Epidemiology and behavior in the time of Ebola

This week the WHO Ebola Response Team published a paper raising the notion that Ebola could become endemic in the human population of West Africa. The idea hadn't occurred to me previously, and it struck me as very unlikely. After all, this is a directly transmissible disease that, as many have told us, we know how to control.

After reflecting on the possibility, however, I don't think it can be discounted out of hand. On the one hand, breaking the chain of transmission can be achieved theoretically with careful attention to infection control and prevention practice, which is well defined in the healthcare environment. On the other hand, this isn't a nosocomial outbreak. Community transmission is the major driver of incident cases, so changing human behavior in the community must occur if this epidemic is to be stopped. In general behavior is hard to affect, and in this case it may be even harder, given recent descriptions of distrust between healthcare providers and the community.

As I've mentioned before, one of the uses of mathematical modeling is to support clear and careful thinking. In this case, epidemiologists have applied models to estimate the basic reproduction ratio, R₀, and have found it to be greater than 1, consistent with estimates from past outbreaks. Such an R₀ suggests that the virus has the potential to circulate permanently in the human population at some non-zero endemic prevalence. Endemic prevalence levels could be, relatively speaking, high or low (or intermediate). If low enough, the disease could fade out stochastically on its own, but at higher prevalences the continual danger of sporadic cases could persist indefinitely. Models can help us gain a sense of the relative likelihood of such outcomes.

The risk factors for acquiring Ebola virus infection are well known. If effective interventions reducing risky behavior are instituted widely and adhered to, they may reduce the effective reproduction ratio, R_eff, to less than 1, thereby breaking the chain of transmission. Achieving that must entail not only nosocomial infection control but also infection prevention through behavioral change in the community.

Changing behavior surely involves building and rebuilding trust between healthcare providers and local people. I suspect and hope that the recent massive pledges of, and plans for, assistance will help build the necessary rapport and trust. Maybe the construction of clinic facilities that better support effective care will help advance such endeavors. One thing is certain, however: the longer those pledges take to become reality, the more likely the worst scenarios for the course of this epidemic become.

(image source: Wikipedia)

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)