Scheduling Mechanisms to Control Spread of Covid-19 (Simulation Results)

This DATSETNAMEreadme.txt file was generated on 2021-06-23 by Khalid Hourani
 
 
GENERAL INFORMATION
 
1. Title of Dataset: Scheduling Mechanisms to Control Spread of Covid-19 (Simulation Results)
 
2. Author Information
    A. Principal Investigator Contact Information
        Name: Gopal Pandurangan
        Institution: University of Houston
        Address: 3551 Cullen Blvd. Room 572, Houston, TX 77204
        Email: gopal@cs.uh.edu
 
    B. Associate or Co-investigator Contact Information
        Name: Khalid Hourani
        Institution: University of Houston
        Address: 3551 Cullen Blvd. Room 520, Houston, TX 77204
        Email:  
 
3. Date of data collection (single date, range, approximate date): 2021-01-25
 
4. Geographic location of data collection: NA  
 
5. Information about funding sources that supported the collection of the data: NSF
 
SHARING/ACCESS INFORMATION
 
1. Licenses/restrictions placed on the data: Code has GPL3 License
 
2. Links to publications that cite or use the data: https://arxiv.org/abs/2009.08872
 
3. Links to other publicly accessible locations of the data: Code (not CSVs) available at https://github.com/Covimulation/covimulation
 
4. Links/relationships to ancillary data sets: https://pubmed.ncbi.nlm.nih.gov/18366252/ used to create contact distribution
 
5. Was data derived from another source? yes/no
    A. If yes, list source(s): https://github.com/skissler/haslemere
 
6. Recommended citation for this dataset:  
 
 
DATA & FILE OVERVIEW
 
1. File List:  
    alias_sampler.py: code for efficient weighted sampling
    contact_distribution.csv: contact distribution based on https://pubmed.ncbi.nlm.nih.gov/18366252/
    contact_distribution.py: reads above csv and returns a probability density function
    contact_graph.py: base class for contact graph model
    csv_helper.py: helper code to generate csvs after simulations
    graph.py: base class that contact_graph inherits from
    grid.py: code to improve efficiency of determining k closest neighbors by gridding
    mechanisms.py: string manipulation for simulation - converts mechanism name from CSV to expected format
    person.py: code for person class (effectively, code for nodes in graph)
    plot_helper.py: code to generate plots (largely unused - plots where generated using PGF from raw data)
    simulation.py: code to run simulation
    simulation_kissler.py: code to grab data from https://github.com/skissler/haslemere and run simulation
    sir_graph.py: inherits structure from contact graph class, adds SIR specific logic
    
    kissler_(0.01).csv: simulation results for kissler data with T_p = 0.01
    kissler_(0.10).csv: simulation results for kissler data with T_p = 0.10
    raw_geometric_(0.01).csv: simulation results for contact graph with T_p = 0.01
    raw_geometric_(0.10).csv: simulation results for contact graph with T_p = 0.10
    raw_seir_(0.01).csv: simulation results for SEIR simulation with T_p = 0.01
    raw_seir_(0.10).csv: simulation results for SEIR simulation with T_p = 0.10
 
    CSVs are formatted:
    day,basic,symptomatic,(1|5|2),(2|5|0),(3|3|0),(4|4|0)
     
    day: the number of days passed since start of the simulation
    basic: number of new infections that day in basic model
    symptomatic: number of new infections that day in symptomatic quarantine model
    (1|5|2): number of new infections that day in group scheduling model with (g, d, t) = (1, 5, 2)
    (2|5|0): number of new infections that day in group scheduling model with (g, d, t) = (2, 5, 0)
    (3|3|0): number of new infections that day in group scheduling model with (g, d, t) = (3, 3, 0)
    (4|4|0): number of new infections that day in group scheduling model with (g, d, t) = (4, 4, 0)
 
2. Relationship between files, if important: simulation should be able to run by calling
        python simulation.py
        python simulation_kissler.py
    which uses the other files, as described in 1.  
 
3. Additional related data collected that was not included in the current data package:  
 
4. Are there multiple versions of the dataset? no
    A. If yes, name of file(s) that was updated:  
        i. Why was the file updated?  
        ii. When was the file updated?  
 
 
METHODOLOGICAL INFORMATION
 
1. Description of methods used for collection/generation of data:  
    We use https://pubmed.ncbi.nlm.nih.gov/18366252/ to create a contact distribution as a probability density function
    then generate the required number of persons. People are placed randomly in the unit square and connect to a
    number (drawn from the contact distribution) of closest neighbors. The people can then infect each contact with
    probability T_p. The simulation is run (for various values of T_p) until there are no remaining infected participants.
     
    The same process is run on the data from https://github.com/skissler/haslemer.
     
2. Methods for processing the data:  
    The number of new infections per day are tracked in the provided source code.
 
3. Instrument- or software-specific information needed to interpret the data:  
    Python3.6 or later is necessary. Script plot_helper.py uses matplotlib. The Person class from person.py
    makes use of various numpy functions. The remaining modules imported are all standard library files.
 
4. Standards and calibration information, if appropriate:  
 
5. Environmental/experimental conditions:  
 
6. Describe any quality-assurance procedures performed on the data:  
 
7. People involved with sample collection, processing, analysis and/or submission:  
    John Augustine
    Khalid Hourani
    Anisur Rahaman Molla
    Gopal Pandurangan
    Adi Pasic

Funding provided by: Science and Engineering Research Board
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100001843
Award Number: EMR/2016/003016

Funding provided by: Department of Science and Technology, Ministry of Science and Technology, India
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100001409
Award Number: DST/INSPIRE/04/2015/002801

Funding provided by: National Science Foundation
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000001
Award Number: IIS-1633720

Funding provided by: National Science Foundation
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000001
Award Number: CCF-BSF-1717075

Funding provided by: National Science Foundation
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000001
Award Number: CCF-1540512