A Novel Tool Improves Existing Estimates of Recent Tuberculosis Transmission in Settings of Sparse Data Collection

Kasaie, Parastu; Mathema, Barun; Kelton, W. David; Azman, Andrew S.; Pennington, Jeff; Dowdy, David W.

doi:10.1371/journal.pone.0144137

Cited by 9 publications

(11 citation statements)

References 45 publications

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“…Transmission chains were inferred by genetic clusters, which were defined as two or more identical genotype patterns identified in the same study location and time period [ 26 ]. Genetic clustering appears not to be a perfect measure of transmission chains since it can be impacted by various factors including social mixing, immigration, age structure, and underlying TB incidence [ 27 , 28 ]. However, we decided that it was an important measure to include because (1) it has been an important tool for TB surveillance [ 29 – 31 ] and (2) it currently has sufficient published data available for global analysis.…”

Section: Methodsmentioning

confidence: 99%

Global variation in bacterial strains that cause tuberculosis disease: a systematic review and meta-analysis

Wiens

Woyczynski

Ledesma

et al. 2018

BMC Med

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BackgroundThe host, microbial, and environmental factors that contribute to variation in tuberculosis (TB) disease are incompletely understood. Accumulating evidence suggests that one driver of geographic variation in TB disease is the local ecology of mycobacterial genotypes or strains, and there is a need for a comprehensive and systematic synthesis of these data. The objectives of this study were to (1) map the global distribution of genotypes that cause TB disease and (2) examine whether any epidemiologically relevant clinical characteristics were associated with those genotypes.MethodsWe performed a systematic review of PubMed and Scopus to create a comprehensive dataset of human TB molecular epidemiology studies that used representative sampling techniques. The methods were developed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We extracted and synthesized data from studies that reported prevalence of bacterial genotypes and from studies that reported clinical characteristics associated with those genotypes.ResultsThe results of this study are twofold. First, we identified 206 studies for inclusion in the study, representing over 200,000 bacterial isolates collected over 27 years in 85 countries. We mapped the genotypes and found that, consistent with previously published maps, Euro-American lineage 4 and East Asian lineage 2 strains are widespread, and West African lineages 5 and 6 strains are geographically restricted. Second, 30 studies also reported transmission chains and 4 reported treatment failure associated with genotypes. We performed a meta-analysis and found substantial heterogeneity across studies. However, based on the data available, we found that lineage 2 strains may be associated with increased risk of transmission chains, while lineages 5 and 6 strains may be associated with reduced risk, compared with lineage 4 strains.ConclusionsThis study provides the most comprehensive systematic analysis of the evidence for diversity in bacterial strains that cause TB disease. The results show both geographic and epidemiological differences between strains, which could inform our understanding of the global burden of TB. Our findings also highlight the challenges of collecting the clinical data required to inform TB diagnosis and treatment. We urge future national TB programs and research efforts to prioritize and reinforce clinical data collection in study designs and results dissemination.Electronic supplementary materialThe online version of this article (10.1186/s12916-018-1180-x) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Global variation in bacterial strains that cause tuberculosis disease: a systematic review and meta-analysis

Wiens

Woyczynski

Ledesma

et al. 2018

BMC Med

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“…Methodological papers, not applied to a specified close-contact infection, mostly describe the conceptual usage of an IBM to simulate heterogeneous disease dynamics and targeted intervention strategies. Other studies were published on validation procedures [ 52 , 53 ], performance issues [ 54 , 55 ] and emulation to improve rapid policy making in various settings [ 55 – 58 ]. Models and model output have been calibrated and validated with observed incidence and (sero)prevalence data [ 49 , 59 , 60 ] but also with data generated by other models, such as deterministic ordinary differential equation models [ 61 ] or meta-populations models [ 62 ].…”

Section: Resultsmentioning

confidence: 99%

“…Parameter values can be drawn from a pre-computed design (e.g., Latin Hypercube) or at random from a distribution. Emulation techniques are promising to capture complex simulators’ behavior in order to improve engaged and perhaps more rapid policy making [ 55 – 58 ]. Given the lack of standards, it is crucial to fully describe the methods and experimental design in the context of the model [ 4 ].…”

Section: Discussionmentioning

confidence: 99%

Lessons from a decade of individual-based models for infectious disease transmission: a systematic review (2006-2015)

et al. 2017

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BackgroundIndividual-based models (IBMs) are useful to simulate events subject to stochasticity and/or heterogeneity, and have become well established to model the potential (re)emergence of pathogens (e.g., pandemic influenza, bioterrorism). Individual heterogeneity at the host and pathogen level is increasingly documented to influence transmission of endemic diseases and it is well understood that the final stages of elimination strategies for vaccine-preventable childhood diseases (e.g., polio, measles) are subject to stochasticity. Even so it appears IBMs for both these phenomena are not well established. We review a decade of IBM publications aiming to obtain insights in their advantages, pitfalls and rationale for use and to make recommendations facilitating knowledge transfer within and across disciplines.MethodsWe systematically identified publications in Web of Science and PubMed from 2006-2015 based on title/abstract/keywords screening (and full-text if necessary) to retrieve topics, modeling purposes and general specifications. We extracted detailed modeling features from papers on established vaccine-preventable childhood diseases based on full-text screening.ResultsWe identified 698 papers, which applied an IBM for infectious disease transmission, and listed these in a reference database, describing their general characteristics. The diversity of disease-topics and overall publication frequency have increased over time (38 to 115 annual publications from 2006 to 2015). The inclusion of intervention strategies (8 to 52) and economic consequences (1 to 20) are increasing, to the detriment of purely theoretical explorations. Unfortunately, terminology used to describe IBMs is inconsistent and ambiguous. We retrieved 24 studies on a vaccine-preventable childhood disease (covering 7 different diseases), with publication frequency increasing from the first such study published in 2008. IBMs have been useful to explore heterogeneous between- and within-host interactions, but combined applications are still sparse. The amount of missing information on model characteristics and study design is remarkable.ConclusionsIBMs are suited to combine heterogeneous within- and between-host interactions, which offers many opportunities, especially to analyze targeted interventions for endemic infections. We advocate the exchange of (open-source) platforms and stress the need for consistent “branding”. Using (existing) conventions and reporting protocols would stimulate cross-fertilization between research groups and fields, and ultimately policy making in decades to come.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-017-2699-8) contains supplementary material, which is available to authorized users.

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“…In particular, one of the technological barriers is the speed at which model simulations of a given scenario can occur. Thus, this introduces the idea of using emulation in lieu of model computation [9][10][11][12][13], where we replace the individual-based model with a statistical model that is more computationally efficient to sample from. These statistical models can be difficult to train and often require the assumption that the model produces unimodal or normal outputs [14].…”

Section: Introductionmentioning

confidence: 99%

The use of mixture density networks in the emulation of complex epidemiological individual-based models

Davis

Hollingsworth

Caudron³

et al. 2020

PLoS Comput Biol

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Complex, highly-computational, individual-based models are abundant in epidemiology. For epidemics such as macro-parasitic diseases, detailed modelling of human behaviour and pathogen life-cycle are required in order to produce accurate results. This can often lead to models that are computationally-expensive to analyse and perform model fitting, and often require many simulation runs in order to build up sufficient statistics. Emulation can provide a more computationally-efficient output of the individual-based model, by approximating it using a statistical model. Previous work has used Gaussian processes (GPs) in order to achieve this, but these can not deal with multi-modal, heavy-tailed, or discrete distributions. Here, we introduce the concept of a mixture density network (MDN) in its application in the emulation of epidemiological models. MDNs incorporate both a mixture model and a neural network to provide a flexible tool for emulating a variety of models and outputs. We develop an MDN emulation methodology and demonstrate its use on a number of simple models incorporating both normal, gamma and beta distribution outputs. We then explore its use on the stochastic SIR model to predict the final size distribution and infection dynamics. MDNs have the potential to faithfully reproduce multiple outputs of an individual-based model and allow for rapid analysis from a range of users. As such, an open-access library of the method has been released alongside this manuscript. Author summaryInfectious disease modellers have a growing need to expose their models to a variety of stakeholders in interactive, engaging ways that allow them to explore different scenarios. This approach can come with a considerable computational cost that motivates providing a simpler representation of the complex model. We propose the use of mixture density networks as a solution to this problem. MDNs are highly flexible, deep neural networkbased models that can emulate a variety of data, including counts and over-dispersion. PLOS COMPUTATIONAL BIOLOGYPLOS Computational Biology | https://doi.org/10.1371/journal.pcbi.1006869 March 16, 2020 1 / 16 OPEN ACCESS Citation: Davis CN, Hollingsworth TD, Caudron Q, Irvine MA (2020) The use of mixture density networks in the emulation of complex epidemiological individual-based models. PLoS Comput Biol 16(3): e1006869. https://doi.org/ 10.We explore their use firstly through emulating a negative binomial distribution, which arises in many places in ecology and parasite epidemiology. Then, we explore the approach using a stochastic SIR model. We also provide an accompanying Python library with code for all examples given in the manuscript. We believe that the use of emulation will provide a method to package an infectious disease model such that it can be disseminated to the widest audience possible. This is a PLOS Computational Biology Methods paper. PLOS COMPUTATIONAL BIOLOGYMixture-density network emulation of complex epidemiological individual-based models PLOS Computational Biology | https://doi...

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A Novel Tool Improves Existing Estimates of Recent Tuberculosis Transmission in Settings of Sparse Data Collection

Cited by 9 publications

References 45 publications

Global variation in bacterial strains that cause tuberculosis disease: a systematic review and meta-analysis

Global variation in bacterial strains that cause tuberculosis disease: a systematic review and meta-analysis

Lessons from a decade of individual-based models for infectious disease transmission: a systematic review (2006-2015)

The use of mixture density networks in the emulation of complex epidemiological individual-based models

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