what evidence is there related to COVID-19 super spreaders

The number of secondary cases from each primary case determines how fast an epidemic grows. It is known that all cases do not spread the infection equally; super spreaders play an important role as they contribute disproportionately to a much larger number of cases including in the ongoing COVID-19 pandemic. Super spreaders have been reported for more than a century, but limited information is available in scientific literature. An epidemic containment strategy needs to include early identification of super spreaders to limit an explosive growth. Super spreaders tend to get stigmatized, resulting in late reporting and hiding of cases. It is important for program managers to be sensitive to the manner in which related information is shared with media and general public.

Significance of super spreader events in COVID-19.

Significance of super spreader events in COVID-19

Uncertainty around the role 'super-spreaders' play in the transmission and escalation of infectious disease is compounded by its broad and vague definition. It is a term that has been much used in relation to COVID-19, particularly in social media. On its widest definition, it refers to a propensity to infect a larger than average number of people. Given the biological, behavioural and environmental variables relevant to infectivity, this might be pertinent to almost any infected individual who is not physically isolated from others. Nor is the term confined to individuals with a propensity to spread infectious disease: it can potentially be used to describe events, policies or settings. This article explores the use of the term and considers circumstances in which the wide definition can be problematic. One problem is that it can lead to undeserved apportionment of moral blame to alleged super-spreaders. Another is that it can detract from scientific investigation of the heterogeneity of COVID-19 transmission. The author calls for a clearer epidemiological definition.

COVID-19 Super-spreaders: Definitional Quandaries and Implications

Uncertainty around the role ‘super-spreaders’ play in the transmission and escalation of infectious disease is compounded by its broad and vague definition. It is a term that has been much used in relation to COVID-19, particularly in social media. On its widest definition, it refers to a propensity to infect a larger than average number of people. Given the biological, behavioural and environmental variables relevant to infectivity, this might be pertinent to almost any infected individual who is not physically isolated from others. Nor is the term confined to individuals with a propensity to spread infectious disease: it can potentially be used to describe events, policies or settings. This article explores the use of the term and considers circumstances in which the wide definition can be problematic. One problem is that it can lead to undeserved apportionment of moral blame to alleged super-spreaders. Another is that it can detract from scientific investigation of the heterogeneity of COVID-19 transmission. The author calls for a clearer epidemiological definition.

Superspreaders and the spread pattern of COVID-19: Response to: "Do superspreaders generate new superspreaders? A hypothesis to explain the propagation pattern of COVID-19", International Journal of Infectious Diseases 96 (2020) 461-463

Superspreaders and the spread pattern of COVID-19: Response to: “Do superspreaders generate new superspreaders? A hypothesis to explain the propagation pattern of COVID-19”, International Journal of Infectious Diseases 96 (2020) 461–463

If the covid-19 virus is transmitted largely by superspreaders, it might not go pandemic, reports Debora MacKenzie

Is it super-spreading?

An outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly. It is imperative to control the epidemic by understanding the epidemiological feature, preventative quarantine, and effective hygiene measures. In the present study, we report a case of super-spreader who transmitted the disease to over twenty-eight persons in Ningbo, Zhejiang. Identifying and isolated super-spreaders, understanding the reasons behind the efficient transmission ability are important for the control and management of the ongoing COVID-19 pandemic.

A super-spreader of COVID-19 in Ningbo city in China

The current global propagation of COVID-19 is heterogeneous, with slow transmission continuing in many countries and exponential propagation in others, where the time that it took for the explosive spread to begin varied greatly. It is proposed that this could be explained by cascading superspreading events, in which new infections caused by a superspreader are more likely to be highly infectious. The mechanism suggested for this is related to viral loads. Exposure to high viral loads may result in high-intensity infection, which exposes new cases to high viral loads. This notion is supported by experimental veterinary research.

Do superspreaders generate new superspreaders? A hypothesis to explain the propagation pattern of COVID-19

Abstract The current global propagation of COVID-19 is heterogeneous, with slow transmission continuing in many countries, and exponential propagation in others, in which the time that took to begin this explosive spread varies greatly. It is proposed that this could be explained by cascading superspreading events, in which new infections caused by a superspreader are more likely to be highly infectious. The mechanism suggested for this is related to viral loads. Exposure to high viral loads may result in infections of high intensity, which exposes new cases to high viral loads, and so on. This notion is supported by experimental veterinary research.

Do superspreaders generate new superspreaders? a hypothesis to explain the propagation pattern of COVID-19

Purpose: The United States has the highest number of confirmed COVID-19 cases in the world to date, with over 94,000 COVID-19-related deaths. The true risk of a COVID-19 resurgence as states prepare to reopen businesses is unknown. This paper aims to classify businesses by their risk of transmission and quantify the relationship between the density of super-spreader businesses and COVID-19 cases. Methods: We constructed a COVID-19 Business Transmission Risk Index based upon the frequency and duration of visits and square footage of businesses pre-pandemic in 2019 in 8 states (Massachusetts, Rhode Island, Connecticut, New Hampshire, Vermont, Maine, New York, and California). We used this index to classify businesses as super-spreaders. Then, we analyzed the association between the density of super-spreader businesses in a county and the rate of COVID-19 cases. We performed significance testing using a negative binomial regression. The main outcome of interest is the cumulative number of COVID-19 cases each week. Results: We found a positive association between the density of super-spreader businesses and COVID-19 cases. A 1 percentage point increase in the density of super-spreader businesses is associated with 5% higher COVID-19 cases, all else equal. Conclusion: Higher densities of super-spreader businesses are associated with higher rates of COVID-19 cases. This may have important implications for how states reopen super-spreader businesses.

Super-Spreader Businesses and Risk of COVID-19 Transmission

Background: Super-spreading events were associated with the outbreaks of SARS and MERS, but their association with the outbreak of COVID-19 remains unknown. Here, we report a super-spreading transmission chain of SARS-CoV-2 involving an index patient, seven cancer patients, 40 health care workers and four family members. Methods: We conducted a retrospective study to identify the index patient and the exposed individuals linked to a chain of transmission associated with COVID-19. We collected and analyzed the data on demographic features, exposure history, clinical presentation, laboratory investigation, radiological examination, and disease outcome of these patients. Results: We identified the index patient and another presumptive super-spreader, who initiated and amplified a super-spreading transmission chain associated with COVID-19, respectively. There were 31 female and 21 male patients in this cohort, and the median age was 37 years (range: 22-79 years). Each of them had an exposure history with the index patient or his close contacts. Approximately 87% (45/52) of the patients had fever or other symptoms, 96% (50/52) had abnormal chest CT-scan findings, 86% of the tested patients (39/45) were positive for SARS-CoV-2 in the nasopharyngeal or throat swab specimen, 85% of the tested patients (29/34) were positive for SARS-CoV-2-specific IgM and/or IgG, 15% of the RT-PCR positive patients were tested negative for the specific IgM and/or IgG at the convalescent phase, and 15% of the RT-PCR negative patients were tested positive for the specific IgM and/or IgG. The severe patients experienced a significant decrease in oximetry saturation, lymphocyte, and platelet counts, along with a significant increase in C-reactive protein, D-dimer, and lactate dehydrogenase. All six fatal cases had comorbidities and five of the seven cancer patients (71%) died within 2-20 days of the disease onset. Conclusions: The super-spreading events were associated with the outbreak of COVID-19 in Wuhan and its impact on disease transmission warrants further investigation. Cancer patients appeared highly vulnerable to COVID-19. The finding that a significant portion of SARS-CoV-2 infected patients were tested negative for the serum specific IgM and IgG at the convalescent phase should be addressed by additional studies.

Identification of a super-spreading chain of transmission associated with COVID-19

Super-spreading events in an outbreak can change the nature of an epidemic. Therefore, it is useful for public health teams to determine if an ongoing outbreak has any contribution from such events, which may be amenable to interventions. We estimated the basic reproductive number (R(0)) and the dispersion factor (k) from empirical data on clusters of epidemiologically-linked COVID-19 cases in Hong Kong, Japan and Singapore. This allowed us to infer the presence or absence of super-spreading events during the early phase of these outbreaks. The relatively large values of k implied that large cluster sizes, compatible with super-spreading, were unlikely.

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

Super-spreading events in an outbreak can change the nature of an epidemic. Therefore, it is useful for public health teams to determine if an ongoing outbreak has any contribution from such events, which may be amenable to interventions. We estimated the basic reproductive number (R0) and the dispersion factor (k) from empirical data on clusters of epidemiologically-linked COVID-19 cases in Hong Kong, Japan and Singapore. This allowed us to infer the presence or absence of super-spreading events during the early phase of these outbreaks. The relatively large values of k implied that large cluster sizes, compatible with super-spreading, were unlikely.

A newly emerged coronavirus, SARS-CoV-2, caused severe pneumonia outbreaks in China in December 2019 and has since spread to various countries around the world. To trace the evolution route and probe the transmission dynamics of this virus, we performed phylodynamic analysis of 247 high quality genomic sequences available in the GISAID platform as of 5 March 2020. Among them, four genetic clusters, defined as super-spreaders (SSs), could be identified and were found to be responsible for the major outbreaks that subsequently occurred in various countries. SS1 was widely disseminated in Asia and the US, and mainly responsible for outbreaks in the states of Washington and California as well as South Korea, whereas SS4 contributed to the pandemic in Europe. Using the signature mutations of each SS as markers, we further analysed 1539 genome sequences reported after 29 February 2020 and found that 90% of these genomes belonged to SSs, with SS4 being the most dominant. The relative degree of contribution of each SS to the pandemic in different continents was also depicted. Identification of these super-spreaders greatly facilitates development of new strategies to control the transmission of SARS-CoV-2.

Genetic cluster analysis of SARS-CoV-2 and the identification of those responsible for the major outbreaks in various countries

Super-spreading occurs when a single patient infects a disproportionate number of contacts. The 2015 MERS-CoV, 2003 SARS-CoV, and to a lesser extent 2014–15 Ebola virus outbreaks were driven by super-spreaders. We summarize documented super-spreading in these outbreaks, explore contributing factors, and suggest studies to better understand super-spreading.

MERS, SARS, and Ebola: The Role of Super-Spreaders in Infectious Disease

The human pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the major pandemic of the 21st century. We analyzed >4,700 SARS-CoV-2 genomes and associated meta-data retrieved from public repositories. SARS-CoV-2 sequences have a high sequence identity (>99.9%), which drops to >96% when compared to bat coronavirus. We built a mutation-annotated reference SARS-CoV-2 phylogeny with two main macro-haplogroups, A and B, both of Asian origin, and >160 sub-branches representing virus strains of variable geographical origins worldwide, revealing a uniform mutation occurrence along branches that could complicate the design of future vaccines. The root of SARS-CoV-2 genomes locates at the Chinese haplogroup B1, with a TMRCA dating to 12 November 2019 - thus matching epidemiological records. Sub-haplogroup A2a originates in China and represents the major non-Asian outbreak. Multiple founder effect episodes, most likely associated with super-spreader hosts, explain COVID-19 pandemic to a large extent.

The impact of super-spreaders in COVID-19: mapping genome variation worldwide

Genetic cluster analysis of SARS-CoV-2 and the identification of those responsible for the major outbreaks in various countries.

Super-spreading events and contribution to transmission of MERS, SARS, and SARS-CoV-2 (COVID-19)

A simple two-cohort SIR like model can explain the qualitative behaviour of the logarithmic derivative estimations of the covid-19 epidemic evolution as observed in several countries. The model consists of a general population in which the R_0 value is slightly below 1, but in which a super-spreading small subgroup with high R_0, coupled to the general population, is contaminating a significant fraction of the population. The epidemic starts to slow down when herd immunity is reached in this subgroup. The dynamics of this system is quite robust against non-pharmaceutical measures.

Super spreader cohorts and covid-19

We use anonymized and aggregated data from Facebook to show that areas with stronger social ties to two early COVID-19"hotspots"(Westchester County, NY, in the U.S. and Lodi province in Italy) generally have more confirmed COVID-19 cases as of March 30, 2020. These relationships hold after controlling for geographic distance to the hotspots as well as for the income and population density of the regions. These results suggest that data from online social networks may prove useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.

The geographic spread of COVID-19 correlates with structure of social networks as measured by Facebook

We use a model of covid-19 spread, an SEIR agent-based model on a graph, which takes into account several important real-life attributes of covid-19: Super-spreaders, realistic epidemiological parameters of the disease, testing and quarantine policies. We provide simulation results and mathematical arguments to argue that certain results of our simulations hold in more general settings. We find that mass-testing is much less effective than testing the symptomatic and contact tracing, and some blend of these with social distancing is required to get suppression. We also find that the fat tail of the degree distribution matters a lot for epidemic growth, and many standard models do not account for this. Additionally, the average reproduction number for individuals is not an upper bound for the effective reproduction number, R. Even with an expectation of less than one new case per person, this model shows that exponential spread is possible. The parameter which closely predicts growth rate is the ratio between 2nd to 1st moments of the degree distribution.

Modeling COVID-19 on a network: super-spreaders, testing and containment

Super-spreading events and contribution to transmission of MERS, SARS, and COVID-19

COVID-19 is a new type of coronavirus disease which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It originated in China in the month of December 2019 and quickly started to spread within the country. On 31st December 2019, it was first reported to country office of World Health Organization (WHO) in China. Since then, it has spread to most of the countries around the globe. However, there has been a recent rise in trend in believing that it would go away during summer days, which has not yet been properly investigated. In this paper, relationship of daily number of confirmed cases of COVID-19 with three environmental factors, viz. maximum relative humidity (RH_max), maximum temperature (T_max) and highest wind speed (WS_max), considering the incubation period, have been investigated statistically, for four of the most affected places of China, viz. Beijing, Chongqing, Shanghai, Wuhan and five of the most affected places of Italy, viz. Bergamo, Cremona, Lodi, Milano. It has been found that the relationship with maximum relative humidity and highest wind is mostly negligible, whereas relationship with maximum temperature is ranging between negligible to moderate.

Statistical investigation of relationship between spread of coronavirus disease (COVID-19) and environmental factors based on study of four mostly affected places of China and five mostly affected places of Italy

Summary Early studies that explored host–pathogen interactions assumed that infected individuals within a population have equal chances of transmitting the infection to others. Subsequently, in what became known as the 20/80 rule, a small percentage of individuals within any population was observed to control most transmission events. This empirical rule was shown to govern inter-individual transmission dynamics for many pathogens in several species, and individuals who infect disproportionately more secondary contacts, as compared to most others, became known as super-spreaders. Studies conducted in the wake of the severe acute respiratory syndrome (SARS) pandemic revealed that, in the absence of super-spreading events, most individuals infect few, if any, secondary contacts. The analysis of SARS transmission, and reports from other outbreaks, unveil a complex scenario in which super-spreading events are shaped by multiple factors, including co-infection with another pathogen, immune suppression, changes in airflow dynamics, delayed hospital admission, misdiagnosis, and inter-hospital transfers. Predicting and identifying super-spreaders open significant medical and public health challenges, and represent important facets of infectious disease management and pandemic preparedness plans.

Super-spreaders in infectious diseases

The novel Coronavirus Disease 2019 (COVID-19) has spread quickly across the globe. Here, we evaluated the role of climate (temperature and precipitation), region-specific susceptibility (BCG vaccination, malaria infection, and elderly population) and international traveller population (human mobility) in shaping the geographical patterns of COVID-19 cases across 1,055 countries/regions, and examined the sequential shift of multiple drivers of the accumulated cases from December, 2019 to April 12, 2020. The accumulated numbers of COVID-19 cases (per 1 million population) were well explained by a simple regression model. The explanatory power (R2) of the model increased up to > 70% in April 2020 as the COVID-19 spread progressed. Climate, host mobility, and host susceptibility largely explained the variance of the COVID-19 cases (per 1 million population), and their explanatory power improved as the pandemic progressed; the relative importance of host mobility and host susceptibility have been greater than that of climate. The number of days from outbreak onset showed greater explanatory power in the earlier stages of COVID-19 spread but rapidly lost its influence. Our findings demonstrate that the COVID-19 pandemic is deterministically driven by climate suitability, cross-border human mobility, and region-specific susceptibility. The present distribution of COVID-19 cases has not reached an equilibrium and is changing daily, especially in the Southern Hemisphere. Nevertheless, the present results, based on mapping the spread of COVID-19 and identifying multiple drivers of this outbreak trajectory, may contribute to a better understanding of the COVID-19 disease transmission risk and the measures against long-term epidemic.

Multiple drivers of the COVID-19 spread: role of climate, international mobility, and region-specific conditions

Mechanisms underlying the acute respiratory distress syndrome (ARDS)-like clinical manifestations leading to deaths in patients who develop COVID-19 remain uncharacterized. While multiple factors could influence these clinical outcomes, we explored if differences in transmissibility and pathogenicity of SARS-CoV2 variants could contribute to these terminal clinical consequences of COVID-19. We analyzed 34,412 SARS-CoV2 sequences deposited in the Global Initiative for Sharing All Influenza Data (GISAID) SARS-CoV2 sequence database to determine if regional differences in circulating strain variants correlated with increased mortality in Europe, the United States, and California. We found two subclades descending from the Wuhan HU-1 strain that rapidly became dominant in Western Europe and the United States. These variants contained nonsynonymous nucleotide mutations in the Orf1ab segment encoding RNA-dependent RNA polymerase (C14408T), the spike protein gene (A23403G), and Orf1a (G25563T), which resulted in non-conservative amino acid substitutions P323L, D614G, and Q57H, respectively. In Western Europe, the A23403G-C14408T subclade dominated, while in the US, the A23403G-C14408T-G25563T mutant became the dominant strain in New York and parts of California. The high cumulative frequencies of both subclades showed inconsistent but significant association with high cumulative CFRs in some of the regions. When the frequencies of the subclades were analyzed by their 7-day moving averages across each epidemic, we found co-circulation of both subclades to temporally correlate with peak mortality periods. We postulate that in areas with high numbers of these co-circulating subclades, a person may get serially infected. The second infection may trigger a hyperinflammatory response similar to the antibody-dependent enhancement (ADE) response, which could explain the ARDS-like manifestations observed in people with co-morbidity, who may not mount sufficient levels of neutralizing antibodies against the first infection. Further studies are necessary but the implication of such a mechanism will need to be considered for all current COVID-19 vaccine designs.

Dissemination and co-circulation of SARS-CoV2 subclades exhibiting enhanced transmission associated with increased mortality in Western Europe and the United States

COVID-19 caused rapid mass infection worldwide. Understanding its transmission characteristics, including heterogeneity and the emergence of super spreading events (SSEs) where certain individuals infect large numbers of secondary cases, is of vital importance for prediction and intervention of future epidemics. Here, we collected information of all infected cases (135 cases) between 21 January and 26 February 2020 from official public sources in Tianjin, a metropolis of China, and grouped them into 43 transmission chains with the largest chain of 45 cases and the longest chain of four generations. Utilizing a heterogeneous transmission model based on branching process along with a negative binomial offspring distribution, we estimated the reproductive number R and the dispersion parameter k (lower value indicating higher heterogeneity) to be 0.67 (95% CI: 0.54-0.84) and 0.25 (95% CI: 0.13-0.88), respectively. A super-spreader causing six infections was identified in Tianjin. In addition, our simulation allowing for heterogeneity showed that the outbreak in Tianjin would have caused 165 infections and sustained for 7.56 generations on average if no control measures had been taken by local government since 28 January. Our results highlighted more efforts are needed to verify the transmission heterogeneity of COVID-19 in other populations and its contributing factors.

Evaluating Transmission Heterogeneity and Super-Spreading Event of COVID-19 in a Metropolis of China

COVID-19 caused rapid mass infection worldwide. Understanding its transmission characteristics, including heterogeneity and the emergence of super spreading events (SSEs) where certain individuals infect large numbers of secondary cases, is of vital importance for prediction and intervention of future epidemics. Here, we collected information of all infected cases (135 cases) between 21 January and 26 February 2020 from official public sources in Tianjin, a metropolis of China, and grouped them into 43 transmission chains with the largest chain of 45 cases and the longest chain of four generations. Utilizing a heterogeneous transmission model based on branching process along with a negative binomial offspring distribution, we estimated the reproductive number R and the dispersion parameter k (lower value indicating higher heterogeneity) to be 0.67 (95% CI: 0.54–0.84) and 0.25 (95% CI: 0.13–0.88), respectively. A super-spreader causing six infections was identified in Tianjin. In addition, our simulation allowing for heterogeneity showed that the outbreak in Tianjin would have caused 165 infections and sustained for 7.56 generations on average if no control measures had been taken by local government since 28 January. Our results highlighted more efforts are needed to verify the transmission heterogeneity of COVID-19 in other populations and its contributing factors.

A newly emerged coronavirus, SARS-CoV-2, caused severe outbreaks of pneumonia in China in December 2019 and has since spread to various countries around the world. To probe the origin and transmission dynamics of this virus, we performed phylodynamic analysis of 247 high quality genomic sequences of viruses available in the GISAID platform as of March 05, 2020. A substantial number of earliest sequences reported in Wuhan in December 2019, including those of viruses recovered from the Huanan Seafood Market (HNSM), the site of the initial outbreak, were genetically diverse, suggesting that viruses of multiple sources were involved in the original outbreak. The viruses were subsequently disseminated to different parts of China and other countries, with diverse mutational profiles being recorded in strains recovered subsequently. Interestingly, four genetic clusters defined as Super-transmitters (STs) were found to become dominant and were responsible for the major outbreaks in various countries. Among the four clusters, ST1 is widely disseminated in Asia and the US and mainly responsible for outbreaks in the states of Washington and California in the US as well as those in South Korea at the end of February and early March, whereas ST4 contributed to the pandemic in Europe. Each ST cluster carried a signature mutation profile which allowed us to trace the origin and transmission patterns of specific viruses in different parts of the world. Using the signature mutations as markers of STs, we further analysed 1539 genome sequences reported after February 29, 2020. We found that around 90% of these genomes belonged to STs with ST4 being the dominant one and their contribution to pandemic in different continents were also depicted. The identification of these super-transmitters provides insight into the control of further transmission of SARS-CoV-2.

Identification of super-transmitters of SARS-CoV-2

Children are not COVID-19 super spreaders: time to go back to school.

On the base of logic discrete equations system mathematical modeling of COVID-19 epidemic spread was carried out in the world and in the countries with the largest number of infected people such as the USA, Brasil, Russia and India in the first half of 2020. It was shown that for the countries with strong restrictive measures the spread of COVID-19 fit on a single wave with small capacity as for a number of countries with violation of restrictive measures the spread of the epidemic fit on a waves superposition. For countries with large population mixing, the spread of the epidemic today also fits into a single wave, but with a huge capacity value (for Brazil - 80 million people, for India - 40 million people). We estimated that the epidemic spread in the world today fits into 5 waves. The first two waves are caused by the epidemic spread in China (the first - in Wuhan), the third - by the epidemic spread in European countries, the fourth mainly by the epidemic spread in Russia and the USA, the fifth wave is mainly caused by the epidemic spread in Latin America and South Asia. It was the fifth wave that led to the spread of the coronavirus epidemic COVID-19 entering a new phase, with an increase in the number of infected more than 100 thousand inhabitants. For all the studied countries and the world, for each of the superposition waves, the wave capacities and growth indicators were calculated. The local peaks of the waves and their ending times are determined. It was the fifth wave that led to the fact that COVID-19 spread is entering a new phase, with the increase in the number of infected people being more than 100 thousand inhabitants. For all the countries being examined and for the whole world, for each of the superposition waves we calculated the waves capacities and index of infected people growth.

Superposition of waves for modeling COVID-19 epidemic in the world and in the countries with the maximum number of infected people in the first half of 2020

Background: COVID-19 caused rapid mass infection worldwide. Understanding its transmission characteristics including heterogeneity is of vital importance for prediction and intervention of future epidemics. In addition, transmission heterogeneity usually envokes super spreading events (SSEs) where certain individuals infect large numbers of secondary cases. Till now, studies of transmission heterogeneity of COVID-19 and its underlying reason are far from reaching an agreement. MethodsWe collected information of all infected cases between January 21 and February 26, 2020 from official public sources in Tianjin, a metropolis of China. Utilizing a heterogeneous transmission model based on branching process along with a negative binomial offspring distribution, we estimated the reproductive number R and the dispersion parameter k which characterized the transmission potential and heterogeneity, respectively. Furthermore, we studied the SSE in Tianjin outbreak and evaluated the effect of control measures undertaken by local government based on the heterogeneous model. Results: A total of 135 confirmed cases (including 34 imported cases and 101 local infections) in Tianjin by February 26th 2020 entered the study. We grouped them into 43 transmission chains with the largest chain of 45 cases and the longest chain of 4 generations. The estimated reproduction number R was at 0.67 (95%CI: 0.54[~]0.84), and the dispersion parameter k was at 0.25 (95% CI: 0.13[~]0.88). A super spreader causing six infections in Tianjin, was identified. In addition, our simulation results showed that the outbreak in Tianjin would have caused 165 infections and sustained for 7.56 generations on average if no control measures had been taken by local government since January 28th. Conclusions: Our analysis suggested that the transmission of COVID-19 was subcritical but with significant heterogeneity and may incur SSE. More efforts are needed to verify the transmission heterogeneity of COVID-19 in other populations and its contributing factors, which is important for developing targeted measures to curb the pandemic.

Evaluating transmission heterogeneity and super-spreading event of COVID-19 in a metropolis of China

The COVID-19 pandemic started in Wuhan, China, and caused the worldwide spread of the RNA virus SARS-CoV-2, the causative agent of COVID-19. Because of its mutational rate, wide geographical distribution, and host response variance this coronavirus is currently evolving into an array of strains with increasing genetic diversity. Most variants apparently have neutral effects for disease spread and symptoms severity. However, in the viral Spike protein, which is responsible for host cell attachment and invasion, an emergent variant, containing the amino acid substitution D to G in position 614 (D614G), was suggested to increase viral infection capability. To test whether this variant has epidemiological impact, the temporal distributions of the SARS-CoV-2 samples bearing D or G at position 614 were compared in the USA, Asia and Europe. The epidemiological curves were compared at early and late epidemic stages. At early stages, where containment measures were still not fully implemented, the viral variants are supposed to be unconstrained and its growth curves might approximate the free viral dynamics. Our analysis shows that the D614G prevalence and the growth rates of COVID-19 epidemic curves are correlated in the USA, Asia and Europe. Our results suggest a selective sweep that can be explained, at least in part, by a propagation advantage of this variant, in other words, that the molecular level effects of D614G have sufficient impact on population transmission dynamics as to be detected by differences in rate coefficients of epidemic growth curves.

Temporal data series of COVID-19 epidemics in the USA, Asia and Europe suggests a selective sweep of SARS-CoV-2 Spike D614G variant

BACKGROUD: Effective communication of accurate information through social media constitutes an important component of public health interventions in modern time, when traditional public health approaches such as contact tracing, quarantine and isolation are among the few options for the containing the disease spread in the population. The success of control of COVID-19 outbreak started from Wuhan, the capital city of Hubei Province of China relies heavily on the resilience of residents to follow public health interventions which induce substantial interruption of social-economic activities, and evidence shows that opinion leaders have been playing significant roles in the propagation of epidemic information and public health policy and implementations. METHODS: We design a mathematical model to quantify the roles of information superspreaders in single specific information which outbreaks rapidly and usually has a short duration period, and to examine the information propagation dynamics in the Chinese Sina-microblog. Our opinion-leader susceptible-forwarding-immune (OL-SFI) model is formulated to track the temporal evolution of forwarding quantities generated by opinion leaders and normal users. RESULTS: Data fitting from the real data of COVID-19 obtained from Chinese Sina-microblog can identify the different contact rates and forwarding probabilities (and hence calculate the basic information forwarding reproduction number of superspreaders), and can be used to evaluate the roles of opinion leaders in different stages of the information propagation and the outbreak unfolding. CONCLUSIONS: The parameterized model can be used to nearcast the information propagation trend, and the model-based sensitivity analysis can help to explore important factors for the roles of opinion leaders.

Quantify the role of superspreaders -opinion leaders- on COVID-19 information propagation in the Chinese Sina-microblog

The global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causing coronavirus disease 2019 (COVID-19) reached over two million confirmed cases worldwide, and numbers are still growing at a fast rate. The majority of new infections are now being reported outside of China, where the outbreak officially originated in December 2019 in Wuhan. Despite the wide outbreak of the infection, a remarkable asymmetry is observed in the number of cases and in the distribution of the severity of the COVID-19 symptoms in patients with respect to the countries/regions. In the early stages of a new pathogen outbreak, it is critical to understand the dynamics of the infection transmission, in order to follow contagion over time and project the epidemiological situation in the near future. While it is possible to reason that observed variation in the number and severity of cases stem from the initial number of infected individuals, the difference in the testing policies and social aspects of community transmissions, the factors that could explain high discrepancy in areas with a similar level of healthcare still remain unknown. Here we introduce a binary classifier based on an artificial neural network that can help in explaining those differences and that can be used to support the design of containment policies. We propose that air pollutants, and specifically particulate matter (PM) 2.5 and ozone, are oppositely related with the SARS-CoV-2 infection frequency and could serve as surrogate markers to complement the infection outbreak anticipation.

Spatial-temporal variations of atmospheric factors contribute to SARS-CoV-2 outbreak

Children are not COVID-19 super spreaders: time to go back to school

Most models of epidemic spread, including many designed specifically for COVID-19, implicitly assume that social networks are undirected, i.e., that the infection is equally likely to spread in either direction whenever a contact occurs. In particular, this assumption implies that the individuals most likely to spread the disease are also the most likely to receive it from others. Here, we review results from the theory of random directed graphs which show that many important quantities, including the reproductive 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 some do not. We also show that some structured SIR models informed by 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 networks have a forward and backward version of the classic"friendship paradox"-- forward links tend to lead to individuals with high risk, while backward links 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.

The role of directionality, heterogeneity and correlations in epidemic risk and spread

We describe in this paper an analysis of the spatial evolution of coronavirus pandemic around the world by using a particular type of unsupervised neural network, which is called self-organizing maps. Based on the clustering abilities of self-organizing maps we are able to spatially group together countries that are similar according to their coronavirus cases, in this way being able to analyze which countries are behaving similarly and thus can benefit by using similar strategies in dealing with the spread of the virus. Publicly available datasets of coronavirus cases around the globe from the last months have been used in the analysis. Interesting conclusions have been obtained, that could be helpful in deciding the best strategies in dealing with this virus. Most of the previous papers dealing with data of the Coronavirus have viewed the problem on temporal aspect, which is also important, but this is mainly concerned with the forecast of the numeric information. However, we believe that the spatial aspect is also important, so in this view the main contribution of this paper is the use of unsupervised self-organizing maps for grouping together similar countries in their fight against the Coronavirus pandemic, and thus proposing that strategies for similar countries could be established accordingly.

Analysis of Spatial Spread Relationships of Coronavirus (COVID-19) Pandemic in the World using Self Organizing Maps

Nosocomial transmission is an important characteristic of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) infection. Risk factors for transmission of MERS-CoV in healthcare settings are not well defined. During the Korean outbreak in 2015, 186 patients had laboratory-confirmed MERS-CoV infection. Those suspected as a source of viral transmission were categorized into the spreader groups (super-spreader [n = 5] and usual-spreader [n = 10]) and compared to the non-spreader group (n = 171). Body temperature of ≥ 38.5°C (adjusted odds ratio [aOR], 5.54; 95% confidence interval [CI], 1.38–22.30; P = 0.016), pulmonary infiltration of ≥ 3 lung zones (aOR, 7.33; 95% CI, 1.93–27.79; P = 0.003), and a more nonisolated in-hospital days (aOR, 1.32 per 1 day; 95% CI, 1.09–1.60; P = 0.004) were significant risk factors in the spreader group. There was no different clinical factor between super-spreaders and usual-spreaders. Nonisolated in-hospital days was the only factor which tended to be higher in super-spreaders than usual-spreaders (Mean, 6.6 vs. 2.9 days; P = 0.061). Early active quarantine might help reducing the size of an outbreak.

Clinical and Epidemiologic Characteristics of Spreaders of Middle East Respiratory Syndrome Coronavirus during the 2015 Outbreak in Korea

The outbreak of corona virus disease 2019 (Covid-19) imposes a major challenge in managing patients undergoing surgical operation. In this study, we analyzed clinical and transmission features of 25 cases of Covid-19 from a single thoracic department, including 13 patients and 12 health care staff. There were 13 males and 12 females. The median age of the patients was 61 (range: 51 to 69) years. The median age of the health care staff was 35 (range: 22 to 51) years. By the end of follow-up date (Mar. 3, 2020), there were 16 non-severe cases (64%) and 9 severe cases (36%), 5 cases were dead (20%). Nineteen (76%) of the infected cases were confirmed by SARS-CoV-2 nucleic acid test, the rest were clinically diagnosed as suspected Covid-19 cases, and 19 (76%) of the infected cases had positive exposure history. We found that COPD was significantly associated with severity and death (P=0.040, and P=0.038, respectively), and chest operation was significantly associated with death for Covid-19 patients (P=0.039). A potential "super spreader" may be the source of the transmission before the implementation of quarantine and comprehensive protection. It was concluded that Covid-19 is associated with poor prognosis for patients undergoing thoracic operation, especially for those with COPD. Implementation of comprehensive protective measures is important to control nosocomial infection.

Clinical and Transmission Characteristics of Covid-19 - A Retrospective Study of 25 Cases from a Single Thoracic Surgery Department

The novel coronavirus COVID-19 originally identified in December 2019, based on the data issued by March 30, 2020 daily report, the epidemic of SARS-CoV-2 so far has caused 693224 cases and resulted in 33106 deaths in more than 200 countries. Referring to the data reported, World Health Organization declared the outbreak a pandemic. We considered the chain-binomial type of the model which involves short stages of high infectivity and approximately constant incubation periods. This research paper is to study and analyze the COVID-19 Virus spreading statistics on the examples of the cases from the different counties. High correlation coefficients (91.64%) and determinants (83.98%) between the total volumes of virus spread and recovery are considered to be high and indicate the correctness of the Bailey model. Thus, as of March 30, with the results of statistical and mathematical data processing, it is difficult to predict the future spread-reduction variables of the pandemic.

Research on COVID-19 virus spreading statistics based on the examples of the cases from different counties

In December 2019, severe cases of pneumonia of unknown aetiology were reported in Wuhan city, in China. Lately, the pneumonia was related to the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), and the diseases was termed coronavirus disease-2019 (COVID-19). At the end of January 2020, the infection spread all over Italy, but with high infection rates and mortality in the northern part, especially in Lombardy, the most industrialized and polluted region of the country. It is noteworthy that a strong association between severe viral respiratory disease and air pollution has been described. Air pollutant could be solid particles, liquid droplets, or gases and can be of natural origin (such as ash from a volcanic eruption) or released from motor vehicle depletes (carbon monoxide gas) or factories (sulfur dioxide). Volcanic eruptions release large amounts of sulphuric acid, hydrogen sulfide, and hydrochloric acid into the atmosphere. Pulmunary diseases spread by means of small droplets in the breath, also called aerosols, and air pollution may facilitate the outside survival of viruses. We suppose that ash and gases emitted from the Mount Etna contributed to air pollution, potentially favouring the major contagion of COVID-19 in the eastern flank of the mountain, as in Catania city. In fact, ash and gases (with regard to radon) are usually particularly intense in winter, with a reduction of emission of specific metals with warmer weather. This is the first paper that elaborates the hypothesis of a potential role of volcanic gases and heavy metals-related air pollution, combined to specific climatic conditions and regional topography, in favouring severe COVID-19 diffusion in Sicily. Clinical and epidemiological studies are needed to support the hypothesis and plan the due prevention and awareness-raising campaigns.

Can volcanic trace elements facilitate Covid-19 diffusion? A hypothesis stemming from the Mount Etna area, Sicily

Abstract We describe a stochastic small-world network model of transmission of the SARS virus. Unlike the standard Susceptible-Infected-Removed models of disease transmission, our model exhibits both geographically localised outbreaks and “super-spreaders”. Moreover, the combination of localised and long range links allows for more accurate modelling of partial isolation and various public health policies. From this model, we derive an expression for the probability of a widespread outbreak and a condition to ensure that the epidemic is controlled. Moreover, multiple simulations are used to make predictions of the likelihood of various eventual scenarios for fixed initial conditions. The main conclusions of this study are: (i) “super-spreaders” may occur even if the infectiousness of all infected individuals is constant; (ii) consistent with previous reports, extended exposure time beyond 3–5 days (i.e. significant nosocomial transmission) was the key factor in the severity of the SARS outbreak in Hong Kong; and, (iii) the spread of SARS can be effectively controlled by either limiting long range links (imposing a partial quarantine) or enforcing rapid hospitalisation and isolation of symptomatic individuals.

Super-spreaders and the rate of transmission of the SARS virus

We present results on the existence of various common patterns in the growth of the total number of patients affected by COVID-19, a disease acquired through infection by a novel coronavirus, in different countries. For this purpose we propose a scaling model that can have general applicability in the understanding of real data of epidemics. This is analogous to the finite-size scaling, a technique used in the literature of phase transition to identify universality classes. In the disease model, the size of a system is proportional to the volume of the population, within a geographical region, that have been infected at the death of the epidemic or are eventually going to be infected when an epidemic ends. Outcome of our study, for COVID-19, via application of this model, suggests that in most of the countries, after the `onset' of spread, the growths are described by rapid exponential function, for significantly long periods. In addition to accurately identifying this superuniversal feature, we point out that the model is helpful in grouping countries into universality classes, based on the late time behavior, characterized by physical distancing practices, in a natural way. This feature of the model can provide direct comparative understanding of the effectiveness of lockdown-like social measures adopted in different places.

Spread of COVID-19: Investigation of universal features in real data

On February 1, 2020, China announced a novel coronavirus CoVID-19 outbreak to the public. CoVID-19 was classified as an epidemic by the World Health Organization (WHO). Although the disease was discovered and concentrated in Hubei Province, China, it was exported to all of the other Chinese provinces and spread globally. As of this writing, all plans have failed to contain the novel coronavirus disease, and it has continued to spread to the rest of the world. This study aimed to explore and interpret the effect of environmental and metrological variables on the spread of coronavirus disease in 30 provinces in China, as well as to investigate the impact of new China regulations and plans to mitigate further spread of infections. This article forecasts the size of the disease spreading based on time series forecasting. The growing size of CoVID-19 in China for the next 210 days is estimated by predicting the expected confirmed and recovered cases. The results revealed that weather conditions largely influence the spread of coronavirus in most of the Chinese provinces. This study has determined that increasing temperature and short-wave radiation would positively increase the number of confirmed cases, mortality rate, and recovered cases. The findings of this study agree with the results of our previous study.

The correlation between the spread of COVID-19 infections and weather variables in 30 Chinese provinces and the impact of Chinese government mitigation plans

As new cases of COVID-19 are being confirmed pressure is mounting to increase understanding of the factors underlying the spread the disease. Using data on local transmissions until the 23rd of March 2020, we develop an ensemble of 200 ecological niche models to project monthly variation in climate suitability for spread of SARS-CoV-2 throughout a typical climatological year. Although cases of COVID-19 are reported all over the world, most outbreaks display a pattern of clustering in relatively cool and dry areas. The predecessor SARS-CoV-1 was linked to similar climate conditions. Should the spread of SARS CoV-2 continue to follow current trends, asynchronous seasonal global outbreaks could be expected. According to the models, temperate warm and cold climates are more favorable to spread of the virus, whereas arid and tropical climates are less favorable. However, model uncertainties are still high across much of sub- Saharan Africa, Latin America and South East Asia. While models of epidemic spread utilize human demography and mobility as predictors, climate can also help constrain the virus. This is because the environment can mediate human-to-human transmission of SARS-CoV-2, and unsuitable climates can cause the virus to destabilize quickly, hence reducing its capacity to become epidemic.

Spread of SARS-CoV-2 Coronavirus likely to be constrained by climate

The new coronavirus known as COVID-19 is rapidly spreading since December 2019. Without any vaccination or medicine, the means of controlling it are limited to quarantine and social distancing. Here we study the spatio-temporal propagation of the COVID-19 virus in China and compare it to other global locations. Our results suggest that the disease propagation is highly related to population migration from Hubei resembling a Lévy flight which is characteristic of human mobility and thus could be controlled by efficient quarantines. Since quarantine is usually applied on a city level, more insight can be obtained in analyzing the epidemic in cities. Our results suggest that the disease spread in a city in China is characterized by two-stages process. At early times, at order of few days, the infection rate in the city is close to constant probably due to the lack of means to detect infected individuals before infection signs are observed and at later times it decays approximately exponentially due to quarantines. These two stages can explain the significant differences between the propagation in China and in other world-wide locations. While most cities in China control the disease which resulted in the decaying stage, in other world-wide countries the situation is still becoming worse probably due to less social interactions control and overloaded health systems which reflects in the death and recovery rates.

Spatio-temporal propagation of COVID-19 pandemics

To evaluate the effectiveness of the containment on the epidemic spreading of the new Coronavirus disease 2019, we carry on an analysis of the time evolution of the infection in a selected number of different Countries, by considering well-known macroscopic growth laws, the Gompertz law, and the logistic law. We also propose here a generalization of Gompertz law. Our data analysis permits an evaluation of the maximum number of infected individuals. The daily data must be compared with the obtained fits, to verify if the spreading is under control. From our analysis it appears that the spreading reached saturation in China, due to the strong containment policy of the national government. In Singapore a large growth rate, recently observed, suggests the start of a new strong spreading. For South Korea and Italy, instead, the next data on new infections will be crucial to understand if the saturation will be reached for lower or higher numbers of infected individuals.

Data analysis on Coronavirus spreading by macroscopic growth laws

Although the unprecedented efforts the world has been taking to control the spread of the human coronavirus disease (COVID-19) and its causative etiology [Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2)], the number of confirmed cases has been increasing drastically. Therefore, there is an urgent need for devising more efficient preventive measures, to limit the spread of the infection until an effective treatment or vaccine is available. The preventive measures depend mainly on the understanding of the transmission routes of this virus, its environmental stability, and its persistence on common touch surfaces. Due to the very limited knowledge about SARS-CoV-2, we can speculate its stability in the light of previous studies conducted on other human and animal coronaviruses. In this review, we present the available data on the stability of coronaviruses (CoVs), including SARS-CoV-2, from previous reports to help understand its environmental survival. According to available data, possible airborne transmission of SARS-CoV-2 has been suggested. SARS-CoV-2 and other human and animal CoVs have remarkably short persistence on copper, latex, and surfaces with low porosity as compared to other surfaces like stainless steel, plastics, glass, and highly porous fabrics. It has also been reported that SARS-CoV-2 is associated with diarrhea and that it is shed in the feces of COVID-19 patients. Some CoVs show persistence in human excrement, sewage, and waters for a few days. These findings suggest a possible risk of fecal-oral, foodborne, and waterborne transmission of SARS-CoV-2 in developing countries that often use sewage-polluted waters in irrigation and have poor water treatment systems. CoVs survive longer in the environment at lower temperatures and lower relative humidity. It has been suggested that large numbers of COVID-19 cases are associated with cold and dry climates in temperate regions of the world and that seasonality of the virus spread is suspected.

Stability of SARS-CoV-2 and other coronaviruses in the environment and on common touch surfaces and the influence of climatic conditions: a review

As lockdowns and stay-at-home orders start to be lifted across the globe, governments are struggling to establish effective and practical guidelines to reopen their economies. In dense urban environments with people returning to work and public transportation resuming full capacity, enforcing strict social distancing measures will be extremely challenging, if not practically impossible. Governments are thus paying close attention to particular locations that may become the next cluster of disease spreading. Indeed, certain places, like some people, can be"super-spreaders."Is a bustling train station in a central business district more or less susceptible and vulnerable as compared to teeming bus interchanges in the suburbs? Here, we propose a quantitative and systematic framework to identify spatial super-spreaders and the novel concept of super-susceptibles, i.e. respectively, places most likely to contribute to disease spread or to people contracting it. Our proposed data-analytic framework is based on the daily-aggregated ridership data of public transport in Singapore. By constructing the directed and weighted human movement networks and integrating human flow intensity with two neighborhood diversity metrics, we are able to pinpoint super-spreader and super-susceptible locations. Our results reveal that most super-spreaders are also super-susceptibles and that counterintuitively, busy peripheral bus interchanges are riskier places than crowded central train stations. Our analysis is based on data from Singapore, but can be readily adapted and extended for any other major urban center. It therefore serves as a useful framework for devising targeted and cost-effective preventive measures for urban planning and epidemiological preparedness.

Spatial super-spreaders and super-susceptibles in human movement networks

The correlation between the spread of COVID-19 infections and weather variables in 30 Chinese provinces and the impact of Chinese government mitigation plans.

To understand the roles of different transport modes in the spread of COVID-19 pandemic across Chinese cities, this paper looks at the factors influencing the number of imported cases from Wuhan and the spread speed and pattern of the pandemic. We find that frequencies of air flights and high-speed train (HST) services out of Wuhan are significantly associated with the number of COVID-19 cases in the destination cities. The presence of an airport or HST station at a city is significantly related to the speed of the pandemic spread, but its link with the total number of confirmed cases is weak. The farther the distance from Wuhan, the lower number of cases in a city and the slower the dissemination of the pandemic. The longitude and latitude coordinates do not have a significant relationship with the number of total cases but can increase the speed of the COVID-19 spread. Specifically, cities in the higher longitudinal region tended to record a COVID-19 case earlier than their counterparties in the west. Cities in the north were more likely to report the first case later than those in the south. The pandemic may emerge in large cities earlier than in small cities as GDP is a factor positively associated with the spread speed.

Exploring the roles of high-speed train, air and coach services in the spread of COVID-19 in China

We explore here how variation in the SARS-CoV-2 virus tropism could influence epidemic spread. We use a compartmental model fit to the existing data. The model indicates that Wuhan quarantine measures were effective but that alternative virus forms (gut tropism) and a second propagation route (through environment) was present. For Singapore and Shenzhen region the secondary route does not seem to be active yet. Adequate prevention measures taking into account both routes should be implemented.

A new transmission route for the propagation of the SARS-CoV-2 coronavirus

Background: The emerging SARS-CoV-2 virus has caused a global pandemic characterized by superspreader events. Heterogeneity in transmission risk is a known phenomenon in infectious diseases and was also seen in SARS and MERS outbreaks since 2003. The pandemic has led countries to control the pandemic spread using unprecedented severe mitigation strategies that include quarantine and lock-down. This has has been highly successful in terms of halting the spread, but had enormous socioeconomic cost. We set out to theoretically explore whether a model that includes the phenomenon of superspreaders may guide us towards a cost-effective mitigation strategy. Methods: We developed an agent-based model that includes persons that spread the disease far more readily than other persons. This allow us to investigate effect of containment strategies in both a random SEIR like scenario, and in a transmission model that work with structured society. Our model considered even contact patterns in three settings: home, work and a category of "other" settings representing diffuse social contacts. We introduced superspreaders either as a fixed proportion of the population, or as a broad spectrum of infectivity. For each choice, the model was then calibrated to an overall realistic daily growth rate of 23 percentage. As sensitivity analyses we varied the fraction of superspreaders as well the distribution of their infectivity. To compare the simulation findings for each mitigation scenario we considered the maximum daily ICU utility. Results: As expected, without mitigation imposed, the inclusion of superspreaders does not meaningfully change the epidemic trajectory. However when introducing mitigation strategies imposed on each of the three settings, we find that the presence of superspreaders made a substantial difference. The simulations demonstrate that the best strategy is to focus on limiting contacts in the other category. This in particular suggests that limiting diffuse social contacts in settings such as bars, transportation, restaurants, parties, concerts and lecture halls is far more effective than limiting the same amount of contact events in the home and work setting. Conclusions: To appreciate effect of heterogeneity in various social spheres we need to rethink disease transmission models. Doing so, we found that wide distribution of infectivity favours strategies that reduces the max, while leaving the typical behaviour undisturbed. We found that most workplaces may be opened without much influence on the epidemic, while one could snuff out transmission with an effective avoidance of other contacts. Interestingly, including a consideration of superspreaders can help explain the dramatic success of even moderate lock-down strategies as that practiced in Denmark and Sweden. And it points to the need to avoid mass gatherings until either flock immunity has been achieved or an effective vaccine is available.

Impact of Superspreaders on dissemination and mitigation of COVID-19

Abstract As the microblogging services are becoming more prosperous in everyday life for users on Online Social Networks (OSNs), it is more favorable for hot topics and breaking news to gain more attraction very soon than ever before, which are so-called “super-spreading events”. In the information diffusion process of these super-spreading events, messages are passed on from one user to another and numerous individuals are influenced by a relatively small portion of users, a.k.a. super-spreaders. Acquiring an awareness of super-spreading phenomena and an understanding of patterns of wide-ranged information propagations benefits several social media data mining tasks, such as hot topic detection, predictions of information propagation, harmful information monitoring and intervention. Taking into account that super-spreading in both information diffusion and spread of a contagious disease are analogous, in this study, we build a parameterized model, the SAIR model, based on well-known epidemic models to characterize super-spreading phenomenon in tweet information propagation accompanied with super-spreaders. For the purpose of modeling information diffusion, empirical observations on a real-world Weibo dataset are statistically carried out. Both the steady-state analysis on the equilibrium and the validation on real-world Weibo dataset of the proposed model are conducted. The case study that validates the proposed model shows that the SAIR model is much more promising than the conventional SIR model in characterizing a super-spreading event of information propagation. In addition, numerical simulations are carried out and discussed to discover how sensitively the parameters affect the information propagation process.

Characterizing super-spreading in microblog: An epidemic-based information propagation model

Background: COVID-19 rapidly escalated into a pandemic, threatening 213 countries, areas, and territories the world over. We aimed to identify potential province-level socioeconomic determinants of the virus's dissemination, and explain between-province differences in the speed of its spread, based on data from 36 provinces of Northern Italy. Methods: This is an ecological study. We included all confirmed cases of SARS-CoV-2 reported between February 24th and March 30th, 2020. For each province, we calculated the trend of contagion as the relative increase in the number of individuals infected between two time endpoints, assuming an exponential growth. Pearson's test was used to correlate the trend of contagion with a set of healthcare-associated, economic, and demographic parameters by province. The virus's spread was input as a dependent variable in a stepwise OLS regression model to test the association between rate of spread and province-level indicators. Findings: Multivariate analysis showed that the spread of COVID-19 was correlated negatively with aging index (p-value=0.003), and positively with public transportation per capita (p-value=0.012), the % of private long-term care hospital beds and, to a lesser extent (p-value=0.070), the % of private acute care hospital beds (p-value=0.006). Interpretation: Demographic and socioeconomic factors, and healthcare organization variables were found associated with a significant difference in the rate of COVID-19 spread in 36 provinces of Northern Italy. An aging population seemed to naturally contain social contacts. The availability of healthcare resources and their coordination could play an important part in spreading infection.

Demographic and Socio-Economic Factors, and Healthcare Resource Indicators Associated with the Rapid Spread of COVID-19 in Northern Italy: An Ecological Study

This study presents an in-depth investigation on the transmission of the novel coronavirus (COVID-19) from the urban perspective. It focuses on the “aftermath” of the outbreak and the spread of the infection among cities. Especially, this study provides insights of the fundamentals of the factors that may affect the spread of the infection in cities, where the marginal effects of some most influential factors to the virus transmission are estimated. It reveals that the distance to epicenter is a very strong influential factor, and is negatively linked with the spread of COVID-19. In addition, subway, wastewater and residential garbage are positively connected with the virus transmission. Moreover, both urban area and population density are negatively associated with the spread of COVID-19 at the early stage of the epidemic. Furthermore, this study also provides high precision estimation of the number of COVID-19 infection in Wuhan city, which is the epicenter of the outbreak in China. Based on the real-world data of cities outside Wuhan on March 2, 2020, the estimated number is 56,944.866 (mean value), which is very close to the officially reported number. The methodology and main conclusions shown in this paper are of general interest, and they can be applied to other countries to help understand the local transmission of COVID-19 as well.

Emerging study on the transmission of the Novel Coronavirus (COVID-19) from urban perspective: Evidence from China

The global spread of the 2019-nCoV is continuing and is fast moving, as indicated by the WHO raising the risk assessment to high. In this article, we provide a preliminary phylodynamic and phylogeographic analysis of this new virus. A Maximum Clade Credibility tree has been built using the 29 available whole genome sequences of 2019-nCoV and two whole genome sequences that are highly similar sequences from Bat SARS-like Coronavirus available in GeneBank. We are able to clarify the mechanism of transmission among the countries which have provided the 2019-nCoV sequence isolates from their patients. The Bayesian phylogeographic reconstruction shows that the 2019-2020 nCoV most probably originated from the Bat SARS-like Coronavirus circulating in the Rhinolophus bat family. In agreement with epidemiological observations, the most likely geographic origin of the new outbreak was the city of Wuhan, China, where 2019-nCoV time of the most recent common ancestor emerged, according to molecular clock analysis, around November 25th, 2019. These results, together with previously recorded epidemics, suggest a recurring pattern of periodical epizootic outbreaks due to Betacoronavirus. Moreover, our study describes the same population genetic dynamic underlying the SARS 2003 epidemic, and suggests the urgent need for the development of effective molecular surveillance strategies of Betacoronavirus among animals and Rhinolophus of the bat family.

The global spread of 2019-nCoV: a molecular evolutionary analysis

Background Most of epidemiological models applied for COVID-19 do not consider heterogeneity in infectiousness and impact of superspreaders, despite the broad viral loading distributions amongst COVID-19 positive people (1-1 000 000 per mL). Also, mass group testing is not used regardless to existing shortage of tests. I propose new strategy for early detection of superspreaders with reasonable number of RT-PCR tests, which can dramatically mitigate development COVID-19 pandemic and even turn it endemic. Methods I used stochastic social-epidemiological SEIAR model, where S-suspected, E-exposed, I-infectious, A-admitted (confirmed COVID-19 positive, who are admitted to hospital or completely isolated), R-recovered. The model was applied to real COVID-19 dynamics in London, Moscow and New York City. Findings Viral loading data measured by RT-PCR were fitted by broad log-normal distribution, which governed high importance of superspreaders. The proposed full scale model of a metropolis shows that top 10% spreaders (100+ higher viral loading than median infector) transmit 45% of new cases. Rapid isolation of superspreaders leads to 4-8 fold mitigation of pandemic depending on applied quarantine strength and amount of currently infected people. High viral loading allows efficient group matrix pool testing of population focused on detection of the superspreaders requiring remarkably small amount of tests. Interpretation The model and new testing strategy may prevent thousand or millions COVID-19 deaths requiring just about 5000 daily RT-PCR test for big 12 million city such as Moscow. Though applied to COVID-19 pandemic the results are universal and can be used for other infectious heterogenous epidemics. Funding No funding

Early detection of superspreaders by mass group pool testing can mitigate COVID-19 pandemic

Background Coronavirus disease 2019 (COVID-19) has been rapidly spreading throughout China and other countries including South Korea. As of March 12, 2020, a total number of 7,869 cases and 66 deaths had been documented in South Korea. Although the first confirmed case in South Korea was identified on January 20, 2020, the number of confirmed cases showed a rapid growth on February 19, 2020 with a total number of 1,261 cases with 12 deaths based on the Korea Centers for Disease Control and Prevention (KCDC). Method Using the data of confirmed cases of COVID-19 in South Korea that are publicly available from the KCDC, this paper aims to create spatial visualizations of COVID-19 transmission between January 20, 2020 and February 19, 2020. Results Using spatial visualization, this paper identified two early transmission clusters in South Korea (Daegu cluster and capital area cluster). Using a degree-weighted centrality measure, this paper proposes potential super-spreaders of the virus in the visualized clusters. Conclusion Compared to various epidemiological measures such as the basic reproduction number, spatial visualizations of the cluster-specific transmission networks and the proposed centrality measure may be more useful to characterize super-spreaders and the spread of the virus especially in the early epidemic phase.

Spatial Visualization of Cluster-Specific COVID-19 Transmission Network in South Korea During the Early Epidemic Phase

A key characteristic of the spread of infectious diseases is their ability to use efficient transmission paths within contact graphs. This enables the pathogen to maximise infection rates and spread within a target population. In this work, we devise techniques to localise infections and decrease infection rates based on a principled analysis of disease transmission paths within human-contact networks (proximity graphs). Experimental results of disease spreading shows that that at low visibility rates contact tracing slows disease spreading. However to stop disease spreading, contact tracing requires both significant visibility (at least 60%) into the proximity graph and the ability to place half of the population under isolation. We find that pro-actively isolating super-links -- key proximity encounters -- has significant benefits: targeted isolation of a fourth of the population based on 35% visibility into the proximity graph prevents an epidemic outbreak. It turns out that isolating super-spreaders is more effective than contact tracing and testing but less effective than targeting super-links. We highlight the important role of topology in epidemic outbreaks. We argue that proactive innoculation of a population by disabling super-links and super-spreaders may have an important complimentary role alongside contact tracing and testing as part of a sophisticated public-health response to epidemic outbreaks.

Unlinking super-linkers: the topology of epidemic response (Covid-19)

BACKGROUND: There is sufficient epidemiological and biological evidence of increased human susceptibility to viral pathogens such as Middle East respiratory syndrome coronavirus, respiratory syncytial virus, human metapneumovirus and influenza virus, in cold weather. The pattern of outbreak of the coronavirus disease 2019 (COVID-19) in China during the flu season is further proof that meteorological conditions may potentially influence the susceptibility of human populations to coronaviruses, a situation that may become increasingly evident as the current global pandemic of COVID-19 unfolds. MAIN BODY: A very rapid spread and high mortality rates have characterized the COVID-19 pandemic in countries north of the equator where air temperatures have been seasonally low. It is unclear if the currently high rates of COVID-19 infections in countries of the northern hemisphere will wane during the summer months, or if fewer people overall will become infected with COVID-19 in countries south of the equator where warmer weather conditions prevail through most of the year. However, apart from the influence of seasons, evidence based on the structural biology and biochemical properties of many enveloped viruses similar to the novel severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 (aetiology of COVID-19), support the higher likelihood of the latter of the two outcomes. Other factors that may potentially impact the rate of virus spread include the effectiveness of infection control practices, individual and herd immunity, and emergency preparedness levels of countries. CONCLUSION: This report highlights the potential influence of weather conditions, seasons and non-climatological factors on the geographical spread of cases of COVID-19 across the globe.

A close look at the biology of SARS-CoV-2, and the potential influence of weather conditions and seasons on COVID-19 case spread

The pandemic state of COVID-19 caused by the SARS CoV-2 put the world in quarantine and is causing an unprecedented economic crisis. However, COVID-19 is spreading in different rates at different countries. Here, we tested the effect of three classes of predictors, i.e., socioeconomic, climatic and transport, on the rate of daily increase of COVID-19. We found that global connections, represented by countries importance in the global air transportation network, is the main explanation for the growth rate of COVID-19 in different countries. Climate, geographic distance and socioeconomics did not affect this big picture analysis. Geographic distance and climate were significant barriers in the past but were surpassed by the human engine that allowed us to colonize almost every corner on Earth. Based on our global analysis, the global network of air transportation could lead to a worst-case scenario of synchronous global pandemic if board control measures in international airports were not taken and are not sustained during this pandemic. Despite all limitations of a global analysis, our results indicate that the current claims that the growth rate of COVID-19 may be lower in tropical countries should be taken very carefully, at risk to disturb well-established and effective policy of social isolation that may help to avoid higher mortality rates due to collapse of national health systems. This is the case of Brazil, a well-connected tropical country that presents the second highest increase rate of COVID-19 and might experience a serious case of human-induced disasters if decision makers take into consideration unsupported claims of the growth rate of COVID-19 might be lower in tropical countries.

Exponential phase of covid19 expansion is not driven by climate at global scale

BACKGROUND Globally, there have been many cases of COVID-2019 cases among medical staff, however, the main factors associated with the infection are not well understood. AIM To identify the super-factors causing COVID-19 infection in medical staff in China. METHODS A cross-sectional study was conducted between Jan. 1st, and Feb. 30th, 2020, where front line members of medical staff that took part in the care and treatment of patients with COVID-19 were enrolled. Epidemiological and demographic data between infected and uninfected groups were collected and compared. Social network analysis (SNA) was used to establish socio-metric social links between influencing factors. FINDINGS A total of 92 medical staff were enrolled. In all participant groups, the super-factor identified by the network was wearing a medical protective mask or surgical mask correctly (degree = 572; closeness =25; betweenness centrality = 3·23). Touching the cheek, nose, and mouth while working was the super-factor in the infected group. This was the biggest node in the network and had the strongest influence (degree = 370; closeness = 29; betweenness centrality = 0·37). Self-protection score was the super-factor in the uninfected group but was the isolated factor in the infected group (degree = 201; closeness = 28; betweenness centrality = 5·64). For family members, the exposure history to Huanan Seafood Wholesale Market and the contact history to wild animals were two isolated nodes. CONCLUSION High self-protection score was the main factor that prevented medical staff from contracting COVID-19 infection. The main factor that contributed to COVID-19 infections among medical staff was touching the cheek, nose and mouth while working.

Super-factors associated with transmission of occupational COVID-2019 infection among healthcare staff in Wuhan , China.

Starting from December 2019 the world has faced an unprecedented health crisis caused by the new Coronavirus (COVID-19) due to the SARS-CoV-2 pathogen. Within this topic, the aim of the paper was to quantify the effect of mobility habits in the spread of the Coronavirus in Italy through a multiple linear regression model. Estimation results showed that mobility habits represent one of the variables that explains the number of COVID-19 infections jointly with the number of tests/day and some environmental variables (i.e. PM pollution and temperature). Nevertheless, a proximity variable to the first outbreak was also significant, meaning that the areas close to the outbreak had a higher risk of contagion, especially in the initial stage of infection (time-decay phenomena). Furthermore, the number of daily new cases was related to the trips performed three weeks before. This threshold of 21â¯days could be considered as a sort of positivity detection time, meaning that the mobility restrictions quarantine commonly set at 14â¯days, defined only according to incubation-based epidemiological considerations, is underestimated (possible delays between contagion and detection) as a containment policy and may not always contribute to effectively slowing down the spread of virus worldwide. This result is original and, if confirmed in other studies, will lay the groundwork for more effective containment of COVID-19 in countries that are still in the health emergency, as well as for possible future returns of the virus.

How mobility habits influenced the spread of the COVID-19 pandemic: Results from the Italian case study

Background: Faced with the global pandemic of COVID-19, declared by World Health Organization (WHO) on March 11th 2020, and the need to better understand the seasonal behavior of the virus, our team conducted this systematic review to describe current knowledge about the emergence and replicability of the virus and its correlation with different weather factors such as temperature and relative humidity. Methods: The review was registered with the PROSPERO database. The electronic databases PubMed, Scopus, Web of Science, Cochrane Library, LILACS, OpenGrey and Google Scholar were examined with the searches restricted to the years 2019 and 2020. Risk of bias assessment was performed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist tool. The GRADE tool was used to assess the quality of the evidence. Results: The initial screening identified 517 articles. After examination of the full texts, seventeen studies met the review's eligibility criteria. Great homogeneity was observed in the findings regarding the effect of temperature and humidity on the seasonal viability and transmissibility of COVID-19. Cold and dry conditions were potentiating factors on the spread of the virus. After quality assessment, four studies had a high risk of bias and thirteen studies were scored as moderate risk of bias. The certainty of evidence was graded as low for both outcomes evaluated. Conclusion: Considering the existing scientific evidence, warm and wet climates seem to reduce the spread of COVID-19. The certainty of the evidence generated was graded as low. However, these variables alone could not explain most of the variability in disease transmission.

Effects of temperature and humidity on the spread of COVID-19: A systematic review.

BACKGROUND Since the outbreak of the novel coronavirus disease 2019 (COVID-19) in December 2019, the coronavirus has spread all over the world at an unprecedented rate. The transmissibility of coronavirus from asymptomatic patients to healthy individuals has received enormous attention. An important study using the COVID-19 data from the city Ningbo, China, was carried out to estimate and compare the transmission rates of the coronavirus by the symptomatic and asymptomatic patients. However, in the original analysis, the usual chi-squared tests were unduly used for some contingency tables with small cell counts including zero, which may violate the assumptions for the chi-squared test. OBJECTIVE We reanalyze the data from the city Ningbo with more appropriate statistical methods to draw more reliable and sound conclusions on the transmission rates of the coronavirus by the symptomatic and asymptomatic patients. METHODS We exclude the cases associated with the super-spreader and adopt more appropriate statistical methods, including the permutation test and Fisher's exact test, to reanalyze the COVID-19 data from the city Ningbo. RESULTS After excluding the cases related to the super-spreader, Fisher's exact test yields p-value=.84, which indicates stronger evidence of no difference in the transmission rates compared with the original analysis. The odds ratio of transmission rates of coronavirus between the symptomatic and asymptomatic patients is 1.2 with a 95% confidence interval [0.5, 2.8]. CONCLUSIONS Through a more in-depth and comprehensive statistical analysis of the Ningbo data, we conclude that there is no difference in the transmission rates of coronavirus between the symptomatic and asymptomatic patients.

Comparison of transmissibility of coronavirus between symptomatic and asymptomatic patients: Reanalysis of the Ningbo COVID-19 data.

The most effective way to stem the spread of a pandemic such as coronavirus disease 2019 (COVID-19) is social distancing, but the introduction of such measures is hampered by the fact that a sizeable part of the population fails to see their need. Three studies conducted during the mass spreading of the virus in the United States toward the end of March 2020 show that this results partially from people's misperception of the virus's exponential growth in linear terms and that overcoming this bias increases support for social distancing. Study 1 shows that American participants mistakenly perceive the virus's exponential growth in linear terms (conservatives more so than liberals). Studies 2 and 3 show that instructing people to avoid the exponential growth bias significantly increases perceptions of the virus's growth and thereby increases support for social distancing. Together, these results show the importance of statistical literacy to recruit support for fighting pandemics such as the coronavirus.

Correcting misperceptions of exponential coronavirus growth increases support for social distancing

The novel coronavirus (2019-nCoV) appeared in Wuhan in late 2019 have infected 34,598 people, and killed 723 among them until 8th February 2020. The new virus has spread to at least 316 cities (until 1st February 2020) in China. We used the traffic flow data from Baidu Map, and number of air passengers who left Wuhan from 1st January to 26th January, to quantify the potential infectious people. We developed multiple linear models with local population and air passengers as predicted variables to explain the variance of confirmed cases in every city across China. We found the contribution of air passengers from Wuhan was decreasing gradually, but the effect of local population was increasing, indicating the trend of local transmission. However, the increase of local transmission is slow during the early stage of novel coronavirus, due to the super strict control measures carried out by government agents and communities.

Tracking the spread of novel coronavirus (2019-nCoV) based on big data

As thousands of new cases of COVID-19 have been confirmed, there is an increasing demand to understand the factors underlying the spread of this disease. Using country-level data, we modeled the early growth in the number of cases for over 480 cities in all Brazilian states. As the main findings, we found that the percentage of people respecting social distancing protocols was the main explanatory factor for the observed growth rate of COVID-19. Those cities that presented the highest spread of the new coronavirus were also those that had lower averages of social distancing. We also underline that total population of cities and connectivity, represented by the city-level importance to the air transportation of people across the country, plays important roles in the dissemination of SARS-CoV-2. Climate and socioeconomic predictors had little contribution to the big-picture scenario. Our results show that different States had high variability in their growth rates, mostly due to quite different public health strategies to retain the outbreak of COVID-19. In spite of all limitations of such a large-scale approach, our results underline that climatic conditions are likely weak limiting factors for the spread of the new coronavirus, and the circulation of people in the city- and country-level are the most responsible factors for the early outbreak of COVID-19 in Brazil. Moreover, we reinforce that social distancing protocols are fundamental to avoid critical scenarios and the collapse of healthcare systems. We also predict that economic-induced decisions for relaxing social distancing might have catastrophic consequences, especially in large cities.

Social distancing and movement constraint as the most likely factors for COVID-19 outbreak control in Brazil

The pandemic spread of severe acute respiratory syndrome coronavirus (SARS-CoV-2) that causes COVID-19 calls for global health emergency with wide prevalence across 94 countries, and around 3073 deaths reported in china on 7thMarch 2020 which created red alert zone in the country. It was further noticed other than China, countries like the republic of Korea ranked first with 6767 cases, Italy with 4747 and Iran with 3513 cases. The spread of COVID-19 made a historical transition between December 2019 to March 2020 by extending the paradigm to a newer territory every day with the highest predicted reproductive number <2. Hence, while combating the epidemic spread, there are spectra of strategies that require crucial validation, some of which include drug repurposing, enzyme inhibition, target drug delivery etc. Among these, the category of drugs called enzyme inhibitors has a unique opportunity in the process of new drug discovery as these enzymes possess structural versatility starting from the host viral interface and up to the release of a new virus. Drugs entrapped within liposomes are highly effective against intracellular microorganisms as per published observations. Regulatory authorities like World Health Organization (WHO) and Centre for disease control and prevention (CDC) strongly recommend the need for the PPE’s like N95 respirator to avoid person to person contact. In this context, Electrospun Nanofiber Technology (ENT) offers ultrathin fibres (20-200nm) with close proximity of 99.97% of high efficient air filtration. Fabrication of ultrafine nano mask by utilizing electospun technology will surely benefit millions of people in a time-dependent manner.

Current strategies and approaches in combating SARS-CoV-2 virus that causes COVID-19

The COVID-19 has caused more than three million infections and over two hundred thousand deaths by April 20201. Limiting socioeconomic activities (SA) is among the most adopted governmental mitigating efforts to combat the transmission of the virus, though the degree varies dramatically among different regimes2. This study aims to quantify the contribution from the SA and weather conditions to the transmission of COVID-19 at global scale. Ruling out the unobservable factors including medical facilities and other control policies (MOC) through region-by-time fixed effects3,4, we show that the limiting SA has a leading contribution to lower the reproductive number by 18.3%, while weather conditions, including ultraviolet, relative humidity, and wind explain a smaller amount of variation. Temperature might have a non-monotonic impact on the transmission. We further show that in developed countries5 and China, the SA effect is more pronounced whereas the weather effect is significantly downplayed possibly because people tend to stay indoors most of the time with a controlled climate. We finally estimate the reduced reproductive number and the population spared from infections due to restricting SA at 40,964, 180,336, 174,494, in China, United States, and Europe respectively. From late January to mid-April, all regions, except for China, Australia, and south Korea show a steep upward trend of spared infections due to restricting SA. US and Europe, in particular, show far steeper upward trends of spared infections in the analyzed timeframe, signaling a greater risk of reopening the economy too soon.

Quantifying socioeconomic activities and weather effects on the global spread of COVID-19 epidemic

BACKGROUND: Since the outbreak of the novel coronavirus disease (COVID-19) in December 2019, the coronavirus has spread all over the world at an unprecedented rate. The transmissibility of the coronavirus from asymptomatic patients to healthy individuals has received enormous attention. An important study using COVID-19 data from the city of Ningbo, China, was carried out to estimate and compare the transmission rates of the coronavirus by the symptomatic and asymptomatic patients. However, in the original analysis, the usual chi-square tests were unduly used for some contingency tables with small cell counts including zero, which may violate the assumptions for the chi-square test. OBJECTIVE: We reanalyze the data from the city of Ningbo with more appropriate statistical methods to draw more reliable and sound conclusions on the transmission rates of the coronavirus by the symptomatic and asymptomatic patients. METHODS: We excluded the cases associated with the super-spreader and adopted a more appropriate statistical method, including the permutation test and the Fisher exact test, to reanalyze the COVID-19 data from the city of Ningbo. RESULTS: After excluding the cases related to the super-spreader, the Fisher exact test yields a P value of .84, which indicates stronger evidence of no difference in the transmission rates compared with the original analysis. The odds ratio of the coronavirus transmission rates between the symptomatic and asymptomatic patients is 1.2 with a 95% confidence interval 0.5-2.8. CONCLUSIONS: Through a more in-depth and comprehensive statistical analysis of the Ningbo data, we concluded that there is no difference in the transmission rates of coronavirus between symptomatic and asymptomatic patients.

Comparison of Transmissibility of Coronavirus Between Symptomatic and Asymptomatic Patients: Reanalysis of the Ningbo COVID-19 Data

From the end of 2019, an unprecedented novel coronavirus, which was named COVID-19 by the World Health Organization (WHO) emerged from Wuhan city, China. Despite rigorous global containment and quarantine efforts, the incidence of COVID-19 has continued to rise, with over 4 million confirmed-cases and over 300,000 deaths worldwide until mid-May. This study aims to present the effect of the promulgation of social distancing measures on the spread of COVID-19 in the cases of 10 highly infected countries. The authors focus on the statistics of the COVID-19 confirmed-cases and deaths in 10 highly infected countries, including The U.S., Spain, Italy, The U.K., France, Germany, Russia, Turkey, Iran and China, and the response to the pandemic of these countries in the period from January 11 to May 2, 2020. The relationships between the social distancing measures and the statistics of COVID-19 confirmed-cases and deaths were analyzed in order to elucidate the effectiveness of the social distancing measures on the spread of COVID-19 in 10 highly infected countries. The results showed it took1-4 weeks since the highest level of social distancing measures promulgation until the daily confirmed-cases and deaths showed signs of decreasing. The effectiveness of the social distancing measures on the spread of COVID-19 was different between the 10 focused countries. This variation is due to the difference in the levels of promulgated social distancing measures, as well as the difference in the COVID-19 spread situation at the time of promulgation between the countries.

Effect of the social distancing measures on the spread of COVID-19 in 10 highly infected countries

COVID-19 pandemic is a major human tragedy. Worldwide, SARS-CoV-2 has already infected over 3 million and has killed about 230,000 people. SARS-CoV-2 originated in China and, within three months, has evolved to an additional 10 subtypes. One particular subtype with a non-silent (Aspartate to Glycine) mutation at 614th position of the Spike protein (D614G) rapidly outcompeted other pre-existing subtypes, including the ancestral. We assessed that D614G mutation generates an additional serine protease (Elastase) cleavage site near the S1-S2 junction of the Spike protein. We also identified that a single nucleotide deletion (delC) at a known variant site (rs35074065) in a cis-eQTL of TMPRSS2, is extremely rare in East Asians but is common in Europeans and North Americans. The delC allele facilitates entry of the 614G subtype into host cells, thus accelerating the spread of 614G subtype in Europe and North America where the delC allele is common. The delC allele at the cis-eQTL locus rs35074065 of TMPRSS2 leads to overexpression of both TMPRSS2 and a nearby gene MX1. The cis-eQTL site, rs35074065 overlaps with a transcription factor binding site of an activator (IRF1) and a repressor (IRF2). IRF1 activator can bind to variant delC allele, but IRF2 repressor fails to bind. Thus, in an individual carrying the delC allele, there is only activation, but no repression. On viral entry, IRF1 mediated upregulation of MX1 leads to neutrophil infiltration and processing of 614G mutated Spike protein by neutrophil Elastase. The simultaneous processing of 614G spike protein by TMPRSS2 and Elastase serine proteases facilitates the entry of the 614G subtype into host cells. Thus, SARS-CoV-2, particularly the 614G subtype, has spread more easily and with higher frequency to Europe and North America where the delC allele regulating expression of TMPRSS2 and MX1 host proteins is common, but not to East Asia where this allele is rare.

Global Spread of SARS-CoV-2 Subtype with Spike Protein Mutation D614G is Shaped by Human Genomic Variations that Regulate Expression of TMPRSS2 and MX1 Genes

The pandemic outbreak of the novel coronavirus epidemic disease (COVID-19) is spreading like a diffusion-reaction in the world and almost 208 countries and territories are being affected around the globe. It became a sever health and socio-economic problem, while the world has no vaccine to combat this virus. This research aims to analyze the connection between the fast spread of COVID-19 and regional climate parameters over a global scale. In this research, we collected the data of COVID-19 cases from the time of 1st reported case to the 5th June 2020 in different affected countries and regional climatic parameters data from January 2020 to 5th June 2020. It was found that most of the countries located in the relatively lower temperature region show a rapid increase in the COVID-19 cases than the countries locating in the warmer climatic regions despite their better socio-economic conditions. A correlation between metrological parameters and COVID-19 cases was observed. Average daylight hours are correlated to total the COVID-19 cases with a coefficient of determination of 0.42, while average high-temperature shows a correlation of 0.59 and 0.42 with total COVID-19 cases and death cases respectively. The finding of the study will help international health organizations and local administrations to combat and well manage the spread of COVID-19.

The effects of regional climatic condition on the spread of COVID-19 at global scale

The COVID-19 disease, a respiratory disease transmitted by a new betacoronavirus SARS-CoV-2. As for other viral respiratory agents, SARS-CoV-2 spreads by person to person through respiratory droplets and direct contact and potentially by indirect contact through fomites. The goal of the current study is to evaluate whether the increase of temperature can influence the environmental endurance of SARS-CoV-2.We tested SARS-CoV-2 environmental stability in parallel at room temperature (RT, 20-25 Celsius degrees) and at average maximum temperature of June (JT) estimated at 28 Celsius degrees in Italy. The virus inoculated on plastic surface was harvested at predefined time-points and tested to evaluate viral titres on Vero cells by TCID50. Our results confirm that fomite transmission of the emerging SARS-CoV2 is possible, since the virus remains viable on surfaces up to 84 hours at both RT and JT. Moreover, a remarkable difference between the two temperatures exists, suggesting that virus vitality can be influenced by the environmental temperature. Our results support the hypothesis that in the hot season the increase of temperature could influence the environmental endurance of SARS-CoV2 and reduce Covid-19 transmission probability.

SARS-CoV-2 infection: the environmental endurance of the virus can be influenced by the increase of temperature

Correcting misperceptions of exponential coronavirus growth increases support for social distancing.

Abstract From the end of 2019, an unprecedented novel coronavirus, which was named COVID-19 by the World Health Organization (WHO) emerged from Wuhan city, China. Despite rigorous global containment and quarantine efforts, the incidence of COVID-19 has continued to rise, with over 4 million confirmed-cases and over 300,000 deaths worldwide until mid-May. This study aims to present the effect of the promulgation of social distancing measures on the spread of COVID-19 in the cases of 10 highly infected countries. The authors focus on the statistics of the COVID-19 confirmed-cases and deaths in 10 highly infected countries, including The U.S., Spain, Italy, The U.K., France, Germany, Russia, Turkey, Iran and China, and the response to the pandemic of these countries in the period from January 11 to May 2, 2020. The relationships between the social distancing measures and the statistics of COVID-19 confirmed-cases and deaths were analyzed in order to elucidate the effectiveness of the social distancing measures on the spread of COVID-19 in 10 highly infected countries. The results showed it took1–4 weeks since the highest level of social distancing measures promulgation until the daily confirmed-cases and deaths showed signs of decreasing. The effectiveness of the social distancing measures on the spread of COVID-19 was different between the 10 focused countries. This variation is due to the difference in the levels of promulgated social distancing measures, as well as the difference in the COVID-19 spread situation at the time of promulgation between the countries.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emergent RNA virus that spread around the planet in about 4 months. The consequences of this rapid spread on the virus evolution are under investigation. In this work, we analyzed ca. 9,000 SARS-CoV-2 genomes from Africa, America, Asia, Europe, and Oceania. We show that the virus is a complex of slightly different genetic variants that are unevenly distributed on Earth. Furthermore, we demonstrate that SARS-CoV-2 phylogeny is spatially structured. We hypothesize this could be the result of founder effects occurring as a consequence of, and local evolution occurring after, long-distance dispersal. In light of our results, we discuss how dispersal may constitute an opportunity for the virus to fix otherwise rare, and/or develop new, mutations. Based on previous studies, the possibility that this could significantly affect the virus phenotype is not remote. Relevance A cluster of pneumonia cases of unknown etiology was reported in December 2019 in Wuhan, Hubei province, China. Since then, hundreds of thousands of people have died all around the planet. Quickly after the pandemic onset, metagenomic studies showed the causative agent, now named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), belonged to the Betacoronavirus genus, responsible for spillover events in 2002 and 2012 (severe acute respiratory syndrome and Middle East respiratory syndrome, respectively). The zoonotic origins of these viruses (possibly bats, camelids, pangolins and/or palm civets) have received much attention. However, other evolutionary aspects, such as spatial variation, have received comparatively little attention. This study shows that SARS-CoV-2 variants, which we call virotypes, are heterogeneously distributed on Earth and demonstrates that the virus phylogeny is geographically structured. We explain how this may be due to founder effects combined with high mutation rates.

Mega-phylogeny sheds light on SARS-CoV-2 spatial phylogenetic structure

Recent analysis of COVID-19 data from China showed that the number of confirmed cases followed a subexponential power-law increase, with a growth exponent of around 2.2. The power-law behavior was attributed to a combination of effective containment and mitigation measures employed as well as behavioral changes by the population. In this work, we report a random walk Monte Carlo simulation study of proximity-based infection spread. Social distancing is incorporated in the simulations through a single parameter, the size of each step in the random walk process. The step size $l$ is taken to be a multiple of $\langle r \rangle$, which is the average separation between individuals. Three temporal growth regimes (quadratic, intermediate power-law and exponential) are shown to emerge naturally from our simulations. For $l = \langle r \rangle$, we get intermediate power-law growth exponents that are in general agreement with available data from China. On the other hand, we obtain a quadratic growth for smaller step sizes $l \lesssim \langle r \rangle/2 $, while for large $l$ the growth is found to be exponential. Together with available data, these results suggest that the early containment of the disease within China was close to optimal. We further performed a comparative case study of data from three other countries, India, Brazil and South Africa. We show that reasonable agreement with these data can be obtained by incorporating small-world-like connections in our simulations.

A random walk Monte Carlo simulation study of COVID-19-like infection spread

Epidemiological forecasts of COVID-19 spread at the country and/or state level have helped shape public health interventions. However, such models leave a scale-gap between the spatial resolution of actionable information (i.e. the county or city level) and that of modeled viral spread. States and nations are not spatially homogeneous and different areas may vary in disease risk and severity. For example, COVID-19 has age-stratified risk. Similarly, ICU units, PPE and other vital equipment are not equally distributed within states. Here, we implement a county-level epidemiological framework to assess and forecast COVID-19 spread through Georgia, where 1,933 people have died from COVID-19 and 44,638 cases have been documented as of May 27, 2020. We find that county-level forecasts trained on heterogeneity due to clustered events can continue to predict epidemic spread over multi-week periods, potentially serving efforts to prepare medical resources, manage supply chains, and develop targeted public health interventions. We find that the premature removal of physical (social) distancing could lead to rapid increases in cases or the emergence of sustained plateaus of elevated fatalities.

Spread of COVID-19 through Georgia, USA. Near-term projections and impacts of social distancing via a metapopulation model

The current outbreak of the novel coronavirus disease 2019 (COVID-19) in more than 250 countries has become a serious threat to the health of people around the world. Human-to-human transmission of the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occurs most often when people are in the incubation stage of the disease or are carriers and have no symptoms. Therefore, in this study, was discussed the role of environmental factors and conditions such as temperature, humidity, wind speed as well as food, water and sewage, air, insects, inanimate surfaces, and hands in COVID-19 transmission. The results of studies on the stability of the SARS-CoV-2 on different levels showed that the resistance of this virus on smooth surfaces was higher than others. Temperature increase and sunlight can facilitate the destruction of SARS-COV-2 and the stability of it on surfaces. When the minimum ambient air temperature increases by 1 °C, the cumulative number of cases decreases by 0.86%. According to the latest evidence, the presence of coronavirus in the sewer has been confirmed, but there is no evidence that it is transmitted through sewage or contaminated drinking water. Also, SARS-COV-2 transmission through food, food packages, and food handlers has not been identified as a risk factor for the disease. According to the latest studies, the possibility of transmitting SARS-COV-2 bioaerosol through the air has been reported in the internal environment of ophthalmology. The results additionally show that infectious bio-aerosols can move up to 6 feet. There have been no reports of SARS-COV-2 transmission by blood-feeding arthropods such as mosquitoes.

The role of environmental factors to transmission of SARS-CoV-2 (COVID-19)

Abstract Starting from December 2019 the world has faced an unprecedented health crisis caused by the new Coronavirus (COVID-19) due to the SARS-CoV-2 pathogen. Within this topic, the aim of the paper was to quantify the effect of mobility habits in the spread of the Coronavirus in Italy through a multiple linear regression model. Estimation results showed that mobility habits represent one of the variables that explains the number of COVID-19 infections jointly with the number of tests/day and some environmental variables (i.e. PM pollution and temperature). Nevertheless, a proximity variable to the first outbreak was also significant, meaning that the areas close to the outbreak had a higher risk of contagion, especially in the initial stage of infection (time-decay phenomena). Furthermore, the number of daily new cases was related to the trips performed three weeks before. This threshold of 21 days could be considered as a sort of positivity detection time, meaning that the mobility restrictions quarantine commonly set at 14 days, defined only according to incubation-based epidemiological considerations, is underestimated (possible delays between contagion and detection) as a containment policy and may not always contribute to effectively slowing down the spread of virus worldwide. This result is original and, if confirmed in other studies, will lay the groundwork for more effective containment of COVID-19 in countries that are still in the health emergency, as well as for possible future returns of the virus.

How mobility habits influenced the spread of the COVID-19 pandemic: Results from the Italian case study

From January 21 through February 23, 2020, public health agencies detected 14 U.S. cases of coronavirus disease 2019 (COVID-19), all related to travel from China (1,2). The first nontravel-related U.S. case was confirmed on February 26 in a California resident who had become ill on February 13 (3). Two days later, on February 28, a second nontravel-related case was confirmed in the state of Washington (4,5). Examination of four lines of evidence provides insight into the timing of introduction and early transmission of SARS-CoV-2, the virus that causes COVID-19, into the United States before the detection of these two cases. First, syndromic surveillance based on emergency department records from counties affected early by the pandemic did not show an increase in visits for COVID-19-like illness before February 28. Second, retrospective SARS-CoV-2 testing of approximately 11,000 respiratory specimens from several U.S. locations beginning January 1 identified no positive results before February 20. Third, analysis of viral RNA sequences from early cases suggested that a single lineage of virus imported directly or indirectly from China began circulating in the United States between January 18 and February 9, followed by several SARS-CoV-2 importations from Europe. Finally, the occurrence of three cases, one in a California resident who died on February 6, a second in another resident of the same county who died February 17, and a third in an unidentified passenger or crew member aboard a Pacific cruise ship that left San Francisco on February 11, confirms cryptic circulation of the virus by early February. These data indicate that sustained, community transmission had begun before detection of the first two nontravel-related U.S. cases, likely resulting from the importation of a single lineage of virus from China in late January or early February, followed by several importations from Europe. The widespread emergence of COVID-19 throughout the United States after February highlights the importance of robust public health systems to respond rapidly to emerging infectious threats.

Evidence for Limited Early Spread of COVID-19 Within the United States, January-February 2020

The global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causing coronavirus disease 2019 (COVID-19) has reached over five million confirmed cases worldwide, and numbers are still growing at a fast rate. Despite the wide outbreak of the infection, a remarkable asymmetry is observed in the number of cases and in the distribution of the severity of the COVID-19 symptoms in patients with respect to the countries/regions. In the early stages of a new pathogen outbreak, it is critical to understand the dynamics of the infection transmission, in order to follow contagion over time and project the epidemiological situation in the near future. While it is possible to reason that observed variation in the number and severity of cases stems from the initial number of infected individuals, the difference in the testing policies and social aspects of community transmissions, the factors that could explain high discrepancy in areas with a similar level of healthcare still remain unknown. Here, we introduce a binary classifier based on an artificial neural network that can help in explaining those differences and that can be used to support the design of containment policies. We found that SARS-CoV-2 infection frequency positively correlates with particulate air pollutants, and specifically with particulate matter 2.5 (PM2.5), while ozone gas is oppositely related with the number of infected individuals. We propose that atmospheric air pollutants could thus serve as surrogate markers to complement the infection outbreak anticipation.

Spatial-Temporal Variations in Atmospheric Factors Contribute to SARS-CoV-2 Outbreak.

INTRODUCTION: Climate change has been known to influence infectious diseases. The reason for this being the fact; disease agents and their vectors each have particular environments that are optimal for growth, survival, transport, and dissemination. MATERIALS AND METHODS: The WHO's website was accessed to look for the Novel Coronavirus (COVID-19) situation dashboard and comprehensively study and assess the report. An attempt was made to look for countries, areas or territories with maximum and minimum number of cases of lab confirmed COVID cases. Further, we entered the words “Climate“ in google for each of the aforementioned countries and searched for the results. A comparison was established by including countries from both the hemispheres (northern and southern). The preliminary analysis was based on the reports from countries with established testing facilities for Covid-19. RESULTS: The report suggests that countries with higher number of cases are the countries with cold weather. These are also the countries with low humidity which could be favoring the transmission and survival of the SARS-COV-2. CONCLUSIONS: The results though preliminary point to a pattern which favors the hypothesis that the extensive spread of Covid-19 maybe limited by temperature and humidity.

Does temperature and humidity influence the spread of Covid-19?: A preliminary report

This study endeavors to explain the relation between air pollution and particulate compounds emissions, wind resources and energy, and the diffusion of COVID-19 infection to provide insights of sustainable policy to prevent future epidemics. The statistical analysis here focuses on case study of Italy, one of the countries to experience a rapid increase in confirmed cases and deaths. Results reveal two main findings: 1) cities in regions with high wind speed and a high wind energy production in MW have a lower number of infected individuals of COVID-19 infection and total deaths; 2) cities located in hinterland zones (mostly those bordering large urban conurbations) with high polluting industrialization, low wind speed and less cleaner production have a greater number of infected individuals and total deaths. Hence, cities with pollution industrialization and low renewable energy have also to consider low wind speed and other climatological factors that can increase stagnation of the air in the atmosphere with potential problems for public health in the presence of viral agents. Results here suggest that current pandemic of Coronavirus disease and future epidemics similar to COVID-19 infection cannot be solved only with research and practice of medicine, immunology and microbiology but also with a proactive strategy directed to interventions for a sustainable development. Overall, then, this study has to conclude that a strategy to prevent future epidemics similar to COVID-19 infection must also be based on sustainability science to support a higher level of renewable energy and cleaner production to reduce polluting industrialization and, as result, the factors determining the spread of coronavirus disease and other infections in society.

How sustainable environments have reduced the diffusion of coronavirus disease 2019: the interaction between spread of COVID-19 infection, polluting industrialization, wind (renewable) energy

Background The COVID-19 pandemic caused by the SARS-CoV-2 virus started in China in December 2019 and has since spread globally. Information about the spread of the virus in a country can inform the gradual reopening of a country and help to avoid a second wave of infections. Denmark is currently opening up after a lockdown in mid-March. Methods We perform a phylogenetic analysis of 742 publicly available Danish SARS-CoV-2 genome sequences and put them into context using sequences from other countries. Result Our findings are consistent with several introductions of the virus to Denmark from independent sources. We identify several chains of mutations that occurred in Denmark and in at least one case find evidence that the virus spread from Denmark to other countries. A number of the mutations found in Denmark are non-synonymous, and in general there is a considerable variety of strains. The proportions of the most common haplotypes is stable after lockdown. Conclusion Our work shows how genetic data can be used to identify routes of introduction of a virus into a region and provide alternative means for verifying existing assumptions. For example, our analysis supports the hypothesis that the virus was brought to Denmark by skiers returning from Ischgl. On the other hand, we identify transmission chains suggesting that Denmark was part of a network of countries among which the virus was being transmitted; thus challenging the common narrative that Denmark only got infected from abroad. Our analysis does not indicate that the major haplotypes appearing in Denmark have a different degree of virality. Our methods can be applied to other countries, regions or even highly localised outbreaks. When used in real-time, we believe they can serve to identify transmission events and supplement traditional methods such as contact tracing.

SARS-CoV-2 transmission chains from genetic data: a Danish case study

Background: The role of aerosols in the transmission of SARS-CoV-2 remains debated. We analysed an outbreak involving three non-associated families in Restaurant X in Guangzhou, China, and assessed the possibility of aerosol transmission of SARS-CoV-2 and characterize the associated environmental conditions. Methods: We collected epidemiological data, obtained a video record and a patron seating-arrangement from the restaurant, and measured the dispersion of a warm tracer gas as a surrogate for exhaled droplets from the suspected index patient. Computer simulations were performed to simulate the spread of fine exhaled droplets. We compared the in-room location of subsequently infected cases and spread of the simulated virus-laden aerosol tracer. The ventilation rate was measured using the tracer decay method. Results: Three families (A, B, C), 10 members of which were subsequently found to have been infected with SARS-CoV-2 at this time, or previously, ate lunch at Restaurant X on Chinese New Year's Eve (January 24, 2020) at three neighboring tables. Subsequently, three members of family B and two members of family C became infected with SARS-CoV-2, whereas none of the waiters or 68 patrons at the remaining 15 tables became infected. During this occasion, the ventilation rate was 0.75-1.04 L/s per person. No close contact or fomite contact was observed, aside from back-to-back sitting by some patrons. Our results show that the infection distribution is consistent with a spread pattern representative of exhaled virus-laden aerosols. Conclusions: Aerosol transmission of SARS-CoV-2 due to poor ventilation may explain the community spread of COVID-19.

Evidence for probable aerosol transmission of SARS-CoV-2 in a poorly ventilated restaurant

The Coronavirus disease 2019 (COVID-19) is spreading around the world, representing a global pandemic, counting, as of June 5th, 2020, over 6,600,000 confirmed cases and more than 390,000 deaths, with exponentially increasing numbers. In the first half of 2020, because of the widespread of the COVID-19, researches were focused on the monitoring of SARS-CoV-2 in water, wastewater, sludge, air, and on surfaces, in order to assess the risk of contracting the viral infection from contaminated environments. So far, the survival of the novel Coronavirus out of the human body has been reported for short time periods (from hours to few days, in optimized in vitro conditions), mainly because of the need of an host organism which could consent the viral attack, and due to the weak external membrane of the virus. SARS-CoV-2 viral shedding strategies in the environment, either through animate and unanimate matrices, or exploiting the organic matter in water, wastewater, and waste in general, have been discussed in the present article. We concluded that, besides the high infectuousness of the novel Coronavirus, the transmission of the pathogen may be efficiently contained applying the adequate preventive measures (e.g., personal protection equipments, and disinfecting agents), indicated by national and international health authories.

Persistence of SARS-CoV-2 in the environment and COVID-19 transmission risk from environmental matrices and surfaces()

BACKGROUND: Globally, there have been many cases of COVID-2019 cases among medical staff, however, the main factors associated with the infection are not well understood. AIM: To identify the super-factors causing COVID-19 infection in medical staff in China. METHODS: A cross-sectional study was conducted between Jan. 1st, and Feb. 30th, 2020, where front line members of medical staff that took part in the care and treatment of patients with COVID-19 were enrolled. Epidemiological and demographic data between infected and uninfected groups were collected and compared. Social network analysis (SNA) was used to establish socio-metric social links between influencing factors. FINDINGS: A total of 92 medical staff were enrolled. In all participant groups, the super-factor identified by the network was wearing a medical protective mask or surgical mask correctly (degree = 572; closeness =25; betweenness centrality = 3·23). Touching the cheek, nose, and mouth while working was the super-factor in the infected group. This was the biggest node in the network and had the strongest influence (degree = 370; closeness = 29; betweenness centrality = 0·37). Self-protection score was the super-factor in the uninfected group but was the isolated factor in the infected group (degree = 201; closeness = 28; betweenness centrality = 5·64). For family members, the exposure history to Huanan Seafood Wholesale Market and the contact history to wild animals were two isolated nodes. CONCLUSION: High self-protection score was the main factor that prevented medical staff from contracting COVID-19 infection. The main factor that contributed to COVID-19 infections among medical staff was touching the cheek, nose and mouth while working.

Super-factors associated with transmission of occupational COVID-2019 infection among healthcare staff in Wuhan , China

Evidence of short-range aerosol transmission of SARS-CoV-2 and call for universal airborne precautions for anesthesiologists during the COVID-19 pandemic

SARS-CoV-2 causing 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 and misused to characterize the transmissibility of the virus, hides the fact that transmission is stochastic, is dominated by a small number of individuals, and is driven by super-spreading events (SSEs). The distinct transmission features, such as high stochasticity under low prevalence, and the central role played by SSEs on transmission dynamics, should not 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, fish factories, cruise ships, family gatherings, parties and night clubs. These SSEs demonstrate the urgent need to understand routes of transmission, while posing an opportunity that outbreak can be effectively contained with targeted interventions to eliminate SSEs. Here, we describe the potential types of SSEs, how they influence transmission, and give recommendations for control of SARS-CoV-2.

Stochasticity and heterogeneity in the transmission dynamics of SARS-CoV-2

We estimate the basic reproduction number R0 and the overdispersion parameter K at two COVID-19 clusters in Indonesia: Jakarta-Depok and Batam. Based on the first 397 confirmed cases in both clusters, we find a high degree of individual-level variation in the transmission. The basic reproduction number R0 is estimated at 6.79 and 2.47, while the overdispersion parameter K of a negative-binomial distribution is estimated at 0.08 and 0.2 for Jakarta-Depok and Batam, respectively. This suggests that superspreading events played a key role in the early stage of the outbreak, i.e., a small number of infected individuals are responsible for large amounts of COVID-19 transmission.

Superspreading in Early Transmissions of COVID-19 in Indonesia

Spatial-Temporal Variations in Atmospheric Factors Contribute to SARS-CoV-2 Outbreak

Abstract This paper uses the exploratory spatial data analysis and the geodetector method to analyze the spatial and temporal differentiation characteristics and the influencing factors of the COVID-19 (corona virus disease 2019) epidemic spread in mainland China based on the cumulative confirmed cases, average temperature, and socio-economic data. The results show that: (1) the epidemic spread rapidly from January 24 to February 20, 2020, and the distribution of the epidemic areas tended to be stable over time. The epidemic spread rate in Hubei province, in its surrounding, and in some economically developed cities was higher, while that in western part of China and in remote areas of central and eastern China was lower. (2) The global and local spatial correlation characteristics of the epidemic distribution present a positive correlation. Specifically, the global spatial correlation characteristics experienced a change process from agglomeration to decentralization. The local spatial correlation characteristics were mainly composed of the‘high-high’ and ‘low-low’ clustering types, and the situation of the contiguous layout was very significant. (3) The population inflow from Wuhan and the strength of economic connection were the main factors affecting the epidemic spread, together with the population distribution, transport accessibility, average temperature, and medical facilities, which affected the epidemic spread to varying degrees. (4) The detection factors interacted mainly through the mutual enhancement and nonlinear enhancement, and their influence on the epidemic spread rate exceeded that of single factors. Besides, each detection factor has an interval range that is conducive to the epidemic spread.

Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors

As the current COVID-19 pandemic continues to impact countries around the globe, refining our understanding of its transmission dynamics and the effectiveness of interventions is imperative. In particular, it is essential to obtain a firmer grasp on the effect of social distancing, potential individual-level heterogeneities in transmission such as age-specific infectivity, and impact of super-spreading. To this end, it is important to exploit multiple data streams that are becoming abundantly available during the pandemic. In this paper, we formulate an individual-level spatio-temporal mechanistic framework to statistically integrate case data with geo-location data and aggregate mobility data, enabling a more granular understanding of the transmission dynamics of COVID-19. We analyze reported cases from surveillance data, between March and early May 2020, in five (urban and rural) counties in the State of Georgia USA. We estimate natural history parameters of COVID-19 and infer unobserved quantities including infection times and transmission paths using Bayesian data-augmentation techniques. First, our results show that the overall median reproductive number was 2.88 (with 95% C.I. [1.85, 4.9]) before the state-wide shelter-in-place order issued in early April, and the effective reproductive number was reduced to below 1 about two weeks by the order. Super-spreading appears to be widespread across space and time, and it may have a particularly important role in driving the outbreak in the rural area and increasing importance towards later stages of outbreaks in both urban and rural settings. Overall, about 2% of cases may have directly infected 20% of all infections. We estimate that the infected children and younger adults (<60 years old) may be 2.38 [1.30, 3.51] times more transmissible than infected elderly (>=60), and the former may be the main driver of super-spreading. Through the synthesis of multiple data streams using our transmission modelling framework, our results enforce and improve our understanding of the natural history and transmission dynamics of COVID-19. More importantly, we reveal the roles of age-specific infectivity and characterize systematic variations and associated risk factors of super-spreading. These have important implications for the planning of relaxing social distancing and, more generally, designing optimal control measures.

Characterizing super-spreading events and age-specific infectivity of COVID-19 transmission in Georgia, USA

Since its emergence and detection in Wuhan, China in late 2019, the novel coronavirus SARS-CoV-2 has spread to nearly every country around the world, resulting in hundreds of thousands of infections to date. The virus was first detected in the Pacific Northwest region of the United States in January, 2020, with subsequent COVID-19 outbreaks detected in all 50 states by early March. To uncover the sources of SARS-CoV-2 introductions and patterns of spread within the U.S., we sequenced nine viral genomes from early reported COVID-19 patients in Connecticut. Our phylogenetic analysis places the majority of these genomes with viruses sequenced from Washington state. By coupling our genomic data with domestic and international travel patterns, we show that early SARS-CoV-2 transmission in Connecticut was likely driven by domestic introductions. Moreover, the risk of domestic importation to Connecticut exceeded that of international importation by mid-March regardless of our estimated impacts of federal travel restrictions. This study provides evidence for widespread, sustained transmission of SARS-CoV-2 within the U.S. and highlights the critical need for local surveillance.

Coast-to-coast spread of SARS-CoV-2 in the United States revealed by genomic epidemiology