Biomedical subjects

Kimber L Gross

Publications and source records attributed to Kimber L Gross.

3 recordsLinked to original sources

PubMed2004

A decrease in drug resistance levels of the HIV epidemic can be bad news.

Transient decreases in the proportion of individuals newly infected with an HIV-resistant virus (primary resistance) are documented for several cities of North America, including San Francisco. Using a staged SI deterministic model, we identified three potential causes consistent with the history of the epidemic: (1) increase in risky behaviour, (2) reduction in the proportion of HIV-acutely infected individuals undergoing treatment, and (3) replacement of mono- and dual-drug therapies with triple-drug therapies. Although observed patterns resemble scenario 1 most closely, these explanations are not mutually exclusive and may have contributed synergistically to the decline. Under scenario 1 the counterintuitive situation arises where, although the proportion of primary resistance cases decreases transiently, the epidemic worsens because the actual numbers of infected individuals and of drug resistance carriers increases. Our results call for improved efforts to control the epidemic in developed nations, and highlight the usefulness of drug resistant strains as epidemiological markers.

Antiretroviral Therapy, Highly Active↗

PubMed2004

The effect of treatment on pathogen virulence.

The optimal virulence of a pathogen is determined by a trade-off between maximizing the rate of transmission and maximizing the duration of infectivity. Treatment measures such as curative therapy and case isolation exert selective pressure by reducing the duration of infectivity, reducing the value of duration-increasing strategies to the pathogen and favoring pathogen strategies that maximize the rate of transmission. We extend the trade-off models of previous authors, and represents the reproduction number of the pathogen as a function of the transmissibility, host contact rate, disease-induced mortality, recovery rate, and treatment rate, each of which may be influenced by the virulence. We find that when virulence is subject to a transmissibility-mortality trade-off, treatment can lead to an increase in optimal virulence, but that in other scenarios (such as the activity-recovery trade-off) treatment decreases the optimal virulence. Paradoxically, when levels of treatment rise with pathogen virulence, increasing control efforts may raise predicted levels of optimal virulence. Thus we show that conflict can arise between the epidemiological benefits of treatment and the evolutionary risks of heightened virulence.

Communicable Diseases↗

PubMed2004

HIV-1 superinfection and viral diversity.

OBJECTIVE: Sequential acquisition of viral variants, or HIV-1 superinfection, has been proposed to explain the high fractions of recombinant viruses observed in some geographical regions, but only a few cases of superinfection in humans have been reported. Animal models suggest that susceptibility to superinfection may be restricted to a short period of time after initial infection, possibly due to maturation of broad antiviral immune responses. METHODS: A mathematical model involving a system of differential equations was developed to identify transmission and superinfection patterns that would lead to the observed global patterns of viral diversity. RESULTS: Requirements for a high prevalence of infections involving recombinant viruses include high viral infectivity, the presence of highly sexually active core groups, and introduction of divergent viruses early in the epidemic spread of HIV-1. Restricted superinfection could explain the persistent predominance of single virus subtypes in regions with well-established HIV-1 epidemics. The rate of recombination within individuals was not strongly related to recombinant fractions in populations. CONCLUSIONS: HIV-1 superinfection restricted to early HIV-1 infection could account for the high fraction of recombinant virus infections observed in populations. The relationship between recombination in cellular infections and recombinant fractions in populations is complex and depends on epidemiological factors and biological factors that can be modeled.

Computer Simulation↗