Estimates Of Basic Reproduction Number Research Articles

Droplet finite-size scaling of the contact process on scale-free networks revisited

We present an alternative finite-size scaling (FSS) of the contact process on scale-free networks compatible with mean-field scaling and test it with extensive Monte Carlo simulations. In our FSS theory, the dependence on the system size enters the external field, which represents spontaneous contamination in the context of an epidemic model. In addition, dependence on the finite size in the scale-free networks also enters the network cutoff. We show that our theory reproduces the results of other mean-field theories on finite lattices already reported in the literature. To simulate the dynamics, we impose quasi-stationary states by reactivation. We insert spontaneously infected individuals, equivalent to a droplet perturbation to the system scaling as [Formula: see text]. The system presents an absorbing phase transition where the critical behavior obeys the mean-field exponents, as we show theoretically and by simulations. However, the quasi-stationary state gives finite-size logarithmic corrections, predicted by our FSS theory, and reproduces equivalent results in the literature in the thermodynamic limit. We also report the critical threshold estimates of basic reproduction number [Formula: see text] of the model as a linear function of the network connectivity inverse [Formula: see text], and the extrapolation of the critical threshold function for [Formula: see text] yields the basic reproduction number [Formula: see text] of the complete graph, as expected. Decreasing the network connectivity increases the critical [Formula: see text] for this model.

Estimation of basic reproduction number (R0) of African swine fever (ASF) in mid-size commercial pig farms in Vietnam.

African swine fever (ASF) is a devastating disease affecting the global swine industry. Recently, it has spread to many countries in Africa, Europe, Asia, and the Caribbean, leaving severe damage to local, regional, national, and global economies. Due to its highly complex molecular characteristics and pathogenesis, the development of a successful vaccine has been an unmet challenge. Therefore, ASF control relies solely on biosecurity, rapid detection, and elimination. Epidemiological information obtained from natural ASF outbreaks is critical for designing and implementing ASF control measures. Basic reproduction number (R0), an epidemiological metric used to describe the contagiousness or transmissibility of infectious agents, is an important epidemiological tool. In this study, we have calculated R0 for the in-farm spread of ASF among fattening pigs and sows in two midsize commercial pig farms, HY1 and HY2, that practice the spot removal approach in controlling ASF outbreaks in Vietnam. The R0 values for the sows and fattening pigs were 1.78 (1.35–2.35) and 4.76 (4.18–5.38) for HY1 and 1.55 (1.08–2.18) and 3.8 (3.33–4.28) for HY2. This is the first study to evaluate the transmission potential of ASF in midsize commercial pig farms in Vietnam. Based on the R0 values, we predict that the spot removal approach could be used to successfully control ASF outbreaks in midsize commercial sow barns but not in fattening pens.

District-wise estimation of Basic reproduction number (R0) for COVID-19 in India in the initial phase

SARS-CoV-2 or COVID-19 was introduced into India by multiple sources generating local clusters and leading to the nationwide spread. A retrospective study has been conducted on the epidemiological features and spatial spread of COVID-19 in India during February 2020–March 2021. For each district, the cumulative number of confirmed COVID-19 cases were fitted to exponential growth model for the initial phase of the outbreak (the first 7–15 days). From the estimated growth rate, epidemiological parameters like the Basic reproduction number (R0) and epidemic doubling time (τ) were determined. Using Q-GIS software, we have generated the all India distribution maps for R0 and τ. COVID-19 spread rapidly covering majority of the districts of India between March and June 2020. As on 1st March 2021, a total of 715 out of 717 districts have been affected. The R0 range is at par with the global average. A few districts, where outbreaks were caused by migrant workers coming home, intense transmission was recorded R0 > 7. We also found that the spread of COVID-19 was not uniform across the different districts of India. The methodology developed in the study can be used by researchers and public health professionals to analyze and study epidemics in future.

Estimates of Intrinsic Growth Rates and Basic Reproduction Number (R0) for the First Historical Zika Outbreak in Salta, Argentina

Scopes and Objectives: After entering South America in May 2015 through northeast Brazil, the Zika virus spread to Argentina between April and June 2016 and reached Salta province the following year. We analyzed some aspects of the first historical outbreak of Zika that occurred in Salta, Argentina, in the year 2017. We obtained elementary estimates, such as the intrinsic growth rate of the cases accumulated in the first weeks of the outbreak, using expressions that relate it to the basic reproduction number thereof. Study design: Retrospective-descriptive studies and relational analysis.
 Place and Duration of Study: Department of Mathematics, Faculty of Exact Sciences and Faculty of Engineering. National University of Salta, Argentina, from September 2019 to June 2020.
 Methodology: Descriptive and relational analysis. Estimates of parameters and Simulation tests were also carried out in order to qualitatively describe the first Zika historical outbreak in Salta. 
 Results: Our study revealed that the Zika virus in the province of Salta mainly affects the localities of the departments of Or´an, General San Mart´ın, and Rivadavia, with infection forces α2017 ≈ 0.42 week-1 (SD 0.05) and α2017 ≈ 0.32 week-1 (SD 0.02) with the refined exponential model. On the other hand, we obtained estimates of the basic reproduction number R0 ≈ 1:105 95% CI[1:104 - 1:106] and R0 ≈ 1:111 95% CI[1:110 - 1:112]. 
 Conclusion: Both the values of the estimates of the infection forces and R0 would seem to indicate that the first outbreak of Zika in Salta was of relatively low intensity and of short duration, coinciding with patterns generally present emerging diseases. We found practically no differences with the estimates provided by the two expressions of basic reproduction number used. Although the estimates slightly exceed the threshold value R0 = 1, with respect to otherestimates, we consider them quite reasonable for the first historical outbreak occurred in Salta, since it was short-lived and of little intensity.

Basic reproduction number for certain infectious porcine diseases: estimation of required level of vaccination or depopulation of susceptible animals

Basic reproduction number ( R 0 ) is one of the fundamental quantitative characteristics in epidemiology of infectious human and animal diseases. This parameter reflects the biological properties of the infectious agent, the social and economic aspects of animal husbandry, natural factors associated with the habitat of the animal population invaded by the virus (microorganism), as well as the effectiveness of methods selected for infection control, in particular, the implementation of preventive measures; it also allows foreseeing the number and probability of occurrence of new secondary outbreaks in the area at risk of the disease spread. The paper presents data on the estimation of basic reproduction number ( R 0 ) for a range of infectious porcine diseases. A systematic analysis has been undertaken with respect to the publications available on the estimation of R 0 for various virus isolates of African swine fever, classical swine fever, foot-and-mouth disease, porcine reproductive and respiratory syndrome, Aujeszky’s disease, hepatitis E, encephalomyocarditis, porcine circovirus type 2, as well as pleuropneumonia associated with Actinobacillus pleuropneumoniae, and diseases caused by pathogenic isolates of Mycoplasma hyopneumoniae . Based on the obtained R 0 values, recommendations for the veterinary services are made on preventive vaccination of pigs against the above mentioned diseases in the areas at risk of infection spread. The necessary conditions for wild boar depopulation aimed to prevent new African swine fever outbreaks are identified, namely, the elimination of at least 75% of the wild boar population living in the risk zone within the period of time equal to one infectious period.

Estimation of Basic Reproduction Number (Ro) of Novel Coronavirus (COVID-19) from SEIR Model in Perspective of Bangladesh

The novel corona virus 19 (nCoV-19) created a global pandemic situation. To control this pandemic effectively, the basic reproduction number (Ro) is an effective epidemic tool. This study was undertaken to estimate the basic reproduction number (Ro) of the novel corona virus (COVID-19) infection in Bangladesh.

Progress on the basic reproduction number of SARS-CoV-2

<p indent="0mm">Coronavirus disease 2019 (COVID-19) is an emerging infectious diseases caused by a new coronavirus named SARS-CoV-2. In the epidemiology of infectious diseases, the basic reproduction number (<italic>R</italic>₀) is a central quantitative parameter that can be used to measure the transmission potential of infectious diseases.<italic> R</italic>₀ represents the average number of new infections generated by an infectious person in a totally naïve population. If <italic>R</italic>₀&gt;1, the number infected is likely to increase. If <italic>R</italic>₀&lt;1, transmission is likely to die out. In this study, we present a review on the estimation of basic reproduction number of SARS-CoV-2, in order to provide a scientific basis for better understanding the dynamic characteristics of SARS-CoV-2 transmission. <italic>R</italic>₀ is usually estimated with various types of complex mathematical models. It can be calculated and estimated by differential equations in the propagation dynamics model (such as SIR model and SEIR model), or estimated using maximum likelihood method, stochastic model, etc. Modeled <italic>R</italic>₀ values are dependent on model structures and assumptions. With the rapid spread of the global COVID-19 epidemic, there is an increasing evidence on <italic>R</italic>₀ of SARS-CoV-2. However, the results of <italic>R</italic>₀ estimation varied a lot in different studies, due to different model assumptions, parameters settings, and data used in the models. The estimated median<italic> R</italic>₀ is about 3.15 (95%CI: 2.26–6.20) in studies published by peer-reviewed journals, 3.01 (95%CI: 1.99–5.44) in preprinted platform without peer-reviewed and online reports, and 2.55 (95%CI: 1.61–3.55) in studies published in Chinese language, and 3.10 (95%CI: 2.09–6.05) in studies published in English language. With the implementation of comprehensive intervention measures, <italic>R</italic>₀ showed a downward trend. In the basic propagation dynamics model, <italic>R</italic>₀ might be estimated with biases, due to the limitations of its basic assumptions (such as fixed population). Regardless of the methods used, characteristics of the virus should be considered fully during estimation. To better estimate <italic>R</italic>₀, some researchers improved the basic propagation dynamics model and considered the assumption of unfixed population, population mobility, infectivity of the virus during incubation period, and quarantine measures, to make it more in line with the characteristics of SARS-CoV-2. When adopting other methods such as exponential growth model and stochastic model to estimate <italic>R</italic>₀, the estimated results is relatively stable because that it is not restricted by assumptions of the propagation dynamics model. However, the estimation are more susceptible to the data used in the model (such as distribution of the data) because that the inherent characteristics of the spread of infectious diseases are not considered. Thus, it is particularly critical to use appropriate data distribution and parameters during estimation.

Estimation of basic reproduction number of the Middle East respiratory syndrome coronavirus (MERS-CoV) during the outbreak in South Korea, 2015

BackgroundIn South Korea, an outbreak of Middle East respiratory syndrome (MERS) occurred in 2015. It was the second largest MERS outbreak. As a result of the outbreak in South Korea, 186 infections were reported, and 36 patients died. At least 16,693 people were isolated with suspicious symptoms. This paper estimates the basic reproduction number of the MERS coronavirus (CoV), using data on the spread of MERS in South Korea.MethodsThe basic reproduction number of an epidemic is defined as the average number of secondary cases that an infected subject produces over its infectious period in a susceptible and uninfected population. To estimate the basic reproduction number of the MERS-CoV, we employ data from the 2015 South Korea MERS outbreak and the susceptible-infected-removed (SIR) model, a mathematical model that uses a set of ordinary differential equations (ODEs).ResultsWe fit the model to the epidemic data of the South Korea outbreak minimizing the sum of the squared errors to identify model parameters. Also we derive the basic reproductive number as the terms of the parameters of the SIR model. Then we determine the basic reproduction number of the MERS-CoV in South Korea in 2015 as 8.0977. It is worth comparing with the basic reproductive number of the 2014 Ebola outbreak in West Africa including Guinea, Sierra Leone, and Liberia, which had values of 1.5–2.5.ConclusionsThere was no intervention to control the infection in the early phase of the outbreak, thus the data used here provide the best conditions to evaluate the epidemic characteristics of MERS, such as the basic reproduction number. An evaluation of basic reproduction number using epidemic data could be problematic if there are stochastic fluctuations in the early phase of the outbreak, or if the report is not accurate and there is bias in the data. Such problems are not relevant to this study because the data used here were precisely reported and verified by Korea Hospital Association.

Estimation of Basic Reproduction Number of Enterovirus 71, Coxsackievirus A6 and A16 in Hand, Foot, and Mouth Disease Outbreaks in Singapore

The major enteroviruses causing nationwide epidemics of hand, foot and mouth disease (HFMD) in Singapore were enterovirus 71 (EV71), coxsackievirus A6 (CA6) and A16 (CA16). A mathematical model was used to estimate the basic reproduction number (R&lt;sub&gt;0&lt;/sub&gt;) of these enteroviruses based on the cumulative number of reported cases in the initial growth phase of each HFMD outbreak. The median R&lt;sub&gt;0&lt;/sub&gt; was estimated to be 3.50 for EV71, 2.42 for CA16, and 5.04 for CA6. The enterovirus-specific R&lt;sub&gt;0&lt;/sub&gt; estimates would be helpful in providing insights into the potential growth of future HFMD epidemics for timely implementation of disease control measures.

Estimation of Hospital Potential Capacity and Basic Reproduction Number

In order to reflect the population covered by institutional medical services, the concept of hospital potential capacity is proposed and a formula for its estimation is developed based on a population dynamic model. Using the collected data on hospital outpatient and inpatient services and the demographical information on Chongqing as an example, the demand for medical resource allocation in Chongqing is dynamically estimated. Moreover, the proposed formula is also useful in the estimation of the basic reproduction number in epidemiology. The results can be contributed to the improvement of decision-making in the allocation of medical resources and the evaluation of the interventions and control efforts of the infectious disease.