Data from: The basic-reproduction number of infectious diseases in spatially structured host populations
Data files
May 23, 2024 version files 52.25 KB
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create_LS.R
7.59 KB
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EpidemicSim.c
34.97 KB
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EpidemicSim.cfg
362 B
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README.md
2.49 KB
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rZero_From_Sims.R
240 B
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rZero_Function.R
6.60 KB
Abstract
The spatial structure of a host population has a profound effect on the dynamics of infectious diseases. The basic reproduction number, a central quantity in the study of epidemic dynamics, is affected by host clustering as well as host density. Several authors have developed methods to quantify the basic reproduction number in a spatially structured host population. The methods used and the expressions derived are however difficult to apply to real life spatial host structures. In this paper we introduce an explicit expression for the basic reproduction number using the O-ring statistic, developed in spatial statistics, that quantifies the host density as a function of the distance from a randomly selected host individual. The O-ring statistic is frequently used in the study of the ecology of spatially structured plant populations, being a convenient summary of the properties of a landscape by way of a single function. The connection we develop between spatial statistics and epidemic dynamics can be used to study the effect of host spatial pattern on the basic reproduction number of infectious diseases. As well as showing how explicit expressions for the basic reproduction number can be derived for landscapes with standard structures, our expression for the basic reproduction number is tested against a simulation model. The model structure in our simulation is motivated by the spread of a plant disease epidemic, although it is applicable more broadly. The agreement between our analytic expression for the basic reproduction number and the corresponding numeric quantity extracted from simulations is close to perfect across a wide range of landscape structures and model parameterisations, and including cases in which more than one species of host is at risk of infection.
https://doi.org/10.5061/dryad.kh18932fw
Computer code to support the above-named article.
Description of the data and file structure
Files provided include two .c files and one .h file which can be compiled to create an executable to simulate epidemics using a stochastic individual based model. The .cfg file (as well as command line flags) can be altered to affect the parameterisation of this simulation. There are also three .R files; running one creates a host landscape for the epidemic simulation; the other two can be used to calculate R0 using the methods set out in our paper, and to calculate the same quantity numerically by fitting a (multi-species) branching process model (i.e., a way of checking that the analytic method gives reliable results).
Sharing/Access information
The code is also available at
Note that compiling the EpidemicSim.c file requires a C implementation of the Mersenne Twister. In doing the runs underneath the paper, a modified version of mt19937ar was used. This was originally written by Takuji Nishimura and Makoto Matsumoto, and cannot be released under a CC0 licence. However, a copy of the files used - with an appropriate copyright declaration - is hosted at https://doi.org/10.5281/zenodo.11244650
Code/Software
The pipeline for creating landscape(s), running epidemics and calculating R0 is described below.
- Compile EpidemicSim.exe from EpidemicSim.c and mt19937ar.c
- Create directory to do the runs
- Copy the following files to directory created in step 2
- EpidemicSim.cfg
- EpidemicSim.exe
- create_LS.R
- rZero_From_Sims.R
- rZero_Function.R
- Create the following subdirectories of directory created in step 2
- Inputs
- Outputs
- Start R and change to the directory created in step 2 using setwd()
- Run create_LS.R
- Options for landscape generation are in the R file
- Running it will fill up Inputs subdirectory
- Run EpidemicSim.exe on command line
- Options for epidemics are in the EpidemicSim.cfg files
- will fill up Outputs subdirectory
- Run rZero_From_Sims.R
- will print estimated and calculated rZero to the screen
The dataset is computer code supporting the article "The basic-reproduction number of infectious diseases in spatially structured host populations" in Oikos by van den Bosch, Helps & Cunniffe.