scholarly journals Media/Psychological Impact on Multiple Outbreaks of Emerging Infectious Diseases

2007 ◽  
Vol 8 (3) ◽  
pp. 153-164 ◽  
Author(s):  
Rongsong Liu ◽  
Jianhong Wu ◽  
Huaiping Zhu
Keyword(s):  
Infectious Diseases ◽  
Compartmental Model ◽  
Psychological Impact ◽  
Emerging Infectious Diseases ◽  
Transmission Dynamics ◽  
Periodic Oscillations ◽  
Nonlinear Incidence ◽  
Incidence Function ◽  
Contact Patterns ◽  
Nonlinear Incidence Function

We use a compartmental model to illustrate a possible mechanism for multiple outbreaks or even sustained periodic oscillations of emerging infectious diseases due to the psychological impact of the reported numbers of infectious and hospitalized individuals. This impact leads to the change of avoidance and contact patterns at both individual and community levels, and incorporating this impact using a simple nonlinear incidence function into the model shows qualitative differences of the transmission dynamics.

scholarly journals Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Chunxiang Cao ◽  
Wei Chen ◽  
Sheng Zheng ◽  
Jian Zhao ◽  
Jinfeng Wang ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Mainland China ◽  
Emerging Infectious Diseases ◽  
Epidemiological Data ◽  
Transmission Dynamics ◽  
Bayesian Maximum Entropy ◽  
Covariance Model ◽  
Spatial And Temporal Scales ◽  
Epidemiological Characteristics ◽  
Spatiotemporal Transmission

Severe acute respiratory syndrome (SARS) is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME) method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases.

scholarly journals INFEKTA: A General Agent-based Model for Transmission of Infectious Diseases: Studying the COVID-19 Propagation in Bogotá - Colombia

2020 ◽  
Author(s):  
Jonatan Gomez ◽  
Jeisson Prieto ◽  
Elizabeth Leon ◽  
Arles Rodríguez
Keyword(s):  
Infectious Disease ◽  
Euclidean Space ◽  
Infectious Diseases ◽  
Complex Network ◽  
Computational Models ◽  
Compartmental Model ◽  
Transmission Dynamics ◽  
Agent Based Model ◽  
Research Groups ◽  
Agent Based

AbstractThe transmission dynamics of the coronavirus - COVID-19-have challenged humankind at almost every level. Currently, research groups around the globe are trying to figure out such transmission dynamics using different scientific and technological approaches. One of those is by using mathematical and computational models like the compartmental model or the agent-based models. In this paper, a general agent-based model, called INFEKTA, that combines the transmission dynamics of an infectious disease with agents (individuals) that can move on a complex network of accessible places defined over a Euclidean space representing a real town or city is proposed. The applicability of INFEKTA is shown by modeling the transmission dynamics of the COVID-19 in Bogotá city, the capital of Colombia.

scholarly journals Agent-Based Modeling for Super-Spreading Events: A Case Study of MERS-CoV Transmission Dynamics in the Republic of Korea

2018 ◽  
Vol 15 (11) ◽  
pp. 2369 ◽  
Author(s):  
Yunhwan Kim ◽  
Hohyung Ryu ◽  
Sunmi Lee
Keyword(s):  
Infectious Diseases ◽  
Transmission Rate ◽  
Emerging Infectious Diseases ◽  
Epidemiological Data ◽  
Agent Based Modeling ◽  
Transmission Dynamics ◽  
Republic Of Korea ◽  
Agent Based ◽  
The Republic ◽  
High Level

Super-spreading events have been observed in the transmission dynamics of many infectious diseases. The 2015 MERS-CoV outbreak in the Republic of Korea has also shown super-spreading events with a significantly high level of heterogeneity in generating secondary cases. It becomes critical to understand the mechanism for this high level of heterogeneity to develop effective intervention strategies and preventive plans for future emerging infectious diseases. In this regard, agent-based modeling is a useful tool for incorporating individual heterogeneity into the epidemic model. In the present work, a stochastic agent-based framework is developed in order to understand the underlying mechanism of heterogeneity. Clinical (i.e., an infectivity level) and social or environmental (i.e., a contact level) heterogeneity are modeled. These factors are incorporated in the transmission rate functions under assumptions that super-spreaders have stronger transmission and/or higher links. Our agent-based model has employed real MERS-CoV epidemic features based on the 2015 MERS-CoV epidemiological data. Monte Carlo simulations are carried out under various epidemic scenarios. Our findings highlight the roles of super-spreaders in a high level of heterogeneity, underscoring that the number of contacts combined with a higher level of infectivity are the most critical factors for substantial heterogeneity in generating secondary cases of the 2015 MERS-CoV transmission.

scholarly journals Mathematical analysis of a fractional-order epidemic model with nonlinear incidence function

2021 ◽  
Vol 7 (2) ◽  
pp. 2160-2175
Author(s):  
Salih Djillali ◽  
◽  
Abdon Atangana ◽  
Anwar Zeb ◽  
Choonkil Park ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Fractional Order ◽  
Graphical Representation ◽  
Order Model ◽  
Derivative Operator ◽  
Nonlinear Incidence ◽  
Incidence Function ◽  
Different Types ◽  
Fractional System ◽  
Nonlinear Incidence Function

<abstract><p>In this paper, we are interested in studying the spread of infectious disease using a fractional-order model with Caputo's fractional derivative operator. The considered model includes an infectious disease that includes two types of infected class, the first shows the presence of symptoms (symptomatic infected persons), and the second class does not show any symptoms (asymptomatic infected persons). Further, we considered a nonlinear incidence function, where it is obtained that the investigated fractional system shows some important results. In fact, different types of bifurcation are obtained, as saddle-node bifurcation, transcritical bifurcation, Hopf bifurcation, where it is discussed in detail through the research. For the numerical part, a proper numerical scheme is used for the graphical representation of the solutions. The mathematical findings are checked numerically.</p></abstract>

scholarly journals Persistent infections support maintenance of a coronavirus in a population of Australian bats (Myotis macropus)

2017 ◽  
Vol 145 (10) ◽  
pp. 2053-2061 ◽  
Author(s):  
J. JEONG ◽  
C. S. SMITH ◽  
A. J. PEEL ◽  
R. K. PLOWRIGHT ◽  
D. H. KERLIN ◽  
...  
Keyword(s):  
Public Health ◽  
Data Analysis ◽  
Infectious Diseases ◽  
Epidemic Model ◽  
Emerging Infectious Diseases ◽  
Viral Transmission ◽  
Transmission Dynamics ◽  
Persistent Infections ◽  
Study Population ◽  
Reservoir Hosts

SUMMARYUnderstanding viral transmission dynamics within populations of reservoir hosts can facilitate greater knowledge of the spillover of emerging infectious diseases. While bat-borne viruses are of concern to public health, investigations into their dynamics have been limited by a lack of longitudinal data from individual bats. Here, we examine capture–mark–recapture (CMR) data from a species of Australian bat (Myotis macropus) infected with a putative novel Alphacoronavirus within a Bayesian framework. Then, we developed epidemic models to estimate the effect of persistently infectious individuals (which shed viruses for extensive periods) on the probability of viral maintenance within the study population. We found that the CMR data analysis supported grouping of infectious bats into persistently and transiently infectious bats. Maintenance of coronavirus within the study population was more likely in an epidemic model that included both persistently and transiently infectious bats, compared with the epidemic model with non-grouping of bats. These findings, using rare CMR data from longitudinal samples of individual bats, increase our understanding of transmission dynamics of bat viral infectious diseases.

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