sir model
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2022 ◽  
Vol 193 ◽  
pp. 1-18
Author(s):  
Patricio Cumsille ◽  
Óscar Rojas-Díaz ◽  
Pablo Moisset de Espanés ◽  
Paula Verdugo-Hernández
Keyword(s):  

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Nariyuki Nakagiri ◽  
Kazunori Sato ◽  
Yukio Sakisaka ◽  
Kei-ichi Tainaka

AbstractThe infectious disease (COVID-19) causes serious damages and outbreaks. A large number of infected people have been reported in the world. However, such a number only represents those who have been tested; e.g. PCR test. We focus on the infected individuals who are not checked by inspections. The susceptible-infected-recovered (SIR) model is modified: infected people are divided into quarantined (Q) and non-quarantined (N) agents. Since N-agents behave like uninfected people, they can move around in a stochastic simulation. Both theory of well-mixed population and simulation of random-walk reveal that the total population size of Q-agents decrease in spite of increasing the number of tests. Such a paradox appears, when the ratio of Q exceeds a critical value. Random-walk simulations indicate that the infection hardly spreads, if the movement of all people is prohibited ("lockdown"). In this case the infected people are clustered and locally distributed within narrow spots. The similar result can be obtained, even when only non-infected people move around. However, when both N-agents and uninfected people move around, the infection spreads everywhere. Hence, it may be important to promote the inspections even for asymptomatic people, because most of N-agents are mild or asymptomatic.


Author(s):  
Agniva Datta ◽  
Muktish Acharyya

The results of Kermack–McKendrick SIR model are planned to be reproduced by cellular automata (CA) lattice model. The CA algorithms are proposed to study the model of an epidemic, systematically. The basic goal is to capture the effects of spreading of infection over a scale of length. This CA model can provide the rate of growth of the infection over the space which was lacking in the mean-field like susceptible-infected-removed (SIR) model. The motion of the circular front of an infected cluster shows a linear behavior in time. The correlation of a particular site to be infected with respect to the central site is also studied. The outcomes of the CA model are in good agreement with those obtained from SIR model. The results of vaccination have been also incorporated in the CA algorithm with a satisfactory degree of success. The advantage of the present model is that it can shed a considerable amount of light on the physical properties of the spread of a typical epidemic in a simple, yet robust way.


Forecasting ◽  
2022 ◽  
Vol 4 (1) ◽  
pp. 72-94
Author(s):  
Roberto Vega ◽  
Leonardo Flores ◽  
Russell Greiner

Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which incorporates machine learning (ML) into the epidemiological SIR model. For each region, SIMLR tracks the changes in the policies implemented at the government level, which it uses to estimate the time-varying parameters of an SIR model for forecasting the number of new infections one to four weeks in advance. It also forecasts the probability of changes in those government policies at each of these future times, which is essential for the longer-range forecasts. We applied SIMLR to data from in Canada and the United States, and show that its mean average percentage error is as good as state-of-the-art forecasting models, with the added advantage of being an interpretable model. We expect that this approach will be useful not only for forecasting COVID-19 infections, but also in predicting the evolution of other infectious diseases.


2022 ◽  
Author(s):  
Sergey A Trigger ◽  
Alexander M. Ignatov

The SIR model of the epidemic spread is used for consideration the problem of the competition of two viruses having different contagiousness. It is shown how the more contagious strain replaces over time the less contagious one. In particular the results can be applied to the current situation when the omicron strain appeared in population affected by the delta strain.


2022 ◽  
Author(s):  
Rajesh Ranjan

India is currently experiencing the third wave of COVID-19, which began on around 28 Dec. 2021. Although genome sequencing data of a sufficiently large sample is not yet available, the rapid growth in the daily number of cases, comparable to South Africa, United Kingdom, suggests that the current wave is primarily driven by the Omicron variant. The logarithmic regression suggests the growth rate of the infections during the early days in this wave is nearly four times than that in the second wave. Another notable difference in this wave is the relatively concurrent arrival of outbreaks in all the states; the effective reproduction number (Rt) although has significant variations among them. The test positivity rate (TPR) also displays a rapid growth in the last 10 days in several states. Preliminary estimates with the SIR model suggest that the peak to occur in late January 2022 with peak caseload exceeding that in the second wave. Although the Omicron trends in several countries suggest a decline in case fatality rate and hospitalizations compared to Delta, a sudden surge in active caseload can temporarily choke the already stressed healthcare India is currently experiencing the third wave of COVID-19, which began on around 28 Dec. 2021. Although genome sequencing data of a sufficiently large sample is not yet available, the rapid growth in the daily number of cases, comparable to South Africa, United Kingdom, suggests that the current wave is primarily driven by the Omicron variant. The logarithmic regression suggests the growth rate of the infections during the early days in this wave is nearly four times than that in the second wave. Another notable difference in this wave is the relatively concurrent arrival of outbreaks in all the states; the effective reproduction number (Rt) although has significant variations among them. The test positivity rate (TPR) also displays a rapid growth in the last 10 days in several states. Preliminary estimates with the SIR model suggest that the peak to occur in late January 2022 with peak caseload exceeding that in the second wave. Although the Omicron trends in several countries suggest a decline in case fatality rate and hospitalizations compared to Delta, a sudden surge in active caseload can temporarily choke the already stressed healthcare infrastructure. Therefore, it is advisable to strictly adhere to COVID-19 appropriate behavior for the next few weeks to mitigate an explosion in the number of infections.


2022 ◽  
Vol 9 ◽  
Author(s):  
Li Tao ◽  
Mutong Liu ◽  
Zili Zhang ◽  
Liang Luo

Identifying multiple influential spreaders, which relates to finding k (k > 1) nodes with the most significant influence, is of great importance both in theoretical and practical applications. It is usually formulated as a node-ranking problem and addressed by sorting spreaders’ influence as measured based on the topological structure of interactions or propagation process of spreaders. However, ranking-based algorithms may not guarantee that the selected spreaders have the maximum influence, as these nodes may be adjacent, and thus play redundant roles in the propagation process. We propose three new algorithms to select multiple spreaders by taking into account the dispersion of nodes in the following ways: (1) improving a well-performed local index rank (LIR) algorithm by extending its key concept of the local index (an index measures how many of a node’s neighbors have a higher degree) from first-to second-order neighbors; (2) combining the LIR and independent set (IS) methods, which is a generalization of the coloring problem for complex networks and can ensure the selected nodes are non-adjacent if they have the same color; (3) combining the improved second-order LIR method and IS method so as to make the selected spreaders more disperse. We evaluate the proposed methods against six baseline methods on 10 synthetic networks and five real networks based on the classic susceptible-infected-recovered (SIR) model. The experimental results show that our proposed methods can identify nodes that are more influential. This suggests that taking into account the distances between nodes may aid in the identification of multiple influential spreaders.


2022 ◽  
Author(s):  
Sewmehon Shimekaw Alemu

Abstract The objective of this paper is to analyse and demonstrate the dynamics of Kala-azar infected group using stochastic model, particularly using simple SIR model with python script over time. The model is used under a closed population with N = 100, transmission rate coefficient β = 0.09, recovery rate γ = 0.03 and initial condition I(0) = 1. In the paper it is discussed how the Kala-azar infected group behaves through simple SIR model. The paper is completed with stochastic SIR model simulation result and shows stochasticity of the dynamics of Kala-azar infected population over time. Fig. 2 below depicts continuous fluctuations which tells us the disease evolves with stochastic nature and shows random process.Subject: Infectious Disease, Global Health, Health Informatics and Statistical and Computational Physics


2022 ◽  
Vol 2148 (1) ◽  
pp. 012002
Author(s):  
Jiale Wang ◽  
Yang Liu ◽  
Xusheng Liu ◽  
Keming Shen

Abstract The analysis of the coronavirus disease 2019 (COVID-19) is of great importance to deeply understand the dynamics of this coronavirus spread. Based on the complexity of it, a modified susceptible-infected-removed (SIR) model is applied to analyse the time dependence of active and hospitalized cases in China. The time evolution of the virus spread in different provinces was adequately modelled. Changeable parameters among them have been obtained and turned to be not naively independent with each other. The non-extensive parameter was found to be strongly connected with the freedom of systems. Taken into the prevention and treatment of disease, more measures by the government lead to higher values of it.


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