Chapter 3.3: Maricopa County’s Estimated Infections and Rate of Detection¶
import pandas as pd
import requests
import numpy as np
import matplotlib.pyplot as plt
from functions import clean_deaths, clean_cases
deaths_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv')
deaths_df = clean_deaths(deaths_df)
cases_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv')
cases_df = clean_cases(cases_df)
deaths_df_MC = deaths_df.iloc[103,:]
deaths_df_MC = deaths_df_MC.reset_index()
cases_df_MC = cases_df.iloc[103,:]
cases_df_MC = cases_df_MC.reset_index()
index_val = len(cases_df_MC.index)
for ind in deaths_df_MC.index:
if (int(ind)+18) > index_val-1:
deaths_df_MC.loc[ind, 'total_infections'] = 0
else:
deaths_df_MC.loc[ind, 'total_infections'] = deaths_df_MC.iloc[ind+18, 1] * 100
deaths_df_MC = deaths_df_MC[0:-18]
merged_df_MC = pd.merge(deaths_df_MC, cases_df_MC, on = 'index', suffixes = ['_deaths', '_cases'])
merged_df_MC['detection_rate'] = merged_df_MC['103_cases'] / merged_df_MC['total_infections']
plt.figure(figsize=(12,8))
plt.plot(merged_df_MC['index'], merged_df_MC['total_infections'], label='Estimated Infections')
plt.plot(merged_df_MC['index'], merged_df_MC['103_cases'], label='Confirmed cases')
plt.xticks(np.arange(0, 111, step=21))
plt.xlabel('days')
plt.ylabel('total infections at 1%')
plt.legend()
<matplotlib.legend.Legend at 0x111f832d0>
This graph displays Maricopa’s estimated number of infections, calculated with death counts and a death rate of 1%, and the the number of confimred cases of COVID 19. Both infection estimate and confirmed cases are on an accelerating trajectory, but similar to LA County, Maricopa’s confirmed case count is substantially lower than the possible true number of infections.
merged_df_MC = merged_df_MC[55:-1]
merged_df_MC.hist(column='detection_rate', bins=10, figsize =(12,8))
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x119d7b5d0>]],
dtype=object)
To determine the average detection rate in Maricopa county, the number of reported cases is divided by its estimated counterpart for each date. As displayed in the histogram, Maricopa appears to detect approximately 10-20% of all COVID 19 cases.
merged_df_MC['detection_cases'] = merged_df_MC['103_cases'] / 0.2
plt.figure(figsize=(12,8))
plt.plot(merged_df_MC['index'], merged_df_MC['total_infections'], label='Death rate infections')
plt.plot(merged_df_MC['index'], merged_df_MC['detection_cases'], label='Detection rate cases')
plt.xticks(np.arange(0, 111, step=21))
plt.xlabel('days')
plt.ylabel('total infections at 1%')
plt.legend()
<matplotlib.legend.Legend at 0x11a06edd0>
Maricopa’s detection rate ranges from approximately 10-20%. Applying these detection rates to confirmed cases would produce another infection estimate, but this metric would incorporate a variable not directly produced by death counts. By cross validating infection estimates based on death rates with those based on detection rates, the validity of both estimation methods can be evaluated. In this graph I created the additional estimate of infections with a conservative 20% detection rate. To cross validate for accuracy, these estimates are compared to the original infection estimation based on a 1% death rate and reported deaths. As you can see, the estimates follow two different trajectories, with death rate calculated infections reporting higher infections until approximately June 2nd, where they cross over. One possible explanation for this is poor testing practices, especially in the early stages of COVID 19. It makes sense that a shortage of testing kits contributed to underreporting of COVID 19 cases. As tests become more available and accessible, more people are able to get tested, regardless of symptoms present.