new location object in json files and load in model

.github/workflows/ci.yaml

@@ -12,7 +12,7 @@ jobs:
   test:
     strategy:
       matrix:
-        python-version: [3.7]
+        python-version: [3.9]
     runs-on: ubuntu-latest
 
     steps:

README.md

@@ -1,9 +1,19 @@
 # COVID19-mesa
 
+
+## Authors 
+
 *Santiago Nunez-Corrales, Informatics and NCSA, UIUC ([email protected])*
 
 *Eric Jakobsson, Molecular and Cell Biology and NCSA, UIUC ([email protected])*
 
+## Developers 
+
+* Angelo Santos
+* Boda Song
+* Xinyi Huang
+
+
 A simple simulation to explore contagion by COVID-19 via agent-based modeling (ABM), as well as potential effectiveness of various measures.
 
 **This software is under development rapidly to attempt to respond to the current situation, and should be used bearing this in mind. Any scenarios for specific communities should be considered provisional at all times and revised constantly against the most recent and best curated field evidence.**
@@ -49,7 +59,7 @@ source .venv/bin/activate
 To execute the dashboard interface:
 
 ```bash
-(.venv) python covidserver.py
+(.venv) python covidserver.py scenarios/[variant_data_filename]
 ```
 
 To execute a scenario stored in `scenarios/`:
@@ -93,7 +103,6 @@ Model callibration requires the best possible clinical data estimates for two pa
 
 To develop scenarios, we strongly recommend starting from the most recently callibrated model that includes policies as well as usin a model without measures as a basis for counterfactual arguments.
 
-
 ## Acknowledgments
 
 The following individuals have contributed directly and indirectly to rapid development of this software package.

agent_data_class.py

@@ -0,0 +1,112 @@
+from scipy.stats import poisson, bernoulli
+
+# The agent class contains all the parameters for an agent
+class AgentDataClass:
+    def __init__(self, model, is_checkpoint, params):
+        # start from time 0
+        if not is_checkpoint:
+            self.age_group = params[1]
+            self.sex_group = params[2]
+            self.vaccine_willingness = bernoulli.rvs(model.model_data.vaccinated_percent)
+            # These are fixed values associated with properties of individuals
+            self.incubation_time = poisson.rvs(model.model_data.avg_incubation)
+            self.dwelling_time = poisson.rvs(model.model_data.avg_dwell)
+            self.recovery_time = poisson.rvs(model.model_data.avg_recovery)
+            self.prob_contagion = model.model_data.prob_contagion_base
+            # Mortality in vulnerable population appears to be around day 2-3
+            self.mortality_value = params[3]
+            # Severity appears to appear after day 5
+            self.severity_value = model.model_data.prob_severe/(model.model_data.dwell_15_day*self.recovery_time)
+            self.curr_dwelling = 0
+            self.curr_incubation = 0
+            self.curr_recovery = 0
+            self.curr_asymptomatic = 0
+            # Isolation measures are set at the model step level
+            self.isolated = False
+            self.isolated_but_inefficient = False
+            # Contagion probability is local
+            self.test_chance = 0
+            # Horrible hack for isolation step
+            self.in_isolation = False
+            self.in_distancing = False
+            self.in_testing = False
+            self.astep = 0
+            self.tested = False
+            self.occupying_bed = False
+            # Economic assumptions
+            self.cumul_private_value = 0
+            self.cumul_public_value = 0
+            # Employment
+            self.employed = True
+            # Contact tracing: this is only available for symptomatic patients
+            self.tested_traced = False
+            # All agents 
+            self.contacts = set()
+            # We assume it takes two full days
+            self.tracing_delay = 2*model.model_data.dwell_15_day
+            self.tracing_counter = 0
+            #vaccination variables
+            self.vaccinated = False
+            self.safetymultiplier = 1
+            self.current_effectiveness = 0
+            self.vaccination_day = 0
+            self.vaccine_count = 0
+            self.dosage_eligible = True
+            self.fully_vaccinated = False
+            self.variant = "Standard"
+            self.variant_immune = {}
+            for variant in model.model_data.variant_data_list:
+                self.variant_immune[variant] = False
+
+        # start from an existing file
+        else:
+            self.age_group = params[2]
+            self.sex_group = params[3]
+            self.vaccine_willingness = params[4]
+            # These are fixed values associated with properties of individuals
+            self.incubation_time = params[5]
+            self.dwelling_time = params[6]
+            self.recovery_time = params[7]
+            self.prob_contagion = params[8]
+            # Mortality in vulnerable population appears to be around day 2-3
+            self.mortality_value = params[9]
+            # Severity appears to appear after day 5
+            self.severity_value = params[10]
+            self.curr_dwelling = params[11]
+            self.curr_incubation = params[12]
+            self.curr_recovery = params[13]
+            self.curr_asymptomatic = params[14]
+            # Isolation measures are set at the model step level
+            self.isolated = params[15]
+            self.isolated_but_inefficient = params[16]
+            # Contagion probability is local
+            self.test_chance = params[17]
+            # Horrible hack for isolation step
+            self.in_isolation = params[18]
+            self.in_distancing = params[19]
+            self.in_testing = params[20]
+            self.astep = params[21]
+            self.tested = params[22]
+            self.occupying_bed = params[23]
+            # Economic assumptions
+            self.cumul_private_value = params[24]
+            self.cumul_public_value = params[25]
+            # Employment
+            self.employed = params[26]
+            # Contact tracing: this is only available for symptomatic patients
+            self.tested_traced = params[27]
+            # All agents
+            self.contacts = params[28]
+            # We assume it takes two full days
+            self.tracing_delay = params[29]
+            self.tracing_counter = params[30]
+            # vaccination variables
+            self.vaccinated = params[31]
+            self.safetymultiplier = params[32]
+            self.current_effectiveness = params[33]
+            self.vaccination_day = params[34]
+            self.vaccine_count = params[35]
+            self.dosage_eligible = params[36]
+            self.fully_vaccinated =  params[37]
+            self.variant = params[38]
+            self.variant_immune = params[39]

config.py

@@ -0,0 +1,20 @@
+#!/usr/bin/python
+from configparser import ConfigParser
+
+
+def config(filename='database/database.ini', section='postgresql'):
+    # create a parser
+    parser = ConfigParser()
+    # read config file
+    parser.read(filename)
+
+    # get section, default to postgresql
+    db = {}
+    if parser.has_section(section):
+        params = parser.items(section)
+        for param in params:
+            db[param[0]] = param[1]
+    else:
+        raise Exception('Section {0} not found in the {1} file'.format(section, filename))
+
+    return db