maximum-likelihood phylogenetic analysis compared sequences from this flight alongside other cases imported from India to Hong Kong on 26 flights between June 2020 and April 2021, as well as sequences from India or associated with India-related travel from February to April 2021 and 1217 reference sequences.Results: Sequence analysis identified six lineages of SARS-CoV-2 belonging to two variants of concern (Alpha and Delta) and one variant of public health interest (Kappa) involved in this outbreak. Phylogenetic analysis confirmed at least three independent sub-lineages of Alpha with limited onward transmission, a superspreading event comprising 37 cases of Kappa and transmission of Delta to only one passenger. Additional analysis of another 26 flights from India to Hong Kong confirmed widespread circulation of all three variants in India since early March 2021.Conclusions: The broad spectrum of disease severity and long incubation period of SARS-CoV-2 pose a challenge for surveillance and control. As illustrated by this particular outbreak, opportunistic infections of SARS-CoV-2 can occur irrespective of variant lineage, and requiring a nucleic acid test within 72 hours of departure may be insufficient to prevent importation or in-flight transmission. [ABSTRACT FROM AUTHOR]

Superspreading and variants of concern (VOC) of the human pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are the main catalyzers of the coronavirus disease 2019 (COVID-19) pandemic. However, measuring their individual impact is challenging. By examining the largest database of SARS-CoV-2 genomes The Global Initiative on Sharing Avian Influenza Data [GISAID; n >1.2 million high-quality (HQ) sequences], we present evidence suggesting that superspreading has had a key role in the epidemiological predominance of VOC. There are clear signatures in the database compatible with large superspreading events (SSEs) coinciding chronologically with the worst epidemiological scenarios triggered by VOC. The data suggest that, without the randomness effect of the genetic drift facilitated by superspreading, new VOC of SARS-CoV-2 would have had more limited chance of success. Viral genome phylogenies reflect patterns of virus transmissions (e.g., signatures left by transmission chains differ from those left by superspreading). Due to an incubation time of ~5 to 6 days and an evolutionary rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the order of ~10–3, superspreading transmissions generate starlike phylogenies that find perfect parallelism in contact tracing networks. It is likely that thousands of variants of concern (VOC) passed unnoticed to genome databases because they have died out before having the opportunity to emerge in the population (or have not been sampled). Given that mutational changes occur in a nearly constant way, it is not obvious how to determine the mutation/s that make the virus more infectious. The algorithm of the pandemic is not simple and superspreading should be considered as one of the main catalyzers of the SARS-CoV-2 pandemic worldwide, independently of the viral variant involved. Evidence points to a key role of superspreading in the success of VOC. Studies analyzing selective forces on VOC should not ignore the power of genetic drift on spreading. [ABSTRACT FROM AUTHOR]

• R t of COVID-19 was predicted using mobility, temperature, and risk awareness. • Predicted R t captured the observed estimates. • Using available data, COVID-19 transmission can be monitored in real time. The effective reproduction number (R t) has been critical for assessing the effectiveness of countermeasures during the coronavirus disease 2019 (COVID-19) pandemic. Conventional methods using reported incidences are unable to provide timely R t data due to the delay from infection to reporting. Our study aimed to develop a framework for predicting R t in real time, using timely accessible data — i.e. human mobility, temperature, and risk awareness. A linear regression model to predict R t was designed and embedded in the renewal process. Four prefectures of Japan with high incidences in the first wave were selected for model fitting and validation. Predictive performance was assessed by comparing the observed and predicted incidences using cross-validation, and by testing on a separate dataset in two other prefectures with distinct geographical settings from the four studied prefectures. The predicted mean values of R t and 95% uncertainty intervals followed the overall trends for incidence, while predictive performance was diminished when R t changed abruptly, potentially due to superspreading events or when stringent countermeasures were implemented. The described model can potentially be used for monitoring the transmission dynamics of COVID-19 ahead of the formal estimates, subject to delay, providing essential information for timely planning and assessment of countermeasures. [ABSTRACT FROM AUTHOR]

Mycobacterium bovis (M. bovis) is a causative agent of bovine tuberculosis, a significant source of morbidity and mortality in the global cattle industry. The Randomised Badger Culling Trial was a field experiment carried out between 1998 and 2005 in the South West of England. As part of this trial, M. bovis isolates were collected from contemporaneous and overlapping populations of badgers and cattle within ten defined trial areas. We combined whole genome sequences from 1,442 isolates with location and cattle movement data, identifying transmission clusters and inferred rates and routes of transmission of M. bovis. Most trial areas contained a single transmission cluster that had been established shortly before sampling, often contemporaneous with the expansion of bovine tuberculosis in the 1980s. The estimated rate of transmission from badger to cattle was approximately two times higher than from cattle to badger, and the rate of within-species transmission considerably exceeded these for both species. We identified long distance transmission events linked to cattle movement, recurrence of herd breakdown by infection within the same transmission clusters and superspreader events driven by cattle but not badgers. Overall, our data suggests that the transmission clusters in different parts of South West England that are still evident today were established by long-distance seeding events involving cattle movement, not by recrudescence from a long-established wildlife reservoir. Clusters are maintained primarily by within-species transmission, with less frequent spill-over both from badger to cattle and cattle to badger. Author summary: Bovine tuberculosis (bTB), predominantly caused by Mycobacterium bovis, is a significant cause of sickness and death amongst cattle globally. Due to its broad host range which includes a variety of wildlife species such as the Eurasian badger, control measures for bTB are often both ineffective and expensive. One method used to attempt to control the spread of bTB in Great Britain is to cull badgers in areas adjacent to infected herds. To assess the effect of badger culling on the prevalence of bTB in nearby herds, the Randomised Badger Culling Trial (RBCT) was set up by the British government and ran between 1998 and 2005. Here we use 1,442 whole genome sequences obtained from isolates collected as part of the RBCT to describe the population structure of Mycobacterium bovis in the trial areas, identify putative transmission clusters and estimate the directionality of transmission, and integrate the genomic data with associated cattle movement data to identify examples of recurrence, superspreading and long-distance transmission. We found that the transmission clusters identified were strongly associated with RBCT trial area and that, whilst badger to cattle transmission was broadly more common than vice versa, the majority of transmission was occurring within the herds or badger populations. Additionally, molecular dating of the transmission clusters showed that the clusters were likely seeded during the 1980s, a period during which the prevalence of bTB in Great Britain increased markedly. Our study provides novel insights into the transmission of bTB between cattle and badger in Great Britain. [ABSTRACT FROM AUTHOR]

Background: Effective vaccines are now available for SARS-CoV-2 in the 2nd year of the COVID-19 pandemic, but there remains significant uncertainty surrounding the necessary vaccination rate to safely lift occupancy controls in public buildings and return to pre-pandemic norms. The aim of this paper is to estimate setting-specific vaccination thresholds for SARS-CoV-2 to prevent sustained community transmission using classical principles of airborne contagion modeling. We calculated the airborne infection risk in three settings, a classroom, prison cell block, and restaurant, at typical ventilation rates, and then the expected number of infections resulting from this risk at varying percentages of occupant immunity.Results: We estimate the setting-specific immunity threshold for control of wild-type SARS-CoV-2 to range from a low of 40% for a mechanically ventilation classroom to a high of 85% for a naturally ventilated restaurant.Conclusions: If vaccination rates are limited to a theoretical minimum of approximately two-thirds of the population, enhanced ventilation above minimum standards for acceptable air quality is needed to reduce the frequency and severity of SARS-CoV-2 superspreading events in high-risk indoor environments. [ABSTRACT FROM AUTHOR]

Present day risk assessment on the spreading of airborne viruses is often based on the classical Wells-Riley model assuming immediate mixing of the aerosol into the studied environment. Here, we improve on this approach and the underlying assumptions by modeling the space-time dependency of the aerosol concentration via a transport equation with a dynamic source term introduced by the infected individual(s). In the present agent-based methodology, we study the viral aerosol inhalation exposure risk in two scenarios including a low/high risk scenario of a "supermarket"/"bar". The model takes into account typical behavioral patterns for determining the rules of motion for the agents. We solve a diffusion model for aerosol concentration in the prescribed environments in order to account for local exposure to aerosol inhalation. We assess the infection risk using the Wells-Riley model formula using a space-time dependent aerosol concentration. The results are compared against the classical Wells-Riley model. The results indicate features that explain individual cases of high risk with repeated sampling of a heterogeneous environment occupied by non-equilibrium concentration clouds. An example is the relative frequency of cases that might be called superspreading events depending on the model parameters. A simple interpretation is that averages of infection risk are often misleading. They also point out and explain the qualitative and quantitative difference between the two cases—shopping is typically safer for a single individual person. [ABSTRACT FROM AUTHOR]

The SARS‐CoV‐2 virus is highly contagious, as demonstrated by numerous well‐documented superspreading events. The infection commonly starts in the upper respiratory tract (URT) but can migrate to the lower respiratory tract (LRT) and other organs, often with severe consequences. Whereas LRT infection can lead to shedding of virus via breath and cough droplets, URT infection enables shedding via abundant speech droplets. Their viral load can be high in carriers with mild or no symptoms, an observation linked to the abundance of SARS‐CoV‐2‐susceptible cells in the oral cavity epithelium. Expelled droplets rapidly lose water through evaporation, with the smaller ones transforming into long‐lived aerosol. Although the largest speech droplets can carry more virions, they are few in number, fall to the ground rapidly and therefore play a relatively minor role in transmission. Of more concern is small speech aerosol, which can descend deep into the LRT and cause severe disease. However, since their total volume is small, the amount of virus they carry is low. Nevertheless, in closed environments with inadequate ventilation, they can accumulate, which elevates the risk of direct LRT infection. Of most concern is the large fraction of speech aerosol that is intermediate‐sized because it remains suspended in air for minutes and can be transported over considerable distances by convective air currents. The abundance of this speech‐generated aerosol, combined with its high viral load in pre‐ and asymptomatic individuals, strongly implicates airborne transmission of SARS‐CoV‐2 through speech as the primary contributor to its rapid spread. [ABSTRACT FROM AUTHOR]

A reanalysis of SARS-CoV-2 deep sequencing data from donor-recipient pairs indicates that transmission bottlenecks are very narrow (one to three virions). [ABSTRACT FROM AUTHOR]

A customized susceptible, exposed, infected, and recovered compartmental model is presented for describing the control of asymptomatic spread of COVID-19 infections on a residential, urban college campus embedded in a large urban community by using public health protocols, founded on surveillance testing, contact tracing, isolation, and quarantine. Analysis in the limit of low infection rates--a necessary condition for successful operation of the campus--yields expressions for controlling the infection and understanding the dynamics of infection spread. The number of expected cases on campus is proportional to the exogenous infection rate in the community and is decreased by more frequent testing and effective contact tracing. Simple expressions are presented for the dynamics of superspreader events and the impact of partial vaccination. The model results compare well with residential data from Boston University's undergraduate population for fall 2020. [ABSTRACT FROM AUTHOR]