Epidemic prevalence information on social networks can mediate emergent collective outcomes in voluntary vaccine schemes

This repository contains data for Figures 2-4 of the manuscript:

Sharma A, Menon SN, Sasidevan V and Sinha S (2019) Epidemic prevalence information on social networks can mediate emergent collective outcomes in voluntary vaccine schemes. PLoS Comput Biol 15(5): e1006977. https://doi.org/10.1371/journal.pcbi.1006977

The data is in the form of .mat files (which can be opened in MATLAB).

Note: Data for the empirical social networks used in Figures 2(a-b) and 3(d) are openly accessible as part of the published article: Banerjee A, Chandrasekhar AG, Duflo E and Jackson MO (2013) The diffusion of microfinance. Science 341(6144): 1236498 https://doi.org/10.1126/science.1236498 and can be directly downloaded from the Harvard Dataverse Repository here.

The following table provides a description of the variables saved in the data files:

Variable	Description
t	time instant
S(t)	the number of susceptible agents at time t
I(t)	the number of infected agents at time t
R(t)	the number of recovered agents at time t
V(t)	the number of vaccinated agents at time t
Ic(t)	the cumulative number of infected agents at time t
inf∞	final fraction of infected agents
vac∞	final fraction of vaccinated agents

Contents of folder Figure_2

1. TimeSeries_Vill55_alpha0.mat: Data for Fig. 2(a) and the left panel of Fig. 2(c)

This file contains a matrix S_rec of dimensions 692x6 that stores time series data for a single simulation on village network #55 for the case α = 0. The 6 columns correspond to: t, S(t), I(t), R(t), V(t) and I_c(t).

2. TimeSeries_Vill55_alpha1.mat: Data for Fig. 2(b) and the right panel of Fig. 2(c)

This file contains a matrix S_rec of dimensions 924x6 that stores time series data for a single simulation on village network #55 for the case α = 1. The 6 columns correspond to: t, S(t), I(t), R(t), V(t) and I_c(t).

3. Subfolder vill55_network: Data for Fig. 2(d) and Fig. 2(e)

This folder contains files for simulations on village network #55 for the cases α = 0 and α = 1. The file names are vill55_qX_alphaY.mat where X is the value of β and Y is the value of α (0 or 1). Each .mat file contains a matrix datavn of dimensions 2x1000 that contains data for inf_∞ and vac_∞ over 1000 simulation runs.

Contents of folder Figure_3

1. TimeSeries_ER_alpha0.mat: Data for the left panel of Fig. 3(a)

This file contains a matrix S_rec of dimensions 885x6 that stores time series data for a single simulation on an Erdős-Rényi network of size 1024 for the case α = 0. The 6 columns correspond to: t, S(t), I(t), R(t), V(t) and I_c(t).

2. TimeSeries_ER_alpha1.mat: Data for the right panel of Fig. 3(a)

This file contains a matrix S_rec of dimensions 1103x6 that stores time series data for a single simulation on an Erdős-Rényi network of size 1024 for the case α = 1. The 6 columns correspond to: t, S(t), I(t), R(t), V(t) and I_c(t).

3. Subfolder ER_network: Data for Fig. 3(b) and Fig. 3(c)

This folder contains files for simulations on Erdős-Rényi networks of size 1024 for the case of local information (α = 0, loc_*.mat) and global information (α = 1, glo_*.mat). The file names are X_glsp_qY.mat, where X is "loc" or "glo" and Y is the value of β. Each .mat file contains a matrix dataq of dimensions 2x1000 that contains data for inf_∞ and vac_∞ over 1000 simulation runs.

4. Subfolder Kavg_ERN_KVN: Data for Fig. 3(d)

This folder contains files for simulations on Erdős-Rényi networks of size 1024 as well as on empirical village networks.

For the case of Erdős-Rényi networks, the file names are glsp_qX_kY_alphaZ.mat, where X is the value of β, Y is the average degree of the network used and Z is the value of α (0 or 1). Each .mat file contains a matrix dataq of dimensions 2x1000. This contains data for inf_∞ and vac_∞ over 1000 simulation runs.

For the case of village networks, the file names are villX_qY_alphaZ.mat, where X is the village id, Y is the value of β and Z is the value of α (0 or 1). Each .mat file contains a matrix datavn of dimensions 2x1000 that contains data for inf_∞ and vac_∞ over 1000 simulation runs.

Contents of folder Figure_4

1. Subfolder ER_All_system_sizes: Data for Fig. 4(a)

This folder contains simulations on Erdős-Rényi networks for a range of system sizes (multiples of 1024), for the case of local and global information. The file names are Xn_alphaY_qZ.mat, where X is the multiple of 1024 that specifies the system size (e.g. X="02" corresponds to a network of size 2*1024=2048), Y is the value of α (0 or 1) and Z is the value of β. Each .mat file contains a matrix dataq of dimensions 2x1000 that contains data for inf_∞ and vac_∞ over 1000 simulation runs.

2. Subfolder ER_16N: Data for Fig. 4(b) and Fig. 4(c)

This folder contains simulations on Erdős-Rényi networks for a fixed system size (16*1024=16384 agents) and over a range of values of α. The file names are 16n_alphaX_qY.mat, where X is the value of α (0 or 1) and Y is the value of β. Each .mat file contains a matrix dataq of dimensions 2x1000 that contains data for inf_∞ and vac_∞ over 1000 simulation runs. An additional 1000 simulation runs are provided in the files 16n2_alphaX_qY.mat, where X is the value of α (0 or 1) and Y is the value of β.