Modeling TB and HIV co-infections.

Tuberculosis (TB) is the leading cause of death among individuals infected with the human immunodeficiency virus (HIV). The study of the joint dynamics of HIV and TB present formidable mathematical challenges due to the fact that the models of transmission are quite distinct. Furthermore, although there is overlap in the populations at risk of HIV and TB infections, the magnitude of the proportion of individuals at risk for both diseases is not known. Here, we consider a highly simplified deterministic model that incorporates the joint dynamics of TB and HIV, a model that is quite hard to analyze. We compute independent reproductive numbers for TB (R1) and HIV (R2) and the overall reproductive number for the system, R=max{R1, R2}. The focus is naturally (given the highly simplified nature of the framework) on the qualitative analysis of this model. We find that if R < 1 then the disease-free equilibrium is locally asymptotically stable. The TB-only equilibrium ET is locally asymptotically stable if R1 < 1 and R2 < 1. However, the symmetric condition, R1 < 1 and R2 > 1, does not necessarily guarantee the stability of the HIV-only equilibrium EH, and it is possible that TB can coexist with HIV when R2 > 1. In other words, in the case when R1 < 1 and R2 > 1 (or when R1 > 1 and R2 > 1), we are able to find a stable HIV/TB coexistence equilibrium. Moreover, we show that the prevalence level of TB increases with R2 > 1 under certain conditions. Through simulations, we find that i) the increased progression rate from latent to active TB in co-infected individuals may play a significant role in the rising prevalence of TB; and ii) the increased progression rates from HIV to AIDS have not only increased the prevalence level of HIV while decreasing TB prevalence, but also generated damped oscillations in the system.

[1]  S. Blower,et al.  Amplification Dynamics: Predicting the Effect of HIV on Tuberculosis Outbreaks , 2001, Journal of acquired immune deficiency syndromes.

[2]  B. Bloom,et al.  Tuberculosis Pathogenesis, Protection, and Control , 1994 .

[3]  D. Kirschner,et al.  Dynamics of co-infection with M. Tuberculosis and HIV-1. , 1999, Theoretical population biology.

[4]  Elizabeth C. Theil,et al.  Epochal Evolution Shapes the Phylodynamics of Interpandemic Influenza A (H3N2) in Humans , 2006, Science.

[5]  Carlos Castillo-Chavez,et al.  On the Role of Variable Latent Periods in Mathematical Models for Tuberculosis , 2001 .

[6]  Rodney Carlos Bassanezi,et al.  An Approach to Estimating the Transmission Coefficients for AIDS and for Tuberculosis Using Mathematical Models , 2003 .

[7]  Carlos Castillo-Chavez,et al.  Markers of disease evolution: the case of tuberculosis. , 2002, Journal of theoretical biology.

[8]  O. Diekmann,et al.  On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations , 1990, Journal of mathematical biology.

[9]  H. Thieme,et al.  Recurrent outbreaks of childhood diseases revisited: the impact of isolation. , 1995, Mathematical biosciences.

[10]  R. May,et al.  The transmission dynamics of human immunodeficiency virus (HIV). , 1988, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[11]  Carlos Castillo-Chavez,et al.  MATHEMATICAL MODELS FOR THE DISEASE DYNAMICS OF TUBERCULOSIS , 1996 .

[12]  H. Hethcote,et al.  Modeling HIV Transmission and AIDS in the United States , 1992 .

[13]  J. Broadhead,et al.  WHO consensus statement. , 1990, The British journal of psychiatry : the journal of mental science.

[14]  Carlos Castillo-Chavez,et al.  Dynamical models of tuberculosis and their applications. , 2004, Mathematical biosciences and engineering : MBE.

[15]  S. Blower,et al.  Quantifying the intrinsic transmission dynamics of tuberculosis. , 1998, Theoretical population biology.

[16]  S. Blower,et al.  Control Strategies for Tuberculosis Epidemics: New Models for Old Problems , 1996, Science.

[17]  C. Castillo-Chavez,et al.  Global stability of an age-structure model for TB and its applications to optimal vaccination strategies. , 1998, Mathematical biosciences.

[18]  C. Castillo-Chavez,et al.  To treat or not to treat: the case of tuberculosis , 1997, Journal of mathematical biology.

[19]  J. R. Thompson,et al.  Modeling the impact of HIV on the spread of tuberculosis in the United States. , 1997, Mathematical biosciences.

[20]  C. Dye,et al.  Consensus statement. Global burden of tuberculosis: estimated incidence, prevalence, and mortality by country. WHO Global Surveillance and Monitoring Project. , 1999, JAMA.

[21]  K Dietz,et al.  The basic reproduction ratio for sexually transmitted diseases: I. Theoretical considerations. , 1991, Mathematical biosciences.

[22]  Carlos Castillo-Chavez,et al.  A Model for Tuberculosis w Exogenous Reinfection , 2000 .

[23]  Agraj Tripathi,et al.  Modelling and analysis of HIV‐TB co‐infection in a variable size population , 2010 .

[24]  R. Schinazi Can HIV invade a population which is already sick? , 2003 .

[25]  C. Castillo-Chavez Review of recent models of HIV/AIDS transmission , 1989 .

[26]  D. Maher,et al.  How human immunodeficiency virus voluntary testing can contribute to tuberculosis control. , 2002, Bulletin of the World Health Organization.

[27]  Zhilan Feng,et al.  Homoclinic Bifurcation in an SIQR Model for Childhood Diseases , 2000 .

[28]  H. Hethcote,et al.  Effects of quarantine in six endemic models for infectious diseases. , 2002, Mathematical biosciences.

[29]  Carlos Castillo-Chavez,et al.  How May Infection-Age-Dependent Infectivity Affect the Dynamics of HIV/AIDS? , 1993, SIAM J. Appl. Math..

[30]  M Schulzer,et al.  A mathematical model for the prediction of the impact of HIV infection on tuberculosis. , 1994, International journal of epidemiology.