Evidence map›Paper›PMID 30540815›Full record

ArticlePloS one2018

Decoupling environmental effects and host population dynamics for anthrax, a classic reservoir-driven disease.

Juan Pablo Gomez, Dawn M Nekorchuk, Liang Mao, Sadie J Ryan, José Miguel Ponciano, Jason K Blackburn

Abstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Seasonality and Ecological Suitability Modelling for Anthrax (Animals : an open access journal from MDPI · 2022
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. ModelingInternational journal of environmental research and public health · 2019
    Review
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

6 authors.

Juan Pablo GomezSpatial Epidemiology and Ecology Research Laboratory, Department of Geography, University of Florida, Gainesville, Florida, United States of America.
Dawn M NekorchukSpatial Epidemiology and Ecology Research Laboratory, Department of Geography, University of Florida, Gainesville, Florida, United States of America.ORCID 0000-0001-8200-2146
Liang MaoEmerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America.
Sadie J RyanEmerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America.
José Miguel PoncianoDepartment of Biology, University of Florida, Gainesville, Florida, United States of America.
Jason K BlackburnSpatial Epidemiology and Ecology Research Laboratory, Department of Geography, University of Florida, Gainesville, Florida, United States of America.ORCID 0000-0003-0928-4831

Funding

Spatio-temporally explicit estimation of R0 for pathogens with environmentally-mediated transmissionR01GM117617 · NIGMS · UNIVERSITY OF FLORIDA · PI BLACKBURN, JASON K · 2015 to 2018
$1.5M
NIGMS NIH HHS R01 GM117617
6 · The paper itself

Abstract

Quantitative models describing environmentally-mediated disease transmission rarely focus on the independent contribution of recruitment and the environment on the force of infection driving outbreaks. In this study we attempt to investigate the interaction between external factors and host's population dynamics in determining the outbreaks of some indirectly transmitted diseases. We first built deterministic and stochastic compartmental models based on anthrax which were parameterized using information from literature and complemented with field observations. Our force of infection function was derived modeling the number of successful transmission encounters as a pure birth process that depends on the pathogen's dispersion effort. After accounting for individual heterogeneity in pathogen's dispersion effort, we allowed the force of infection to vary seasonally according to external factors recreating a scenario in which disease transmission increases in response to an environmental variable. Using simulations we demonstrate that anthrax disease dynamics in mid-latitude grasslands is decoupled from hosts population dynamics. When seasonal forcing was ignored, outbreaks matched hosts reproductive events, a scenario that is not realistic in nature. Instead, when allowing the force of infection to vary seasonally, outbreaks were only present in years were environmental variables were appropriate for the outbreaks to develop. We used the stochastic formulation of the force of infection to derive R0 under scenarios with different assumptions. The derivation of R0 allowed us to conclude that during epizootic years, pathogen contribution to disease persistence is nearly independent of dispersion. In endemic years, only pathogens with high dispersion significantly prevent disease extinction. Finally, we used our model in a maximum likelihood framework to estimate the parameters that determined a significant anthrax outbreak in Montana in 2008. Our study highlights the importance of the environment in determining anthrax outbreak intensity and could be useful to predict future events that could result in significant wildlife and domestic livestock losses.

Indexed as

Models, TheoreticalAnimalsAnthraxBacillus anthracisDisease OutbreaksDisease ReservoirsHumansLikelihood FunctionsMontanaPopulation DynamicsSeasons

Identifiers

PMID30540815
PMCPMC6291251

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.