Analyzing Intervention Strategies for Containing a Pandemic Outbreak

Strategic intervention to control infectious disease spread is of paramount importance in preventing a potential global epidemic. We develop a biological-behavioral-operational computer model to help policy makers choose the best intervention strategies to rapidly contain an infectious disease outbreak. The analysis covers the dynamics of disease transmission across different environments and social settings. The modeling system gives on-the-ground policymakers critical information about how to mitigate infection, monitor risk and trace disease during a pandemic.

The system can utilize many types of data, including biosurveillance, environmental, climate, viral, host, human behavior and social factors. If genetic information for the disease carriers are available, they also can be incorporated. The modeling system provides the ability to predict disease spread, assess risk and determine effective containment methods. It can also help public health leaders optimize deployment of limited resources to help prevent and reduce the extent of future outbreaks.

Modeling the Zika outbreak in Brazil, we identify strategies that are most effective for disease containment. The model shows that the easiest and most productive way to contain the outbreak is to reduce the biting rate of mosquitoes by using insect repellents/mosquito-wristbands, wearing long-sleeved shirts and long pants, and employing air conditioning and window/door screens to keep mosquitoes out. The result is practical. For example, the model demonstrates that only 20% compliance can reduce the total infection by half. This strategy is more successful than just widely applying insecticide and lasers to kill mosquitoes. The model offers policymakers a decision-support framework to estimate the cost-effectiveness of each prevention measure.

For Puerto Rico, self-protection from mosquito bites could drastically reduce total infection by 90%, when the compliance reaches 30%. The system returns a visualized pareto frontier that offers an economic-decision-framework for policy makers. They can review results, contrast different outcomes, and select a strategy portfolio (with the minimum total infection) that is compatible with their local environment and regional demographics.

The system can be applied to help contain a wide variety of epidemics, including not only Zika but also dengue, Ebola, and many other types. The modeling framework accommodates various transmission mechanisms. This allows public health officials to adapt rapidly to changing disease environments and different emerging epidemics.