Modeling disease eradication

Diseases evolve in response to treatment, frustrating efforts to eradicate them. In 2016 the IIASA Evolution and Ecology Program explored how evolution, population dynamics, and economic factors interact, providing new insight that could help inform efforts to control diseases like malaria.

Efforts to eradicate a disease are likely to fail if medical professionals only know the target of an eradication campaign, but cannot predict the course for reaching it, according to IIASA research. The 2016 study examined the interplay between disease evolution, human populations, and economic factors to determine how diseases can be controlled, using a new, model-based view of disease eradication.

Despite many efforts to eliminate specific diseases, there have only been two success stories: smallpox and rinderpest. Most of the world’s deadly illnesses have survived repeated efforts to eradicate them, resurging in vulnerable populations and in some cases gaining resistance to standard treatment. Malaria, for instance, infected over 200 million people in 2010, killing around 500,000 according to the World Health Organization.

The problem is that it’s easy to make progress at the beginning, but in the last stages of eradication, when there are only a few cases, it becomes very difficult to make further progress.

A graph of this process would show a fast decline in the incidence of the disease, which peters out into a long tail that the researchers call an “eradication tail.” The reasons for this are multiple. The microbes that cause disease evolve in response to changes in their environment, sometimes gaining resistance to the drugs used against them. Likewise, eradication efforts may target the animal that spreads a disease, such as mosquitoes, which may thus evolve or adapt in response.

Also the way human populations are structured plays a role—since diseases spread between individuals, predicting eradication tails requires mapping a population and links between diseased individuals. And crucially, economic factors contribute as well—if the money for interventions runs out, the disease may come back.

The study examined how much each of these factors affected eradication in a model system. It showed that while all three factors were important, the economic factor played a deciding role in shaping the trajectory of disease eradication.

The researchers found that extending the money and time spent on an eradication campaign can make up for the tendency of evolutionary and population factors to allow a disease to persist. While evolution could allow the disease to develop resistance or become more virulent or deadly, population dynamics could allow the disease to ‘hide’ in an isolated subset of the population, and spread back to the general population in the future.

Most epidemiology research today has an on-the-ground view of specific disease data. This study shows that a model-based perspective of disease eradication can provide useful information for public health institutions aiming to eradicate diseases.


To reduce an infectious disease’s incidence level, health officials need to invest precious resources. As an eradication campaign progresses, these investments have less and less impact, resulting in so-called eradication tails. The shape of these depends on the disease characteristics (colors), as well as on whether the investments help increase the recovery rate of patients from the disease (left panel), decrease the contact rate among healthy and infected patients (middle panel), or restrict the mobility of patients (right panel).


[1] Mazzucco R, Dieckmann U & Metz JAJ (2016). Epidemiological, evolutionary, and economic determinants of eradication tails. Journal of Theoretical Biology 405: 58–65.


  • Department of Microbiology and Ecosystem Science, University of Vienna, Austria
  • Institute of Biology and Mathematical Institute, Leiden University, Netherlands
  • Netherlands Centre for Biodiversity, Netherlands