A multiscale model of viral infection

Usage

Tweak parameters in parameters.h.

make
./gendata       # Generate random data (environments, contacts)
./model         # Run model, save data
./plot.p        # Visualize

Model outputs are present in the output folder as .dat files, in csv format.

Note: Plotting currently requires gnuplot.

Run ./model -h for help on more options.

Visualize the degree distribution of the generated network using ./degree_distribution.R, and see figures/degree_distribution.pdf.

Multiple runs

Execute ./multirun.sh 1 200 to recreate model outputs expected by the scripts multirun.R and peaks.R which generate figures.

The command ./multirun.sh START END will run the model multiple times for different values for P_INFECT, and produce output files indexed by integers from START to END. For example, ./multirun.sh 1 10 will produce files countdata_01.dat to countdata_10.dat in the output/p2 to output/p8 folders.

Here, we have adjusted the parameter P_INFECT (probability of infection on contact) as $X \times 10^{-3}$ for $X \in \{2, 3, 4, 5, 6, 8\}$.

Change the statement P_INFECT=(2 3 4 5 6 8) in multirun.sh to use other values for $X$; the corresponding output files will be placed in the output/pX folder.

Tweak the variables trials and trial_p in multirun.R and peaks.R to reflect the number of trials and the values of $X$.

One agent

Tweak parameters in oneagent.c, then make and run ./oneagent.c > output/oneagent.dat. Visualize using oneagent.p, or oneagent.R which produces figures/oneagent.pdf.

Description

The model specification can be found in report/model.pdf.

TODO

Implement status ($S, I, R$) and shedding dynamics based on actual viral load curves
Implement immune response
Implement mortality
Create variation among agents, environments
Better contact networks

References

Wang X, Wang S, Wang J, Rong L. A Multiscale Model of COVID-19 Dynamics. Bull Math Biol. 2022 Aug 9;84(9):99. doi: 10.1007/s11538-022-01058-8.
Ciupe SM, Heffernan JM. In-host modeling. Infect Dis Model. 2017 Apr 29;2(2):188-202. doi: 10.1016/j.idm.2017.04.002.