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Behavioural Infectious Disease Simulator: The 2014 Ebola Outbreak

By Jeroen Struben
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The current Ebola outbreak is unprecedented. While experts understand the diffusion patterns of infectious diseases, this knowledge is often unavailable to policy makers, journalists, and citizens.

Further, expert models include information about the virus and disease (transmission rate, incubation period, and infectious period); however, they typically do not consider how endogenous responses of the population and policy makers alter the spread of the virus.

The purpose of this simulation is to show whether and, if so, how quickly an outbreak can escalate into an epidemic depending on the following: • Infectivity of the virus • Contact rate • Infection from deceased population • Reported cases of infection and death • How citizens alter contact intensity in response to reports of infection and death • Government communication policies (informing citizens and hospital personnel) • Government quarantine policies • Government vaccine policies

The model allows users to alter the transmission parameters and the behavioral responses of citizens and policy makers, creating multiple scenarios.

Overall, the model demonstrates the fundamental nonlinear dynamics of infectious diseases, illustrating the danger of underestimating the potential of epidemic outbreaks. Swift and comprehensive responses can however mitigate the risk of epidemics.

At its core the model is based on the susceptible-exposed-infectious-removed (SEIR) model, commonly used in epidemiology. However, I adapt this model to capture behavioral responses of the population and health policy makers as the disease spreads.

The simulator shows WHO data on the 2014 outbreak within the West African countries (with frequent updates to follow) - with widely varying breakout patterns among them. The default parameters of the core SEIR model structure (and with that initial reproductive number R0) are based on empirical data from well-documented Ebola outbreaks in Congo and Uganda in 1995 and 2000. These parameters can be adjusted to analyze how the dynamics change for other infectious diseases, such as SARS, HIV, H1N1, and H5N1, and alternative contact rates within specific populations.

The simulator is designed for classroom settings (with accompanying discussion) but is also available to independent users.

Comments are appreciated. Simulator updates will follow.

Jeroen Struben McGill University October 2014

Photo Credit: AP Photo / Abbas Dulleh