dolphinsync

This repository contains data and code for "Breathing in sync: how a social behavior structures respiratory epidemic risk in bottlenose dolphins"

Data files:

1. Clean_Degree_Data_PCDP.csv contains focal follow data from PC. Column descriptions:
   Follow_ID = unique ID for the focal follow conducted
   Dolphin-ID = unique ID of the focal dolphin
   Demo = Demographic group: AM = Adult Male, AF= Adult Female, AFNN= Mother and neonate calf pair, AFNNN = mother and nonneonate calf pair, JX = juvenile
   _Demo_Confidenc_e = Confidence in demo assignment based on Appdendix 1 in paper
   Degree = total sync degree during follow
   AM_deg = total degree during follow that was an AM
   AF_deg = total degree during follow that was an AF
   JX_deg = total degree during follow that was a JX
   low_AM_deg = total AM_deg that was low confidence demo assignment
   low_AF_deg = total AF_deg that was low confidence demo assignment
   low_JX_deg = total JX_deg that was low confidence demo assignment
   Follow_length = length of follow in minutes
   Date = date of follow
   Number_Syncs = number of unique synchronized breathing events that occurred in the follow
   Group_Size = total individuals present during the focal follow
   Year = year of follow
   
2. Clean_Degree_Data_SB.csv contains focal follow data from SB. Column descriptions:
   Follow_ID = unique ID for the focal follow conducted
   Dolphin-ID = unique ID of the focal dolphin
   Demo = Demographic group: AM = Adult Male, AF= Adult Female, AFNN= Mother and neonate calf pair, AFNNN = mother and nonneonate calf pair, JX = juvenile
   Degree = total sync degree during follow
   AM_deg = total degree during follow that was an AM
   AF_deg = total degree during follow that was an AF
   JX_deg = total degree during follow that was a JX
   Follow_length = length of follow in minutes
   Date = date of the follow
   Number_Syncs = number of unique synchronized breathing events that occurred in the follow
   Group_Size = total individuals present during the focal follow
   Year = year of the follow
   
3. Stranding_Data_background_removed_by_Adult_Sex.csv contains the stranding records during the UME with the background strandings removed for each adult sex class and state based on our methods
   
4. Stranding_Data_background_removed_by_Age.csv contains the stranding records during the UME with the background strandings removed for adult and juvenile age classes and state based on our methods.
   
5. PC_degree_distributions.csv and SB_degree_distributions.csv are generated using code file 1. Each row represents a degree (e.g. row 1 = degree 0, row 2 = degree of 1, etc) and each column (AM, AF, JX) shows the proportion of that demographic that would have that degree over an average DMV infectious period.
   
6. Mixing_Matrix_data_PC.csv and Mixing_Matrix_data_SB.csv are generated using code file 2. Column descriptions:
   Comb: demo combination based on focal:contact
   Mean: average degree for that combination across focal follows
   Lower: the lower degree estimate of that combination
   Upper: the upper estimate of that combination
   Mean_Ratio: The proportion of all observed syncs that are of that combination acorss all follows
   SD-Ratio: the std of that proportion
   empirical_lower_ratio/upper_ratio: the upper and lower bounds of that proportion
   
7. generated_graphs: a sub directory with all network files for 25 networks created based on PC data. All files in this sub directory were generated using code file 3
   
   

Code Files:

1. Estimate_degree_distribution_for_infectious_period.R: R code to generate a degree distribution for AM, AF and JX dolphins using data from PC and SB (data files 1 and 2)
   
2. Generate_Mixing_Matrices.R: Code to generate a mixing matrix across demographic groups using the PC and SB data files.
   
3. generate_synthetic_networks.py: Code to generate synthetic networks based on emprical degree distributions and mixing matrices generated code files 1 and 2 (i.e. data files 5 and 6). Requires the user make a subflder in their working directory called "generated_networks". Also requires code files general_tools.py and pretty_print.py
   
4. Create_graphml_networks_for_disease_Simulations.py: Code to create a graphml file for all generated networks to be used for disease simulations.
   
5. Disease_Simulation_Code.py: Code to run disease simulations on each generated network and write all model results into a csv file. Requires the code file: Simulation_Functions.py