Multi-agent modeling of the South Korean avian influenza epidemic

<p>Abstract</p> <p>Background</p> <p>Several highly pathogenic avian influenza (AI) outbreaks have been reported over the past decade. South Korea recently faced AI outbreaks whose economic impact was estimated to be 6.3 billion dollars, equivalent to nearly 50% of the profit generated by the poultry-related industries in 2008. In addition, AI is threatening to cause a human pandemic of potentially devastating proportions. Several studies show that a stochastic simulation model can be used to plan an efficient containment strategy on an emerging influenza. Efficient control of AI outbreaks based on such simulation studies could be an important strategy in minimizing its adverse economic and public health impacts.</p> <p>Methods</p> <p>We constructed a spatio-temporal multi-agent model of chickens and ducks in poultry farms in South Korea. The spatial domain, comprised of 76 (37.5 km × 37.5 km) unit squares, approximated the size and scale of South Korea. In this spatial domain, we introduced 3,039 poultry flocks (corresponding to 2,231 flocks of chickens and 808 flocks of ducks) whose spatial distribution was proportional to the number of birds in each province. The model parameterizes the properties and dynamic behaviors of birds in poultry farms and quarantine plans and included infection probability, incubation period, interactions among birds, and quarantine region.</p> <p>Results</p> <p>We conducted sensitivity analysis for the different parameters in the model. Our study shows that the quarantine plan with well-chosen values of parameters is critical for minimize loss of poultry flocks in an AI outbreak. Specifically, the aggressive culling plan of infected poultry farms over 18.75 km radius range is unlikely to be effective, resulting in higher fractions of unnecessarily culled poultry flocks and the weak culling plan is also unlikely to be effective, resulting in higher fractions of infected poultry flocks.</p> <p>Conclusions</p> <p>Our results show that a prepared response with targeted quarantine protocols would have a high probability of containing the disease. The containment plan with an aggressive culling plan is not necessarily efficient, causing a higher fraction of unnecessarily culled poultry farms. Instead, it is necessary to balance culling with other important factors involved in AI spreading. Better estimations for the containment of AI spreading with this model offer the potential to reduce the loss of poultry and minimize economic impact on the poultry industry.</p

Multimodal Epidemic Visual Analytics and Modeling

The risk of infectious disease increases due to various factors including the dense population, development of various transportations, urbanization, and abnormal weather conditions. Since the speed of epidemic spread is fast, it is necessary to respond quickly in order to prevent the high fatality rate. Therefore, a fast search for the highly accurate spreading model has to be focused on the proper analysis of disease spreading. There have been many studies to understand the disease spreading and the epidemic model is often used to analyze and predict the spread of infectious disease. However, it is limited to apply the epidemic model for the spread analysis because the model captures spreading changes only within the defined area. In this paper, we propose a framework for the disease spreading simulation with multimodal factors in the epidemic model and networks of possible spread routes. Our system provides an interactive simulation environment with the interregional disease spreading according to various spread parameters. Moreover, in order to understand the spreading directions, we extract vector fields over time and visualize the vector fields with the fatality of the disease. Therefore, users are able to understand the disease spreading phenomena and obtain appropriate models through our framework

Exploring the role of spatial configuration and behavior on the spread of the epidemic: A study of factors that affect Covid-19 spreading in the city

This research explores how exterior public space - defined through the configuration of the city - and human behavior affect the spread of disease. In order to understand the virus spreading mechanism and influencing factors of the epidemic which accompany residents' movement, this study attempts to reproduce the process of virus spreading in city areas through computer simulation. The simulation can be divided into residents' movement simulation and the virus spreading simulation. First, the Agent-based model (ABM) can effectively simulate the behavior of the individual and crowd;real location data - uploaded by residents via mobile phone applications - is used as a behavioral driving force for the agent's movement. Second, a mathematical model of infectious diseases is constructed based on SIR (SEIR) Compartmental models in epidemiology. Finally, by analyzing the simulation results of the agent's movement in the city and the virus spreading under different conditions, the influence of multiple factors of city configuration and human behavior on the virus spreading process is explored, and the effectiveness of countermeasures such as social distancing and lockdown are further demonstrated

Assessing potential impact of avian influenza on poultry in West Africa: a spatial equilibrium model analysis

"In this paper, the authors analyze the potential economic impacts of avian influenza (AI) in West Africa, taking Nigeria as an example. They find that, depending on the size of the affected areas, the direct impact of the spread of AI along the two major migratory bird flyways would be the loss of about 4 percent of national chicken production. However, the indirect effect—consumers' reluctance to consume poultry if AI is detected, causing a decline in chicken prices—is generally larger than the direct effect. The study estimates that Nigerian chicken production would fall by 21 percent and chicken farmers would lose US$250 million of revenue if the worst-case scenario occurred. The negative impact of AI would be unevenly distributed in the country, and some states and districts would be seriously hurt. This study is based on a spatial equilibrium model that makes use of the most recent spatial distribution data sets for poultry and human populations in West Africa. The study shows that, while most of the attention has focused on preventing global influenza pandemic, preventive measures are also needed at the national, subnational, and local levels, because AI could potentially have a huge negative impact on the poultry industry and the livelihood of smallholder farmers in many regions in West Africa.." Authors' AbstractComputable general equilibrium (CGE) modeling, Small farmers, Spatial analysis (Statistics),