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A Mathematical Model of HIV and Malaria Co-Infection in Sub-Saharan Africa | OMICS International | Abstract
ISSN 2155-6113

Journal of AIDS & Clinical Research
Open Access

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Research Article

A Mathematical Model of HIV and Malaria Co-Infection in Sub-Saharan Africa

Kamal Barley1, David Murillo1, Svetlana Roudenko1, Ana M. Tameru2* and SharquettaTatum3

1Department of Mathematics and Statistics, Arizona State University, Tempe, AZ 85287, USA

2Department of Mathematics and Computer Science, Alabama State University, Montgomery, AL 36101, USA

3Department of Mathematics, Alabama A & M University, Normal, AL 35762, USA

*Corresponding Author:
Ana M. Tameru
Department of Mathematics and Computer Science
Alabama State University, 915 Jackson St
Montgomery, AL 36101, USA
Tel: 334-229-6828
E-mail: [email protected]

Received Date: July 10, 2012; Accepted Date: September 04, 2012; Published Date: September 14, 2012

Citation: Barley K, Murillo D, Roudenko S, Tameru AM, Tatum S (2012) A Mathematical Model of HIV and Malaria Co-Infection in Sub-Saharan Africa. J AIDS Clinic Res 3:173. doi:10.4172/2155-6113.1000173

Copyright: © 2012 Barley K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Malaria and HIV are two of the most deadly diseases in Africa. Combined they account for 4 million deaths each year, and according to the Center for Disease Control and Prevention (CDC), there is an estimated 5 percent increase in malaria deaths in those who tested positive for HIV than those without HIV infection. Since the co-infections were recorded, malaria has seen a 28 percent increase in its incidence. These results raise the possibility that biological differences could alter the effect of co-infection and underscore the importance of identifying these factors for the implementation of control interventions focused on co-infection. Malaria associated death rates have nearly doubled for those with co-infections. The biological integrations between the malaria parasite and HIV are not fully understood, but it is conceivable that the parasite or viral load can increase by an order of magnitude due to coinfection. HIV-infected persons are at increased risk for clinical malaria; the risk is greatest when immune suppression is advanced. Malaria is associated with increases in HIV viral load that, while modest, may impact HIV progression or the risk of HIV transmission. We also showed that in the Full Model, total cause of deaths are from co-infection when the amplification factor , i ρ , i =1, 2, 3, 4, is larger than 25. We introduce a system of differential equations linking the host-vector system of malaria with co-infection with HIV. Data were collected from Sub-Saharan Africa for the global parameter estimates and we simulated for sensitivity analysis using data collected from Malawi. Finally, these simulations show that the HIV-induced increase in susceptibility to malaria infection has marginal effect on the new cases of HIV and malaria but increases the number of new cases of the dual HIV-malaria infection.


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