Modeling secondary level of HIV contact tracing: its impact on HIV intervention in Cuba

Abstract

Background: Universal HIV testing/treatment program has currently been suggested and debated as a useful strategy for elimination of HIV epidemic in Africa, although not without practical issues regarding the costs and feasibility of a fully implemented program.

Methods: A mathematical model is proposed which considers two levels of detection of HIV-infectives through contact tracing of known infectives in addition to detections through other means such as random screening. Simulations based on Cuban contact tracing data were performed to ascertain the potential impact of the different levels of contact tracing.

Results: Simulation studies illustrate that: (1) contact tracing is an important intervention measure which, while less effective than random screening, is perhaps less costly and hence ideal for large-scale intervention programs in developing countries with less resources; (2) the secondary level of contact tracing could significantly change the basic disease transmission dynamics, depending on the parameter values; (3) the prevalence of the epidemic at the time of implementation of contact tracing program might be a crucial factor in determining whether the measure will be effective in preventing disease infections and its eventual eradication.

Conclusions: Our results indicate that contact tracing for detection of HIV infectives could be suitably used to remedy inadequacies in a universal HIV testing program when designing timely and effective intervention measures.

Figure 1

Histogram of the numbers of contacts declared by the 4091 HIV-infected persons in the 1986-2001 Cuban contact tracing data. X-axis is the number of contacts and y-axis is the number of detected HIV-positive individuals with the given number of contacts.

Figure 2

Distribution of number of detections for levels 1-6 of contact tracing in the 1986-2001 Cuban contact tracing data.

Figure 3

Model flow diagram. The mortality rates of all compartments are omitted for sake of brevity.

Figure 4

Simulation of the case (k₁, k₂, k₃) = (0.25,0.5,0.25) for rows 3 and 4 of Table 1, where the dotted blue trajectory approaches the endemic steady state E* while solid green trajectory approaches DFE E_B3.

Figure 5

Simulation for the cases of row 1 (solid blue trajectory approaching E*) and row 2 (dash green trajectory approaching E_B3 ) in Table 1, and row 2 (dotted red trajectory approaching E_B1) in Table 2, where initial fractions for all three trajectories are (x(0), y₂(0), y₃(0)) = (0.3,0.17,0.04).