/
GlobalModel.py
450 lines (349 loc) · 21.8 KB
/
GlobalModel.py
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"""
Copyright (C) 2020 Eili Klein
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
# System Imports
import numpy as np
import random
import os, shutil
import gc
import math
import time
import pandas as pd
from datetime import datetime
from datetime import timedelta
import traceback
import copy
## Model Imports
import ProcessManager
import GlobalLocationSetup
import ParameterSet
import data.ConstructInteractionMatrix
import Utils
import PostProcessing
def getCountyHHsAgesMatrix(dfHH,dfNational57,fip_code,zip_code):
SelectedCounty = dfHH.loc[fip_code,:].values
SelectedCountyAges = SelectedCounty * dfNational57.values
HHSizeDist = list(SelectedCounty*100)
HHSizeDist = [round(elem, 2) for elem in HHSizeDist]
HHSizeAgeDist = {}
for i in range(len(HHSizeDist)):
HHSizeAgeDist[i+1] = [round(elem, 2) for elem in SelectedCountyAges[:,i]*100]
return HHSizeDist, HHSizeAgeDist
def getHospitalData(ModelType,modelvals,popdata):
#""" Import Adjacency Matrix from the Input Folder """
if int(modelvals['UseHospital']) == 1:
try:
ComHosAdj = pd.read_csv(os.path.join("data",ModelType,modelvals['HospitalMatrixFile']), index_col=0)
except:
print("Error reading Hospital Matrix file. Please ensure this is correctly specified")
if ParameterSet.logginglevel == "debug":
print(traceback.format_exc())
raise Exception("File Read Error")
#""" Create Adjacency Flow Matrix """
td = pd.DataFrame(popdata[modelvals['GeographicScale']].copy())
td = td.merge(pd.DataFrame(ComHosAdj), how='left',
left_on=modelvals['GeographicScale'], right_index=True) # left join on population > 0
td = td.set_index(modelvals['GeographicScale'])
TranCH = np.asarray(td.values)
HosNames = list(ComHosAdj.columns)
#FacList = pd.read_csv(os.path.join("data",ModelType,modelvals['HospitalNamesFile']))
#HosNames = []
# This makes sure it runs on all unix systems
#for i in range(0,len(FacList.HOSPID)):
# HosNames.append(FacList.ProviderNames[i].encode("ascii",errors="ignore").decode())
else:
PopulationData = np.asarray(popdata['POPULATION'])
RegionalNames = np.asarray(popdata[modelvals['RegionalPopName']])
hospitals = np.unique(np.asarray(popdata[modelvals['RegionalPopName']]))
HospitalInteractionMatrix = np.empty([len(PopulationData), len(hospitals)], np.single)
for i in range(0, len(PopulationData)):
for j in range(0, len(hospitals)):
if RegionalNames[i] == hospitals[j]:
HospitalInteractionMatrix[i][j] = (1)
else:
HospitalInteractionMatrix[i][j] = (0)
TranCH = np.empty([len(PopulationData), len(hospitals)], np.single)
for i in range(0, len(PopulationData)):
rowSum = 0.0
for j in range(0, len(hospitals)):
rowSum = rowSum + HospitalInteractionMatrix[i, j]
for j in range(0, len(hospitals)):
TranCH[i][j] = HospitalInteractionMatrix[i, j] / rowSum
HosNames = []
for i in range(0,len(hospitals)):
HosNames.append(hospitals[i])
return TranCH, HosNames
def LoadModel(ModelType,modelvals,DiseaseParameters,substate=None):
print("Loading",ModelType," ...")
PopData = pd.read_csv(os.path.join("data",ModelType,modelvals['PopulationFile']))
if substate:
PopData = PopData.loc[PopData['stname'] == substate]
# just check that there are no NAs in the file
PopData = PopData.dropna(subset=['POPULATION'])
PopData = PopData[PopData.POPULATION != 0].copy()
# delete all rows not in ED Matrix
PopulationData = np.asarray(PopData['POPULATION'])
PopulationDensity = np.asarray(PopData['POPULATION']/PopData['SQMI'])
GeoArea = np.asarray(PopData[modelvals['GeographicScale']])
CountyFIP = np.asarray(PopData['STCOUNTYFP'])
LPNames = np.asarray(PopData[modelvals['LocalPopName']])
RegionalNames = np.asarray(PopData[modelvals['RegionalPopName']])
LongCentroid = np.asarray(PopData['Longitude'])
LatCentroid = np.asarray(PopData['Latitude'])
BAProportion = np.asarray(PopData['BAProportion'])
NursingCareFacilities = np.asarray(PopData['NursingCareFacilities'])
AssistedLivingFacilities = np.asarray(PopData['AssistedLivingFacilities'])
LTCF = np.asarray(PopData['LTCF'])
HealthcareWorkerPercent = np.asarray(PopData['HealthcareWorkerPercent'])
# Now get the hospital data
HospitalTransitionRate, HospitalColNames = getHospitalData(ModelType,modelvals,PopData)
numpops = len(PopulationData) # number of HRRs
print("Found: ", numpops, " Populations, with a total population of ", sum(PopulationData), " (mean:",
sum(PopulationData) / len(PopulationData), " max:", max(PopulationData), " min:", min(PopulationData),")")
InteractionMatrix = data.ConstructInteractionMatrix. \
CreateInteractionMatrix(LongCentroid, LatCentroid, PopulationData)
# Get the data to create the household matrices
dfHH = pd.read_csv(os.path.join("data","HHSize_USCounty.csv"), index_col = 'FIPS')
dfHH = dfHH.loc[:,'1.Person.Household':].div(dfHH.Total, axis=0) # get the percentage
dfNational57 = pd.read_csv(os.path.join("data","AgeAvgHH_Matrix.csv"), index_col = 0)
#county_fips county_name PRE.mean APRIL.mean MAY.LastWeek.mean
#print(dfPhoneData)
if DiseaseParameters['UseCountyLevel'] == 1:
# Get the county level phonse use data if using
dfPhoneData = pd.read_csv(os.path.join("data",ModelType,DiseaseParameters['CountyEncountersFile']), index_col = 'county_fips')
print("Using County Level Data",DiseaseParameters['CountyEncountersFile'])
# Now load the global locations
GlobalLocations = []
countytransmissionrates = {}
countyvisitrates = {}
for G in range(0, numpops):
HHSizeDist, HHSizeAgeDist = getCountyHHsAgesMatrix(dfHH,dfNational57,CountyFIP[G],GeoArea[G])
newdeclinevals = []
newdeclinevalsLow = []
if DiseaseParameters['UseCountyLevel'] == 1:
countyrows = dfPhoneData.loc[CountyFIP[G],:]
#try:
# visitrateraw = countyrows.iloc[:,dfPhoneData.columns.tolist().index('AverageDiff')].values.tolist()
#except:
# print("AverageDiffMissing")
visitrateraw = countyrows.iloc[:,dfPhoneData.columns.tolist().index('daily_visitation_diff')].values.tolist()
visitratedateraw = countyrows.iloc[:,dfPhoneData.columns.tolist().index('date')].values.tolist()
fixvisitrate = {}
for d in range(0,len(visitratedateraw)):
dval = Utils.dateparser(visitratedateraw[d])
fixvisitrate[dval] = visitrateraw[d]
visitrate = []
visitratetest = []
for k in sorted (fixvisitrate.keys()) :
visitrate.append(fixvisitrate[k])
visitratetest.append(k)
#if CountyFIP[G] not in countyvisitrates.keys():
# countyvisitrates[CountyFIP[G]] = visitrate
unacaststdate = Utils.dateparser('2020-02-24')
day_count = (unacaststdate - DiseaseParameters['startdate']).days
TransProbC = []
TransProbCLow = []
for i in range(0,day_count):
TransProbC.append(DiseaseParameters['TransProb'][i])
TransProbCLow.append(DiseaseParameters['TransProbLow'][i])
lastseven = []
for i in range(0,len(visitrate)):
#DiseaseParameters['TransProb_AH'].append((1-1/(1+0.4*math.exp(-float(ahvals[i])*.1)))*probtransscale)
#DiseaseParameters['TransProb_intnumval'].append(intnumval[i])
if math.isnan(float(visitrate[i])):
visitrate[i] = (visitrate[i-1]+visitrate[i+1])/2
if math.isnan(float(visitrate[i])):
visitrate[i] = visitrate[i-1]
if math.isnan(float(visitrate[i])):
visitrate[i] = visitrate[i+1]
if math.isnan(float(visitrate[i])):
print("Visit rates are not correct. Please check and rerun")
exit()
day_count+=1
transprobval = DiseaseParameters['TransProb_AH'][day_count]*(1+float(visitrate[i])+DiseaseParameters['TransProb_intnumval'][day_count])
if transprobval < .001:
transprobval = .001
transprobvallow = max(transprobval*.5,.001)
transprobvalhigh = max((transprobval - transprobvallow*.4)/.6,.001)
TransProbC.append(transprobvalhigh)
TransProbCLow.append(transprobvallow)
if i >= (len(visitrate)-7):
lastseven.append(visitrate[i])
unacaststdate += timedelta(days=1)
lson = 0
lastval = 0
day_count+=1
while unacaststdate < Utils.dateparser('2021-03-01'):
unacaststdate += timedelta(days=1)
transprobval = DiseaseParameters['TransProb_AH'][day_count]*(1+float(lastseven[lson])+DiseaseParameters['TransProb_intnumval'][day_count])
lastval = lastseven[lson]
transprobvallow = max(transprobval*.5,.001)
transprobvalhigh = max((transprobval - transprobvallow*.4)/.6,.001)
TransProbC.append(transprobvalhigh)
TransProbCLow.append(transprobvallow)
if lson >= (len(lastseven)-1):
lson = 0
else:
lson+=1
day_count+=1
delta = lastval/15/2
while day_count < len(DiseaseParameters['TransProb_AH']):
unacaststdate += timedelta(days=1)
if unacaststdate < Utils.dateparser('2021-03-15'):
lastval -= delta
if unacaststdate > Utils.dateparser('2021-04-15') and unacaststdate < Utils.dateparser('2021-05-01'):
lastval -= delta
transprobval = DiseaseParameters['TransProb_AH'][day_count]*(1+float(lastval)+DiseaseParameters['TransProb_intnumval'][day_count])
transprobvallow = max(transprobval*.5,.001)
transprobvalhigh = max((transprobval - transprobvallow*.4)/.6,.001)
TransProbC.append(transprobvalhigh)
TransProbCLow.append(transprobvallow)
day_count+=1
DiseaseParameters['TransProb'] = TransProbC.copy()
DiseaseParameters['TransProbLow'] = TransProbCLow.copy()
#if CountyFIP[G] not in countytransmissionrates.keys():
# countytransmissionrates[CountyFIP[G]] = TransProbC
if DiseaseParameters['AdjustPopDensity']:
transmissonmodifier = 1/(1+ DiseaseParameters['pdscale1']*math.exp(-1*DiseaseParameters['pdscale2']*PopulationDensity[G])) ## pdscale1 = .25 / pdscale2 = .001
for TP in range(0,len(DiseaseParameters['TransProb'])):
newdeclinevals.append(DiseaseParameters['TransProb'][TP]*transmissonmodifier)
newdeclinevalsLow.append(DiseaseParameters['TransProbLow'][TP]*transmissonmodifier)
else:
newdeclinevals = DiseaseParameters['TransProb'].copy()
newdeclinevalsLow = DiseaseParameters['TransProbLow'].copy()
GL = GlobalLocationSetup.\
GlobalLocationSetup(G, PopulationData[G], HHSizeDist,HHSizeAgeDist,
DiseaseParameters, LPNames[G],RegionalNames[G],
BAProportion[G]-HealthcareWorkerPercent[G],NursingCareFacilities[G]+AssistedLivingFacilities[G]+LTCF[G],newdeclinevals,newdeclinevalsLow)
GlobalLocations.append(GL)
print("Loaded Populations")
#csvFile = "countytransmissionrates.csv"
#with open(csvFile, 'w') as f:
# for key in countytransmissionrates.keys():
# f.write("%s,%s\n" % (key,countytransmissionrates[key]))
# f.write("\n")
#csvFile = "countyvisitrates.csv"
#with open(csvFile, 'w') as f:
# for key in countyvisitrates.keys():
# f.write("%s,%s\n" % (key,countyvisitrates[key]))
# f.write("\n")
return PopulationData, InteractionMatrix, HospitalTransitionRate, HospitalColNames,GlobalLocations
def modelSetup(ModelType,modelvals,PopulationParameters,DiseaseParameters,substate=None):
## Need error check here to make sure that modelpopnames is a valid system name
#if modelPopNames is None:
# modelPopNames = 'region'
GlobalInteractionMatrix = []
HospitalTransitionRate = []
PopulationDensity = []
WuhanCoordDict = {}
GlobalLocations = []
PopulationData, GlobalInteractionMatrix, HospitalTransitionRate, HospitalNames, GlobalLocations = LoadModel(ModelType,modelvals,DiseaseParameters,substate=substate)
LocationImportationRisk = []
popsum = sum(PopulationData)
for i in range(0,len(PopulationData)):
LocationImportationRisk.append(PopulationData[i]/popsum)
return PopulationData, GlobalInteractionMatrix, HospitalTransitionRate, HospitalNames, GlobalLocations, LocationImportationRisk
# This is called from main
def RunDefaultModelType(ModelType,modelvals,modelPopNames,resultsName,PopulationParameters,DiseaseParameters,endTime,mprandomseed,stepLength=1,writefolder='',startDate=datetime(2020,2,1),fitdates=[],hospitalizations=[],deaths=[],cases=[],fitper=.3,StartInfected=-1,historyData={},vaccinationdata={}):
cleanUp(modelPopNames)
ParameterVals = PopulationParameters
ParameterVals.update(DiseaseParameters)
PopulationData, GlobalInteractionMatrix, HospitalTransitionRate, HospitalNames, GlobalLocations, LocationImportationRisk = modelSetup(ModelType,modelvals,PopulationParameters,DiseaseParameters)
RegionalList, timeRange, fitinfo = ProcessManager.RunModel(GlobalLocations, GlobalInteractionMatrix, HospitalTransitionRate,LocationImportationRisk,PopulationParameters,DiseaseParameters,endTime,resultsName,mprandomseed,startDate=startDate,stepLength=1,numregions=-1,modelPopNames=modelPopNames,fitdates=fitdates,hospitalizations=hospitalizations,deaths=deaths,cases=cases,fitper=fitper,burnin=False,StartInfected=StartInfected,historyData=historyData,vaccinationdata=vaccinationdata)
PostProcessing.WriteParameterVals(resultsName,ModelType,ParameterVals,writefolder)
results = PostProcessing.CompileResults(resultsName,modelPopNames,RegionalList,timeRange)
PostProcessing.WriteAggregatedResults(results,ModelType,resultsName,modelPopNames,RegionalList,HospitalNames,endTime,writefolder)
cleanUp(modelPopNames,len(RegionalList))
if os.path.exists(os.path.join(ParameterSet.ResultsFolder,"Results_"+resultsName+".pickle")):
os.remove(os.path.join(ParameterSet.ResultsFolder,"Results_"+resultsName+".pickle"))
return fitinfo
# This is called from main
def RunSavedRegionModelType(ModelType,modelvals,modelPopNames,resultsName,PopulationParameters,DiseaseParameters,endTime,mprandomseed,stepLength=1,writefolder='',startDate=datetime(2020,2,1),SavedRegionFolder='',numregions=-1,FolderContainer='',vaccinationdata={}):
cleanUp(modelPopNames)
ParameterVals = PopulationParameters
ParameterVals.update(DiseaseParameters)
PopulationData, GlobalInteractionMatrix, HospitalTransitionRate, HospitalNames, GlobalLocations, LocationImportationRisk = modelSetup(ModelType,modelvals,PopulationParameters,DiseaseParameters)
RegionalList, timeRange, fitinfo = ProcessManager.RunModel(GlobalLocations, GlobalInteractionMatrix, HospitalTransitionRate,LocationImportationRisk,PopulationParameters,DiseaseParameters,endTime,resultsName,mprandomseed,startDate=startDate,modelPopNames=modelPopNames,SavedRegionFolder=SavedRegionFolder,numregions=numregions,FolderContainer=FolderContainer,vaccinationdata=vaccinationdata)
if fitinfo['fitted']:
PostProcessing.WriteFitvals(resultsName,ModelType,fitinfo['SLSH'], fitinfo['SLSD'], fitinfo['SLSC'], fitinfo['avgperdiffhosp'], fitinfo['avgperdiffdeaths'], fitinfo['avgperdiffcases'],writefolder)
PostProcessing.WriteParameterVals(resultsName,ModelType,ParameterVals,writefolder)
results = PostProcessing.CompileResults(resultsName,modelPopNames,RegionalList,timeRange)
PostProcessing.WriteAggregatedResults(results,ModelType,resultsName,modelPopNames,RegionalList,HospitalNames,endTime,writefolder)
cleanUp(modelPopNames,len(RegionalList))
if os.path.exists(os.path.join(ParameterSet.ResultsFolder,"Results_"+resultsName+".pickle")):
os.remove(os.path.join(ParameterSet.ResultsFolder,"Results_"+resultsName+".pickle"))
return fitinfo
def RunBurnin(ModelType,modelvals,modelPopNames,resultsName,PopulationParameters,DiseaseParameters,endTime,mprandomseed,stepLength=1,writefolder='',startDate=datetime(2020,2,1),fitdates=[],hospitalizations=[],deaths=[],cases=[],fitper=.3,FolderContainer='',saveRun=False,historyData={},SavedRegionFolder=ParameterSet.SavedRegionFolder,burnin=True,vaccinationdata={}):
if saveRun:
if not os.path.exists(os.path.join(SavedRegionFolder,FolderContainer)):
os.makedirs(os.path.join(SavedRegionFolder,FolderContainer))
cleanUp(modelPopNames)
print("RunBurnin: TransProb_AH Len:",len(DiseaseParameters['TransProb_AH']))
PopulationData, GlobalInteractionMatrix, HospitalTransitionRate, HospitalNames, GlobalLocations, LocationImportationRisk = modelSetup(ModelType,modelvals,PopulationParameters,DiseaseParameters)
ParameterVals = PopulationParameters
ParameterVals.update(DiseaseParameters)
RegionalList, timeRange, fitinfo = ProcessManager.RunModel(GlobalLocations, GlobalInteractionMatrix, HospitalTransitionRate,LocationImportationRisk,PopulationParameters,DiseaseParameters,endTime,resultsName,mprandomseed,startDate=startDate,modelPopNames=modelPopNames,fitdates=fitdates,hospitalizations=hospitalizations,deaths=deaths,cases=cases,fitper=fitper,burnin=burnin,FolderContainer=FolderContainer,saveRun=saveRun,historyData=historyData,SavedRegionFolder=SavedRegionFolder,vaccinationdata=vaccinationdata)
if saveRun and fitinfo['fitted']:
PostProcessing.WriteFitvals(resultsName,ModelType,fitinfo['SLSH'], fitinfo['SLSD'], fitinfo['SLSC'], fitinfo['avgperdiffhosp'], fitinfo['avgperdiffdeaths'], fitinfo['avgperdiffcases'],writefolder)
Utils.PickleFileWrite(os.path.join(SavedRegionFolder,FolderContainer,"PopulationParameters.pickle"), PopulationParameters)
Utils.PickleFileWrite(os.path.join(SavedRegionFolder,FolderContainer,"DiseaseParameters.pickle"), DiseaseParameters)
else:
if os.path.exists(os.path.join(SavedRegionFolder,FolderContainer)):
os.rmdir(os.path.join(SavedRegionFolder,FolderContainer))
cleanUp(modelPopNames,len(RegionalList))
if os.path.exists(os.path.join(ParameterSet.ResultsFolder,"Results_"+resultsName+".pickle")):
os.remove(os.path.join(ParameterSet.ResultsFolder,"Results_"+resultsName+".pickle"))
return fitinfo
def cleanUp(modelPopNames='',lengthnum=1000):
# Cleanup population data
for i in range(0, lengthnum):
try:
os.remove(os.path.join(ParameterSet.PopDataFolder,str(modelPopNames)+str(i)+".pickle"))
except:
#print("error removing main model")
pass
try:
os.remove(os.path.join(ParameterSet.PopDataFolder,str(modelPopNames)+str(i)+"STATS.pickle"))
except:
#print("error removing Stats")
pass
try:
os.remove(os.path.join(ParameterSet.PopDataFolder,str(modelPopNames)+str(i)+"AgeStats.pickle"))
except:
#print("error removing AgeStats")
pass
try:
os.remove(os.path.join(ParameterSet.PopDataFolder,str(modelPopNames)+str(i)+"RegionStats.pickle"))
except:
#print("error removing RegionStats")
pass
try:
os.remove(os.path.join(ParameterSet.PopDataFolder,str(modelPopNames)+str(i)+"HOSPLIST.pickle"))
except:
#print("error removing HOSPLIST")
pass
try:
os.remove(os.path.join(ParameterSet.QueueFolder,str(modelPopNames)+str(i)+"Queue.pickle"))
except:
#print("error removing Queue")
pass
try:
os.remove(os.path.join(ParameterSet.QueueFolder,str(modelPopNames)+str(i)+"testextra.pickle"))
except:
#print("error removing testextra")
pass
try:
os.remove(os.path.join(ParameterSet.PopDataFolder,str(modelPopNames)+str(i)+"R0Stats.pickle"))
except:
#print("error removing ROstats")
pass