Parasite responses to resource provisioning can be altered by within-host co-infection interactions
Data files
Aug 25, 2025 version files 522.56 KB
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analysis_coinfection.R
4.39 KB
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analysis_population.R
4.10 KB
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coinf_det_I9.R
2.31 KB
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coinf_det_L9.R
2.50 KB
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Data_coinfection.RData
3.79 KB
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Data_population.RData
4.12 KB
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functions.R
30.87 KB
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main_coinfection.R
3.80 KB
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main_population.R
4.76 KB
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plots.nb
456.27 KB
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popdyn_det.R
3.20 KB
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README.md
2.44 KB
Sep 03, 2025 version files 524.74 KB
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analysis_coinfection.R
4.39 KB
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analysis_population.R
4.10 KB
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coinf_det_I9.R
2.31 KB
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coinf_det_L9.R
2.50 KB
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Ehungaryensis.csv
1.94 KB
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functions.R
30.87 KB
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Hpolygyrus.csv
1.23 KB
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main_coinfection.R
3.74 KB
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main_population.R
4.67 KB
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plots.nb
456.27 KB
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popdyn_det.R
3.20 KB
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population.csv
4.15 KB
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README.md
5.37 KB
Abstract
Anthropogenic changes to the environment significantly impact wildlife infectious diseases by modifying food resources and impacting host-parasite interactions through changes in host demography, behaviour, and immune defences. Supplemental resource provisioning has been found to both enhance and mitigate parasite transmission; however, the role of co-infecting parasites in mediating these effects remains understudied. We developed a mathematical model to explore these dynamics, motivated by the empirical system of wood mice (Apodemus sylvaticus) infected with the nematode Heligmosomoides polygyrus, which suppresses co-infections by the apicomplexan microparasite Eimeria hungaryensis. Our model shows that the effects of resource provisioning on parasite epidemiology can be mediated, and potentially reversed, by within-host co-infection interactions, through effects on host-parasite contact rates and host susceptibility. Provisioning may elevate microparasite prevalence by reducing nematode burdens, thereby releasing the microparasite from the negative effects of co-infection. However, if provisioning increases host contact rates with parasite infective stages in the environment, the associated increase in nematode burdens can result in concomitant reductions in the microparasite, due to the negative within-host co-infection interaction. Our study highlights the need for experimental designs that decouple the complex factors of provisioning on co-infecting parasite dynamics and provides a framework for interpreting outcomes in multi-parasite systems.
Short Summary
This repository contains code for a mathematical model exploring how supplemental food provisioning influences parasite dynamics in wildlife, specifically focusing on co-infections in wood mice (Apodemus sylvaticus). The model examines interactions between the nematode Heligmosomoides polygyrus and the microparasite Eimeria hungaryensis, showing that resource provisioning can either increase or decrease parasite prevalence depending on how it alters host contact rates, susceptibility, and within-host co-infection interactions. The code provides a framework for studying how environmental changes shape multi-parasite epidemiology.
https://doi.org/10.5061/dryad.msbcc2g72
Description of the data and file structure
Code and Package Versions
- Manual version number:
v1.0 (initial release) - R version: 4.2.3 (2023-03-15)
- Platform: x86_64-apple-darwin17.0 (64-bit)
- OS: macOS Monterey 12.5.1
R packages used (with versions):
gridExtra 2.3, stringr 1.5.1, plyr 1.8.9, reshape2 1.4.4, lattice 0.20-45,
deSolve 1.40, coda 0.19-4.1, ggplot2 3.5.2, tidyr 1.3.1, MASS 7.3-58.2,
fitR 0.2.1, readr 2.1.4, adaptivetau 2.3, dplyr 1.1.4, lubridate 1.9.4,
mvtnorm 1.1-3, bbmle 1.0.25.1, emdbook 1.3.13, tmvtnorm 1.6
Licensing Information
This code and data are released under the CC0 License.
Files and variables
File: population.csv
Description: data for population dynamics model - collected Apodemus sylvaticus per week
population.csv: Contains population data with collection date (date), and number of collected individuals (obs). Time column corresponds to the time step in the simulation.
File: Ehungaryensis.csv and Hpolygyrus.csv
Description: data for co-infection model - Heligmosomoides polygyrus infective stage counts and number of Eimeria hungaryensis infected mice (Apodemus sylvaticus ) per week
Ehungaryensis.csv: Contains Eimeria hungaryensis data with collection date (date), observed infected individuals (obs), and number of collected individuals (num). Time column corresponds to the time step in the simulation.Hpolygyrus.csv: Contains Helygmosomoides polygyrus data with collection date (date), and total number of observed eggs (obs) in the population. Time column corresponds to the time step in the simulation.
Code/software
Overview
This repository contains R code and Mathematica scripts for parameter estimation and analysis of two models: a population dynamics model and a coinfection model.
Directory
Contains all necessary scripts and data for estimating parameters of the population and coinfection dynamics models.
Key files:
main_population.R: The master script that initiates the population dynamics analysis.popdyn_det.R: Implements the population dynamics model.analysis_population.R: Analyzes the output frommain_population.R.population.csv: Contains population data with collection date (date), and number of collected individuals (num). Time column corresponds to the time step in the simulation.
main_coinfection.R: The master script that initiates the coinfection analysis.coinf_det_I9.R: Implements the I9 coinfection dynamics.coinf_det_L9.R: Implements the L9 coinfection dynamics.analysis_coinfection.R: Analyzes the output frommain_coinfection.R.Ehungaryensis.csv: Contains Eimeria hungaryensis data with collection date (date), observed infected individuals (obs), and number of collected individuals (num). Time column corresponds to the time step in the simulation.Hpolygyrus.csv: Contains Helygmosomoides polygyrus data with collection date (date), and total number of observed eggs (obs) in the population. Time column corresponds to the time step in the simulation.
3. functions.R
A collection of functions used across both models, promoting code reusability and modularity.
4. plots.nb
A Mathematica notebook used to generate Figures 3 and 4 from the manuscript, visualizing the results of the models.
Summary of Workflow
main_population.Rserves as the main entry code for population dynamics fitting and calls the detailed model scriptpopdyn_det.Rfor population dynamics.- After running the simulations and the fitting,
analysis_population.Rprocesses the output data and performs further analysis. main_coinfection.Rserves as the main entry code for coinfection dynamics fitting and calls the detailed model scriptscoinf_det_I9.Randcoinf_det_L9.Rfor coinfection dynamics.- After running the simulations and the fitting,
analysis_coinfection.Rprocesses the output data and performs further analysis. - The
plots.nbnotebook is then used to create Figures 4 and 5, displaying the model results graphically.
Version changes
03-09-2025: population (population.csv) and coinfection (Ehungaryensis.csv and Hpolygyrus.csv) data were included as .csv files (previously Data_coinfection.RData and Data_population.RData files). main_population.R and main_coinfection.R were updated to import these .csv files.