Control and Cost-benefit Analysis of Fast Spreading Diseases: The case of Ebola

Romarie Rosado Morales, Lauren Charles-Smith, Brent Daniel


IntroductionMitigating the spread of infectious disease is of great importancefor policy makers. Taking the recent outbreak of Ebola as an example,it was difficult for policy makers to identify the best course of actionbased on the cost-effectiveness of what was available.In effort to address the needs of policy makers to mitigate the spreadof infectious disease before an outbreak becomes uncontrollable, wehave devised a cost-benefit disease control model to simulate theeffect of various control methods on disease incidence and the costassociated with each of the scenarios. Here, we present a case studyof Ebola used to quantify the cost effectiveness of vaccination andisolation methods to minimize the spread of the disease. We evaluatethe impact of changing strategy levels on the incidence of the diseaseand address the benefits of choosing one strategy over the other withregards to cost of vaccine and isolation.MethodsDisease.We use a general SEIRJ model for disease transmission.Here, S-Susceptible, E- Exposed (latent), IA– Infected (asymptomatic),IM– Infected (mild symptoms), IS– Infected (severe symptoms),JM– Isolated (mild symptoms at home), JS– Isolated (severesymptoms in hospital), and R- Recovered individuals. In this model,we consider the dynamics of the system and the effect of the relativetransmissibility of isolated individuals (L) compared to other infectedindividuals1.Cost.Ebola vaccination and treatment are very expensive andnot widely available. Some preliminary data shows that it will take$73 million (M) to produce 27 M vaccines2plus the cost for vaccinedelivery and health care professionals (not included here). On theother hand, the treatment for Ebola in the U.S. would cost $25,000dollars a day per person3to ensure proper isolation and adequate care(treatment, health care professionals, facilities and special equipment).Although not included in this research, the proper isolation of Ebolapatients would also lead to a loss in hospital revenue of $148,000per day due to reduced patient capacity3. Here, we use $27,000 perindividual hospitalized per day and $2.70 per person vaccinated.Model.To evaluate the cost-effectiveness of control methods ondisease transmission, we assessed the affect of different levels ofvaccination coverage on the resulting number of infected individuals.Then, we calculated the overall estimated cost of vaccination andresulting hospitalization for each scenario to identify the lowest cost-benefit ratio.ResultsUsing a base population of 10 M individuals, we ran scenarios fordifferent levels of vaccination (μ= 0.01, 0.05, 0.1) while varying therelative transmissibility of isolated individuals (L = 0.5, 0.6, 0.65).For each combination, we calculated the incidence, vaccination andhospitalization cost per individual per day (Fig 1). We note that anincrease in the relative transmissibility of isolated individuals leads toa higher number of infected people and, therefore, a reduced numberof candidates for vaccination and an overall increase in cost. Since thecost of vaccination is 1 ten-thousandth of the cost of hospitalization,our results clearly show the cost-benefit of vaccinating over hospitaltreatment. In every scenario studied, we observed a measurablereduction in disease incidence when vaccinating a higher fraction ofthe population compared to isolating individuals post infection.ConclusionsGiven these preliminary results, we plan to extend the frameworkof our model to a dynamic control system where we consider the costof vaccination and isolation embedded in the system of differentialequations. This approach will allow us see the best availablecontrol implementation while minimizing the cost of treatment andvaccination.KeywordsControl; Epidemiological Modeling; Transmission Dynamics; Cost;EBOLAReferences1. Chowell D, Castillo-Chavez C, Krishna S, Qiu X, Anderson KS. 2015.Modeling the effect of early detection of Ebola.The Lancet InfectiousDiseases, 15(2), 148-149.2.

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Online Journal of Public Health Informatics * ISSN 1947-2579 *