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February  2017, 14(1): 95-109. doi: 10.3934/mbe.2017007

Controlling imported malaria cases in the United States of America

Bassidy Dembele 1, and  Abdul-Aziz Yakubu 2,,

1. 

Department of Mathematics and Physics, Grambling State University, Grambling, LA 71245, USA
2. 

Department of Mathematics, Howard University, Washington, DC 20059, USA

* Corresponding author: Abdul-Aziz Yakubu

Received  November 24, 2015 Accepted  July 02, 2016 Published  October 2016

We extend the mathematical malaria epidemic model framework of Dembele et al. and use it to “capture” the 2013 Centers for Disease Control and Prevention (CDC) reported data on the 2011 number of imported malaria cases in the USA. Furthermore, we use our “fitted” malaria models for the top 20 countries of malaria acquisition by USA residents to study the impact of protecting USA residents from malaria infection when they travel to malaria endemic areas, the impact of protecting residents of malaria endemic regions from mosquito bites and the impact of killing mosquitoes in those endemic areas on the CDC number of imported malaria cases in USA. To significantly reduce the number of imported malaria cases in USA, for each top 20 country of malaria acquisition by USA travelers, we compute the optimal proportion of USA international travelers that must be protected against malaria infection and the optimal proportion of mosquitoes that must be killed.

Keywords: CDC, imported malaria, mosquito, USA travelers.
Mathematics Subject Classification: 92B05.
Citation: Bassidy Dembele, Abdul-Aziz Yakubu. Controlling imported malaria cases in the United States of America. Mathematical Biosciences & Engineering, 2017, 14 (1) : 95-109. doi: 10.3934/mbe.2017007
References:
[1]

P. -L. Alonso, A. Djimde, H. Hughes and S. -A. Ward, A research agenda for malaria eradication: Drugs, DOI: 10.1371/journal.pmed.1000402,January25,2011. Google Scholar

[2]

P. Carnevale, J. Mouchet, M. Coosemans, J. Julvez, S. Manguin, R. -D. Lenoble and S. Sircoulou, Biodiversite du Paludisme dans le Monde Editions John Libbey Eurotext, Paris, 2004. Google Scholar

[3]

R. -P. Cody and J. -K. Smith, Applied Statistics and the SAS Programming Language Pearson Prentice Hall, Fifth Edition, 2006. Google Scholar

[4]

K. -A. Cullen and P. -M. Arguin, Malaria Surveillance-United States, 2011 Centers for Diseases Control and Prevention, (2013), 1-17.   Google Scholar

[5]

B. Dembele, A. Friedman and A. -A. Yakubu, Mathematical Model for optimal use of sulfadoxine-pyrimethamine as a temporary vaccine, Bulletin of Mathematical Biology, 72 (2010), 914-930.  doi: 10.1007/s11538-009-9476-9.  Google Scholar

[6]

B. Dembele and A. -A. Yakubu, Optimal treated mosquito bed nets and insecticides for eradication of malaria in Missira, Discrete and Continuous Dynamical Systems Series B, 17 (2012), 1831-1840.  doi: 10.3934/dcdsb.2012.17.1831.  Google Scholar

[7]

A. -M. Dondorp, F. Nosten, P. Yi, D. Das and A. -P. Phyo, Artemisinin resistance in Plasmodium falciparum malaria, N Engl J Med, 361 (2009), 455-467.   Google Scholar

[8]

J. -C. Koella and R. Antia, Epidemiological models for the spread of anti-malaria resistance, Malaria Journal, (2003), 2-3.   Google Scholar

[9]

W. -O. Kermack and A. G. McKendrick, Contributions to the mathematical theory of epidemics, Proc. R. Soc A, 115 (1927), 700-721.   Google Scholar

[10]

A. J. Lokta, Contributions to the analysis of malaria epidemiology, Am. J. Hyg., 3 (1923), 11-21.   Google Scholar

[11]

G. Macdonald, The analysis of infection rate in diseases in which superinfection occurs, Trop. Dis., 47 (1950), 907-915.   Google Scholar

[12]

C. -A. Mertler and R. -A. Vannatta, Advanced and Multivariate Statistical Methods: Pratical Application and Interpretation Pyrczak Publishing, Fifth Edition, 2013. Google Scholar

[13]

K. O. Okosun, R. Ouifki and N. Marcus, Optimal control analysis of a malaria disease transmission model that includes treatment and vaccination with waning immunity, Biosystems, 106 (2011), 136-145.  doi: 10.1016/j.biosystems.2011.07.006.  Google Scholar

[14]

C. Y. -J. Peng, Data Analysis Using SAS Sage, 2009. doi: 10.4135/9781452230146.  Google Scholar

[15]

R. Ross, The Prevention of Malaria $ \ 2^{nd}$ edition, With Addendum on the Theory of Happenings, Murray, London, 1911. Google Scholar

[16]

C. H. Sibley, J. E. Hyde, P. F. G. Sims, C. V. Plowe, J. G. Kublin, E. K. Mberu, A. F. Cowman, P. A. Winstanley, W. M. Watkins and A. M. Nzila, Pyrimethamine-sulfadoxine resistance in Plasmodium falciparum: What next?, Trends in Parasitology, 17 (2001), 570-571.   Google Scholar

[17]

N. Sogoba, S. Doumbia, P. Vounatsou, I. Baber, M. Keita, M. Maiga, S. Toure, G. Dolo, T. Smith and J. M. C. Ribeiro, Monotoring of larval habitats and mosquito densities in the sudan savanna of Mali: Implications of malaria vector control, Am. J. Trop. Med. Hyg., 77 (2007), 82-88.   Google Scholar

[18]

B. -G. Tabachnick and L. -S. Fidell, Using Multivariate Statistics Pearson, Sixth Edition, 2013. Google Scholar

[19]

J. -F. Trape, A. Tall, C. Sokhna, A. -B. Ly, N. Diagne, O. Ndiath, C. Mazenot and V. Richard, The rise and fall of malaria in a West African rural community, Dielmo, Senegal, from 1990 to 2010: A 22 year longitudinal study, The Lancet Infectious Diseases, 14 (2014), 476-488.   Google Scholar

[20]

http://www.cdc.gov/mmwr/preview/mmwrhtml/ss6205a1.htm. Accessed7/2/2016. Google Scholar

[21]

World-Health-Organization, Malaria, Available from: http://www.who.int/mediacentre/factsheets/fs094/en/. Accessed 7/2/2016. Google Scholar

show all references

References:
[1]

P. -L. Alonso, A. Djimde, H. Hughes and S. -A. Ward, A research agenda for malaria eradication: Drugs, DOI: 10.1371/journal.pmed.1000402,January25,2011. Google Scholar

[2]

P. Carnevale, J. Mouchet, M. Coosemans, J. Julvez, S. Manguin, R. -D. Lenoble and S. Sircoulou, Biodiversite du Paludisme dans le Monde Editions John Libbey Eurotext, Paris, 2004. Google Scholar

[3]

R. -P. Cody and J. -K. Smith, Applied Statistics and the SAS Programming Language Pearson Prentice Hall, Fifth Edition, 2006. Google Scholar

[4]

K. -A. Cullen and P. -M. Arguin, Malaria Surveillance-United States, 2011 Centers for Diseases Control and Prevention, (2013), 1-17.   Google Scholar

[5]

B. Dembele, A. Friedman and A. -A. Yakubu, Mathematical Model for optimal use of sulfadoxine-pyrimethamine as a temporary vaccine, Bulletin of Mathematical Biology, 72 (2010), 914-930.  doi: 10.1007/s11538-009-9476-9.  Google Scholar

[6]

B. Dembele and A. -A. Yakubu, Optimal treated mosquito bed nets and insecticides for eradication of malaria in Missira, Discrete and Continuous Dynamical Systems Series B, 17 (2012), 1831-1840.  doi: 10.3934/dcdsb.2012.17.1831.  Google Scholar

[7]

A. -M. Dondorp, F. Nosten, P. Yi, D. Das and A. -P. Phyo, Artemisinin resistance in Plasmodium falciparum malaria, N Engl J Med, 361 (2009), 455-467.   Google Scholar

[8]

J. -C. Koella and R. Antia, Epidemiological models for the spread of anti-malaria resistance, Malaria Journal, (2003), 2-3.   Google Scholar

[9]

W. -O. Kermack and A. G. McKendrick, Contributions to the mathematical theory of epidemics, Proc. R. Soc A, 115 (1927), 700-721.   Google Scholar

[10]

A. J. Lokta, Contributions to the analysis of malaria epidemiology, Am. J. Hyg., 3 (1923), 11-21.   Google Scholar

[11]

G. Macdonald, The analysis of infection rate in diseases in which superinfection occurs, Trop. Dis., 47 (1950), 907-915.   Google Scholar

[12]

C. -A. Mertler and R. -A. Vannatta, Advanced and Multivariate Statistical Methods: Pratical Application and Interpretation Pyrczak Publishing, Fifth Edition, 2013. Google Scholar

[13]

K. O. Okosun, R. Ouifki and N. Marcus, Optimal control analysis of a malaria disease transmission model that includes treatment and vaccination with waning immunity, Biosystems, 106 (2011), 136-145.  doi: 10.1016/j.biosystems.2011.07.006.  Google Scholar

[14]

C. Y. -J. Peng, Data Analysis Using SAS Sage, 2009. doi: 10.4135/9781452230146.  Google Scholar

[15]

R. Ross, The Prevention of Malaria $ \ 2^{nd}$ edition, With Addendum on the Theory of Happenings, Murray, London, 1911. Google Scholar

[16]

C. H. Sibley, J. E. Hyde, P. F. G. Sims, C. V. Plowe, J. G. Kublin, E. K. Mberu, A. F. Cowman, P. A. Winstanley, W. M. Watkins and A. M. Nzila, Pyrimethamine-sulfadoxine resistance in Plasmodium falciparum: What next?, Trends in Parasitology, 17 (2001), 570-571.   Google Scholar

[17]

N. Sogoba, S. Doumbia, P. Vounatsou, I. Baber, M. Keita, M. Maiga, S. Toure, G. Dolo, T. Smith and J. M. C. Ribeiro, Monotoring of larval habitats and mosquito densities in the sudan savanna of Mali: Implications of malaria vector control, Am. J. Trop. Med. Hyg., 77 (2007), 82-88.   Google Scholar

[18]

B. -G. Tabachnick and L. -S. Fidell, Using Multivariate Statistics Pearson, Sixth Edition, 2013. Google Scholar

[19]

J. -F. Trape, A. Tall, C. Sokhna, A. -B. Ly, N. Diagne, O. Ndiath, C. Mazenot and V. Richard, The rise and fall of malaria in a West African rural community, Dielmo, Senegal, from 1990 to 2010: A 22 year longitudinal study, The Lancet Infectious Diseases, 14 (2014), 476-488.   Google Scholar

[20]

http://www.cdc.gov/mmwr/preview/mmwrhtml/ss6205a1.htm. Accessed7/2/2016. Google Scholar

[21]

World-Health-Organization, Malaria, Available from: http://www.who.int/mediacentre/factsheets/fs094/en/. Accessed 7/2/2016. Google Scholar

Table 1.  Model Parameters and Descriptions.
Parameter Description
αhm Human infectivity rate
αmh Mosquito infectivity rate
bm Mosquito biting rate
λh Human birth rate
λm Mosquito birth rate
βh Human loss of immunity rate
αh Human recovery rate
μd Malaria induced death rate
μh, μm Human, mosquito death rates
θm Mosquito loss of incubation rate
ch Proportion of humans using bed net
cm Proportion of mosquitoes killed
cu Proportion of USA travellers to endemic countries
$\gamma =\frac{\alpha _{hm}b_{m}N_{m}}{N_{h}}$ Infection rate of humans in endemic countries
γu Infection rate of USA travellers to endemic countries
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Parameter Description
αhm Human infectivity rate
αmh Mosquito infectivity rate
bm Mosquito biting rate
λh Human birth rate
λm Mosquito birth rate
βh Human loss of immunity rate
αh Human recovery rate
μd Malaria induced death rate
μh, μm Human, mosquito death rates
θm Mosquito loss of incubation rate
ch Proportion of humans using bed net
cm Proportion of mosquitoes killed
cu Proportion of USA travellers to endemic countries
$\gamma =\frac{\alpha _{hm}b_{m}N_{m}}{N_{h}}$ Infection rate of humans in endemic countries
γu Infection rate of USA travellers to endemic countries
Table 2.  2011 CDC Data: Number of Imported Malaria Cases and Country Of Acquisition.
Malaria Acquisition Country 2011 CDC Data
Afghanistan 60
Cameroon 62
Cote D'Ivoire 28
Ethiopia 55
Ghana 156
Guinea 40
Guyana 19
Haiti 72
Honduras 21
India 223
Kenya 37
Liberia 90
Nigeria 213
Pakistan 39
Sierra Leone 116
Sudan 32
Uganda 61
Senegal 17
Eritrea 18
Gambia 21
Other 540
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Malaria Acquisition Country 2011 CDC Data
Afghanistan 60
Cameroon 62
Cote D'Ivoire 28
Ethiopia 55
Ghana 156
Guinea 40
Guyana 19
Haiti 72
Honduras 21
India 223
Kenya 37
Liberia 90
Nigeria 213
Pakistan 39
Sierra Leone 116
Sudan 32
Uganda 61
Senegal 17
Eritrea 18
Gambia 21
Other 540
Table 3.  Estimates of Model Parameters Per Day.
Country αmh αhm u λh βh αh μh μm θm μd
Afghanistan 0.014 0.014 0.020845 0.000124 0.03 0.25 0.0000219 0.033 0.1 3.1144*10-9
Cameroon 0.096 0.096 0.0043860 0.000094 0.03 0.25 0.0000329 0.033 0.1 6.9372*10-7
Cote D'Ivoire 0.088 0.088 0.002231 0.00009 0.03 0.25 0.0000411 0.033 0.1 2.3603*10-6
Ethiopia 0.037 0.037 0.010205 0.000098 0.03 0.25 0.0000219 0.033 0.1 3.7081*10-8
Ghana 0.080 0.080 0.013519 0.00008 0.03 0.25 0.0000247 0.033 0.1 3.8825*10-7
Guinea 0.081 0.081 0.00343 0.000102 0.03 0.25 0.0000329 0.033 0.1 1.5945*10-7
Guyana 0.018 0.018 0.005986 0.000045 0.03 0.25 0.0000192 0.033 0.1 3.01337*10-7
Haiti 0.005 0.005 0.02941 0.000071 0.03 0.25 0.0000247 0.033 0.1 2.4658*10-7
Honduras 0.001 0.001 0.008831 0.000075 0.03 0.25 0.000037 0.033 0.1 3.6696*10-10
India 0.001 0.001 0.09373 0.00006 0.03 0.25 0.0000219 0.033 0.1 2.5911*10-9
Kenya 0.204 0.204 0.001605 0.000099 0.03 0.25 0.0000247 0.033 0.1 1.8904*10-6
Liberia 0.220 0.220 0.0037256 0.000099 0.03 0.25 0.0000247 0.033 0.1 1.1818*10-6
Nigeria 0.028 0.028 0.0506 0.0001 0.03 0.25 0.0000384 0.033 0.1 1.3319*10-7
Pakistan 0.023 0.023 0.010676 0.000075 0.03 0.25 0.0000192 0.033 0.1 1.5343*10-6
Sierra Leone 0.114 0.114 0.0074855 0.0001 0.03 0.25 0.0000493 0.033 0.1 8.3397*10-7
Sudan 0.064 0.064 0.0034228 0.000089 0.03 0.25 0.0000247 0.033 0.1 9.05*10-8
Uganda 0.3 0.3 0.0021354 0.000121 0.03 0.25 0.0000193 0.033 0.1 5.2734*10-7
Senegal 0.01773 0.01773 0.005383 0.0001 0.03 0.25 0.0000219 0.033 0.1 1.2547*10-7
Eritrea 0.0042 0.0042 0.007413 0.000092 0.03 0.25 0.0000192 0.033 0.1 1.2421*10-8
Gambia 0.28 0.28 0.0007616 0.000094 0.03 0.25 0.0000274 0.033 0.1 3.86*10-7
Other 0.005 0.005 0.220536 0.00009 0.03 0.25 0.00006 0.033 0.1 1.4227*10-5
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Country αmh αhm u λh βh αh μh μm θm μd
Afghanistan 0.014 0.014 0.020845 0.000124 0.03 0.25 0.0000219 0.033 0.1 3.1144*10-9
Cameroon 0.096 0.096 0.0043860 0.000094 0.03 0.25 0.0000329 0.033 0.1 6.9372*10-7
Cote D'Ivoire 0.088 0.088 0.002231 0.00009 0.03 0.25 0.0000411 0.033 0.1 2.3603*10-6
Ethiopia 0.037 0.037 0.010205 0.000098 0.03 0.25 0.0000219 0.033 0.1 3.7081*10-8
Ghana 0.080 0.080 0.013519 0.00008 0.03 0.25 0.0000247 0.033 0.1 3.8825*10-7
Guinea 0.081 0.081 0.00343 0.000102 0.03 0.25 0.0000329 0.033 0.1 1.5945*10-7
Guyana 0.018 0.018 0.005986 0.000045 0.03 0.25 0.0000192 0.033 0.1 3.01337*10-7
Haiti 0.005 0.005 0.02941 0.000071 0.03 0.25 0.0000247 0.033 0.1 2.4658*10-7
Honduras 0.001 0.001 0.008831 0.000075 0.03 0.25 0.000037 0.033 0.1 3.6696*10-10
India 0.001 0.001 0.09373 0.00006 0.03 0.25 0.0000219 0.033 0.1 2.5911*10-9
Kenya 0.204 0.204 0.001605 0.000099 0.03 0.25 0.0000247 0.033 0.1 1.8904*10-6
Liberia 0.220 0.220 0.0037256 0.000099 0.03 0.25 0.0000247 0.033 0.1 1.1818*10-6
Nigeria 0.028 0.028 0.0506 0.0001 0.03 0.25 0.0000384 0.033 0.1 1.3319*10-7
Pakistan 0.023 0.023 0.010676 0.000075 0.03 0.25 0.0000192 0.033 0.1 1.5343*10-6
Sierra Leone 0.114 0.114 0.0074855 0.0001 0.03 0.25 0.0000493 0.033 0.1 8.3397*10-7
Sudan 0.064 0.064 0.0034228 0.000089 0.03 0.25 0.0000247 0.033 0.1 9.05*10-8
Uganda 0.3 0.3 0.0021354 0.000121 0.03 0.25 0.0000193 0.033 0.1 5.2734*10-7
Senegal 0.01773 0.01773 0.005383 0.0001 0.03 0.25 0.0000219 0.033 0.1 1.2547*10-7
Eritrea 0.0042 0.0042 0.007413 0.000092 0.03 0.25 0.0000192 0.033 0.1 1.2421*10-8
Gambia 0.28 0.28 0.0007616 0.000094 0.03 0.25 0.0000274 0.033 0.1 3.86*10-7
Other 0.005 0.005 0.220536 0.00009 0.03 0.25 0.00006 0.033 0.1 1.4227*10-5
Table 4.  2011 CDC Data Versus Model Results: Number of Imported Malaria Cases and Country Of Acquisition.
Malaria Country CDC Data Model Result
Afghanistan 60 60.02
Cameroon 62 62.04
Cote D'Ivoire 28 28.02
Ethiopia 55 55.01
Ghana 156 156.06
Guinea 40 40.04
Guyana 19 19.06
Haiti 72 72.04
Honduras 21 21.02
India 223 223.03
Kenya 37 37.09
Liberia 90 90.04
Nigeria 213 213.03
Pakistan 39 39.02
Sierra Leone 116 116.05
Sudan 32 32.06
Uganda 61 61.01
Senegal 17 17.02
Eritrea 18 18.01
Gambia 21 21.02
Other 540 540.03
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Malaria Country CDC Data Model Result
Afghanistan 60 60.02
Cameroon 62 62.04
Cote D'Ivoire 28 28.02
Ethiopia 55 55.01
Ghana 156 156.06
Guinea 40 40.04
Guyana 19 19.06
Haiti 72 72.04
Honduras 21 21.02
India 223 223.03
Kenya 37 37.09
Liberia 90 90.04
Nigeria 213 213.03
Pakistan 39 39.02
Sierra Leone 116 116.05
Sudan 32 32.06
Uganda 61 61.01
Senegal 17 17.02
Eritrea 18 18.01
Gambia 21 21.02
Other 540 540.03
Table 5.  Policy 1: Percentage of USA Protection and Optimal Number of Imported Malaria Cases.
Country 10% 20% 30% 40% 50% 60% 70% 80% 90%
Afghanistan 54.03 48.04 42.05 36.03 30.04 24.02 18.02 12.02 6.01
Cameroon 55.84 49.65 43.46 37.25 31.05 24.83 18.62 12.02 6.21
Cote D'Ivoire 25.23 22.43 19.63 16.82 14.02 11.22 8.41 5.61 2.8
Ethiopia 49.52 44.03 38.54 33.03 27.53 22.02 16.51 11.01 5.5
Ghana 140.49 124.91 109.34 93.7 78.11 62.48 46.85 31.25 15.62
Guinea 36.04 32.05 28.05 24.04 20.04 16.03 12.02 8.02 4.01
Guyana 17.16 15.26 13.36 11.45 9.54 7.63 5.72 3.82 1.91
Haiti 64.85 57.66 50.47 43.26 36.06 28.84 21.63 14.42 7.21
Honduras 18.92 16.82 14.73 12.63 10.52 8.41 6.31 4.21 2.1
India 200.78 178.53 156.27 133.92 111.64 89.3 66.96 44.66 22.32
Kenya 33.39 26.69 25.99 22.27 18.57 14.85 11.13 7.43 3.71
Liberia 81.05 72.07 63.08 54.06 45.06 36.04 27.03 18.03 9.01
Nigeria 191.77 170.52 149.26 127.92 106.63 85.29 63.96 42.66 21.32
Pakistan 35.12 31.23 27.34 23.43 19.53 15.62 11.71 7.81 3.9
Sierra Leone 104.47 92.89 81.31 69.68 58.09 46.46 34.84 23.24 11.61
Sudan 28.86 25.66 22.46 19.25 16.04 12.83 9.62 6.42 3.21
Uganda 54.92 48.83 42.74 36.63 30.54 24.42 18.31 12.21 6.1
Senegal 15.32 13.62 11.93 10.22 8.52 6.81 5.11 3.41 1.7
Eritrea 16.21 14.41 12.61 10.81 9.01 7.21 5.4 3.6 1.8
Gambia 18.92 16.82 14.73 12.62 10.52 8.41 6.31 4.21 2.1
Other 486.17 432.30 378.42 324.33 270.38 216.27 162.19 108.18 54.07
Total 1,729.06 1,537.42 1,345.77 1,153.35 961.44 768.99 576.66 384.64 192.22
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Country 10% 20% 30% 40% 50% 60% 70% 80% 90%
Afghanistan 54.03 48.04 42.05 36.03 30.04 24.02 18.02 12.02 6.01
Cameroon 55.84 49.65 43.46 37.25 31.05 24.83 18.62 12.02 6.21
Cote D'Ivoire 25.23 22.43 19.63 16.82 14.02 11.22 8.41 5.61 2.8
Ethiopia 49.52 44.03 38.54 33.03 27.53 22.02 16.51 11.01 5.5
Ghana 140.49 124.91 109.34 93.7 78.11 62.48 46.85 31.25 15.62
Guinea 36.04 32.05 28.05 24.04 20.04 16.03 12.02 8.02 4.01
Guyana 17.16 15.26 13.36 11.45 9.54 7.63 5.72 3.82 1.91
Haiti 64.85 57.66 50.47 43.26 36.06 28.84 21.63 14.42 7.21
Honduras 18.92 16.82 14.73 12.63 10.52 8.41 6.31 4.21 2.1
India 200.78 178.53 156.27 133.92 111.64 89.3 66.96 44.66 22.32
Kenya 33.39 26.69 25.99 22.27 18.57 14.85 11.13 7.43 3.71
Liberia 81.05 72.07 63.08 54.06 45.06 36.04 27.03 18.03 9.01
Nigeria 191.77 170.52 149.26 127.92 106.63 85.29 63.96 42.66 21.32
Pakistan 35.12 31.23 27.34 23.43 19.53 15.62 11.71 7.81 3.9
Sierra Leone 104.47 92.89 81.31 69.68 58.09 46.46 34.84 23.24 11.61
Sudan 28.86 25.66 22.46 19.25 16.04 12.83 9.62 6.42 3.21
Uganda 54.92 48.83 42.74 36.63 30.54 24.42 18.31 12.21 6.1
Senegal 15.32 13.62 11.93 10.22 8.52 6.81 5.11 3.41 1.7
Eritrea 16.21 14.41 12.61 10.81 9.01 7.21 5.4 3.6 1.8
Gambia 18.92 16.82 14.73 12.62 10.52 8.41 6.31 4.21 2.1
Other 486.17 432.30 378.42 324.33 270.38 216.27 162.19 108.18 54.07
Total 1,729.06 1,537.42 1,345.77 1,153.35 961.44 768.99 576.66 384.64 192.22
Table 6.  Policy 3: Percentage of Mosquitoes Killed in Endemic Regions and Optimal Number of Imported Malaria Cases.
Country 10% 20% 30% 40% 50% 60% 70% 80% 90%
Afghanistan 58.12 56.16 54.14 51.87 49.37 46.42 42.75 37.73 30.16
Cameroon 55.81 49.43 42.91 36.34 29.79 23.42 17.45 12.14 7.66
Cote D'Ivoire 25.18 22.27 19.32 16.36 13.44 10.63 8.01 5.67 3.66
Ethiopia 50.15 45.46 40.95 36.63 32.46 28.41 24.37 20.12 15.22
Ghana 140.02 123.75 107.33 91.00 75.05 59.78 45.64 32.99 21.83
Guinea 35.93 31.75 27.54 23.34 19.24 15.31 11.66 8.41 5.55
Guyana 18.25 17.43 16.60 15.74 14.81 13.78 12.55 10.98 8.71
Haiti 71.15 70.1 68.84 67.26 65.21 62.43 58.44 52.38 42.38
Honduras 20.84 20.61 20.31 19.91 19.37 18.61 17.48 15.71 12.74
India 221.1 218.65 215.49 211.32 205.58 197.49 185.47 166.69 135.18
Kenya 33.91 30.51 26.90 23.70 19.04 14.88 10.67 6.66 3.35
Liberia 82.45 74.33 65.67 56.43 46.67p> 36.51 26.19 16.28 8.01
Nigeria 198.11 183.65 169.59 155.75 142.02 128.04 113.13 96.19 74.54
Pakistan 36.80 34.62 32.47 30.30 28.07 25.72 23.10 19.92 15.63
Sierra Leone 104.75 93.06 80.98 68.66 56.19 43.87 32.13 21.61 12.96
Sudan 28.71 25.38 22.08 18.86 15.78 12.87 10.17 7.70 5.35
Uganda 56.29 51.16 45.59 39.53 33.00 26.03 18.76 11.55 5.31
Senegal 16.31 15.59 14.86 14.10 13.28 12.36 11.27 9.86 7.83
Eritrea 17.80 17.56 17.26 16.88 16.38 15.7 14.71 13.19 10.68
Gambia 19.36 17.56 15.62 13.52 11.26 8.86 6.38 3.93 1.83
Other 533.34 525.49 516.02 504.20 488.82 468.03 438.16 392.72 317.79
Total 1,824.38 1,724.52 1,620.43 1,511.7 1,394.83 1,269.15 1,128.49 962.43 746.37
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Country 10% 20% 30% 40% 50% 60% 70% 80% 90%
Afghanistan 58.12 56.16 54.14 51.87 49.37 46.42 42.75 37.73 30.16
Cameroon 55.81 49.43 42.91 36.34 29.79 23.42 17.45 12.14 7.66
Cote D'Ivoire 25.18 22.27 19.32 16.36 13.44 10.63 8.01 5.67 3.66
Ethiopia 50.15 45.46 40.95 36.63 32.46 28.41 24.37 20.12 15.22
Ghana 140.02 123.75 107.33 91.00 75.05 59.78 45.64 32.99 21.83
Guinea 35.93 31.75 27.54 23.34 19.24 15.31 11.66 8.41 5.55
Guyana 18.25 17.43 16.60 15.74 14.81 13.78 12.55 10.98 8.71
Haiti 71.15 70.1 68.84 67.26 65.21 62.43 58.44 52.38 42.38
Honduras 20.84 20.61 20.31 19.91 19.37 18.61 17.48 15.71 12.74
India 221.1 218.65 215.49 211.32 205.58 197.49 185.47 166.69 135.18
Kenya 33.91 30.51 26.90 23.70 19.04 14.88 10.67 6.66 3.35
Liberia 82.45 74.33 65.67 56.43 46.67p> 36.51 26.19 16.28 8.01
Nigeria 198.11 183.65 169.59 155.75 142.02 128.04 113.13 96.19 74.54
Pakistan 36.80 34.62 32.47 30.30 28.07 25.72 23.10 19.92 15.63
Sierra Leone 104.75 93.06 80.98 68.66 56.19 43.87 32.13 21.61 12.96
Sudan 28.71 25.38 22.08 18.86 15.78 12.87 10.17 7.70 5.35
Uganda 56.29 51.16 45.59 39.53 33.00 26.03 18.76 11.55 5.31
Senegal 16.31 15.59 14.86 14.10 13.28 12.36 11.27 9.86 7.83
Eritrea 17.80 17.56 17.26 16.88 16.38 15.7 14.71 13.19 10.68
Gambia 19.36 17.56 15.62 13.52 11.26 8.86 6.38 3.93 1.83
Other 533.34 525.49 516.02 504.20 488.82 468.03 438.16 392.72 317.79
Total 1,824.38 1,724.52 1,620.43 1,511.7 1,394.83 1,269.15 1,128.49 962.43 746.37
Table 7.  Policy 4: Percentage of Protected USA Residents Plus Percentage of Mosquitoes Killed and the Resulting Number of Imported Malaria Cases.
Country USA Protected Mosquitoes Killed Malaria cases
Afghanistan 90% 70% 4.27
Cameroon 90% 70% 1.75
Cote D'Ivoire 90% 70% 0.80
Ethiopia 90% 70% 2.44
Ghana 90% 70% 4.56
Guinea 90% 70% 1.17
Guyana 90% 70% 1.26
Haiti 90% 70% 5.85
Honduras 90% 70% 1.75
India 90% 70% 18.56
Kenya 90% 70% 1.07
Liberia 90% 70% 2.62
Nigeria 90% 70% 11.32
Pakistan 90% 70% 2.31
Sierra Leone 90% 70% 3.21
Sudan 90% 70% 1.02
Uganda 90% 70% 1.88
Senegal 90% 70% 1.13
Eritrea 90% 70% 1.47
Gambia 90% 60% 0.64
Other 90% 70% 43.85
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Country USA Protected Mosquitoes Killed Malaria cases
Afghanistan 90% 70% 4.27
Cameroon 90% 70% 1.75
Cote D'Ivoire 90% 70% 0.80
Ethiopia 90% 70% 2.44
Ghana 90% 70% 4.56
Guinea 90% 70% 1.17
Guyana 90% 70% 1.26
Haiti 90% 70% 5.85
Honduras 90% 70% 1.75
India 90% 70% 18.56
Kenya 90% 70% 1.07
Liberia 90% 70% 2.62
Nigeria 90% 70% 11.32
Pakistan 90% 70% 2.31
Sierra Leone 90% 70% 3.21
Sudan 90% 70% 1.02
Uganda 90% 70% 1.88
Senegal 90% 70% 1.13
Eritrea 90% 70% 1.47
Gambia 90% 60% 0.64
Other 90% 70% 43.85
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