Epidemiological models for the spread of anti-malarial resistance

• Type of Material: Article
• Authors: JC Koella, Laboratoire de Parasitologie Evolutive; Rustom Antia, Emory University
• Language: English
• Date: 2003
• Publisher: BioMed Central
• Publication Version: Final Published Version
• Copyright Statement: © 2003 Koella and Antia; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.
• Final Published Version (URL): http://www.malariajournal.com/content/2/1/3
• Title of Journal or Parent Work: Malaria Journal
• ISSN: 1475-2875
• Volume: 2
• Issue: 3
• Grant/Funding Information: RA acknowledges support from the NIH (R29-GM-54268 and AI 49334).
• Research Categories: Biology, Virology; Health Sciences, Epidemiology; Biology, General

Abstract

Background The spread of drug resistance is making malaria control increasingly difficult. Mathematical models for the transmission dynamics of drug sensitive and resistant strains can be a useful tool to help to understand the factors that influence the spread of drug resistance, and they can therefore help in the design of rational strategies for the control of drug resistance. Methods We present an epidemiological framework to investigate the spread of anti-malarial resistance. Several mathematical models, based on the familiar Macdonald-Ross model of malaria transmission, enable us to examine the processes and parameters that are critical in determining the spread of resistance. Results In our simplest model, resistance does not spread if the fraction of infected individuals treated is less than a threshold value; if drug treatment exceeds this threshold, resistance will eventually become fixed in the population. The threshold value is determined only by the rates of infection and the infectious periods of resistant and sensitive parasites in untreated and treated hosts, whereas the intensity of transmission has no influence on the threshold value. In more complex models, where hosts can be infected by multiple parasite strains or where treatment varies spatially, resistance is generally not fixed, but rather some level of sensitivity is often maintained in the population. Conclusions The models developed in this paper are a first step in understanding the epidemiology of anti-malarial resistance and evaluating strategies to reduce the spread of resistance. However, specific recommendations for the management of resistance need to wait until we have more data on the critical parameters underlying the spread of resistance: drug use, spatial variability of treatment and parasite migration among areas, and perhaps most importantly, cost of resistance.