Evidence map›Paper›PMID 31465448›Full record

ArticlePLoS computational biology2019

Network analyses to quantify effects of host movement in multilevel disease transmission models using foot and mouth disease in Cameroon as a case study.

Laura W Pomeroy, Hyeyoung Kim, Ningchuan Xiao, Mark Moritz, Rebecca Garabed

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 18% of its field
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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
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

5 authors at 2 institutions in 2 countries.

Laura W PomeroyDivision of Environmental Health Sciences, College of Public Health, The Ohio State University, Columbus, OH, United States of America.ORCID 0000-0002-9520-0392
Hyeyoung KimDepartment of Geography, The Ohio State University, Columbus, OH, United States of America.
Ningchuan XiaoDepartment of Geography, The Ohio State University, Columbus, OH, United States of America.ORCID 0000-0002-6585-6294
Mark MoritzDepartment of Anthropology, The Ohio State University, Columbus, OH, United States of America.ORCID 0000-0003-0644-0069
Rebecca GarabedDepartment of Veterinary Preventive Medicine, The Ohio State University, Columbus, OH, United States of America.
The Ohio State University · USSwedish Veterinary Agency · SE

Funding

Statistics and Survey Methods CoreR24HD058484 · NICHD · OHIO STATE UNIVERSITY · PI QIAN, ZHENCHAO · 2009 to 2013
$2.1M
NICHD NIH HHS R24 HD058484
6 · The paper itself

Abstract

The dynamics of infectious diseases are greatly influenced by the movement of both susceptible and infected hosts. To accurately represent disease dynamics among a mobile host population, detailed movement models have been coupled with disease transmission models. However, a number of different host movement models have been proposed, each with their own set of assumptions and results that differ from the other models. Here, we compare two movement models coupled to the same disease transmission model using network analyses. This application of network analysis allows us to evaluate the fit and accuracy of the movement model in a multilevel modeling framework with more detail than established statistical modeling fitting methods. We used data that detailed mobile pastoralists' movements as input for 100 stochastic simulations of a Spatio-Temporal Movement (STM) model and 100 stochastic simulations of an Individual Movement Model (IMM). Both models represent dynamic movement and subsequent contacts. We generated networks in which nodes represent camps and edges represent the distance between camps. We simulated pathogen transmission over these networks and tested five network metrics-strength, betweenness centrality, three-step reach, density, and transitivity-to determine which could predict disease simulation outcomes and thereby be used to correlate model simulation results with disease transmission simulations. We found that strength, network density, and three-step reach of movement model results correlated with the final epidemic size of outbreak simulations. Betweenness centrality only weakly correlated for the IMM model. Transitivity only weakly correlated for the STM model and time-varying IMM model metrics. We conclude that movement models coupled with disease transmission models can affect disease transmission results and should be carefully considered and vetted when modeling pathogen spread in mobile host populations. Strength, network density, and three-step reach can be used to evaluate movement models before disease simulations to predict final outbreak sizes. These findings can contribute to the analysis of multilevel models across systems.

Indexed as

Models, BiologicalAnimal MigrationAnimalsCameroonCattleCattle DiseasesComputational BiologyComputer SimulationEpidemicsFoot-and-Mouth DiseaseHost Microbial InteractionsHumansSpatio-Temporal AnalysisStochastic Processes

Identifiers

PMID31465448
PMCPMC6776348
OpenAlexW2970976941

What OpenQuestion holds

Textmetadata
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.