Mathematical modelling is commonly used to evaluate infectious disease control policy and is influential in shaping policy and budgets. Mathematical models necessarily make assumptions about disease natural history and, if these assumptions are not valid, the results of these studies can be biased. We did a systematic review of published tuberculosis transmission models to assess the validity of assumptions about progression to active disease after initial infection (PROSPERO ID CRD42016030009). We searched PubMed, Web of Science, Embase, Biosis, and Cochrane Library, and included studies from the earliest available date (Jan 1, 1962) to Aug 31, 2017. We identified 312 studies that met inclusion criteria. Predicted tuberculosis incidence varied widely across studies for each risk factor investigated. For population groups with no individual risk factors, annual incidence varied by several orders of magnitude, and 20-year cumulative incidence ranged from close to 0% to 100%. A substantial proportion of modelled results were inconsistent with empirical evidence: for 10-year cumulative incidence, 40% of modelled results were more than double or less than half the empirical estimates. These results demonstrate substantial disagreement between modelling studies on a central feature of tuberculosis natural history. Greater attention to reproducing known features of epidemiology would strengthen future tuberculosis modelling studies, and readers of modelling studies are recommended to assess how well those studies demonstrate their validity.

We propose a class of mathematical models for the transmission of infectious diseases in large populations. This class of models, which generalizes the existing discrete-time Markov chain models of infectious diseases, is compatible with efficient dynamic optimization techniques to assist real-time selection and modification of public health interventions in response to evolving epidemiological situations and changing availability of information and medical resources. While retaining the strength of existing classes of mathematical models in their ability to represent the within-host natural history of disease and between-host transmission dynamics, the proposed models possess two advantages over previous models: (1) these models can be used to generate optimal dynamic health policies for controlling spreads of infectious diseases, and (2) these models are able to approximate the spread of the disease in relatively large populations with a limited state space size and computation time.

We estimated long-term tuberculosis (TB) trends in the US population and assessed prospects for TB elimination. We used a detailed simulation model allowing for changes in TB transmission, immigration, and other TB risk determinants. Five hypothetical scenarios were evaluated from 2017 to 2100: 1) maintain current TB prevention and treatment activities (base case); 2) provision of latent TB infection testing and treatment for new legal immigrants; 3) increased uptake of latent TB infection screening and treatment among high-risk populations, including a 3-month isoniazid-rifapentine regimen; 4) improved TB case detection; and 5) improved TB treatment quality. Under the base case, we estimate that by 2050, TB incidence will decline to 14 cases per million, a 52% (95% posterior interval (PI): 35, 67) reduction from 2016, and 82% (95% posterior interval: 78, 86) of incident TB will be among persons born outside of the United States. Intensified TB control could reduce incidence by 77% (95% posterior interval: 66, 85) by 2050. We predict TB may be eliminated in US-born but not non-US-born persons by 2100. Results were sensitive to numbers of people entering the United States with latent or active TB, and were robust to alternative interpretations of epidemiologic evidence. TB elimination in the United States remains a distant goal; however, strengthening TB prevention and treatment could produce important health benefits.

The global tuberculosis (TB) control plan has historically emphasized passive case finding (PCF) as the most practical approach for identifying TB suspects in high burden settings. The success of this approach in controlling TB depends on infectious individuals recognizing their symptoms and voluntarily seeking diagnosis rapidly enough to reduce onward transmission. It now appears, at least in some settings, that more intensified case-finding (ICF) approaches may be needed to control TB transmission; these more aggressive approaches for detecting as-yet undiagnosed cases obviously require additional resources to implement. Given that TB control programs are resource constrained and that the incremental yield of ICF is expected to wane over time as the pool of undiagnosed cases is depleted, a tool that can help policymakers to identify when to implement or suspend an ICF intervention would be valuable. In this article, we propose dynamic case-finding policies that allow policymakers to use existing observations about the epidemic and resource availability to determine when to switch between PCF and ICF to efficiently use resources to optimize population health. Using mathematical models of TB/HIV coepidemics, we show that dynamic policies strictly dominate static policies that prespecify a frequency and duration of rounds of ICF. We also find that the use of a diagnostic tool with better sensitivity for detecting smear-negative cases (e.g., Xpert MTB/RIF) further improves the incremental benefit of these dynamic case-finding policies. (1), emphasizes passive case finding (PCF) as a central tactic for identifying infectious cases requiring treatment. PCF approaches depend on individuals with symptomatic TB to seek out treatment on their own, a practice that is supported by studies indicating that the most infectious patients are aware of their symptoms and seek care (2, 3). Adoption of PCF strategies has been motivated by practical considerations as well. In most high TB incidence settings, resources are limited and PCF allows diagnostic efforts to be focused within existing health facilities and concentrated among suspects at highest risk of TB.The DOTS strategy has significantly improved treatment success rates for individual patients (4) and, where studies have been attempted, has been associated with reduced TB-related mortality in populations (5, 6). Despite clear successes of DOTS programs, there are inherent shortcomings of PCF since this approach may result in either delayed or missed opportunities for diagnosis. These limitations may be especially important in settings where HIV has emerged and triggered large and rapid increases in TB incidence (7).There are many different types of interventions that could be used to increase the vigorousness of TB case detection efforts beyond PCF; in this article, we broadly refer to these alternative approaches as intensified case finding (ICF). ICF approaches are often subclassified as either "enhanced" or "active" case finding and are differentiated by whether emph...