Table 1 Population size n, policy maker’s funds f, and the ratio δ1 impacting the disease contraction probability q for 43 countries. Policy makers’ empirically estimated strategic choice pe that impact the empirically estimated disease contraction probabilities qe, where Int means Intermediate and Lo/I means Low/Int. Additionally, the ratio δ2 impacting the probability x that the average individual remains sick or dies, and the empirically estimated strategic choice Fe that impact the empirically estimated probabilities xe that the average individual remains sick or diesa
Country | nb | fc | δ1 d | pe e | qe | δ2 f | Fe g $ mill(%) 2009-2011 | xe h (%) |
|---|---|---|---|---|---|---|---|---|
Angola | 21256000 | High(18.8%) | Low | 0.342’ | Low | Low | 20.45(0%) | 0.061 |
Benin | 9742000 | Int(15.4%) | Low | 0.134” | Low | Low | 27.80(1%) | 0.031 |
Botswana | 2096000 | High(35.2%) | High | 0.096’ | High | High | 123.14(3%) | 0.286 |
Burkina Faso | 17323000 | Int(11.5%) | Low | - | Low | Low | 35.63(1%) | 0.035 |
Burundi | 9023000 | Int(17.4%) | Low | 0.203” | Low | Low | 26.79(1%) | 0.055 |
Cameroon | 20930000 | Int(18.3%) | Low | - | Low | Int | 22.00(1%) | 0.167 |
Chad | 12948000 | Int | Low | 0.294” | Low | Int | 15.12(0%) | 0.108 |
Congo, Dem Rep | 74618000 | Int(13.2%) | Low | - | Low | Low | 56.44(0%) | 0.043 |
Cote d'Ivoire | 23919000 | Int(15.2%) | Low | - | Low | Int | 80.54(2%) | 0.130 |
Egypt | 84605000 | Int(15.8%) | Low | - | Low | - | - | - |
Equatorial Guinea | 1837000 | Low(1.7%) | Int | - | Int | Low | 1.06(1%) | 0.054 |
Eritrea | 4980000 | - | Low | - | Low | Low | 15.53(0%) | 0.02 |
Ethiopia | 86614000 | Int(11.6%) | Low | - | Low | Low | 367.59(8%) | 0.054 |
Gabon | 2204000 | Int(10.3% + Oil) | Lo/I | 0.167” | Low | Low | 2.94(0%) | 0.091 |
Gambia | 1794000 | Int(18.9%) | Low | - | Low | - | 6.76(0%) | - |
Ghana | 26441000 | High(20.8%) | Low | 0.281’ | Low | Low | 51.80(0%) | 0.045 |
Guinea | 11861000 | Low(8.2%) | Low | 0.135” | Low | Low | 8.49(0%) | 0.042 |
Guinea-Bissau | 1699000 | Int(11.5%) | Lo/I | - | Lo/I | Int | 6.24(0%) | 0.118 |
Kenya | 43291000 | Int(18.4%) | Int | 0.270’ | Int | Int | 425.86(10%) | 0.132 |
Lesotho | 1887000 | High(15%) | High | - | High | High | 52.70(1%) | 0.795 |
Liberia | 3881000 | Int(13%) | Low | 0.313’ | Low | Low | 12.90(0%) | 0.052 |
Madagascar | 21852000 | Int(10.7%) | Low | 0.515” | Low | Low | 10.15(0%) | 0.027 |
Malawi | 15316000 | High(20.7%) | High | 0.113’ | High | High | 146.23(3%) | 0.300 |
Mali | 16678000 | Int(15.3%) | Low | - | Low | Low | 22.04(1%) | 0.030 |
Mauritania | 3461000 | Int(12.9%) | Low | 0.144” | Low | - | 0.61(0%) | - |
Mauritius | 1273000 | Int(19%) | Low | - | Low | - | 1.58(0%) | - |
Morocco | 32950000 | Int(13.4%) | Low | - | Low | Low | - | 0.003 |
Mozambique | 24491000 | High(22.3%) | High | 0.422” | High | High | 240.32(5%) | 0.314 |
Namibia | 2170000 | High(28.8%) | High | - | High | High | 114.22(3%) | 0.230 |
Niger | 17493000 | Int(11%) | Low | 0.421” | Low | Low | 11.52(0%) | 0.017 |
Nigeria | 177096000 | Low(6.1%) | Low | - | Low | Int | 401.22(9%) | 0.136 |
Rwanda | 10780000 | Int(14.1%) | Low | - | Low | Low | 187.99(4%) | 0.056 |
São Tomé and Príncipe | 194000 | Int(17.4%) | Low | 0.046” | Low | - | 0.30(0%) | - |
Senegal | 13567000 | Int(19.2%) | Low | 0.383’ | Low | Low | 25.34(1%) | 0.015 |
Sierra Leone | 5823000 | Lo/I(10.5%) | Low | - | Low | Low | 17.83(0%) | 0.052 |
South Africa | 52982000 | High(26.9%) | High | - | High | High | 595.11(14%) | 0.453 |
Swaziland | 1077000 | High(39.8%) | High | - | High | High | 50.58(1%) | 0.557 |
Tanzania | 45950000 | Int(12%) | Int | - | Int | Int | 341.80(8%) | 0.174 |
Togo | 6675000 | Int(15.5%) | Low | 0.257” | Low | Int | 14.20(0%) | 0.105 |
Tunisia | 10889000 | Int(14.9%) | - | - | - | - | - | - |
Uganda | 35363000 | Int(16.1%) | High | - | High | Int | 284.60(7%) | 0.178 |
Zambia | 14129000 | Int(16.1%) | High | - | High | High | 255.15(6%) | 0.212 |
Zimbabwe | 13098000 | High(49.3%) | High | 0.152” | High | High | 98.95(2%) | 0.298 |