Recently developed molecular techniques have revolutionized the epidemiology of tuberculosis. Multiple studies have used these tools to examine the population structure of Mycobacterium tuberculosis isolates in different communities. The distributions of clusters of M. tuberculosis isolates in these settings may variously reflect social mixing patterns or the differential fitness of specific clones of the organism. We developed an individual-based microsimulation of tuberculosis transmission to explore social and demographic determinants of cluster distribution and to observe the effect of transmission dynamics on the empiric data from molecular epidemiologic studies. Our results demonstrate that multiple hostrelated factors contribute to wide variation in cluster distributions even when all strains of the organism are assumed to be equally transmissible. These host characteristics include interventions such as chemotherapy, vaccination and chemoprophylaxis, HIV prevalence, the age structure of the population, and the prevalence of latent tuberculosis infection. We consider the implications of these results for the interpretation of cluster studies of M. tuberculosis as well as the more general application of microsimulation models to infectious disease epidemiology. O ver the past 10 years, molecular tools have become available that have changed the way that epidemiologists study the transmission of infectious disease (1). In addition to their role in detecting unsuspected transmission links (2-4), molecular markers are increasingly being used to study transmission patterns within populations and to evaluate host-and strain-specific risk factors for disease spread (5, 6). Nowhere has this approach been used more rigorously than in the pioneering work on the molecular epidemiology of tuberculosis (TB). Since the development of standardized methods for DNA fingerprinting of Mycobacterium tuberculosis, molecular techniques have been used to estimate the fraction of cases attributable to recent transmission of M. tuberculosis (7-14), identify host-specific risk factors for disease spread (15-18), document exogenous reinfection (19-21), and study patterns of drug resistance (22) (23) (24) . Investigators also have begun to use these methods to explore potential strain-specific differences in bacterial phenotypes such as tissue tropism, virulence, and transmissibility (25, 26) . This research has shown that the genetic diversity of M. tuberculosis isolates from different human communities can vary considerably (27, 28) . Clusters of identical isolates are assumed to share DNA fingerprints as a result of the spread of the organism among the human hosts who harbor the isolates in the cluster. Patients with TB whose isolates cannot be grouped into clusters, i.e., those with unique DNA fingerprints, are assumed to have disease that results from the reactivation of latent infection acquired in the past. Variation in the distribution of clusters of M. tuberculosis isolates in different communities is thought to reflect different TB transmission dynamics and intensities in diverse parts of the world. A high proportion of clustered isolates in a community suggests ongoing TB transmission, whereas a predominance of unique cases implies that most TB cases are caused by reactivation of remote infection without further spread (29, 30). The distribution of genotypes within bacterial populations also may reflect differences in the selective advantage of specific clones, especially those that are more transmissible or ''fit'' than their counterparts (31, 32) . Clones that are especially transmissible would be expected to infect more human hosts per contact and therefore may belong to larger clusters than lesstransmissible clones. Multiple molecular studies of Neisseria meningitides, for example, have used this approach to characterize pandemic strains that were shown to be genetically distinct from the strains isolated in sporadic cases of meningitis (33). Several recent epidemiologic studies of TB have focused on large clusters of cases caused by a single genotype or strain of M. tuberculosis (34) (35) (36) (37) (38) . In some cases, these outbreaks were attributed to an increased capacity for transmission and͞or replication by the specific strain (37). Other studies described highly mobile or complex social networks that may have facilitated the prolific spread of the organism (34) (35) (36) 38) . In practice, it can be difficult to distinguish between the effects of variability in strain behavior and the often complex transmission dynamics of TB in the analysis of differential cluster size. One way to address this problem is to consider the range of cluster sizes that might be expected in the absence of strainspecific variation in transmissibility, i.e., to establish an expectation of cluster distributions consistent with the null hypothesis that all strains are equally transmissible. Over the past decade, mathematical models of the transmission dynamics of TB have been developed that have helped elucidate the factors that impact TB epidemics (39) (40) (41) (42) (43) (44) . Several recent studies have begun to use some of the concepts developed in these models to examine the relationship between model parameters such as the basic reproductive rate and cluster size (45, 46) . Although this research has important implications for interpreting the results of molecular epidemiology, few studies have explicitly examined the impact of the social and demographic variables that affect TB dynamics on the population structure of mycobacterial isolates. This report describes a stochastic model of TB transmission that generates cluster distributions as well as estimates of the annual incidences of TB infection and disease for a range of different input variables. Because few of the parameters relevant to TB transmission have been well characterized, the purpose of this study is not to predict distributions of cluster size in specific contexts. It is rather to explore the impact of different TB transmission dynamics on the population structure of isolates of M. tuberculosis and, more generally, to provide a link between the disciplines of infectious disease dynamics and molecular epidemiology. Simulation Model. A microsimulation model of TB transmission was used to generate dynamic transmission chains of TB. The
In an older but still relevant paper, REF used a microsimulation model to generate dynamic transmission chains of tuberculosis and examined the epidemiological and demographic determinants of the mean size of clusters and the proportion of isolates.
Determinants of cluster distribution in the molecular epidemiology of tuberculosis
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America
Medicine Biology