Academic articles

In December 2019, there was a case of viral pneumonia in Wuhan. After confirming that the pathogen of this disease is a new coronavirus, the World Health Organization (WHO) confirmed and named it 2019-nCoV. The pneumonia caused by this pathogen infection is called a novel corona virus pneumonia.

To better understand the mode of transmission of 2019-nCoV among the population and the effects of control measures, the study was conducted using agent-based modeling (ABM) to simulate an interactive environment over a certain space-time range. The study simulates the trend of 2019-nCoV infection at different levels of close contact in order to provide relevant information and references.

Pertussis control remains a challenge due to recently observed effects of waning immunity to acellular vaccine and suboptimal vaccine coverage. Multiple outbreaks have been reported in different ages worldwide. For certain outbreaks, publichealth authorities can launch an outbreak response immunization (ORI) campaign to control pertussis spread. The authors developed an agent-based model to investigate effects of outbreak response immunization campaigns targeting young adolescents in averting pertussis cases. The experience proved that ABM offers a promising methodology to evaluate other public health interventions used in pertussis control. The authors also identified the strong need for further research into application of modeling to further our understanding of pertussis epidemiology.

Until relatively recently, developing hybrid simulation models using more than one simulation paradigm was a challenging task which required a degree of ingenuity on behalf of the modeler. Generally speaking, such hybrid models either had to be coded from scratch in a programming language, or developed using two (or more) different off-the-shelf software tools which had to communicate with each other through a user-written interface. Nowadays a number of simulation tools are available which aim to make this task easier. This paper does not set out to be a formal review of such software, but it discusses the increasing popularity of hybrid simulation and the rapidly developing market in hybrid modeling tools, focusing specifically on applications in health and social care and using experience from the Care Life Cycle project and elsewhere.

Developing countries are faced with finding novel and humane ways to permanently reduce and control their dog population. Agent-based models developed to describe dog populations represent a unique, platform for using computer based simulation to identify control strategies with the greatest potential for success, aid in the design of more effective control measures, and provide a means to evaluate the success of different interventions.

Grapevine leafroll disease threatens the economic sustainability of the grape and wine industry in the United States and around the world. This viral disease reduces yield, delays fruit ripening, and affects wine quality. Although there is new information on the disease spatial-dynamic diffusion, little is known about profit-maximizing control strategies. Using cellular automata, we model the disease spatial-dynamic diffusion for individual plants in a vineyard, evaluate nonspatial and spatial control strategies, and rank them based on vineyard expected net present values.

Like most countries, Canada faces rising rates of diabetes and diabetic ESRD, which adversely affect cost, morbidity/mortality and quality of life. These trends raise great challenges for financial, human resource and facility planning and place a premium on understanding tradeoffs between different intervention strategies. We describe here our hybrid simulation model built to inform such efforts.

Hybrid simulation, the combination of simulation paradigms to address a problem is becoming more popular as the problems we are presented with become more complex. This is evidenced by an increase in the number of hybrid papers published in specific domains and the number of hybrid simulation frameworks being produced across domains.

Public health agencies traditionally rely heavily on epidemiological reporting for notifiable disease control, but increasingly apply simulation models for forecasting and to understand intervention tradeoffs. Unfortunately, such models traditionally lack capacity to easily incorporate information from epidemiological data feeds.

The desire to better understand the transmission of infectious disease in the real world has motivated the representation of epidemic diffusion in the context of quantitative simulation. In recent decades, both individual-based (such as Agent-Based) models and aggregate models (such as System Dynamics) are widely used in epidemiological modeling. This paper compares the difference between system dynamics models and agent-based models in the context of Tuberculosis (TB) transmission, considering smoking as a risk factor.

We propose an epidemic agent-based simulation model for disease (TB) transmission dynamics study and to find out the role of various contact networks. Our model simulates the TB epidemic course across a single population and uses a hierarchical network of contacts in three levels, typical to the transmission of airborne diseases (Mossong et al. 2005). Parameters are chosen from the literature, and the model is calibrated to a setting of high TB incidence. We use our model to study the transmission dynamics at an individual level with regard to the timing and distribution of secondary infections from a single source. The average time for disease diffusion to reach 50% of infections at an individual level is estimated, and the timing patterns are compared among different networks. We perform sensitivity analysis of results with regard to multiple parameter values, and discuss the implications for TB control policy.