Treating cofactors can reverse the expansion of a primary disease epidemic

• Main Authors: Gibson Lee R, Li Bingtuan, Remold Susanna K
• Format: Article
• Language: English
• Published: BMC 2010-08-01
• Series: BMC Infectious Diseases
• Online Access: http://www.biomedcentral.com/1471-2334/10/248

<p>Abstract</p> <p>Background</p> <p>Cofactors, "nuisance" conditions or pathogens that affect the spread of a primary disease, are likely to be the norm rather than the exception in disease dynamics. Here we present a "simplest possible" demographic model that incorporates two distinct effects of cofactors: that on the transmission of the primary disease from an infected host bearing the cofactor, and that on the acquisition of the primary disease by an individual that is not infected with the primary disease but carries the cofactor.</p> <p>Methods</p> <p>We constructed and analyzed a four-patch compartment model that accommodates a cofactor. We applied the model to HIV spread in the presence of the causal agent of genital schistosomiasis, <it>Schistosoma hematobium</it>, a pathogen commonly co-occurring with HIV in sub-Saharan Africa.</p> <p>Results</p> <p>We found that cofactors can have a range of effects on primary disease dynamics, including shifting the primary disease from non-endemic to endemic, increasing the prevalence of the primary disease, and reversing demographic growth when the host population bears only the primary disease to demographic decline. We show that under parameter values based on the biology of the HIV/<it>S. haematobium </it>system, reduction of the schistosome-bearing subpopulations (e.g. through periodic use of antihelminths) can slow and even reverse the spread of HIV through the host population.</p> <p>Conclusions</p> <p>Typical single-disease models provide estimates of future conditions and guidance for direct intervention efforts relating only to the modeled primary disease. Our results suggest that, in circumstances under which a cofactor affects the disease dynamics, the most effective intervention effort might not be one focused on direct treatment of the primary disease alone. The cofactor model presented here can be used to estimate the impact of the cofactor in a particular disease/cofactor system without requiring the development of a more complicated model which incorporates many other specific aspects of the chosen disease/cofactor pair. Simulation results for the HIV/<it>S. haematobium </it>system have profound implications for disease management in developing areas, in that they provide evidence that in some cases treating cofactors may be the most successful and cost-effective way to slow the spread of primary diseases.</p>