DOI: 10.5061/dryad.kwh70rz6k
title: Impact of infectious diseases on wild bovidae populations in Thailand: Insights from population modelling and disease dynamics

description: The wildlife and livestock interface is vital for wildlife conservation
 and habitat management. Infectious diseases maintained by domestic species
 may impact threatened species such as Asian bovids, as they share natural
 resources and habitats. To predict the population impact of infectious
 diseases with different traits, we used stochastic mathematical models to
 simulate the population dynamics over 100 years for 100 times a model gaur
 (Bos gaurus) population with and without disease. We simulated repeated
 introductions from a reservoir, such as domestic cattle. We selected six
 bovine infectious diseases; anthrax, bovine tuberculosis, hemorrhagic
 septicaemia, lumpy skin disease, foot and mouth disease and brucellosis,
 all of which have caused outbreaks in wildlife populations. From a
 starting population of 300, the disease-free population increased by an
 average of 228% over 100 years. Brucellosis with frequency-dependent
 transmission showed the highest average population declines (-97%), with
 population extinction occurring 16% of the time. Foot and mouth disease
 with frequency-dependent transmission showed the lowest impact, with an
 average population increase of 200%. Overall, acute infections with very
 high or low fatality had the lowest impact, whereas chronic infections
 produced the greatest population decline. These results may help disease
 management and surveillance strategies support wildlife conservation.
description: The R codes were developed to simulate the population dynamics
 of the gaur with and without disease infection. The disease and animal
 biological parameters were collected from peer-reviewed literature. We
 built the model structures from basic <em>SIR </em>models,
 with compartments varying based on disease traits. In the scripts, we used
 the Poisson distribution to calculate the probability of events and then
 calculated the average population changes (see equations 2.4 and 2.5 in
 the manuscript) to identify which diseases have the most impact on
 population changes. The population change results can be found in the
 supplementary file (ndiff_mean_all.csv).   
description: # InfectiousModel Population dynamics and infectious disease modelling of
 wild bovids. Explore the population impact of infectious diseases with
 different traits. ``` Description of the data and file structure ``` There
 are three R scripts: 1. **script_models_&_plotting_thisone.R**: for
 model simulations and plotting population trends, the main parts contain:
 * **Non-infectious disease model**: includes birth rate (mu_b), natural
 death rate (mu_), and ageing rate (delta_) with 3 age classes: calf (c),
 subadult (sa), adult (a). * **Six Bovine infectious disease models** with
 five different model structures: * Anthrax, SI * Bovine tuberculosis, SEI
 * Haemorrhagic septicaemia, SIRS * Lumpy skin disease, SEIRS * Foot and
 mouth disease, SEIRMS/E * Brucellosis, SEIRMS/E  * Note: *S* = Susceptible
 animals, *E* = Exposed animals; *I* = Infected animal; *R* = Recovered
 animal; *M* = Calves with maternally derived immunity. Two modes of
 disease transmission were used in the script: frequency-dependent (FD) and
 density-dependent (DD). * Plotting the figures for all the model results
 and comparing the the population changes using the boxplots. 2.
 **script_population_dynamic_5sp.R**: includes: * **Non-infectious disease
 model** for five bovid species (gaur, banteng, water buffalo, mainland
 serow, and Chinese goral), includes birth rate (mu_b), natural death
 rate (mu_), aging rate (delta_)  within 3 age classes. * Each species has
 different demographic parameters. 3. **PCA.R**: for creating PCA biplots
 using the results from pca_table.xlsx. PCA is use here to identify which
 diseases showed similar traits group. The result files from our models can
 be downloaded here: 1. **df_ndiff_allmodels.csv**: for generating summary
 statistics and box plots. * *Ndiff*: Population change at the end of the
 simulation (100 years); negative values indicate a population reduction. *
 *run*: Number of model simulations. * *model*: Model name. * *model2*:
 Disease name used in modeling. * *transmission*: Mode of disease
 transmission; no = non-infectious disease model, FD = frequency-dependent,
 DD = density-dependent. 2. **ndiff_mean_all.csv**: the mean population
 change for each model. * *Model name*: Model name. * *Model code*: Code
 for each model. * *Average of the total population changes (%)*: Average
 percentage change in the total population, calculated from the starting
 population minus the ending population, divided by the number of
 simulations. 3. **pca_table.xlsx**: for generating the PCA
 biplot, includes four important disease parameters. * *model*: Model name.
 * *beta*: Disease transmission rate. * *incubation*: Disease incubation
 rate. * *infectious*: Infectious rate. * *fatality*: Disease fatality
 rate. * *Nchange_p*: Average percentage of the total population changes. *
 *disease*: Disease name used in modeling.  * *type*: Modes of disease
 transmission; FD = frequency-dependent, DD = density-dependent. ```
 Sharing/Access information ``` This data also accessible
 via [https://github.com/Wantidah/InfectiousModel](https://github.com/Wantidah/InfectiousModel/)  ``` Code/Software ``` The R scripts were developed using R version 4.2.1 (2022-06-23). R software is required, and the necessary packages are provided within the scripts.