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)

Ebola: Mutation, selection, and all that

The New York Times recently published an op-ed on the Ebola situation in western Africa. One of the things discussed by Michael Osterholmn in that essay is the possibility that "an Ebola virus could mutate to become transmissible through the air." I think this is an interesting idea; if the currently circulating virus were to become dramatically more transmissible, it would make an already desperate situation dire.

The issue of mutation and selection is complex, as described in an excellent post by Jamie Jones. What selective pressures are acting upon Ebola viruses circulating in western Africa currently, and how those might alter the clinical epidemiology of the disease, are interesting and relevant questions.

Many have reacted to the question of how real the risk described by Osterholm is. I think positing such possibilities, and the ensuing discussion, is helpful. Even if the possibility is a false alarm, having people think through the issue, likelihood, and potential impact has value. It should not, however, distract us from important and desperately needed public health operations on the ground, or advocacy efforts to increase the resources for those operations.

(image source: Wikipedia)

On truthiness, celebrities, and math

Katy Waldman recently published an article discussing how people's minds tend to grasp at the "low-hanging cognitive fruit" in daily life. She describes how sometimes we accept ideas as facts according their "truthiness" (a term coined by Steven Colbert in 2005). It's an interesting article and I recommend reading it. It makes me wonder about the role of truthiness in health-related behavior.

Colbert has described the notion of truthiness:

It used to be, everyone was entitled to their own opinion, but not their own facts. But that's not the case anymore. Facts matter not at all. Perception is everything. It's certainty. . . . Truthiness is "What I say is right, and [nothing] anyone else says could possibly be true." It's not only that I feel it to be true, but that I feel it to be true. There's not only an emotional quality, but there's a selfish quality.

Waldman surveys some of the evidence for truthiness: how people, instead of analyzing data critically to draw conclusions, sometimes accept ideas based on seemingly unrelated criteria, like the aesthetic presentation of a written message or the familiarity of a message bearer's name.

Within the realm of health behavior, truthiness can be devastating. In epidemiology, for example, it is often said that people who suffer from a condition tend to look for and find a cause, whether one exists or not. Truthiness reminds me of Jenny McCarthy's views on vaccination and autism, which she described during an interview on The Oprah Winfrey Show:

Winfrey: So what do you think triggered the autism [in your son]? I know you have a theory.
McCarthy: I do have a theory.
Winfrey: Mom instinct.
McCarthy: Mommy instinct. You know, everyone knows the stats, which being one in one hundred and fifty children have autism.
Winfrey: It used to be one in ten thousand.
McCarthy: And, you know, what I have to say is this: What number does it have to be? What number will it take for people just to start listening to what the mothers of children who have autism have been saying for years? Which is that we vaccinated our baby and something happened. . . .
McCarthy: Right before his MMR shot, I said to the doctor, I have a very bad feeling about this shot. This is the autism shot, isn’t it? And he said, “No, that is ridiculous. It is a mother’s desperate attempt to blame something on autism.” And he swore at me. . . . And not soon thereafter, I noticed that change in the pictures: Boom! Soul, gone from his eyes.

The post hoc, ergo propter hoc fallacy of the passage has a high truthiness to many, even though scientific bodies have debunked the notion that vaccination causes autism.

How do "truthy" fringe ideas persist and grow in the general population? Deffuant and coworkers in 2002 published a study applying agent-based modeling to analyze the propagation of extremist views. Nigel Gilbert's book describes the study succinctly:

In Duffuant et al's model, agents [individuals] have an opinion . . . and a degree of doubt about their opinion, called uncertainty . . . An agent's opinion segment is defined as the band centered on the agent's opinion, spreading to the right and left by the agent's value for uncertainty. Agents interact randomly. When they meet, one agent may influence the other if their opinion segments overlap. If the opinion segments do not overlap, the agents are assumed to be so different in the opinions that they have no chance of influencing each other. If an agent does influence another, the opinion of one agent (j) is affected by the opinion of another agent (i) by an amount proportional to the difference between their opinions, multiplied by the amount of overlap divided by agent i's uncertainty minus one. The effect of this formula is that very uncertain agents influence other agents less than those that are certain.

 . . . The model shows that a few extremists with opinions that are not open to influence from other agents can have a dramatic effect on the opinions of the majority . . .

In the case of vaccination and autism, the high truthiness (to some) of the idea that MMR vaccine causes autism produces a group of people very certain in their beliefs. Duffuant et al, working within the context of political ideas, show that such a group can impact the opinions of others, thereby propagating their ideas.

Perhaps similar dynamics apply to the case of vaccination, and potentially other health-related memes such as the raw milk movement. I also wonder about the truthiness of the local myths surrounding Ebola and how those might spread more widely, potentially affecting public health in outbreak areas adversely.

(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)