# If there is no intervention to control the disease

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If there is no intervention to control the disease, the control factor is 0 and the rate of transmission in the population will stay constant over time. In this case, the rate of transmission of the disease at any time will be β it = β 0i If the intervention is efficient, the control factor will be high, and the transmission rate will decrease exponentially. If a country maintains its control factor constant, the transmission rate will become zero as time increases. SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
85 Based on the discussion above, the distribution of the probability of EVD prevalence was modelled. Ebola follows an exponential function dynamics over time with a control factor, which varies from one country to another. For each scenario, either high or low Ebola scenario, the probability of a new Ebola case in country i at time t is given by: P i0 e ki ( t t i0 ) i f t i0 ≤ t ≤ t peak P it = P t peak e –ki ( t–tpeak ) i f t ≥ t peak (3) P i0 e –ki ( t i0 t ) i f t t i0 Where: t i0 is the initial date. In this study it is equal to the month the first case of Ebola occurs in country i. In the case the country never experiences Ebola, we use the initial date observed in the nearest country with effective Ebola cases. t peak is the date of the highest probability. This report assumes that the Ebola situation will not worsen in the future. Therefore, the highest probability is equal to the estimated probability for November 2014 reported in the World Bank report. k i is the control factor set up in country i to fight Ebola. P i0 is the probability at the first occurrence date t_i0, of having Ebola cases during the next month. SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
86 Annex 3. Modelling food security and poverty incidence To assess the social impact of the EVD, the model focuses on food security and poverty aspects. Two approaches are used for this purpose. The analysis of the impact of the EVD on poverty is based on the approach developed by Son and Kakwani (2004) applied to Côte d’Ivoire by Aka and Diallo (2009). Based on the results of the EVD impact on economic growth, we estimate a link with poverty indices starting from the equation below: G dG d P dP (4) Where P α is the FGT index, α is the mean income of the population and G is the Gini index. We assume that the mean income of the population grows at the same rate as the GDP per capita. We also assume that, in the short term, change in income distribution will be negligible so that the change in poverty is mainly due to the change in the average income of the population. This therefore yields: d P dP (5) where = P dP * , the growth elasticity of poverty. It follows that: P α ,t+1 = (1+ ) P α,t (6) The growth elasticity is from Kouadio, Gbongué and Ouattara (2006) for Côte d’Ivoire, Boccanfuso and Kaboré (2003) for Burkina Faso and Senegal, and Chukwu (2014) for Nigeria. For the other West African countries, the estimate of Anyanwu (2013) for sub-Saharan Africa is used. Combining the growth

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