Modeling an epidemic to help guide public health policies (Ebola, West Africa)

Projected daily hospital demand in 2015 for Ebola patients in Liberia if 85% of cases are hospitalized.By mid-December 2014, about 18,000 individuals had been reported to be infected by the Ebola virus currently causing an epidemic in West Africa. On January 12, 2015, the World Health Organization’s (WHO) latest report stated an updated total of more than 21,000 cases. As a substantial proportion of cases remain unreported, however, the true number of infected individuals is believed to be much higher. The fatality rate (the proportion of infected people dying from the infection) for this Ebola epidemic, based on known cases, is estimated to be about 70%.

Starting as an outbreak in Guinea in December 2013, the Ebola epidemic spread to Liberia and Sierra Leone in March and May 2014, respectively. It was declared a “Public Health Emergency of International Concern” by the WHO in August 2014, leading to a ramping up of international efforts to contain the epidemic.

But how to decide on what needs to be done to most efficiently contain the epidemic? For example, how much should hospital capacity increase? What is the impact of population outreach and education? Experts and policymakers rely on epidemic modeling to analyze crucial parameters, make predictions, and decide on what interventions need to be implemented. A new model for the current Ebola epidemic, published last week in PLoS Biology, suggests that the epidemic outcome still depends not only on hospital capacity but also, importantly, on individual behavior (seeking medical care, isolating infected individuals, burying the deceased in secure conditions).

Several models of the 2014 Ebola epidemic have already been published. Many of them have tried to estimate as accurately as possible an important epidemiological parameter called the basic reproductive number, R0. Basically, R0 represents how many people an infected individual will contaminate, in a population where everyone is susceptible to the pathogen (that is, no one has already been infected, then recovered, and become immune to the virus); it gives an idea of how transmissible a pathogen is and, for example, helps estimate what proportion of the population would have to be vaccinated to achieve herd immunity.

According to an analysis performed by the WHO Ebola Response team in September 2014, R0 was estimated to be 1.7 for Guinea, 1.8 for Liberia, and 2 for Sierra Leone. By comparison, R0 for the SARS epidemic in Hong-Kong and Beijing in 2003 was estimated to be 2-3; for measles, R0 is about 12-18, and for chickenpox, 4-9.

However, knowing R0 is not sufficient to model an epidemic whose course can be modified by changes in individual behavior and public health interventions. Two other studies (one by the WHO Response Team, another by the CDC) have therefore tried to establish models of the epidemic that integrate more parameters to predict the evolution of the epidemic. The recent PLoS Biology study follows the same lines, though also allowing for additional variation in several parameters to model epidemic outcomes as human behaviors change over time and public health interventions are implemented (for example, as hospitalization rates or burial practices change over time).

The researchers used their model to assess the likely progression of the Ebola epidemic in Liberia under a range of different scenarios. Their initial results suggested that if hospitalization rates could be increased to 85%, the epidemic would probably be contained. An update of the model based on data collected during the months of October and November confirmed this prediction and further suggested that if the hospitalization rate could be maintained at 85%, the epidemic could be eliminated sometime in the spring of 2015. However, the researchers also note that, should the hospitalization rate fall back to its previous background level of around 70%, the epidemic could take much longer to be eliminated, allowing for more people to be infected. In short, an increase in hospital capacity can only optimally contribute to bringing the epidemic to an end if individual behavior also changes to allow for rapid hospitalization of new cases. This, in turn, can only be achieved with sufficient outreach efforts and education of the population.

Ebola cases and health system demand in Liberia. Drake JM, Kaul RB, Alexander LW, O’Regan SM, Kramer AM, Pulliam JT, Ferrari MJ, Park AW. PLoS Biol. 2015 Jan 13;13(1):e1002056. doi: 10.1371/journal.pbio.1002056
PMID: 25585384

ResearchBlogging.orgDrake JM, Kaul RB, Alexander LW, O’Regan SM, Kramer AM, Pulliam JT, Ferrari MJ, & Park AW (2015). Ebola cases and health system demand in liberia. PLoS biology, 13 (1) PMID: 25585384


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