Impacto Socioeconómico:

Data from the two most affected countries in terms of number of deaths due to Ebola, are used in the fitting process. Liberia accounted for 42.5%

and Sierra Leone accounted for 34.9% of the deaths of EVD [129]. Figures 3.5and 3.6show the model fit for EVD data from Sierra Leone and Liberia, collected by the Centers for Disease Control and Prevention (CDC) [129]. At the national level, the maximum number of beds for hospitalisation of EVD patients was very large.

Mar 14

Apr MayJun Jul AugSeptOct NovDec Jan 15

Feb Mar Apr MayJun July AugSeptOct NovDec

Months

Cumulative number of infected in Sierra Leone

Numerical result WHO data 15000

Figure 3.5: Curve fitting for data from Sierra Leone. 14 stands for the year 2014 and 15 stands for the year 2015 on the x-axis.

Mar 14

Apr MayJun Jul AugSeptOct NovDec Jan 15

Feb Mar Apr MayJun July AugSeptOct NovDec

Months

Cumulative number of infected in Liberia

Numerical result WHO data 20 000

Figure 3.6: Curve fitting for data from Liberia. 14 stands for the year 2014 and 15 stands for the year 2015 on the x-axis.

The estimated parameter values from the fitting process are given in Ta-ble3.2for each country. The value of the reproduction number in each case is Rh = 2 for Liberia and Rh = 2.5 for Sierra Leone. These values of the reproduction numbers are comparable to those obtained from the litera-ture. Rivers et al. [32] found an overall basic reproduction number of 2.2 for the two considered countries, with improved contact tracing, pharma-ceutical interventions and improved infection control. Althaus [38] found the maximum likelihood estimate of the basic reproduction number as 1.59

for Liberia and 2.53 for Sierra Leone in 2014. In the absence of effective con-trol measure, the basic reproduction number of EVD was 2.02 for Liberia according to Xia et al. [130], who were investigating the different transmis-sion routes of EVD. Nishiura and Chowell [39] estimated the reproduction number to be between 1 and 2 for Liberia and Sierra Leone, from March to August 2014. These estimations of the secondary number of Ebola infections have been made from different compartmental models, during different pe-riods of the year 2014 and for diverse types of control measures. This could explain the differences in the values of the reproduction numbers obtained.

Parameters Λ β c α µ ω δ1 δ2 ρ η1 η2 µ0 µ1 π bmax ETU

Liberia 2.1 0.48 4.3 0.5 2.1 0.2 0.37 0.01 0.7 0.16 1.7 0.008 0.4 0.5 45 30 Sierra Leone 3.53 0.47 2.9 0.5 3.53 0.2 0.6 0.009 0.7 0.5 3.43 0.0076 0.52 0.5 50 20

Table 3.2: Estimated parameters’ value obtained from the fitting process. b_maxis the maximum bed capacity of an ETU.

Figures3.5and3.6show that the model formulated in this work fits well to data for parameter values given in Table 3.2. We notice from the esti-mated parameters in Table3.2that Sierra Leone had a larger hospitalisation rate. This is consistent with the fact that the country had the highest num-ber of infected individuals [129]. The efficacy of hospitalisation is indicated by death rate, which is lower with hospital admission of EVD infected in-dividuals in both countries. However, despite the utility of hospitalisation, in Liberia more patients died in these hospitals than in Sierra Leone. This could mean that Liberia had more difficulties in handling its patients in ETU than Sierra Leone. This could have been due to a shortage of beds for a proper hospital admission or insufficient drugs for treatment.

The period during which new ETUs with more available beds were built was an important factor in the limitation of EVD transmission. In fact, Kucharski et al. [35] and Dubois et al. [131] claim that an early allocation of sufficient beds for treatment or holding centers would have helped to avoid many transmissions and deaths. Figures 3.7 and 3.8 indicate the monthly number of new Ebola infected [132]. They also indicate the total number of available beds per country for a period of eight months. Ebola incidence

here represents the number of new cases reported the 25th of the indicated month [133]. The number of beds is the total sum of beds available in Ebola CCCs, ETUs and Ebola holding centers. The maximum number of beds for each month is represented on the graphs and WebPlotDigitizer was used to extract data from the plots in [35]. Data from March to May 2014 were missing in [35] and this explains the absence of some data points in Figures 3.7and3.8.

Figure 3.7: Comparison between the moments when the maximum number of in-fected and beds are reached in Liberia. 14 stands for the year 2014 and 15 stands for the year 2015 on the x-axis.

Figure 3.8: Comparison between the moments when the maximum number of in-fected and beds are reached in Sierra Leone. 14 stands for the year 2014 and 15 stands for the year 2015 on the x-axis.

We notice in Figures3.7 and 3.8 that the maximum number of infected and beds do not always coincide. In Liberia, the maximum number of beds is reached one month after the maximum number of infected is reached. In, Sierra Leone, many beds were expected during two months, from Novem-ber 2014 when the maximum numNovem-ber of infected was reached, to DecemNovem-ber 2014 when the maximum number of available beds was reached. This delay of two months certainly led to more infections and deaths, since the time from infection to death of EVD was estimated to be only 10 days in Liberia and sierra Leone [32]. This emphasises the necessity of timely allocation of funds and implementation of recommendations to build enough Ebola health care facilities with sufficient beds early.

The aim of providing beds for EVD exposed individuals is to limit the dis-ease transmission within the hospital setting and incrdis-ease access to treat-ment. This can be attained when beds are allocated according to the needs of each affected country and if proper maintenance of these beds is imple-mented. Our results suggest a more balanced distribution and maintenance of hospital beds.