Evidence map›Paper›PMID 28279459›Full record

ArticleEpidemics2017

Comparison and validation of two computational models of Chagas disease: A thirty year perspective from Venezuela.

Sarah M Bartsch, Jennifer K Peterson, Daniel L Hertenstein, Laura Skrip, Martial Ndeffo-Mbah, Alison P Galvani, Andrew P Dobson, Bruce Y Lee

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Epidemics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–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

10 citing papers in PubMed.

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  9. Counting Down the 2020 Goals for 9 Neglected Tropical Diseases: What Have We Learned From Quantitative Analysis and Transmission Modeling?Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2018
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Sarah M BartschPublic Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, United States; Global Obesity Prevention Center, Johns Hopkins University, United States.
Jennifer K PetersonDepartment of Ecology and Evolutionary Biology, Princeton University, United States.
Daniel L HertensteinPublic Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, United States; Global Obesity Prevention Center, Johns Hopkins University, United States.
Laura SkripCenter for Infectious Disease Modeling and Analysis (CIDMA), Yale School of Public Health, United States.
Martial Ndeffo-MbahCenter for Infectious Disease Modeling and Analysis (CIDMA), Yale School of Public Health, United States.
Alison P GalvaniCenter for Infectious Disease Modeling and Analysis (CIDMA), Yale School of Public Health, United States.
Andrew P DobsonDepartment of Ecology and Evolutionary Biology, Princeton University, United States.
Bruce Y LeePublic Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, United States; Global Obesity Prevention Center, Johns Hopkins University, United States. Electronic address: brucelee@jhu.edu.

Funding

MULTILEVEL SYSTEMS-ORIENTED CHILDHOOD OBESITY STUDY IN CHINAU54HD070725 · NICHD · JOHNS HOPKINS UNIVERSITY · PI CURRIERO, FRANK · 2011 to 2016
$15.9M
Early Life Determinants of Obesity in U.S. Urban Low Income Minority Birth CohortR01HD086013 · NICHD · JOHNS HOPKINS UNIVERSITY · PI LEE, BRUCE Y, WANG, XIAOBIN · 2016 to 2020
$3.3M
Virtual Population Obesity Prevention (VPOP) Labs: Computational, Multi-Scale Models for Obesity SolutionsU01HD086861 · NICHD · JOHNS HOPKINS UNIVERSITY · PI LEE, BRUCE Y · 2015 to 2019
$2.8M
NICHD NIH HHS R01 HD086013NICHD NIH HHS U01 HD086861NICHD NIH HHS U54 HD070725
6 · The paper itself

Abstract

backgroundMathematical models can help aid public health responses to Chagas disease. Models are typically developed to fulfill a particular need, and comparing outputs from different models addressing the same question can help identify the strengths and weaknesses of the models in answering particular questions, such as those for achieving the 2020 goals for Chagas disease.

methodsUsing two separately developed models (PHICOR/CIDMA model and Princeton model), we simulated dynamics for domestic transmission of Trypanosoma cruzi (T. cruzi). We compared how well the models targeted the last 9 years and last 19 years of the 1968-1998 historical seroprevalence data from Venezuela.

resultsBoth models were able to generate the T. cruzi seroprevalence for the next time period within reason to the historical data. The PHICOR/CIDMA model estimates of the total population seroprevalence more closely followed the trends seen in the historic data, while the Princeton model estimates of the age-specific seroprevalence more closely followed historic trends when simulating over 9 years. Additionally, results from both models overestimated T. cruzi seroprevalence among younger age groups, while underestimating the seroprevalence of T. cruzi in older age groups.

conclusionThe PHICOR/CIDMA and Princeton models differ in level of detail and included features, yet both were able to generate the historical changes in T. cruzi seroprevalence in Venezuela over 9 and 19-year time periods. Our model comparison has demonstrated that different model structures can be useful in evaluating disease transmission dynamics and intervention strategies.

Indexed as

Models, TheoreticalChagas DiseaseHumansReproducibility of ResultsSeroepidemiologic StudiesTrypanosoma cruziVenezuelaChagas diseaseModelModel comparisonSimulationTrypanosoma cruzi

Identifiers

PMID28279459
PMCPMC5549789

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.