def tabulateStateResults(states, showMethods=False):
results = OrderedDict()
results["State"] = []
if showMethods:
for m in methodsToAvgOver: results[m+"(%)"] = []
results["Cases"] = []; results["Deaths"] = []; results["IncidenceRate"] = []
results["InfChance"] = []; results["ContactBudget"] = [];
#results["ActiveInf"] = []; results["infOnDate"] = []
diffAvg = {x:0 for x in methodsToAvgOver}
odds = {x:0 for x in methodsToAvgOver}
for s in states:
results["State"].append(s)
cumCases = dataIO.getEntryFromCTPData(s, userParams["consideredDate"], "positive")
cumCasesPerMil = float(cumCases)*1000000.0 / statePopulation[s]
results["Cases"].append(int(cumCasesPerMil))
cumDeaths = dataIO.getEntryFromCTPData(s, userParams["consideredDate"], "death")
cumDeathsPerMil = float(cumDeaths)*1000000.0 / statePopulation[s]
results["Deaths"].append(int(cumDeathsPerMil))
infectionsOverPast14Days = dataIO.getEntryFromCTPData(s, userParams["consideredDate"], "positiveIncrease", 14)
incidenceRate = (1000000.0*infectionsOverPast14Days)/statePopulation[s]
results["IncidenceRate"].append(int(incidenceRate*100)/100.0)
oddsAvg = 100 * model.getInfecProbability(s, userParams["TP"], userParams["contacts"], methodsToAvgOver, tqspace=userParams["timeToQuar"])
# results["InfProb(%)"].append(oddsAvg)
results["InfChance"].append(str(int(oddsAvg*100)/100.0)+"%")
outcome, maxM = model.getContactsBudget(s, userParams["TP"], userParams["comfortProb"], methodsToAvgOver,
tqspace=userParams["timeToQuar"])
if outcome != "NO LIMIT":
results["ContactBudget"].append(int(maxM))
else:
results["ContactBudget"].append(outcome)
# activeInfections = model.getPhi_i(s, userParams["TP"], userParams["contacts"], ["A"], tqspace=userParams["timeToQuar"])
# results["ActiveInf"].append(ceil(activeInfections))
#
# infOnDate = model.getInfectionsOnRefDateInRegion(userParams["consideredDate"], s, ["A"], tqspace=lag)
# results["infOnDate"].append(ceil(infOnDate))
if showMethods:
for method in methodsToAvgOver:
odds[method] = 100 * model.getInfecProbability(s, userParams["TP"], userParams["contacts"], method)
diffAvg[method] += (odds[method] - oddsAvg)
results[method+"(%)"].append(odds[method])
resultsCache["results"] = results
resultsCache["lastUpdate"] = datetime.datetime.now()
print("Cached results at", resultsCache["lastUpdate"])
outputResults(results, showMethods, diffAvg)
def study_pim_vs_m_CompareQuar(region, mspace, methods, quarValues):
for tq in quarValues:
pim = model.getInfecProbability(region, userParams["TP"], mspace, methods, tq)
plt.plot(mspace, pim, label=["No Quar" if tq==lag else "Quar aft 5d"])
figTitle = "Infection Probability vs contacts for " + region + ": p_t=" + str(int(userParams["TP"] * 100)) + "%"
showPlot(figTitle, "Number of contacts (m)", "Infection probability")
def study_pim_vs_m_compareDays(region, mspace, methods, dateValues):
for date in dateValues:
pim = model.getInfecProbability(region, userParams["TP"], mspace, methods, userParams["timeToQuar"], date)
plt.plot(mspace, pim, label=date)
figTitle = "Infection Probability vs contacts for " + region + ": p_t=" + str(int(userParams["TP"] * 100)) + "%"
showPlot(figTitle, "Number of contacts (m)", "Infection probability")
def study_pim_vs_m_Compare_p_t(region, mspace, methods, p_t_values):
for pt in p_t_values:
pim = model.getInfecProbability(region, pt, mspace, methods)
plt.plot(mspace, pim, label="p_t = " + str(pt))
figTitle = "Infection Probability vs contacts for " + region + " for different p_t values"
showPlot(figTitle, "Number of contacts (m)", "Infection probability")
def study_pim_vs_p_t_CompareMethodsForGivenRegion(region, ptspace, methodList):
for method in methodList:
pim = model.getInfecProbability(region, ptspace, userParams["contacts"], method)
plt.plot(ptspace, pim, methColors[method], label="Method-" + method)
figTitle = "Infection Prob vs Transfer Prob for " + region + ": " + str(userParams["contacts"]) + " contacts"
showPlot(figTitle, "Transfer probability (p_t)", "Infection probability")
def study_pim_vs_m_CompareMethodsForGivenRegion(region, mspace, methodList):
for method in methodList:
pim = model.getInfecProbability(region, userParams["TP"], mspace, method)
plt.plot(mspace, pim, methColors[method], label="Method-" + method)
figTitle = "Infection Prob vs contacts for " + region + ": p_t=" + str(int(userParams["TP"]*100)) + "%"
showPlot(figTitle, "Number of contacts (m)", "Infection probability")
def study_pim_vs_tq_CompareRegions(regionList, tqspace, methods):
for region in regionList:
pim = model.getInfecProbability(region, userParams["TP"], userParams["contacts"], methods, tqspace)
plt.plot(tqspace, pim, label=region)
figTitle = "Infection Probability vs days-to-quarantine " + ": p_t=" + str(int(userParams["TP"] * 100)) + "%"
plt.xlim(24,0)
showPlot(figTitle, "Days to quarantine (t_q)", "Infection probability")
def study_pim_vs_m_CompareRegions(regionList, mspace, methods):
for region in regionList:
pim = model.getInfecProbability(region, userParams["TP"], mspace, methods)
#plt.plot(mspace, pim, regColors[region], label=region)
plt.plot(mspace, pim, label=region)
figTitle = "Infection Probability vs contacts:" + " p_t=" + str(int(userParams["TP"]*100)) + "%"
showPlot(figTitle, "Number of contacts (m)", "Infection probability")
def tabulateRoleInfProbabilities(states):
scenarioVals = {
"Grocery Store Shopper": (500, 15),
"Grocery Store Cashier": (200, 100),
"Warehouse Worker": (100, 50),
"Meatpacking Worker": (10, 50),
"Subway Rider": (75, 30),
"Takeout Cashier": (200, 30),
"Non ER Nurse": (20, 20)
}
for p in scenarioVals.keys():
print(p, " & ", "1/" + str(scenarioVals[p][0]), " & ", scenarioVals[p][1], end=" ")
for s in states:
pim = model.getInfecProbability(s, 1 / scenarioVals[p][0], scenarioVals[p][1], methodsToAvgOver)
print(" & ", "{:.2f}".format(100 * pim) + "\%", " ", end="")
print("\\\\ \hline")
def study_pim_vs_days_CompareRegions(regionList, drange, methods):
movAvgWin = userParams["avgOverDays"]
for region in regionList:
pims = []
for d in drange:
refDate = userParams["consideredDate"] - datetime.timedelta(days=d)
pim = model.getInfecProbability(region, userParams["TP"], userParams["contacts"], methods, userParams["timeToQuar"], refDate)
pims.append(pim)
pims.reverse()
pimsMA = model.movingAverage(pims, movAvgWin)
plt.plot(drange[(movAvgWin-1):], pimsMA, label=region)
figTitle = "Infection Prob. history " + ": " + str(userParams["contacts"]) + " contacts" + " p_t=" + \
str(int(userParams["TP"] * 100)) + "%, " + str(movAvgWin) + " day avg"
showPlot(figTitle, "Days since" + str(userParams["consideredDate"] - datetime.timedelta(days=userParams["history"])),
"Infection probability")
