Current research
Globally HIV is expanding. For every two individuals placed on anti-retrovirals (ARV) treatment, five new people acquire the disease (CDC). The author of this web site has designed several models to investigate the effect of testing rates, Condom usage, ARV efficacy, and treatment availability on HIV progression within the U.S. population. Models are created because mathematics are the only way to predict the future. The authors models are based upon a modification of the standard SI (susceptible-infected) model:
dS/dt = rS(t) - u1S(t) - BS(t)I(t)(1-AE)
dI/dt = BS(t)I(t)(1-AE) - u2I(t) - pI(t)
dIt/dt = pI(t) - u3It(t)
where dS/dt = total susceptible at time t, dI/dt = total infected at time t,
dIt/dt = total infected-treated at time t, r= population source rate, u1,u2,u3=
mortality rates, B=infectivity rate, (1-AE)= mitigation term where A=
treatment availability/usage, E=efficacy of treatment, p= testing rate
The above coupled system of equations was the authors first model. His latest model (HIVSM1) consists of 90 coupled equations, where the model considers that HIV has 4 stages of disease progression. The population is separated into 5 age-structured groups of monogamous and polygamous individuals. Age dependent mortality and coital act rates are used, and infidelity may also be investigated.
Results
The HIVSM1 model predicts that there are currently 24 million infected in the U.S.
The predicted prevalence for 2009 is 16.2%. The model predicts that HIV will peak in 2016 at 17.7%
In simple models, the mathematics reveal that testing and treatment rates are the most effective mitigation force. There is good evidence to support why this should be so; Quinn et al, reported a dose-response between a patients blood plasma viral load (BPVL) and seroconversion in their corresponding partners. Dr Quinns study of discordant heterosexual couples in Uganda found that infected individuals with low viral loads were highly unlikely to transmit HIV to their partners. No transmissions were found in individuals with HIV RNA levels < 1,500 copies/ml, and for every 10-fold increase in viral load there was a >2 fold risk of transmission. Since ARVs can reduce plasma HIV RNA to below detectable levels, it follows that the treated individual will be much less infectious.
Unfortunately simply increasing testing/treatment rates may not reduce HIV prevalence, and may in fact greatly increase it. McCormick et al, has reported that the potential for reducing secondary transmission may be offset by the fact that ARVs extend the duration of infectiousness. The author of this website has observed this in his latest model:
