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MATHEMATICAL MODELS are effective tools that facilitate our understanding of sexually transmitted disease (STD) transmission dynamics.1 Combined with other tools such as disease and behavioral surveillance, population, and clinical epidemiologic studies, mathematical models advance the interpretation of observed epidemiologic trends, highlight the existing information gaps, and in general, facilitate efforts to control spread of infection in populations. The mathematical model was introduced to the STD field as a technical tool in the early to mid 1980s.2 The introduction of the conceptual/theoretical model of the transmission dynamics of sexually transmitted infections at the beginning of the 1990s3,4 rendered mathematical models more accessible to many people and had a major impact on the field. Since then, the conceptual model based on the basic reproductive number of an infection, Ro, which represents the average number of secondary cases generated by one primary case in a defined population of sexually active individuals,1 has stimulated a large body of research and has provided a framework for the organization of information on STD epidemiology. According to the model, three component parameters determine the magnitude of Ro: the average probability of transmission per partner sexual contact (β), the average duration of infectiousness of an infected person (D), and the average number of sexual partnerships formed per unit of time (C). Thus, EQUATION. Starting with the simple case represented above, the mathematical modeling of STDs has advanced rapidly in the past two decades through “sequential refinements associated with recorded heterogeneities in the component parameters that determine the magnitude of Ro.”1 Important advances in the mathematical modeling of STD transmission dynamics and in the more refined definition of component parameters have stimulated empirical research that has enhanced our understanding of transmission probability and duration of infectiousness for specific sexually transmitted infections, and partnership formation and dissolution patterns in different societies. Descriptions of factors that influence the epidemiology of STDs have been developed, particularly in the area of sex partnerships; examples include recent work on sexual networks and their role in the spread of specific STDs,5 descriptions of concurrent partnerships6 and partner selection processes,7 and patterns of mixing across sexual activity classes8 and racial-ethnic subpopulations.9 Mathematical models have also been used to explore the potential impact of proposed interventions.2,10–12 Theoretical models of the transmission dynamics of sexually transmitted pathogens help in both the design of intervention studies and the interpretation of findings from such studies. Prevention and control strategies may be classified on the basis of the component parameter they are intended to impact. For example, condom use is aimed at reducing the probability of transmission, early diagnosis and treatment is aimed at reducing duration of infectiousness, and monogamy is aimed at reducing the number of sexual partnerships. Mathematical models also facilitate programmatic decisions related to the targeting of interventions; in general, interventions targeting core groups have a disproportionately greater effect on the rate of spread of STDs in a population compared with interventions targeting other subgroups. Finally, theoretical models of STD transmission dynamics are helpful in assessing potential costs and benefits of interventions and intervention packages and in providing comparative estimates across intervention options. This issue of Sexually Transmitted Diseases includes a compilation of articles based on presentations made at the “Advances in Mathematical Modeling of Sexually Transmitted Diseases” conference, which was held in Santa Fe, New Mexico on April 26-29, 1999. This conference, co-sponsored by the Division of STD Prevention, Centers for Disease Control and Prevention, and the American Sexually Transmitted Disease Association was the first meeting to bring together researchers working on the mathematical modeling of STDs with epidemiologists, behavioral scientists, and health services researchers working in the field of STDs. The articles cover a wide spectrum of subjects and accurately reflect the types of ongoing modeling work. In the overview article by Anderson, future directions for mathematical and epidemiologic research in STDs are discussed. The articles on sexual partnerships and sexual networks focus on methodological issues, discuss the role of sexual networks in determining risks of acquiring and transmitting STDs, explore the impact of changing numbers of sex partners on the rate of spread of HIV, and describe the effects of membership in core groups on STD risk. The articles on bacterial STD focus on the implications of a “cure” for the transmission dynamics of STD and describe the spread of curable STDs in the context of sex partner networks. The influence of mathematical modeling of HIV/AIDS on policies and programs in the developing world is discussed in the articles on viral STDs. This issue also includes articles relevant to intervention research, focusing on the role of models in the evaluation of network statistics and surveillance procedures, and the implications of sexual network structure for STD interventions. Finally, the articles on the cost-effectiveness of antiviral therapy in the care of HIV-infected patients, and the effectiveness of screening and treatment in the prevention of chlamydial infections and pelvic inflammatory disease provide examples of economic modeling in the STD field. The articles in this issue reflect accomplishments in the mathematical modeling of STD transmission dynamics as we enter a new millennium. Advances in this type of work have been remarkably rapid over the past two decades. The theoretical frameworks that have been developed “provide an increasingly robust template for interpretation and prediction”1; nevertheless, we would expect the expansion of the field to continue in the near future. Evolution and spread of drug-resistant bacterial and viral STDs pose a major challenge to prevention and control efforts. There have been few attempts to model the spread of drug-resistant sexually transmitted pathogens, and the potential impact of alternative control strategies on such spread. Future work focusing on models of drug-resistant infections may prove very helpful to STD programs. Recent work on strain structures in infectious agent populations13 may be of great significance to STD epidemiology,1 and may usher in a new level of understanding. It is important to maintain a multidisciplinary interest in the mathematical modeling of STD transmission dynamics, so that empirical and theoretical work may continue to mutually stimulate further advances in both.
Aral et al. (Wed,) studied this question.