scholarly journals Rising evidence of COVID-19 transmission potential to and between animals: do we need to be concerned?

2020 ◽  
Author(s):  
Andrei R. Akhmetzhanov ◽  
Natalie M. Linton ◽  
Hiroshi Nishiura
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Confidence Interval ◽  
Reproduction Number ◽  
Close Contact ◽  
Case Reports ◽  
Domestic Dogs ◽  
Transmission Potential ◽  
Pet Owners ◽  
Using Data ◽  
The Basic Reproduction Number

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—the virus that causes coronavirus disease (COVID-19)—has been detected in domestic dogs and cats, raising concerns of transmission from, to, or between these animals. There is currently no indication that feline- or canine-to-human transmission can occur, though there is rising evidence of the reverse. To explore the extent of animal-related transmission, we aggregated 17 case reports on confirmed SARS-CoV-2 infections in animals as of 15 May 2020. All but two animals fully recovered and had only mild respiratory or digestive symptoms. Using data from probable cat-to-cat transmission in Wuhan, China, we estimated the basic reproduction number R0 under this scenario at 1.09 (95% confidence interval: 1.05, 1.13). This value is much lower than the R0 reported for humans and close to one, indicating that the sustained transmission between cats is unlikely to occur. Our results support the view that the pet owners and other persons with COVID-19 in close contact with animals should be cautious of the way they interact with them.

scholarly journals A Conceptual Discussion About the Basic Reproduction Number of Severe Acute Respiratory Syndrome Coronavirus 2 in Healthcare Settings

2020 ◽  
Author(s):  
Laura Temime ◽  
Marie-Paule Gustin ◽  
Audrey Duval ◽  
Niccolò Buetti ◽  
Pascal Crépey ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Healthcare Settings ◽  
Contact Data ◽  
Conceptual Discussion ◽  
The Basic Reproduction Number

Abstract To date, no specific estimate of R0 for SARS-CoV-2 is available for healthcare settings. Using interindividual contact data, we highlight that R0 estimates from the community cannot translate directly to healthcare settings, with pre-pandemic R0 values ranging 1.3–7.7 in 3 illustrative healthcare institutions. This has implications for nosocomial COVID-19 control.

scholarly journals Estimating the reproduction number and forecasting the impact of COVID-19 in Kuwait using a modified compartmental epidemiological model

2020 ◽  
Author(s):  
Mohammad AlHamli
Keyword(s):  
Reproduction Number ◽  
Intrinsic Growth Rate ◽  
General Hospitals ◽  
Maximum Value ◽  
Reproduction Numbers ◽  
Next Generation Matrix ◽  
The State Of Kuwait ◽  
Using Data ◽  
The Impact ◽  
The Basic Reproduction Number

Abstract A modified compartmental epidemic model was developed to simulate the state of Kuwait protocol in fighting COVID-19 pandemic. The next generation matrix method was used to drive an expression for the basic reproduction number, R0. Basic and effective reproduction numbers were calculated using data from the intrinsic growth rate of the confirmed COVID-19 cases. R0 was found to be 2.18. Three scenarios that varied by effective reproduction number were used to estimate the future course of the disease: a high value of R = 1.98, a middle value of R = 1.62, and a low value of R = 1.2. The maximum number of beds required in general hospitals in each scenario were estimated at 141 184, 85 341, and 16 412, respectively. For intensive care units, the estimated numbers of beds required were 16 461, 9 645, and 1788. Maximum deaths also varied and were estimated to be 29 202, 23 973, and 11 565. For the maximum value of R, it is estimated to peak on August 27, 2020. For the middle value of R, it is estimated to peak on September 20, 2020. For the minimum value of R, it is estimated to peak on December 21, 2020.

scholarly journals Accounting for super-spreading gives the basic reproduction number R0 of COVID-19 that is higher than initially estimated

2020 ◽  
Author(s):  
Marek Kochańczyk ◽  
Frederic Grabowski ◽  
Tomasz Lipniacki
Keyword(s):  
Basic Reproduction Number ◽  
Doubling Time ◽  
Reproduction Number ◽  
Case Reports ◽  
Individual Case ◽  
Epidemic Spread ◽  
Empirical Estimation ◽  
Seir Model ◽  
Initial Stage ◽  
The Basic Reproduction Number

Transmission of infectious diseases is characterized by the basic reproduction number R0, a metric used to assess the threat posed by an outbreak and inform proportionate preventive decision-making. Based on individual case reports from the initial stage of the coronavirus disease 2019 epidemic, R0 is often estimated to range between 2 and 4. In this report, we show that a SEIR model that properly accounts for the distribution of the incubation period suggests that R0 lie in the range 4.4–11.7. This estimate is based on the doubling time observed in the near-exponential phases of the epidemic spread in China, United States, and six European countries. To support our empirical estimation, we analyze stochastic trajectories of the SEIR model showing that in the presence of super-spreaders the calculations based on individual cases reported during the initial phase of the outbreak systematically overestimate the doubling time and thus underestimate the actual value of R0.

scholarly journals Estimation of the basic reproduction number, average incubation time, asymptomatic infection rate, and case fatality rate for COVID-19: Meta-analysis and sensitivity analysis

2020 ◽  
Author(s):  
Wenqing He ◽  
Grace Y. Yi ◽  
Yayuan Zhu
Keyword(s):  
Confidence Interval ◽  
Basic Reproduction Number ◽  
Incubation Time ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Meta Analysis ◽  
Case Fatality ◽  
Asymptomatic Infection ◽  
Fatality Rate ◽  
The Basic Reproduction Number

AbstractThe coronavirus disease 2019 (COVID-19) has been found to be caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, comprehensive knowledge of COVID-19 remains incomplete and many important features are still unknown. This manuscripts conduct a meta-analysis and a sensitivity study to answer the questions: What is the basic reproduction number? How long is the incubation time of the disease on average? What portion of infections are asymptomatic? And ultimately, what is the case fatality rate? Our studies estimate the basic reproduction number to be 3.15 with the 95% interval (2.41, 3.90), the average incubation time to be 5.08 days with the 95% confidence interval (4.77, 5.39) (in day), the asymptomatic infection rate to be 46% with the 95% confidence interval (18.48%, 73.60%), and the case fatality rate to be 2.72% with 95% confidence interval (1.29%, 4.16%) where asymptomatic infections are accounted for.

scholarly journals Accounting for symptomatic and asymptomatic in a SEIR-type model of COVID-19

2020 ◽  
Vol 15 ◽  
pp. 34 ◽  
Author(s):  
Jayrold P. Arcede ◽  
Randy L. Caga-anan ◽  
Cheryl Q. Mentuda ◽  
Youcef Mammeri
Keyword(s):  
Mathematical Model ◽  
Numerical Simulations ◽  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Type Model ◽  
Treatment Results ◽  
Hospital Capacity ◽  
The Usa ◽  
Using Data ◽  
The Basic Reproduction Number

A mathematical model was developed describing the dynamic of the COVID-19 virus over a population considering that the infected can either be symptomatic or not. The model was calibrated using data on the confirmed cases and death from several countries like France, Philippines, Italy, Spain, United Kingdom, China, and the USA. First, we derived the basic reproduction number, R0, and estimated the effective reproduction Reff for each country. Second, we were interested in the merits of interventions, either by distancing or by treatment. Results revealed that total and partial containment is effective in reducing the transmission. However, its duration may be long to eradicate the disease (104 days for France). By setting the end of containment as the day when hospital capacity is reached, numerical simulations showed that the duration can be reduced (up to only 39 days for France if the capacity is 1000 patients). Further, results pointed out that the effective reproduction number remains large after containment. Therefore, testing and isolation are necessary to stop the disease.

scholarly journals The long-term dynamics of tuberculosis and other diseases with long serial intervals: implications of and for changing reproduction numbers

1998 ◽  
Vol 121 (2) ◽  
pp. 309-324 ◽  
Author(s):  
E. VYNNYCKY ◽  
P. E. M. FINE
Keyword(s):  
Infectious Disease ◽  
Basic Reproduction Number ◽  
Reproduction Number ◽  
England And Wales ◽  
Transmission Potential ◽  
Reproduction Numbers ◽  
Serial Interval ◽  
Net Reproduction ◽  
Epidemiological Parameters ◽  
The Basic Reproduction Number

The net and basic reproduction numbers are among the most widely-applied concepts in infectious disease epidemiology. A net reproduction number (the average number of secondary infectious cases resulting from each case in a given population) of above 1 is conventionally associated with an increase in incidence; the basic reproduction number (defined analogously for a ‘totally susceptible’ population) provides a standard measure of the ‘transmission potential’ of an infection. Using a model of the epidemiology of tuberculosis in England and Wales since 1900, we demonstrate that these measures are difficult to apply if disease can follow reinfection, and that they lose their conventional interpretations if important epidemiological parameters, such as the rate of contact between individuals, change over the time interval between successive cases in a chain of transmission (the serial interval).The net reproduction number for tuberculosis in England and Wales appears to have been approximately 1 from 1900 until 1950, despite concurrent declines in morbidity and mortality rates, and it declined rapidly in the second half of this century. The basic reproduction number declined from about 3 in 1900, reached 2 by 1950, and first fell below 1 in about 1960. Reductions in effective contact between individuals over this period, measured in terms of the average number of individuals to whom each case could transmit the infection, meant that the conventional basic reproduction number measure (which does not consider subsequent changes in epidemiological parameters) for a given year failed to reflect the ‘actual transmission potential’ of the infection. This latter property is better described by a variant of the conventional measure which takes secular trends in contact into account. These results are relevant for the interpretation of trends in any infectious disease for which epidemiological parameters change over time periods comparable to the infectious period, incubation period or serial interval.

scholarly journals The basic reproduction number of COVID-19 across Africa

2021 ◽  
Author(s):  
Sarafa Adewale Iyaniwura ◽  
Muhammad Rabiu Musa ◽  
Jummy F. David ◽  
Jude Dzevela Kong
Keyword(s):  
Bayesian Inference ◽  
Severe Acute Respiratory Syndrome ◽  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Transmission Dynamics ◽  
Clear Understanding ◽  
Seir Model ◽  
The World ◽  
Bayesian Inference Framework ◽  
The Basic Reproduction Number

The pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) took the world by surprise. Following the first outbreak of COVID-19 in December 2019, several models have been developed to study and understand its transmission dynamics. Although the spread of COVID-19 is being slowed down by vaccination and other interventions, there is still a need to have a clear understanding of the evolution of the pandemic across countries, states and communities. To this end, there is a need to have a clearer picture of the initial spread of the disease in different regions. In this project, we used a simple SEIR model and a Bayesian inference framework to estimate the basic reproduction number of COVID-19 across Africa. Our estimates vary between 1.98 (Sudan) and 9.66 (Mauritius), with a median of 3.67 (90% CrI: 3.31 - 4.12). The estimates provided in this paper will help to inform COVID-19 modeling in the respective countries/regions.

scholarly journals The basic reproduction number of SARS-CoV-2: a scoping review of available evidence

2020 ◽  
Author(s):  
Ann Barber ◽  
John M Griffin ◽  
Miriam Casey ◽  
Aine Collins ◽  
Elizabeth A Lane ◽  
...  
Keyword(s):  
Scoping Review ◽  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Quantitative Measure ◽  
Infectious Period ◽  
Complex Nature ◽  
Fundamental Parameter ◽  
Transmission Potential ◽  
Serial Interval ◽  
The Basic Reproduction Number

Background: The transmissibility of SARS-CoV-2 determines both the ability of the virus to invade a population and the strength of intervention that would be required to contain or eliminate the spread of infection. The basic reproduction number, R0, provides a quantitative measure of the transmission potential of a pathogen. Objective: Conduct a scoping review of the available literature providing estimates of R0 for SARS-CoV-2, provide an overview of the drivers of variation in R0 estimates and the considerations taken in the calculation of the parameter. Design: Scoping review of available literature between the 01 December 2019 and 07 May 2020. Data sources: Both peer-reviewed and pre-print articles were searched for on PubMed, Google Scholar, MedRxiv and BioRxiv. Selection criteria: Studies were selected for review if (i) the estimation of R0 represented either the initial stages of the outbreak or the initial stages of the outbreak prior to the onset of widespread population restriction (lockdown), (ii) the exact dates of the study period were provided and (iii) the study provided primary estimates of R0. Results: A total of 20 R0 estimates were extracted from 15 studies. There was substantial variation in the estimates reported. Estimates derived from mathematical models fell within a wider range of 1.94-6.94 than statistical models which fell between the range of 2.2 to 4.4. Several studies made assumptions about the length of the infectious period which ranged from 5.8-20 days and the serial interval which ranged from 4.41-14 days. For a given set of parameters a longer duration of infectiousness or a longer serial interval equates to a higher R0. Several studies took measures to minimise bias in early case reporting, to account for the potential occurrence of super-spreading events, and to account for early sub-exponential epidemic growth. Conclusions: The variation in reported estimates of R0 reflects the complex nature of the parameter itself, including the context (i.e. social/spatial structure), the methodology used to estimate the parameter, and model assumptions. R0 is a fundamental parameter in the study of infectious disease dynamics however it provides limited practical applicability outside of the context in which it was estimated, and should be calculated and interpreted with this in mind.

scholarly journals Variation in natural exposure to anopheles mosquitoes and its effects on malaria transmission

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Wamdaogo M Guelbéogo ◽  
Bronner Pamplona Gonçalves ◽  
Lynn Grignard ◽  
John Bradley ◽  
Samuel S Serme ◽  
...  
Keyword(s):  
Malaria Transmission ◽  
Significant Variation ◽  
Reproduction Number ◽  
Infection Risk ◽  
Transmission Potential ◽  
Anopheles Mosquitoes ◽  
Natural Exposure ◽  
Number Of Individuals ◽  
Blood Meals ◽  
The Basic Reproduction Number

Variation in biting frequency by Anopheles mosquitoes can explain some of the heterogeneity in malaria transmission in endemic areas. In this study in Burkina Faso, we assessed natural exposure to mosquitoes by matching the genotype of blood meals from 1066 mosquitoes with blood from residents of local households. We observed that the distribution of mosquito bites exceeded the Pareto rule (20/80) in two of the three surveys performed (20/85, 76, and 96) and, at its most pronounced, is estimated to have profound epidemiological consequences, inflating the basic reproduction number of malaria by 8-fold. The distribution of bites from sporozoite-positive mosquitoes followed a similar pattern, with a small number of individuals within households receiving multiple potentially infectious bites over the period of a few days. Together, our findings indicate that heterogeneity in mosquito exposure contributes considerably to heterogeneity in infection risk and suggest significant variation in malaria transmission potential.

scholarly journals Modeling the early evolution of the COVID-19 in Brazil: Results from a Susceptible–Infectious–Quarantined–Recovered (SIQR) model

2020 ◽  
Vol 31 (10) ◽  
pp. 2050135 ◽  
Author(s):  
Nuno Crokidakis
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Doubling Time ◽  
Reproduction Number ◽  
Early Evolution ◽  
World Health ◽  
Initial Growth ◽  
Department Of Health ◽  
The World ◽  
Health Organization ◽  
The Basic Reproduction Number

The world evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-Cov2 or simply COVID-19) led the World Health Organization to declare it a pandemic. The disease appeared in China in December 2019, and it has spread fast around the world, especially in European countries like Italy and Spain. The first reported case in Brazil was recorded in February 26, and after that the number of cases grew fast. In order to slow down the initial growth of the disease through the country, confirmed positive cases were isolated to not transmit the disease. To better understand the early evolution of COVID-19 in Brazil, we apply a Susceptible–Infectious–Quarantined–Recovered (SIQR) model to the analysis of data from the Brazilian Department of Health, obtained from February 26, 2020 through March 25, 2020. Based on analytical and numerical results, as well on the data, the basic reproduction number is estimated to [Formula: see text]. In addition, we estimate that the ratio between unidentified infectious individuals and confirmed cases at the beginning of the epidemic is about 10, in agreement with previous studies. We also estimated the epidemic doubling time to be [Formula: see text] days.

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