The macro economic impact model for the ebola virus

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The macro-economic impact model for the Ebola virus disease To assess the macro-economic impact of the EVD, two different modelling schemes were used, derived from previous studies on HIV/AIDS. The first model considered is the dual macro-Ebola model. It allows to analyse, in the context of the Solow-Swan growth model, the macro-economic impact of a temporarily shock on the labour force and capital accumulation caused by the Ebola epidemic. The macro-Ebola model is designed as in Cuddington and Hancock (1993a and b). In this scheme, the path of economic indicators is simulated under a scenario of no Ebola and then under a scenario with presence of EVD cases. The former represents a no-EVD benchmark against which any EVD scenario might be compared. Although different parameters are assumed, the simulation mainly focuses on the impact of lost labour productivity per EVD case. SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
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39 A cross-sectional empirical model was used for the estimation. The aim is to estimate the coefficient of a variable that captures the potential impact of EVD prevalence on an economy. To this extent, a non-linear model similar to the one designed in Bloom and Mahal (1995) is used. The transmission of an epidemic disease is determined by different factors, including economic conditions. Having realized that this might create issues with simultaneity bias, the models use non-linear two-stage least square techniques. This model is different from most of the epidemiologic models found in the literature. Indeed, for the EVD, the first symptoms appear about 21 days after being infected, and infected workers may be completely or partially unable to work, which affects their families and communities. In the meantime, they may have contaminated other workers and family members. To avoid further contagion, workers are isolated at the appearance of the first symptoms. With the high fatality rate, the chances of returning to work are minimal. Hence, one of the main impacts of the EVD is the huge reduction of labour supply. In our model, we estimate the production per capita for the case with and without EVD, to enable the estimation of the cost of the EVD on the economy (see Annexes 1 and 2 for the detailed methodology). 3.3. Key findings from empirical results 3.3.1 Results of the probability distribution of Ebola virus disease prevalence This report simulates the probability of having Ebola cases under low and high scenarios for each of the 15 West African countries between 2013 and 2017. Annex 1b provides the assumptions and equations for estimating the probabilities. Based on the results, countries are grouped into four categories of probability of EVD prevalence. The first category is the high probability of EVD prevalence in the three epicentre countries, which is expected according to the current local situation. Second, Ghana and The Gambia also have a relatively high probability of EVD prevalence – 0.4 and 0.6, respectively. This calls
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  • Fall '19
  • West Africa

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