Evidence map›Paper›PMID 29226167›Full record

ArticleOpen forum infectious diseases2017

Mathematical Modeling of "Chronic" Infectious Diseases: Unpacking the Black Box.

Anthony T Fojo, Emily A Kendall, Parastu Kasaie, Sourya Shrestha, Thomas A Louis, David W Dowdy

Erratum issuedAbstract read
In one paragraph

Article in Open forum infectious diseases, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. The Potential Effect of Ending Centers for Disease Control and Prevention Funding for HIV Tests: A Modeling Study in 18 States.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026
    Article
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  8. Potential Effects of the Coronavirus Disease 2019 (COVID-19) Pandemic on Human Immunodeficiency Virus (HIV) Transmission: A Modeling Study in 32 US Cities.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022
    Article
  9. Article
  10. Article
  11. Article
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  14. Article
  15. Improving the quality of modelling evidence used for tuberculosis policy evaluation.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2019
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Anthony T FojoDivision of General Internal Medicine.
Emily A KendallDivision of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Parastu KasaieDepartment of Epidemiology.
Sourya ShresthaDepartment of Epidemiology.
Thomas A LouisDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
David W DowdyDepartment of Epidemiology.

Funding

Translational modeling of individual- and population-level outcomes of novel tuberculosis drug regimensK08AI127908 · NIAID · JOHNS HOPKINS UNIVERSITY · PI KENDALL, EMILY A · 2017 to 2021
$879k
Projecting the Epidemiologic and Economic Impact of HIV Prevention Efforts in the Context of Other Sexually Transmitted InfectionsK01AI138853 · NIAID · JOHNS HOPKINS UNIVERSITY · PI KASAIE, PARASTU · 2019 to 2023
$591k
NIAID NIH HHS K01 AI138853NIAID NIH HHS K08 AI127908
6 · The paper itself

Abstract

backgroundMathematical models are increasingly used to understand the dynamics of infectious diseases, including "chronic" infections with long generation times. Such models include features that are obscure to most clinicians and decision-makers.

methodsUsing a model of a hypothetical active case-finding intervention for tuberculosis in India as an example, we illustrate the effects on model results of different choices for model structure, input parameters, and calibration process.

resultsUsing the same underlying data, different transmission models produced different estimates of the projected intervention impact on tuberculosis incidence by 2030 with different corresponding uncertainty ranges. We illustrate the reasons for these differences and present a simple guide for clinicians and decision-makers to evaluate models of infectious diseases.

conclusionsMathematical models of chronic infectious diseases must be understood to properly inform policy decisions. Improved communication between modelers and consumers is critical if model results are to improve the health of populations.

Indexed as

Bayesian analysishepatitis CHIVtheoretical modelstuberculosis

Identifiers

PMID29226167
PMCPMC5716064

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.