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 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.

Investigating transmission dynamics of influenza in a public indoor venue: An agent-based modeling approach

2021 ◽  
Vol 157 ◽  
pp. 107327
Author(s):  
Yuan Zhou ◽  
Alexander Nikolaev ◽  
Ling Bian ◽  
Li Lin ◽  
Lin Li
Keyword(s):  
Agent Based Modeling ◽  
Transmission Dynamics ◽  
Agent Based ◽  
Modeling Approach

scholarly journals Elucidating the Local Transmission Dynamics of Highly Pathogenic Avian Influenza H5N6 in the Republic of Korea by Integrating Phylogenetic Information

Pathogens ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 691
Author(s):  
Dae-Sung Yoo ◽  
Byungchul Chun ◽  
Kyung-Duk Min ◽  
Jun-Sik Lim ◽  
Oun-Kyoung Moon ◽  
...  
Keyword(s):  
Avian Influenza ◽  
Highly Pathogenic Avian Influenza ◽  
Transmission Dynamics ◽  
Republic Of Korea ◽  
Transmission Risk ◽  
Highly Pathogenic ◽  
Pathogenic Avian Influenza ◽  
The Republic ◽  
The Impact ◽  
Local Transmission

Highly pathogenic avian influenza (HPAI) virus is one of the most virulent and infectious pathogens of poultry. As a response to HPAI epidemics, veterinary authorities implement preemptive depopulation as a controlling strategy. However, mass culling within a uniform radius of the infection site can result in unnecessary depopulation. Therefore, it is useful to quantify the transmission distance from infected premises (IPs) before determining the optimal area for preemptive depopulation. Accordingly, we analyzed the transmission risk within spatiotemporal clusters of IPs using transmission kernel estimates derived from phylogenetic clustering information on 311 HPAI H5N6 IPs identified during the 2016–2017 epidemic, Republic of Korea. Subsequently, we explored the impact of varying the culling radius on the local transmission of HPAI given the transmission risk estimates. The domestic duck farm density was positively associated with higher transmissibility. Ring culling over a radius of 3 km may be effective for areas with high dense duck holdings, but this approach does not appear to significantly reduce the risk for local transmission in areas with chicken farms. This study provides the first estimation of the local transmission dynamics of HPAI in the Republic of Korea as well as insight into determining an effective ring culling radius.

scholarly journals Toward a transdisciplinary understanding and a global control of emerging infectious diseases

2011 ◽  
Vol 5 (12) ◽  
pp. 903-905 ◽  
Author(s):  
Jean-Paul Gonzalez ◽  
Gérard Lambert ◽  
Anaïs Legand ◽  
Patrice Debré
Keyword(s):  
Social Sciences ◽  
Infectious Diseases ◽  
Animal Health ◽  
Emerging Infectious Diseases ◽  
Public Health Professionals ◽  
Human And Social Sciences ◽  
High Level ◽  
International Experts ◽  
Near Future ◽  
Scientific Debates

The Franceville International Centre for Medical Research (CIRMF) organized a first international symposium on infectious diseases, environments, and biodiversity. Over 200 international experts gathered in Gabon to forecast and work to prevent the emergence of infectious diseases. This symposium aimed to strengthen the regional and international fight against the emergence of infectious diseases with high-level scientific debates. Toward this goal, it brought together experts in human and animal health, the environment, and ecology, including biologists, climatologists, microbiologists, epidemiologists, public health professionals, and human and social sciences specialists. National, regional and international participants were present to debate on the challenges related to the emergence of infectious diseases and on the responses to be implemented. The symposium was very successful, and plans for a second symposium of this kind to be held in the near future in another high-biodiversity area are already underway.

scholarly journals Modelling infectious diseases with herd immunity in a randomly mixed population

2021 ◽  
Author(s):  
Kian Boon Law ◽  
Kalaiarasu M. Peariasamy ◽  
Hishamshah Mohd. Ibrahim ◽  
Noor Hisham Abdullah
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Transmission Rate ◽  
Herd Immunity ◽  
Sir Model ◽  
Mixed Population ◽  
Transmission Dynamics ◽  
Transmission Model ◽  
Infection Model ◽  
Vaccination Rates

Abstract Background The conventional susceptible-infectious-recovered (SIR) model tends to overestimate the transmission dynamics of infectious diseases and ends up with total infections and total immunized population exceeding the threshold required for control and eradication of infectious diseases. The study aims to overcome the limitation by allowing the transmission rate of infectious disease to decline along with the reducing risk of contact infection. Methods Two new SIR models were developed to mimic the declining transmission rate of infectious diseases at different stages of transmission. Model A mimicked the declining transmission rate along with the reducing risk of transmission following infection, while Model B mimicked the declining transmission rate following recovery. Then, the conventional SIR model, Model A and Model B were used to simulate an infectious disease with a basic reproduction number (r0) of 3.0 and a herd immunity threshold (HIT) of 0.667 with and without vaccination. The infectious disease was expected to be controlled or eradicated when the total immunized population either through infection or vaccination reached the level predicted by the HIT. Outcomes of simulations were assessed at the time when the total immunized population reached the level predicted by the HIT, and at the end of simulations. Findings All three models performed likewise at the beginning of the transmission when sizes of infectious and recovered were relatively small as compared with the population size. The infectious disease modelled using the conventional SIR model appeared completely out of control even when the HIT was achieved in all scenarios with and without vaccination. The infectious disease modelled using Model A appeared to be controlled at the level predicted by the HIT in all scenarios with and without vaccination. Model B projected the infectious disease to be controlled at the level predicted by the HIT only at high vaccination rates. At lower vaccination rates or without vaccination, the level at which the infectious disease was controlled cannot be accurately predicted by the HIT. Conclusion Transmission dynamics of infectious diseases with herd immunity can accurately be modelled by allowing the transmission rate of infectious disease to decline along with the combined risk of contact infection. Model B provides a more credible framework for modelling infectious diseases with herd immunity in a randomly mixed population.

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 The impact of social distancing and public behavior changes on COVID-19 transmission dynamics in the Republic of Korea

PLoS ONE ◽  
2020 ◽  
Vol 15 (9) ◽  
pp. e0238684
Author(s):  
Soyoung Kim ◽  
Youngsuk Ko ◽  
Yae-Jean Kim ◽  
Eunok Jung
Keyword(s):  
Transmission Dynamics ◽  
Republic Of Korea ◽  
Behavior Changes ◽  
Social Distancing ◽  
Public Behavior ◽  
The Republic ◽  
The Impact
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