Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel

Universal Nonlinear Infection Kernel from Heterogeneous Exposure on Higher-Order Networks

The collocation of individuals in different environments is an important prerequisite for exposure to infectious diseases on a social network. Standard epidemic models fail to capture the potential complexity of this scenario by (1) neglecting the higher-order structure of contacts that typically occur through environments like workplaces, restaurants, and households, and (2) assuming a linear relationship between the exposure to infected contacts and the risk of infection. Here, we leverage a hypergraph model to embrace the heterogeneity of environments and the heterogeneity of individual participation in these environments. We find that combining heterogeneous exposure with the concept of minimal infective dose induces a universal nonlinear relationship between infected contacts and infection risk. Under nonlinear infection kernels, conventional epidemic wisdom breaks down with the emergence of discontinuous transitions, superexponential spread, and hysteresis.

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.

Characteristics of SARS-CoV-2 Infections in Israeli Children During the Circulation of Different SARS-CoV-2 Variants

This cohort study compares the characteristics of infections from SARS-CoV-2 variants spreading during August to October 2020 vs the variants spreading during December 2020 to February 2021 among children in Israel.

Intrafamilial Spread and Altered Symptomatology of SARS-CoV-2, During Predominant Circulation of Lineage B.1.1.7 Variant in Israel

The dynamics of intrafamilial spread of SARS-CoV-2 during January-February 2021 when variant B.1.1.7 predominated were compared with data from April to May 2020, when other circulating variants prevailed. Much higher intrafamilial transmission rates among all age groups, in particular in young children, and lower rates of sensory impairment were demonstrated during January-February 2021.

Effect of Non-lockdown Social Distancing and Testing-Contact Tracing During a COVID-19 Outbreak in Daegu, South Korea, February to April 2020: A Modeling Study

Objective

In Spring 2020, South Korea applied non-lockdown social distancing (avoiding mass gathering and non-essential social engagement, without restricting the movement of people who were not patients or contacts), testing-and-isolation (testing), and tracing-and-quarantine the contacts (contact tracing) to successfully control the first large-scale COVID-19 outbreak outside China. However, the relative contributions of these two interventions remain uncertain.

Methods

We constructed an SEIR model of SARS-CoV-2 transmission (disproportionately through superspreading events) and fit the model to outbreak data in Daegu, South Korea, from February to April 2020. We assessed the effect of non-lockdown social distancing (population-wide control measures) and/or testing-contact tracing (individual-specific control measures), alone or combined, in terms of the basic reproductive number (R0) and the trajectory of the epidemic.

Results

The point estimate for baseline R0 is 3.6 (sensitivity analyses range: 2.3 to 5.6). Combined interventions of non-lockdown social distancing and testing-contact tracing can suppress R0 to less than one and rapidly contain the epidemic, even under the worst scenario with a high baseline R0 of 5.6. In contrast, either intervention alone will fail to suppress R0. Non-lockdown social distancing alone just postpones the peak of the epidemic, while testing-contact tracing alone only flattens the curve but does not contain the outbreak.

Conclusions

To successfully control a large-scale COVID-19 outbreak, both non-lockdown social distancing and testing-contact tracing must be implemented. The two interventions are synergistic.

COVID-19 false dichotomies and a comprehensive review of the evidence regarding public health, COVID-19 symptomatology, SARS-CoV-2 transmission, mask wearing, and reinfection

Scientists across disciplines, policymakers, and journalists have voiced frustration at the unprecedented polarization and misinformation around coronavirus disease 2019 (COVID-19) pandemic. Several false dichotomies have been used to polarize debates while oversimplifying complex issues. In this comprehensive narrative review, we deconstruct six common COVID-19 false dichotomies, address the evidence on these topics, identify insights relevant to effective pandemic responses, and highlight knowledge gaps and uncertainties. The topics of this review are: 1) Health and lives vs. economy and livelihoods, 2) Indefinite lockdown vs. unlimited reopening, 3) Symptomatic vs. asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, 4) Droplet vs. aerosol transmission of SARS-CoV-2, 5) Masks for all vs. no masking, and 6) SARS-CoV-2 reinfection vs. no reinfection. We discuss the importance of multidisciplinary integration (health, social, and physical sciences), multilayered approaches to reducing risk ("Emmentaler cheese model"), harm reduction, smart masking, relaxation of interventions, and context-sensitive policymaking for COVID-19 response plans. We also address the challenges in understanding the broad clinical presentation of COVID-19, SARS-CoV-2 transmission, and SARS-CoV-2 reinfection. These key issues of science and public health policy have been presented as false dichotomies during the pandemic. However, they are hardly binary, simple, or uniform, and therefore should not be framed as polar extremes. We urge a nuanced understanding of the science and caution against black-or-white messaging, all-or-nothing guidance, and one-size-fits-all approaches. There is a need for meaningful public health communication and science-informed policies that recognize shades of gray, uncertainties, local context, and social determinants of health.

Heterogeneity matters: Contact structure and individual variation shape epidemic dynamics

In the recent COVID-19 pandemic, mathematical modeling constitutes an important tool to evaluate the prospective effectiveness of non-pharmaceutical interventions (NPIs) and to guide policy-making. Most research is, however, centered around characterizing the epidemic based on point estimates like the average infectiousness or the average number of contacts. In this work, we use stochastic simulations to investigate the consequences of a population's heterogeneity regarding connectivity and individual viral load levels. Therefore, we translate a COVID-19 ODE model to a stochastic multi-agent system. We use contact networks to model complex interaction structures and a probabilistic infection rate to model individual viral load variation. We observe a large dependency of the dispersion and dynamical evolution on the population's heterogeneity that is not adequately captured by point estimates, for instance, used in ODE models. In particular, models that assume the same clinical and transmission parameters may lead to different conclusions, depending on different types of heterogeneity in the population. For instance, the existence of hubs in the contact network leads to an initial increase of dispersion and the effective reproduction number, but to a lower herd immunity threshold (HIT) compared to homogeneous populations or a population where the heterogeneity stems solely from individual infectivity variations.

The COVID-19 pandemic: key considerations for the epidemic and its control

The response to the ongoing COVID‐19 pandemic has been characterized by draconian measures and far too many important unknowns, such as the true mortality risk, the role of children as transmitters and the development and duration of immunity in the population. More than a year into the pandemic much has been learned and insights into this novel type of pandemic and options for control are shaping up. Using a historical lens, we review what we know and still do not know about the ongoing COVID‐19 pandemic. A pandemic caused by a member of the coronavirus family is a new situation following more than a century of influenza A pandemics. However, recent pandemic threats such as outbreaks of the related and novel deadly coronavirus SARS in 2003 and of MERS since 2012 had put coronaviruses on WHOs blueprint list of priority diseases. Like pandemic influenza, SARS‐CoV‐2 is highly transmissible (R₀ ~ 2.5). Furthermore, it can fly under the radar due to a broad clinical spectrum where asymptomatic and pre‐symptomatic infected persons also transmit the virus—including children. COVID‐19 is far more deadly than seasonal influenza; initial data from China suggested a case fatality rate of 2.3%—which would have been on par with the deadly 1918 Spanish influenza. But, while the Spanish influenza killed young, otherwise healthy adults, it is the elderly who are at extreme risk of dying of COVID‐19. We review available seroepidemiological evidence of infection rates and compute infection fatality rates (IFR) for Denmark (0.5%), Spain (0.85%), and Iceland (0.3%). We also deduce that population age structure is key. SARS‐CoV‐2 is characterized by superspreading, so that ~10% of infected individuals yield 80% of new infections. This phenomenon turns out to be an Achilles heel of the virus that may explain our ability to effectively mitigate outbreaks so far. How will this pandemic come to an end? Herd immunity has not been achieved in Europe due to intense mitigation by non‐pharmaceutical interventions; for example, only ~8% of Danes were infected across the 1^st and 2^nd wave. Luckily, we now have several safe and effective vaccines. Global vaccine control of the pandemic depends in great measure on our ability to keep up with current and future immune escape variants of the virus. We should thus be prepared for a race between vaccine updates and mutations of the virus. A permanent reopening of society highly depends on winning that race.

COVID-19 in schools: Mitigating classroom clusters in the context of variable transmission

Widespread school closures occurred during the COVID-19 pandemic. Because closures are costly and damaging, many jurisdictions have since reopened schools with control measures in place. Early evidence indicated that schools were low risk and children were unlikely to be very infectious, but it is becoming clear that children and youth can acquire and transmit COVID-19 in school settings and that transmission clusters and outbreaks can be large. We describe the contrasting literature on school transmission, and argue that the apparent discrepancy can be reconciled by heterogeneity, or “overdispersion” in transmission, with many exposures yielding little to no risk of onward transmission, but some unfortunate exposures causing sizeable onward transmission. In addition, respiratory viral loads are as high in children and youth as in adults, pre- and asymptomatic transmission occur, and the possibility of aerosol transmission has been established. We use a stochastic individual-based model to find the implications of these combined observations for cluster sizes and control measures. We consider both individual and environment/activity contributions to the transmission rate, as both are known to contribute to variability in transmission. We find that even small heterogeneities in these contributions result in highly variable transmission cluster sizes in the classroom setting, with clusters ranging from 1 to 20 individuals in a class of 25. None of the mitigation protocols we modeled, initiated by a positive test in a symptomatic individual, are able to prevent large transmission clusters unless the transmission rate is low (in which case large clusters do not occur in any case). Among the measures we modeled, only rapid universal monitoring (for example by regular, onsite, pooled testing) accomplished this prevention. We suggest approaches and the rationale for mitigating these larger clusters, even if they are expected to be rare.

During the COVID-19 pandemic many jurisdictions closed schools in order to limit transmission of SARS-CoV-2. Because school closures are costly and damaging to students, schools were later reopened despite the risk of contact among students contributing to increased prevalence of the virus. Early data showed schools being mostly a low risk setting, but occasionally large outbreaks were observed. We argue that this heterogenous behaviour can be explained by variability in the rate of transmission, both at the level of the individual student and at the level of the classroom. We created a mathematical model of transmission in the classroom to explore the consequences of this variability for cluster size and control measures, considering what happens when a single infectious individual attends a classroom of susceptible students. We used the model to study different interventions with the aim of reducing the size of infection clusters, in situations where such clusters would be large. We found that interventions based on acting after symptomatic students receive a positive test, as is standard practice in many jurisdictions, are ineffective at preventing most infections, and instead found that only frequent screening of the entire class was able to reduce the size of clusters substantially.

Testing at scale during the COVID-19 pandemic

Assembly and publication of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome in January 2020 enabled the immediate development of tests to detect the new virus. This began the largest global testing programme in history, in which hundreds of millions of individuals have been tested to date. The unprecedented scale of testing has driven innovation in the strategies, technologies and concepts that govern testing in public health. This Review describes the changing role of testing during the COVID-19 pandemic, including the use of genomic surveillance to track SARS-CoV-2 transmission around the world, the use of contact tracing to contain disease outbreaks and testing for the presence of the virus circulating in the environment. Despite these efforts, widespread community transmission has become entrenched in many countries and has required the testing of populations to identify and isolate infected individuals, many of whom are asymptomatic. The diagnostic and epidemiological principles that underpin such population-scale testing are also considered, as are the high-throughput and point-of-care technologies that make testing feasible on a massive scale.

Covasim: An agent-based model of COVID-19 dynamics and interventions

The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America.

A nationwide analysis of population group differences in the COVID-19 epidemic in Israel, February 2020-February 2021

Background

Social inequalities affect the COVID-19 burden and vaccine uptake. The aim of this study was to explore inequalities in the incidence and mortality rate of SARS-CoV-2 infection and vaccine uptake in various sociodemographic and population group strata in Israel.

Methods

We analysed nationwide publicly available, aggregated data on PCR-confirmed SARS-CoV-2 infections and COVID-19 deaths between March 2020 and February 2021, as well as the first three months of COVID-19 immunisation according to sociodemographics, including population group and residential socioeconomic status (SES). We computed incidence and mortality rates of COVID-19. Comparisons between towns with predominantly Arab, ultra-Orthodox Jewish (the minorities), general Jewish populations, and according to SES, were conducted using generalised linear models with negative binomial distribution.

Findings

Overall, 774,030 individuals had SARS-CoV-2 infection (cumulative incidence 84•5 per 1,000 persons) and 5687 COVID-19 patients had died (mortality rate 62•8 per 100,000 persons). The highest mortality rate was found amongst the elderly. Most (>75%) individuals aged 60 years or above have been vaccinated with BNT162b2 vaccine. The risk of SARS-CoV-2 infection was higher in towns with predominantly Arab and ultra-Orthodox Jewish populations than in the general Jewish population, and in low SES communities. COVID-19 mortality rate was highest amongst Arabs. Conversely, vaccine uptake was lower amongst Arab and ultra-Orthodox Jewish populations and low SES communities.

Interpretation

Ethnic and religious minorities and low SES communities experience substantial COVID-19 burden, and have lower vaccine uptake, even in a society with universal accessibility to healthcare. Quantifying these inequalities is fundamental towards reducing these gaps, which imposes a designated apportion of resources to adequately control the pandemic.

Funding

No external funding was available for this study.

Slight reduction in SARS-CoV-2 exposure viral load due to masking results in a significant reduction in transmission with widespread implementation

Masks are a vital tool for limiting SARS-CoV-2 spread in the population. Here we utilize a mathematical model to assess the impact of masking on transmission within individual transmission pairs and at the population level. Our model quantitatively links mask efficacy to reductions in viral load and subsequent transmission risk. Our results reinforce that the use of masks by both a potential transmitter and exposed person substantially reduces the probability of successful transmission, even if masks only lower exposure viral load by ~ 50%. Slight increases in mask adherence and/or efficacy above current levels would reduce the effective reproductive number (R_e) substantially below 1, particularly if implemented comprehensively in potential super-spreader environments. Our model predicts that moderately efficacious masks will also lower exposure viral load tenfold among people who get infected despite masking, potentially limiting infection severity. Because peak viral load tends to occur pre-symptomatically, we also identify that antiviral therapy targeting symptomatic individuals is unlikely to impact transmission risk. Instead, antiviral therapy would only lower R_e if dosed as post-exposure prophylaxis and if given to ~ 50% of newly infected people within 3 days of an exposure. These results highlight the primacy of masking relative to other biomedical interventions under consideration for limiting the extent of the COVID-19 pandemic prior to widespread implementation of a vaccine. To confirm this prediction, we used a regression model of King County, Washington data and simulated the counterfactual scenario without mask wearing to estimate that in the absence of additional interventions, mask wearing decreased R_e from 1.3–1.5 to ~ 1.0 between June and September 2020.

Superspreading of airborne pathogens in a heterogeneous world

Epidemics are regularly associated with reports of superspreading: single individuals infecting many others. How do we determine if such events are due to people inherently being biological superspreaders or simply due to random chance? We present an analytically solvable model for airborne diseases which reveal the spreading statistics of epidemics in socio-spatial heterogeneous spaces and provide a baseline to which data may be compared. In contrast to classical SIR models, we explicitly model social events where airborne pathogen transmission allows a single individual to infect many simultaneously, a key feature that generates distinctive output statistics. We find that diseases that have a short duration of high infectiousness can give extreme statistics such as 20% infecting more than 80%, depending on the socio-spatial heterogeneity. Quantifying this by a distribution over sizes of social gatherings, tracking data of social proximity for university students suggest that this can be a approximated by a power law. Finally, we study mitigation efforts applied to our model. We find that the effect of banning large gatherings works equally well for diseases with any duration of infectiousness, but depends strongly on socio-spatial heterogeneity.

SARS-CoV-2 transmission dynamics in Belarus revealed by genomic and incidence data analysis

Since the emergence of COVID-19, a series of non-pharmaceutical interventions (NPIs) has been implemented by governments and public health authorities world-wide to control and curb the ongoing pandemic spread. From that perspective, Belarus is one of a few countries with a relatively modern healthcare system, where much narrower NPIs have been put in place. Given the uniqueness of this Belarusian experience, the understanding its COVID-19 epidemiological dynamics is essential not only for the local assessment, but also for a better insight into the impact of different NPI strategies globally. In this work, we integrate genomic epidemiology and surveillance methods to investigate the emergence and spread of SARS-CoV-2 in the country. The observed Belarusian SARS-CoV-2 genetic diversity originated from at least eighteen separate introductions, at least five of which resulted in on-going domestic transmissions. The introduction sources represent a wide variety of regions, although the proportion of regional virus introductions and exports from/to geographical neighbors appears to be higher than for other European countries. Phylodynamic analysis indicates a moderate reduction in the effective reproductive number ℛ e after the introduction of limited NPIs, with the reduction magnitude generally being lower than for countries with large-scale NPIs. On the other hand, the estimate of the Belarusian ℛ e at the early epidemic stage is comparable with this number for the neighboring ex-USSR country of Ukraine, where much broader NPIs have been implemented. The actual number of cases by the end of May, 2020 was predicted to be 2-9 times higher than the detected number of cases.

Lessons from applied large-scale pooling of 133,816 SARS-CoV-2 RT-PCR tests

Pooling multiple swab samples before RNA extraction and real-time reverse transcription polymerase chain reaction (RT-PCR) analysis has been proposed as a strategy to reduce costs and increase throughput of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests. However, reports on practical large-scale group testing for SARS-CoV-2 have been scant. Key open questions concern reduced sensitivity due to sample dilution, the rate of false positives, the actual efficiency (number of tests saved by pooling), and the impact of infection rate in the population on assay performance. Here, we report an analysis of 133,816 samples collected between April and September 2020 and tested by Dorfman pooling for the presence of SARS-CoV-2. We spared 76% of RNA extraction and RT-PCR tests, despite the frequently changing prevalence (0.5 to 6%). We observed pooling efficiency and sensitivity that exceeded theoretical predictions, which resulted from the nonrandom distribution of positive samples in pools. Overall, our findings support the use of pooling for efficient large-scale SARS-CoV-2 testing.

Non-invasive adapted N-95 mask sampling captures variation in viral particles expelled by COVID-19 patients: Implications in understanding SARS-CoV2 transmission

Infectious respiratory particles expelled by SARS-CoV-2 positive patients are attributed to be the key driver of COVID-19 transmission. Understanding how and by whom the virus is transmitted can help implement better disease control strategies. Here we have described the use of a noninvasive mask sampling method to detect and quantify SARS-CoV-2 RNA in respiratory particles expelled by COVID-19 patients and discussed its relationship to transmission risk. Respiratory particles of 31 symptomatic SARS-CoV-2 positive patients and 31 asymptomatic healthy volunteers were captured on N-95 masks layered with a gelatin membrane in a 30-minute process that involved talking/reading, coughing, and tidal breathing. SARS-CoV-2 viral RNA was detected and quantified using rRT-PCR in the mask and in concomitantly collected nasopharyngeal swab (NPS) samples. The data were analyzed with respect to patient demographics and clinical presentation. Thirteen of 31(41.9%) patients showed SARS-COV-2 positivity in both the mask and NPS samples, while 16 patients were mask negative but NPS positive. Two patients were both mask and NPS negative. All healthy volunteers except one were mask and NPS negative. The mask positive patients had significantly lower NPS Ct value (26) compared to mask negative patients (30.5) and were more likely to be rapid antigen test positive. The mask positive patients could be further grouped into low emitters (expelling <100 viral copies) and high emitters (expelling >1000 viral copies). The study presents evidence for variation in emission of SARS-CoV-2 virus particles by COVID-19 patients reflecting differences in infectivity and transmission risk among individuals. The results conform to reported secondary infection rates and transmission and also suggest that mask sampling could be explored as an effective tool to assess individual transmission risks, at different time points and during different activities.

Genetic evidence for the association between COVID-19 epidemic severity and timing of non-pharmaceutical interventions

Unprecedented public health interventions including travel restrictions and national lockdowns have been implemented to stem the COVID-19 epidemic, but the effectiveness of non-pharmaceutical interventions is still debated. We carried out a phylogenetic analysis of more than 29,000 publicly available whole genome SARS-CoV-2 sequences from 57 locations to estimate the time that the epidemic originated in different places. These estimates were examined in relation to the dates of the most stringent interventions in each location as well as to the number of cumulative COVID-19 deaths and phylodynamic estimates of epidemic size. Here we report that the time elapsed between epidemic origin and maximum intervention is associated with different measures of epidemic severity and explains 11% of the variance in reported deaths one month after the most stringent intervention. Locations where strong non-pharmaceutical interventions were implemented earlier experienced much less severe COVID-19 morbidity and mortality during the period of study.

Temporal dynamics of SARS-CoV-2 mutation accumulation within and across infected hosts

Analysis of SARS-CoV-2 genetic diversity within infected hosts can provide insight into the generation and spread of new viral variants and may enable high resolution inference of transmission chains. However, little is known about temporal aspects of SARS-CoV-2 intrahost diversity and the extent to which shared diversity reflects convergent evolution as opposed to transmission linkage. Here we use high depth of coverage sequencing to identify within-host genetic variants in 325 specimens from hospitalized COVID-19 patients and infected employees at a single medical center. We validated our variant calling by sequencing defined RNA mixtures and identified viral load as a critical factor in variant identification. By leveraging clinical metadata, we found that intrahost diversity is low and does not vary by time from symptom onset. This suggests that variants will only rarely rise to appreciable frequency prior to transmission. Although there was generally little shared variation across the sequenced cohort, we identified intrahost variants shared across individuals who were unlikely to be related by transmission. These variants did not precede a rise in frequency in global consensus genomes, suggesting that intrahost variants may have limited utility for predicting future lineages. These results provide important context for sequence-based inference in SARS-CoV-2 evolution and epidemiology.

Overdispersion in COVID-19 increases the effectiveness of limiting nonrepetitive contacts for transmission control

Evidence indicates that superspreading plays a dominant role in COVID-19 transmission, so that a small fraction of infected people causes a large proportion of new COVID-19 cases. We developed an agent-based model that simulates a superspreading disease moving through a society with networks of both repeated contacts and nonrepeated, random contacts. The results indicate that superspreading is the virus’ Achilles’ heel: Reducing random contacts—such as those that occur at sporting events, restaurants, bars, and the like—can control the outbreak at population scales.

Increasing evidence indicates that superspreading plays a dominant role in COVID-19 transmission. Recent estimates suggest that the dispersion parameter k for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is on the order of 0.1, which corresponds to about 10% of cases being the source of 80% of infections. To investigate how overdispersion might affect the outcome of various mitigation strategies, we developed an agent-based model with a social network that allows transmission through contact in three sectors: “close” (a small, unchanging group of mutual contacts as might be found in a household), “regular” (a larger, unchanging group as might be found in a workplace or school), and “random” (drawn from the entire model population and not repeated regularly). We assigned individual infectivity from a gamma distribution with dispersion parameter k. We found that when k was low (i.e., greater heterogeneity, more superspreading events), reducing random sector contacts had a far greater impact on the epidemic trajectory than did reducing regular contacts; when k was high (i.e., less heterogeneity, no superspreading events), that difference disappeared. These results suggest that overdispersion of COVID-19 transmission gives the virus an Achilles’ heel: Reducing contacts between people who do not regularly meet would substantially reduce the pandemic, while reducing repeated contacts in defined social groups would be less effective.

Hospital load and increased COVID-19 related mortality in Israel

The spread of Coronavirus disease 19 (COVID-19) has led to many healthcare systems being overwhelmed by the rapid emergence of new cases. Here, we study the ramifications of hospital load due to COVID-19 morbidity on in-hospital mortality of patients with COVID-19 by analyzing records of all 22,636 COVID-19 patients hospitalized in Israel from mid-July 2020 to mid-January 2021. We show that even under moderately heavy patient load (>500 countrywide hospitalized severely-ill patients; the Israeli Ministry of Health defined 800 severely-ill patients as the maximum capacity allowing adequate treatment), in-hospital mortality rate of patients with COVID-19 significantly increased compared to periods of lower patient load (250–500 severely-ill patients): 14-day mortality rates were 22.1% (Standard Error 3.1%) higher (mid-September to mid-October) and 27.2% (Standard Error 3.3%) higher (mid-December to mid-January). We further show this higher mortality rate cannot be attributed to changes in the patient population during periods of heavier load.

COVID-19 has caused many healthcare systems to become overwhelmed, potentially impacting patient care. Here, the authors show that COVID-19-related in-hospital mortality rates in Israel increased in periods of moderate or high hospital load, independent of patient characteristics.

COVID-19 Superspreading Suggests Mitigation by Social Network Modulation

Although COVID-19 has caused severe suffering globally, the efficacy of nonpharmaceutical interventions has been greater than typical models have predicted. Meanwhile, evidence is mounting that the pandemic is characterized by superspreading. Capturing this phenomenon theoretically requires modeling at the scale of individuals. Using a mathematical model, we show that superspreading drastically enhances mitigations which reduce the overall personal contact number and that social clustering increases this effect.

SARS-CoV-2 superspreading in cities vs the countryside

The first wave of the COVID‐19 pandemic was characterized by an initial rapid rise in new cases followed by a peak and a more erratic behaviour that varies between regions. This is not easy to reproduce with traditional SIR models, which predict a more symmetric epidemic. Here, we argue that superspreaders and population heterogeneity would predict such behaviour even in the absence of restrictions on social life. We present an agent‐based lattice model of a disease spreading in a heterogeneous population. We predict that an epidemic driven by superspreaders will spread rapidly in cities, but not in the countryside where the sparse population limits the maximal number of secondary infections. This suggests that mitigation strategies should include restrictions on venues where people meet a large number of strangers. Furthermore, mitigating the epidemic in cities and in the countryside may require different levels of restrictions.

Viral load and contact heterogeneity predict SARS-CoV-2 transmission and super-spreading events

SARS-CoV-2 is difficult to contain because many transmissions occur during pre-symptomatic infection. Unlike influenza, most SARS-CoV-2-infected people do not transmit while a small percentage infect large numbers of people. We designed mathematical models which link observed viral loads with epidemiologic features of each virus, including distribution of transmissions attributed to each infected person and duration between symptom onset in the transmitter and secondarily infected person. We identify that people infected with SARS-CoV-2 or influenza can be highly contagious for less than 1 day, congruent with peak viral load. SARS-CoV-2 super-spreader events occur when an infected person is shedding at a very high viral load and has a high number of exposed contacts. The higher predisposition of SARS-CoV-2 toward super-spreading events cannot be attributed to additional weeks of shedding relative to influenza. Rather, a person infected with SARS-CoV-2 exposes more people within equivalent physical contact networks, likely due to aerosolization.

Evolutionary Dynamics and Dissemination Pattern of the SARS-CoV-2 Lineage B.1.1.33 During the Early Pandemic Phase in Brazil

A previous study demonstrates that most of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) Brazilian strains fell in three local clades that were introduced from Europe around late February 2020. Here we investigated in more detail the origin of the major and most widely disseminated SARS-CoV-2 Brazilian lineage B.1.1.33. We recovered 190 whole viral genomes collected from 13 Brazilian states from February 29 to April 31, 2020 and combined them with other B.1.1 genomes collected globally. Our genomic survey confirms that lineage B.1.1.33 is responsible for a variable fraction of the community viral transmissions in Brazilian states, ranging from 2% of all SARS-CoV-2 genomes from Pernambuco to 80% of those from Rio de Janeiro. We detected a moderate prevalence (5-18%) of lineage B.1.1.33 in some South American countries and a very low prevalence (<1%) in North America, Europe, and Oceania. Our study reveals that lineage B.1.1.33 evolved from an ancestral clade, here designated B.1.1.33-like, that carries one of the two B.1.1.33 synapomorphic mutations. The B.1.1.33-like lineage may have been introduced from Europe or arose in Brazil in early February 2020 and a few weeks later gave origin to the lineage B.1.1.33. These SARS-CoV-2 lineages probably circulated during February 2020 and reached all Brazilian regions and multiple countries around the world by mid-March, before the implementation of air travel restrictions in Brazil. Our phylodynamic analysis also indicates that public health interventions were partially effective to control the expansion of lineage B.1.1.33 in Rio de Janeiro because its median effective reproductive number (R e ) was drastically reduced by about 66% during March 2020, but failed to bring it to below one. Continuous genomic surveillance of lineage B.1.1.33 might provide valuable information about epidemic dynamics and the effectiveness of public health interventions in some Brazilian states.

Biased Mutation and Selection in RNA Viruses

RNA viruses are responsible for some of the worst pandemics known to mankind, including outbreaks of Influenza, Ebola, and COVID-19. One major challenge in tackling RNA viruses is the fact they are extremely genetically diverse. Nevertheless, they share common features that include their dependence on host cells for replication, and high mutation rates. We set out to search for shared evolutionary characteristics that may aid in gaining a broader understanding of RNA virus evolution, and constructed a phylogeny-based data set spanning thousands of sequences from diverse single-stranded RNA viruses of animals. Strikingly, we found that the vast majority of these viruses have a skewed nucleotide composition, manifested as adenine rich (A-rich) coding sequences. In order to test whether A-richness is driven by selection or by biased mutation processes, we harnessed the effects of incomplete purifying selection at the tips of virus phylogenies. Our results revealed consistent mutational biases toward U rather than A in genomes of all viruses. In +ssRNA viruses, we found that this bias is compensated by selection against U and selection for A, which leads to A-rich genomes. In -ssRNA viruses, the genomic mutational bias toward U on the negative strand manifests as A-rich coding sequences, on the positive strand. We investigated possible reasons for the advantage of A-rich sequences including weakened RNA secondary structures, codon usage bias, and selection for a particular amino acid composition, and conclude that host immune pressures may have led to similar biases in coding sequence composition across very divergent RNA viruses.

Genomic epidemiology of the early stages of the SARS-CoV-2 outbreak in Russia

The ongoing pandemic of SARS-CoV-2 presents novel challenges and opportunities for the use of phylogenetics to understand and control its spread. Here, we analyze the emergence of SARS-CoV-2 in Russia in March and April 2020. Combining phylogeographic analysis with travel history data, we estimate that the sampled viral diversity has originated from at least 67 closely timed introductions into Russia, mostly in late February to early March. All but one of these introductions were not from China, suggesting that border closure with China has helped delay establishment of SARS-CoV-2 in Russia. These introductions resulted in at least 9 distinct Russian lineages corresponding to domestic transmission. A notable transmission cluster corresponded to a nosocomial outbreak at the Vreden hospital in Saint Petersburg; phylodynamic analysis of this cluster reveals multiple (2-3) introductions each giving rise to a large number of cases, with a high initial effective reproduction number of 3.0 [1.9, 4.3].

Temporal dynamics of SARS-CoV-2 mutation accumulation within and across infected hosts

Analysis of SARS-CoV-2 genetic diversity within infected hosts can provide insight into the generation and spread of new viral variants and may enable high resolution inference of transmission chains. However, little is known about temporal aspects of SARS-CoV-2 intrahost diversity and the extent to which shared diversity reflects convergent evolution as opposed to transmission linkage. Here we use high depth of coverage sequencing to identify within-host genetic variants in 325 specimens from hospitalized COVID-19 patients and infected employees at a single medical center. We validated our variant calling by sequencing defined RNA mixtures and identified a viral load threshold that minimizes false positives. By leveraging clinical metadata, we found that intrahost diversity is low and does not vary by time from symptom onset. This suggests that variants will only rarely rise to appreciable frequency prior to transmission. Although there was generally little shared variation across the sequenced cohort, we identified intrahost variants shared across individuals who were unlikely to be related by transmission. These variants did not precede a rise in frequency in global consensus genomes, suggesting that intrahost variants may have limited utility for predicting future lineages. These results provide important context for sequence-based inference in SARS-CoV-2 evolution and epidemiology.

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant.

'Dark matter', second waves and epidemiological modelling

Recent reports using conventional Susceptible, Exposed, Infected and Removed models suggest that the next wave of the COVID-19 pandemic in the UK could overwhelm health services, with fatalities exceeding the first wave. We used Bayesian model comparison to revisit these conclusions, allowing for heterogeneity of exposure, susceptibility and transmission. We used dynamic causal modelling to estimate the evidence for alternative models of daily cases and deaths from the USA, the UK, Brazil, Italy, France, Spain, Mexico, Belgium, Germany and Canada over the period 25 January 2020 to 15 June 2020. These data were used to estimate the proportions of people (i) not exposed to the virus, (ii) not susceptible to infection when exposed and (iii) not infectious when susceptible to infection. Bayesian model comparison furnished overwhelming evidence for heterogeneity of exposure, susceptibility and transmission. Furthermore, both lockdown and the build-up of population immunity contributed to viral transmission in all but one country. Small variations in heterogeneity were sufficient to explain large differences in mortality rates. The best model of UK data predicts a second surge of fatalities will be much less than the first peak. The size of the second wave depends sensitively on the loss of immunity and the efficacy of Find-Test-Trace-Isolate-Support programmes. In summary, accounting for heterogeneity of exposure, susceptibility and transmission suggests that the next wave of the SARS-CoV-2 pandemic will be much smaller than conventional models predict, with less economic and health disruption. This heterogeneity means that seroprevalence underestimates effective herd immunity and, crucially, the potential of public health programmes.

Superspreading events in the transmission dynamics of SARS-CoV-2: Opportunities for interventions and control

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiological agent of the Coronavirus Disease 2019 (COVID-19) disease, has moved rapidly around the globe, infecting millions and killing hundreds of thousands. The basic reproduction number, which has been widely used-appropriately and less appropriately-to characterize the transmissibility of the virus, hides the fact that transmission is stochastic, often dominated by a small number of individuals, and heavily influenced by superspreading events (SSEs). The distinct transmission features of SARS-CoV-2, e.g., high stochasticity under low prevalence (as compared to other pathogens, such as influenza), and the central role played by SSEs on transmission dynamics cannot be overlooked. Many explosive SSEs have occurred in indoor settings, stoking the pandemic and shaping its spread, such as long-term care facilities, prisons, meat-packing plants, produce processing facilities, fish factories, cruise ships, family gatherings, parties, and nightclubs. These SSEs demonstrate the urgent need to understand routes of transmission, while posing an opportunity to effectively contain outbreaks with targeted interventions to eliminate SSEs. Here, we describe the different types of SSEs, how they influence transmission, empirical evidence for their role in the COVID-19 pandemic, and give recommendations for control of SARS-CoV-2.

A single holiday was the turning point of the COVID-19 policy of Israel

Objectives

Despite an initial success, Israel’s quarantine-isolation COVID-19 policy has abruptly collapsed. This study’s aim is to identify the causes that led to this exponential rise in the accumulation of confirmed cases.

Methods

Epidemiological investigation reports were used to reconstruct chains of transmission as well as assess the net contribution of local infections relative to imported cases, infected travelers arriving from abroad. A mathematical model was implemented in order to describe the efficiency of the quarantine-isolation policy and the inflow of imported cases. The model’s simulations included two scenarios for the actual time series of the symptomatic cases, providing insights into the conditions that lead to the abrupt change.

Results

The abrupt change followed a Jewish holiday, Purim, in which many public gatherings were held. According to the first scenario, the accumulation of confirmed cases before Purim was driven by imported cases resulting in a controlled regime, with an effective reproduction number, Re, of 0.69. In the second scenario, which followed Purim, a continuous rise of the local to imported cases ratio began, which led to an exponential growth regime characterized by an Re of 4.34.

It was found that the change of regime cannot be attributed to super-spreader events, as these consisted of approximately 5% of the primary cases, which resulted in 17% of the secondary cases.

Conclusions

A general lesson for health policymakers should be that even a short lapse in public responsiveness can lead to dire consequences.

Socioeconomic inequalities in the risk of SARS-CoV-2 infection - First results from an analysis of surveillance data from Germany

Experiences with acute respiratory diseases which caused virus epidemics in the past and initial findings in the research literature on the current COVID-19 pandemic suggest a higher SARS-CoV-2 infection risk for socioeconomically disadvantaged populations. Nevertheless, further research on such a potential association between socioeconomic status and SARS-CoV-2 incidence in Germany is required. This article reports on the results of a first Germany-wide analysis of COVID-19 surveillance data to which an area-level index of socioeconomic deprivation was linked. The analysis included 186,839 laboratory-confirmed COVID-19 cases, the data of which was transferred to the Robert Koch Institute by 16 June 2020, 00:00. During the early stage of the epidemic up to mid-April, the data show a socioeconomic gradient with higher incidence in less deprived regions of Germany. Over the course of the epidemic, however, this gradient becomes less measurable and finally reverses in south Germany, the region hardest hit by the epidemic, to the greater detriment of the more deprived regions. These results highlight the need to continue monitoring social epidemiological patterns in COVID-19 and analysing the underlying causes to detect dynamics and trends early on and countering a potential exacerbation of health inequalities.

Mortality of the elderly population during the COVID-19 pandemic. Are there north-south differences?

COVID-19 disease courses are dynamic and in some cases, fatal. In this article, we aim to identify the periods where overall mortality is higher, and therefore to more precisely measure excess mortality. We analysed mortality rate development for the population aged 65 years and older in Germany as a whole, a south Germany region (comprising the federal states of Baden-Wuerttemberg and Bavaria) and a north Germany region (comprising the federal states of Schleswig Holstein, Mecklenburg Western-Pomerania and Brandenburg). The article analyses the mortality rates per calendar week that have been published by Germany's Federal Statistical Office (Destatis) for the first 23 calendar weeks of 2020. We compare these figures with those for the same period 2016, the last year in which there was no influenza-related excess mortality. In calendar weeks ten to 15, mortality rates for the elder population rose exceptionally in the south compared to the north Germany region as well as compared to the 2016 figures. A peak was reached in calendar weeks 14 and 15. Mortality rates peaked around two to three weeks after incidence. Since this peak, mortality rates have decreased again, but up to calendar week 18 have remained above the 2016 rates. Overall the rise in mortality rates observed appears to be related to the COVID-19 pandemic and not the annual influenza wave.