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Dynamic Modeling on Malware and Its Defense in Wireless Computer Network Using Pre-Quarantine
Published in Gautam Kumar, Dinesh Kumar Saini, Nguyen Ha Huy Cuong, Cyber Defense Mechanisms, 2020
Yerra Shankar Rao, Hemraj Saini, Ranjita Rath, Tarini Charan Panda
Basic reproduction number is determined using the essential rule for the technical and biological network in the epidemiology, which shows whether the malware in the network exists or not. So R0 is the basic reproduction number defined as the average number of secondary infection that single viral computer can produce in a totally susceptible class during its life cycle. This can be obtained as the linearization of last two equations: (IQ1)=(F−V)(IQ1),
Epidemiology, Pharmacology, Diagnosis, and Treatment of COVID-19
Published in Joystu Dutta, Srijan Goswami, Abhijit Mitra, COVID-19 and Emerging Environmental Trends, 2020
Joystu Dutta, Srijan Goswami, Abhijit Mitra
Similar to CFR, the concept of R-Naught (R0) is significant in understanding the epidemiology of disease under consideration. The R0 stands for the basic reproduction number, which explains the degree of spreadability of any disease. For example, by considering R0, the epidemiologists can predict how many persons one individual can infect. Let’s consider that Patient 1 (P1) is suffering from COVID-19. The P1 can spread the infection to other individuals through the respiratory droplets. Researchers have reported that one COVID-19 patient (P1) can potentially spread the disease to two to three individuals. This makes the R0 for COVID-19 to be between 2 and 3. Let’s consider the worst-case scenario, that is, one person (P1) can spread the disease to three other people. Each of these three people transmits the disease to three other people, thus giving rise to nine COVID-19 patients. These nine patients give rise to 27 COVID-19 patients, and the transmission continues following the same pattern (see Figure 3.4). From the scenario, it is clear that COVID-19 cases increase exponentially. In Figure 3.5, the upward rising curve represents (R0 COVID-19) the exponential rise of COVID-19. In comparison, the R0 for influenza (see Table 3.1) is approximately around 1.3 (for convenience, the value is rounded to 1), which means one person has the ability to infect one other person. So, P1 infected with influenza can pass it on to the next person and so on. From the scenario, it is clear that influenza cases follow a steady growth pattern. In Figure 3.5, the flat curve represents (R0 influenza) steady progress of influenza. Thus comparison of transmission rate indicates that COVID-19 has larger spreadability compared to influenza.
Modeling the Transmission Dynamics of Zika Virus
Published in Ranjit Kumar Upadhyay, Satteluri R. K. Iyengar, Spatial Dynamics and Pattern Formation in Biological Populations, 2021
Ranjit Kumar Upadhyay, Satteluri R. K. Iyengar
Analysis of equilibrium points: The DFE is given by E0=(Λh/μh,0,0,Λv/μv,0). The basic reproduction number R0 is defined as the average number of secondary infections produced by a single individual introduced into a fully susceptible population during an individual’s entire infectious period [7,69]. Using the next-generation matrix approach [104], R0 was derived as R02=βvhβhvc2ΛhΛvμv(μh+λ)(ημh+Λh)(μvδ+Λv).
Estimation of the doubling time and reproduction number for COVID-19
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2022
Shamim Ahmed, Mohammad Shemanto, Hasin Azhari, Golam Zakaria
The basic reproduction number or the reproduction number (R0) specifies the average number of the secondary infections caused by one infected individual during the entire infectious period at the start of an outbreak. The definition describes the state where all individuals are susceptible to infection and no other individuals are infected or immunized (naturally or through vaccination). It is one of the fundamental and most often used metrics for the study of infectious disease dynamics. An outbreak is expected to continue if R0 has a value > 1 and to end if R0 is < 1. The first modern application of R0 in epidemiology can be traced by the work of Macdonald (1952). Although MacDonald used Z0 to represent the metric and he called it basic reproduction rate. The use of the word rate suggests a quantity having a unit with a per-time dimension. If R0 were a rate involving time, the metric would provide information about how quickly an epidemic will spread through a population. But R0 does not indicate whether new cases will occur within a specific period of time. That is why calling R0 a rate rather than a number creates confusion. Throughout this paper, we refer this parameter reproduction number.
An optimization-based framework to minimize the spread of diseases in social networks with heterogeneous nodes
Published in IISE Transactions, 2023
Having identified all the necessary subject-specific parameters, we next shift attention to establishing the necessary disease-specific parameters (i.e., the transmissibility, and the recovery rates of COVID-19, γ). These parameters are heavily correlated with the basic reproduction number, denoted by R0, which represents the expected number of positive cases directly generated by adding a single infected case to a population comprised of all susceptible individuals (Delamater et al., 2019). For the specific case of COVID-19, R0 is estimated to lie within a 95% confidence interval ranging from 3.8 to 8.9 (Sanche et al., 2020). Since R0 does not represent a rate, it cannot be directly used within the considered network-based model. Instead, we must first transform R0 into a transmission rate on a single interaction (edge) between a pair of subjects (nodes). According to Miller et al. (2012), the transmission rate can be estimated as follows: where is the average degree of the nodes and is the average squared degrees of all nodes. The average recovery time of COVID-19 is estimated to be around 11 days (Alagoz et al., 2020). By assuming that recovery occurs uniformly within this time span, the recovery rate (per day) is set to Consequently, for a given network topology (which describes both and ), the corresponding for the given network can be calculated through (10).