scholarly journals The Complete Parameters Analysis of the Asymptotic Behaviour of a Logistic Epidemic Model with Two Stochastic Perturbations

2009 ◽  
Vol 2009 ◽  
pp. 1-7 ◽  
Author(s):  
Dejun Fan ◽  
Ke Wang ◽  
Ling Hong
Keyword(s):  
Infectious Disease ◽  
Simple Model ◽  
Mortality Rate ◽  
Probability Distribution ◽  
Disease Transmission ◽  
Environmental Variability ◽  
Transmission Rate ◽  
Gaussian White Noise ◽  
Stochastic Perturbations ◽  
Parameters Analysis

A simple model of the dynamics of an infectious disease, taking into account environmental variability in the form of Gaussian white noise in the disease transmission rate and the increase in mortality rate due to disease, has been investigated. The probability distribution for the proportion of infected animals, plus its mean, mode, and variance, is found explicitly.

A disease transmission model based on individual cognition

2020 ◽  
Vol 34 (32) ◽  
pp. 2050323
Author(s):  
Fuzhong Nian ◽  
Yayong Shi ◽  
Zhongkai Dang
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Transmission Rate ◽  
Small World ◽  
Transmission Model ◽  
Model Based ◽  
Individual Cognition ◽  
Disease Transmission Model ◽  
The Impact ◽  
Transmission Of Disease

Recently, the study about the disease transmission has received widespread attention. In the dynamics process of infectious disease, individual’s cognition about disease-related knowledge is an important factor that controls disease transmission. The disease-related information includes the cause, symptoms, transmission route and so on. Disease-related knowledge would influence the individual’s attitude toward disease, and influence the transmission rate and scale of the infectious disease. In order to study the impact of individual cognition on the transmission of disease, the disease transmission model based on individual cognition is proposed in this paper. Based on this model, we numerically simulate the transmission of disease in the small-world network and the BA scale-free network, respectively, and analyze the transmission dynamics behavior of the infectious disease. The simulation experiment verifies the validity of the theoretical result, which shows that this model is closer to the reality than traditional models.

scholarly journals Transient prophylaxis and multiple epidemic waves

2022 ◽  
Vol 7 (4) ◽  
pp. 5616-5633
Author(s):  
Rebecca C. Tyson ◽  
◽  
Noah D. Marshall ◽  
Bert O. Baumgaertner ◽  
◽  
...  
Keyword(s):  
Infectious Disease ◽  
Dynamical System ◽  
Public Opinion ◽  
Simple Model ◽  
Transmission Rate ◽  
Opinion Dynamics ◽  
Disease Prevalence ◽  
Final Size ◽  
Total Size ◽  
Epidemic Size

<abstract><p>Public opinion and opinion dynamics can have a strong effect on the transmission rate of an infectious disease for which there is no vaccine. The coupling of disease and opinion dynamics however, creates a dynamical system that is complex and poorly understood. We present a simple model in which susceptible groups adopt or give up prophylactic behaviour in accordance with the influence related to pro- and con-prophylactic communication. This influence varies with disease prevalence. We observe how the speed of the opinion dynamics affects the total size and peak size of the epidemic. We find that more reactive populations will experience a lower peak epidemic size, but possibly a larger final size and more epidemic waves, and that an increase in polarization results in a larger epidemic.</p></abstract>

scholarly journals Dynamics of Drug Resistance: Optimal Control of an Infectious Disease

2019 ◽  
Vol 67 (3) ◽  
pp. 619-650 ◽  
Author(s):  
Naveed Chehrazi ◽  
Lauren E. Cipriano ◽  
Eva A. Enns
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Optimal Control ◽  
Drug Resistance ◽  
Disease Transmission ◽  
Transmission Rate ◽  
Treatment Options ◽  
Treatment Resistance ◽  
State Variables ◽  
Public Health Challenge

Antimicrobial use contributes to the growing public health challenge of infectious diseases that are resistant to all but a few remaining treatments via natural selection. When few treatment options remain, should the last effective treatment be reserved for controlling larger outbreaks in the future? In “Dynamics of Drug Resistance: Optimal Control of an Infectious Disease,” N. Chehrazi, L. E. Cipriano, and E. A. Enns formulate this important policy question as a control problem with two state variables—disease prevalence and the level of treatment resistance—for an established family of SIS infectious disease models with resistance. They prove that when the disease transmission rate is constant, it is optimal to treat everyone until the level of resistance is so high that it is no longer economical to treat anyone. Public health policies and social distancing can cause a nonconstant disease transmission rate; in these cases, it may be optimal to preserve the drug for relatively larger outbreaks or to use the drug to treat some, but not all, infected individuals.

Travel Connectivity and Infectious Disease Transmission Risks in Oceania

2020 ◽  
Author(s):  
Angela Maria Cadavid Restrepo ◽  
Luis Furuya-Kanamori ◽  
Helen Mayfield ◽  
Eric J. Nilles ◽  
Colleen L. Lau
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Infectious Disease Transmission

scholarly journals Estimating asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan: a mathematical modeling study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xi Huo ◽  
Jing Chen ◽  
Shigui Ruan
Keyword(s):  
Disease Transmission ◽  
Large Scale ◽  
Transmission Rate ◽  
Control Strategies ◽  
Herd Immunity ◽  
Antibody Prevalence ◽  
Screening Process ◽  
Vaccination Programs ◽  
Prevalence Level ◽  
The Impact

Abstract Background The COVID-19 outbreak in Wuhan started in December 2019 and was under control by the end of March 2020 with a total of 50,006 confirmed cases by the implementation of a series of nonpharmaceutical interventions (NPIs) including unprecedented lockdown of the city. This study analyzes the complete outbreak data from Wuhan, assesses the impact of these public health interventions, and estimates the asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan. Methods By taking different stages of the outbreak into account, we developed a time-dependent compartmental model to describe the dynamics of disease transmission and case detection and reporting. Model coefficients were parameterized by using the reported cases and following key events and escalated control strategies. Then the model was used to calibrate the complete outbreak data by using the Monte Carlo Markov Chain (MCMC) method. Finally we used the model to estimate asymptomatic and undetected cases and approximate the overall antibody prevalence level. Results We found that the transmission rate between Jan 24 and Feb 1, 2020, was twice as large as that before the lockdown on Jan 23 and 67.6% (95% CI [0.584,0.759]) of detectable infections occurred during this period. Based on the reported estimates that around 20% of infections were asymptomatic and their transmission ability was about 70% of symptomatic ones, we estimated that there were about 14,448 asymptomatic and undetected cases (95% CI [12,364,23,254]), which yields an estimate of a total of 64,454 infected cases (95% CI [62,370,73,260]), and the overall antibody prevalence level in the population of Wuhan was 0.745% (95% CI [0.693%,0.814%]) by March 31, 2020. Conclusions We conclude that the control of the COVID-19 outbreak in Wuhan was achieved via the enforcement of a combination of multiple NPIs: the lockdown on Jan 23, the stay-at-home order on Feb 2, the massive isolation of all symptomatic individuals via newly constructed special shelter hospitals on Feb 6, and the large scale screening process on Feb 18. Our results indicate that the population in Wuhan is far away from establishing herd immunity and provide insights for other affected countries and regions in designing control strategies and planing vaccination programs.

scholarly journals How does globalization affect COVID-19 responses?

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Steve J. Bickley ◽  
Ho Fai Chan ◽  
Ahmed Skali ◽  
David Stadelmann ◽  
Benno Torgler
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
State Capacity ◽  
International Travel ◽  
Health Crisis ◽  
Government Effectiveness ◽  
Policy Responses ◽  
Travel Restrictions ◽  
High Government

Abstract Background The ongoing COVID-19 pandemic has highlighted the vast differences in approaches to the control and containment of coronavirus across the world and has demonstrated the varied success of such approaches in minimizing the transmission of coronavirus. While previous studies have demonstrated high predictive power of incorporating air travel data and governmental policy responses in global disease transmission modelling, factors influencing the decision to implement travel and border restriction policies have attracted relatively less attention. This paper examines the role of globalization on the pace of adoption of international travel-related non-pharmaceutical interventions (NPIs) during the coronavirus pandemic. This study aims to offer advice on how to improve the global planning, preparation, and coordination of actions and policy responses during future infectious disease outbreaks with empirical evidence. Methods and data We analyzed data on international travel restrictions in response to COVID-19 of 185 countries from January to October 2020. We applied time-to-event analysis to examine the relationship between globalization and the timing of travel restrictions implementation. Results The results of our survival analysis suggest that, in general, more globalized countries, accounting for the country-specific timing of the virus outbreak and other factors, are more likely to adopt international travel restrictions policies. However, countries with high government effectiveness and globalization were more cautious in implementing travel restrictions, particularly if through formal political and trade policy integration. This finding is supported by a placebo analysis of domestic NPIs, where such a relationship is absent. Additionally, we find that globalized countries with high state capacity are more likely to have higher numbers of confirmed cases by the time a first restriction policy measure was taken. Conclusions The findings highlight the dynamic relationship between globalization and protectionism when governments respond to significant global events such as a public health crisis. We suggest that the observed caution of policy implementation by countries with high government efficiency and globalization is a by-product of commitment to existing trade agreements, a greater desire to ‘learn from others’ and also perhaps of ‘confidence’ in a government’s ability to deal with a pandemic through its health system and state capacity. Our results suggest further research is warranted to explore whether global infectious disease forecasting could be improved by including the globalization index and in particular, the de jure economic and political, and de facto social dimensions of globalization, while accounting for the mediating role of government effectiveness. By acting as proxies for a countries’ likelihood and speed of implementation for international travel restriction policies, such measures may predict the likely time delays in disease emergence and transmission across national borders.

scholarly journals The effect of social distancing on the reach of an epidemic in social networks

2021 ◽  
Author(s):  
Gregory Gutin ◽  
Tomohiro Hirano ◽  
Sung-Ha Hwang ◽  
Philip R. Neary ◽  
Alexis Akira Toda
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Social Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Disease Transmission ◽  
Global Level ◽  
Social Distancing ◽  
The Public ◽  
Simulation Results

AbstractHow does social distancing affect the reach of an epidemic in social networks? We present Monte Carlo simulation results of a susceptible–infected–removed with social distancing model. The key feature of the model is that individuals are limited in the number of acquaintances that they can interact with, thereby constraining disease transmission to an infectious subnetwork of the original social network. While increased social distancing typically reduces the spread of an infectious disease, the magnitude varies greatly depending on the topology of the network, indicating the need for policies that are network dependent. Our results also reveal the importance of coordinating policies at the ‘global’ level. In particular, the public health benefits from social distancing to a group (e.g. a country) may be completely undone if that group maintains connections with outside groups that are not following suit.

scholarly journals SPATIAL HETEROGENEITY IN SIMPLE DETERMINISTIC SIR MODELS ASSESSED ECOLOGICALLY

2012 ◽  
Vol 54 (1-2) ◽  
pp. 23-36 ◽  
Author(s):  
E. K. WATERS ◽  
H. S. SIDHU ◽  
G. N. MERCER
Keyword(s):  
Infectious Disease ◽  
Spatial Heterogeneity ◽  
Disease Transmission ◽  
Herd Immunity ◽  
Epidemic Models ◽  
Final Size ◽  
Infectious Disease Transmission ◽  
Epidemic Dynamics ◽  
Mean Crowding ◽  
Rate Of Movement

AbstractPatchy or divided populations can be important to infectious disease transmission. We first show that Lloyd’s mean crowding index, an index of patchiness from ecology, appears as a term in simple deterministic epidemic models of the SIR type. Using these models, we demonstrate that the rate of movement between patches is crucial for epidemic dynamics. In particular, there is a relationship between epidemic final size and epidemic duration in patchy habitats: controlling inter-patch movement will reduce epidemic duration, but also final size. This suggests that a strategy of quarantining infected areas during the initial phases of a virulent epidemic might reduce epidemic duration, but leave the population vulnerable to future epidemics by inhibiting the development of herd immunity.

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