Why model infectious disease: Ebola

Several weeks ago I wrote a blog on why modeling infectious disease is useful. Now seems like a good time to highlight a few issues regarding "why model?" within the context of the current Ebola event. Science Insider recently published a very nice piece on Ebola modeling and some initial results from different groups working the issue. Discussing the article with a few colleagues who are not modelers, however, I sensed some skepticism regarding the past track record of models and why it's useful to model this outbreak.

As described by many authors previously (see the links in the previous blog), a major use of modeling is to help researchers think carefully about a problem. That's especially true in the current situation, where models can help analyze complex issues. A few examples include:

• What can be derived from data in hand, or data that can be collected, to improve our ability to clarify the situation? 
• Can we infer how quickly the virus is being transmitted and whether it is decreasing, increasing, or staying the same (questions regarding the basic reproduction ratio, R₀, and the effective reproduction ratio, R_eff)?  
• If vaccines become available, what coverage and efficacy might be necessary to control the outbreak (i.e., reduce R_eff below 1)? What vaccination strategies are likely to make optimal use of resources?
• Are there combination interventions that might prove effective at reducing the incidence of infection? 
• What is the likelihood of Ebola cases arriving in distant nations via air travel? 

In short, there are plenty of questions that modeling can help elucidate.

One should be skeptical about any epidemiologic method, including mathematical and computer modeling, when the stakes for public health are so high. Ultimately, however, policymakers need timely and defensible analytic guidance to support allocation of scarce resources. Modeling is one component of such guidance.

(image source: David Hartley)