The Mathematics of Infectious Diseases
SIAM Review
Algorithms and Networking for Computer
Algorithms and Networking for Computer
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Traditional approaches in epidemiological modeling assume a fully mixed population with uniform contact rates. These assumptions are inaccurate in a real epidemic. We propose an agent-based and spatially explicit epidemiological model to simulate the spread of influenza for nosocomial environments with high heterogeneity in interactions and susceptibilities. A field survey was conducted to obtain the activity patterns of individuals in a ward of Tan Tock Seng Hospital in Singapore. The data collected supports modeling of social behaviors constrained by roles and physical locations so as to achieve a highly precise simulation of the ward's activity. Our results validate the long-standing belief that within the ward, influenza is typically transmitted through staff and less directly between patients, thereby emphasizing the importance of staff-oriented prophylaxis. The model predicts that outbreak size (and attack rate) will increase exponentially with increasing disease infectiousness beyond a certain threshold but eventually tapers due to a target-limited finite population. The latter constraint also gives rise to a peak in epidemic duration (at the threshold level of infectiousness) that decreases to a steady value for increasing infectiousness. Finally, the results show that the rate of increase in distinct cumulated contacts will be highest within the first 24 hours and gives the highest yield for contact tracing among patients that had prolonged periods of non-isolation. We conclude that agent-based models are a necessary and viable tool for validating epidemiological beliefs and for prediction of disease dynamics when local environmental and host factors are sufficiently heterogeneous.