Transmission and Dynamics of Tuberculosis on Generalized Households

Aparicio, Juan Pablo; Capurro, Ángel F.; Castillo‐Chavez, Carlos

doi:10.1006/jtbi.2000.2129

Cited by 82 publications

(55 citation statements)

References 21 publications

(28 reference statements)

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“…The observation that cluster size will be reduced in partially immune populations is entirely consistent with the well known nonlinear dynamics of communicable diseases. Despite the considerable recent focus on the dynamics of TB transmission (39)(40)(41)(42)(43)(44)46), few studies have explicitly extended these principles to understanding and interpreting data from molecular epidemiologic investigations of TB. Here, the use of an individual-based approach to modeling TB transmission allows us to generate the cluster distributions that comprise the empirical data from such studies and to observe the impact of transmission dynamics on the structure of this data directly.…”

Section: Discussionmentioning

confidence: 99%

“…The wide range of cluster sizes found within simulations reflects variable levels of risk among different individuals as well as the social structure and mixing included in the model. Much recent analytical work has explored the impact of heterogeneous mixing and infectivity on estimates of R 0 for infectious diseases (46,48,49); this work explores the implications of such heterogeneity for the molecular epidemiology of TB.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

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Determinants of cluster distribution in the molecular epidemiology of tuberculosis

Murray

2002

Proc. Natl. Acad. Sci. U.S.A.

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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...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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Determinants of cluster distribution in the molecular epidemiology of tuberculosis

Murray

2002

Proc. Natl. Acad. Sci. U.S.A.

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“…В 1997-2004 годах Карлосом Кастильо-Чавезом (Carlos Castillo-Chavez) и при его участии было опубликовано большое количество работ [49,50,51,52,53,37,54,55,56,57,58,59,60,61,62], посвященных математическим моделям распространения и контроля туберкулеза, а так же анализу их свойств. В подавляющем большинстве ста-тей этого цикла наибольшее внимание уделяется анализу положений равновесия си-стем уравнений, задающих динамику моделей, а также условиям их устойчивости.…”

Section: аналитические исследования моделей эпеидемиологии туберкулезаunclassified

“…С другой стороны, существуют подтверждения возможности заразиться тубер-кулезной инфекцией и при сравнительно непродолжительном контакте. В статье Жуана П. Апарицио (Juan P. Aparicio) с соавторами [53] произведена оценка вкладов этих двух механизмов. В более поздней работе [56] произведен анализ математической модели, построенной в [53].…”

Section: концепция "обобщенных семей" для описания структуры контактоunclassified

Mathematical Models of Tuberculosis Extension and Control of it

Avilov¹

2007

Math.Biol.Bioinf.

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Институт вычислительной математики РАН, Россия, Москва, 119991, ул.Губкина, д.8 Аннотация. В обзоре рассмотрены математические модели эпидемиологиче-ских процессов, определяющих динамику заболеваемости туберкулезом. Пер-вые модели эпидемиологии туберкулеза были разработаны и опубликованы в начале 60-х годов прошлого века. В этих и последующих работах 70-х годов сформулированы и описаны особенности эпидемиологии туберкулеза: (нали-чие длительной латентной фазы инфекции, очень низкая вероятность полно-го освобождения от инфекции, возможность быстрого развития заболевания после инфицирования и зависимость вероятности активации инфекции от со-стояния носителя и длительности латентного периода). Важнейшей областью приложений математических моделей эпидемиологии туберкулеза является оценка эффективности стратегий борьбы с этим заболеванием. Новая волна интереса к математическим моделям распространения туберкулеза связана с ростом заболеваемости в развивающихся странах из-за эпидемией ВИЧ и по-явлению штаммов микобактерий устойчивых к одному или нескольким препа-ратам. Модели 80-х и 90-х годов посвящены взаимодействию инфекции ВИЧ и микобактерий, формированию и распространению лекарственно устойчи-вых штаммов; большое внимание уделяется исследованию свойств моделей, оценке параметров, сравнению с реальными данными. В этот период моде-ли становятся важным средством для выработки и обоснования деятельности как национальных, так и международных организаций, отвечающих за борьбу с этой инфекцией. Для удобства в обзоре используется единая система обо-значений переменных и параметров, приводятся блок-схемы моделируемых процессов, оценки значений параметров, обсуждаются предположения и до-пущения, использованные при построении моделей. Работа является первым полным обзором этого класса моделей до 2006 года.

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Active case finding for increasing case detection of tuberculosis

Fox

Marks

2010

Cochrane Database of Systematic Reviews

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Transmission and Dynamics of Tuberculosis on Generalized Households

Cited by 82 publications

References 21 publications

Determinants of cluster distribution in the molecular epidemiology of tuberculosis

Determinants of cluster distribution in the molecular epidemiology of tuberculosis

Mathematical Models of Tuberculosis Extension and Control of it

Active case finding for increasing case detection of tuberculosis

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