Reference no: EM132974538

After reading this "Agent-Based Simulation Helps Analyze Spread of a Pandemic Outbreak answer the following:

Application Case 10.5

Agent-Based Simulation Helps Analyze Spread of a Pandemic Outbreak

Knowledge about the spread of a disease plays an important role in both preparing for and responding to a pandemic outbreak. Previous models for such analyses are mostly homogenous and make use of simplistic assumptions about transmission and the infection rates. These models assume that each individual in the population is identical and typically has the same number of potential contacts with an infected individual in the same time period. Also each infected individual is assumed to have the same probability to transmit the disease. Using these models, implementing any mitigation strategies to vaccinate the susceptible individuals and treating the infected individuals become extremely difficult under limited resources.

In order to effectively choose and implement a mitigation strategy, modeling of the disease spread has to be done across the specific set of individuals, which enables researchers to prioritize the selection of individuals to be treated first and also gauge the effectiveness of mitigation strategy.

Although nonhomogenous models for spread of a disease can be built based on individual characteristics using the interactions in a contact network, such individual levels of infectivity and vulnerability require complex mathematics to obtain the information needed for such models.

Simulation techniques can be used to generate hypothetical outcomes of disease spread by simulating events on the basis of hourly, daily, or other periods and tallying the outcomes throughout the simulation. A nonhomogenous agent-based simulation approach allows each member of the population to be simulated individually, considering the unique individual characteristics that affect the transmission and infection probabilities. Furthermore, individual behaviors that affect the length of contact between individuals, and the possibility of infected individuals recovering and becoming immune, can also be simulated via agent-based models.

One such simulation model, built for the Ontario Agency for Health Protection and Promotion (OAHPP) following the global outbreak of severe acute respiratory syndrome (SARS) in 2002-2003, simulated the spread of disease by applying various mitigation strategies. The simulation models each state of an individual in each time unit, based on the individual probabilities to transition from susceptible state to infected stage and then to recovered state and back to susceptible state. The simulation model also uses an individual's duration of contact with infected individuals. The model also accounts for the rate of disease transmission per time unit based on the type of contact between individuals and for behavioral changes of individuals in a disease progression (being quarantined or treated or recovered). It is flexible enough to consider several factors affecting the mitigation strategy, such as an individual's age, residence, level of general interaction with other members of population, number of individuals in each household, distribution of households, and behavioral aspects involving daily commutes, attendance at schools, and asymptotic time period of disease.

The simulation model was tested to measure the effectiveness of a mitigation strategy involving an advertising campaign that urged individuals who have symptoms of disease to stay at home rather than commute to work or school. The model was based on a pandemic influenza outbreak in the greater Toronto area. Each individual agent, generated from the population, was sequentially assigned to households. Individuals were also assigned to different ages based on census age distribution; all other pertinent demographic and behavioral attributes were assigned to the individuals.

The model considered two types of contact: close contact, which involved members of the same household or commuters on the public transport; and causal contact, which involved random individuals among the same census tract. Influenza pandemic records provided past disease transmission data, including transmission rates and contact time for both close and causal contacts. The effect of public transportation was simplified with an assumption that every individual of working age used the nearest subway line to travel. An initial outbreak of infection was fed into the model. A total of 1,000 such simulations was conducted.

The results from the simulation indicated that there was a significant decrease in the levels of infected and deceased persons as an increasing number of infected individuals followed the mitigation strategy of staying at home. The results were also analyzed by answering questions that sought to verify issues such as the impact of 20 percent of infected individuals staying at home versus 10 percent staying at home. The results from each of the simulation outputs were fed into geographic information system software, ESRI ArcGIS, and detailed shaded maps of the greater Toronto area, showing the spread of disease based on the average number of cumulative infected individuals. This helped to determine the effectiveness of a particular mitigation strategy. This agent-based simulation model provides a what-if analysis tool that can be used to compare relative outcomes of different disease scenarios and mitigation strategies and help in choosing the effective mitigation strategy

• Discuss the characteristics of an agent-based simulation model.
• List the various factors that were fed into the agent-based simulation model described in the case and elaborate on the benefits of using agent-based simulation models.
• Describe the general process of simulation.
• Discuss some of the major advantages of simulation over optimization and vice versa.