covid疫苗效率的数据

接近 3 年

楼主 (北美华人网)

年底事情不多，刚发现10月的数据也出来了就把原来做过的分析又跑了下：看了下死亡率(定义为death/case，就是各个组里面得了covid死亡的比例），传统的感染和死亡保护率（1- (#vaxx outcome /#vaxx population)/ (#unvaxx outcome/#unvaxx population), outcome= case/death) ，数据还是非常明显说明疫苗有用（最近两个月的数据估计得1-2月才能有，不管如何这半年的数据已经说明没有vaccination只会更惨），anti-vaxx的人可以闭嘴了(特别是那些拿钱发帖自己打了还不停板上黑疫苗的有效性的精粉id，比如啥beaver/storm这些）
数据在这里，包含全美20多个州，https://data.cdc.gov/Public-Health-Surveillance/Rates-of-COVID-19-Cases-or-Deaths-by-Age-Group-and/3rge-nu2a/
死亡率：

死亡率比：unvaxx mortality rate /vaxx mortality rate (没有数据的说明vaxx组那一周没人死）

感染保护率：

死亡保护率:

代码：把数据下载存成covid_data.csv
#!/usr/bin/env python # coding: utf-8
# In[1]:

import pandas as pd import numpy as np import matplotlib.pyplot as plt
df = pd.read_csv("covid_data.csv", usecols = ["outcome", "MMWR week", "Age group", "Vaccine product",                                             "Vaccinated with outcome", "Unvaccinated with outcome",                                             "Fully vaccinated population", "Unvaccinated population"]) df.columns = df.columns.str.replace(" ", "_") df = df.query("Vaccine_product == 'all_types'") # no detailed data to do vaccine-type*age_group analysis
vaxx_outcome_col = "Vaccinated_with_outcome" unvaxx_outcome_col = "Unvaccinated_with_outcome" total_outcome_col = "Total_with_outcome" df[total_outcome_col] = df[vaxx_outcome_col] + df[unvaxx_outcome_col]
buckets = { "age_below_50" : ["12-17", "18-29", "30-49"], "age_above_50" : ["50-64", "65-79", "80+"]}
outcome_cols = [vaxx_outcome_col, unvaxx_outcome_col, total_outcome_col] agg_cols = outcome_cols + ["Fully_vaccinated_population", "Unvaccinated_population"] grouped = df.groupby(["outcome", "MMWR_week", "Vaccine_product"])
df_list = [df] for key, value in buckets.items():    summary = grouped.apply(lambda group: group.query("Age_group in @value")[agg_cols].sum())    summary = summary.reset_index()    summary["Age_group"] = key    df_list.append(summary)
df = pd.concat(df_list) df = df.sort_values("MMWR_week")

# In[2]:

table = df.pivot_table(index=["MMWR_week","Age_group", "Vaccine_product"], columns = ["outcome"],                       values = outcome_cols) table = table.swaplevel(axis=1)
mortality_rate = table["death"] / table["case"]
mortality_rate_cols = [name.split('_')[0] + "_rate" for name in mortality_rate.columns] mortality_rate.columns = mortality_rate_cols
mortality_rate["ratio"] = mortality_rate["Unvaccinated_rate"] / mortality_rate["Vaccinated_rate"] mortality_rate = mortality_rate.reset_index().set_index("MMWR_week")
fig, axes = plt.subplots(3,3, figsize=(25,10)) plt.subplots_adjust(hspace=0.5) for n, bucket in enumerate(['all_ages_adj', 'age_below_50', 'age_above_50', '12-17','18-29', '30-49', '50-64', '65-79', '80+']):    i, j = np.divmod(n, 3)
   subset = "Age_group == @bucket and Vaccine_product == 'all_types'"    mortality_rate.query(subset)[mortality_rate_cols].plot(ax = axes[i, j], title = bucket)

# In[3]:

df = df.set_index("MMWR_week")

# In[4]:

df["vaxx_outcome_rate"] = df["Vaccinated_with_outcome"] / df["Fully_vaccinated_population"] df["unvaxx_outcome_rate"] = df["Unvaccinated_with_outcome"] / df["Unvaccinated_population"] df["total_outcome_rate"] = (df["Vaccinated_with_outcome"] + df["Unvaccinated_with_outcome"]) / (df["Fully_vaccinated_population"] + df["Unvaccinated_population"])

# In[5]:

outcome = "case" cols = ["total_outcome_rate", "vaxx_outcome_rate", "unvaxx_outcome_rate"]
fig, axes = plt.subplots(3,3, figsize=(25,10)) plt.subplots_adjust(hspace=0.5)
for n, bucket in enumerate(['all_ages_adj', 'age_below_50', 'age_above_50', '12-17','18-29', '30-49', '50-64', '65-79', '80+']):    i, j = np.divmod(n, 3)
   subset = "Age_group == @bucket and outcome == @outcome"    df.query(subset)[cols].plot(ax = axes[i, j], title = bucket)
plt.savefig(outcome + "_rate_by_age_group.jpg")

# In[6]:

outcome = "death" cols = ["total_outcome_rate", "vaxx_outcome_rate", "unvaxx_outcome_rate"]
fig, axes = plt.subplots(3,3, figsize=(25,10)) plt.subplots_adjust(hspace=0.5)
for n, bucket in enumerate(['all_ages_adj', 'age_below_50', 'age_above_50', '12-17','18-29', '30-49', '50-64', '65-79', '80+']):    i, j = np.divmod(n, 3)
   subset = "Age_group == @bucket and outcome == @outcome"    df.query(subset)[cols].plot(ax = axes[i, j], title = bucket)
plt.savefig(outcome + "_rate_by_age_group.jpg")

# In[7]:

outcome = "case" cols = ["Unvaccinated_with_outcome", "Vaccinated_with_outcome", "Total_with_outcome"]
fig, axes = plt.subplots(3,3, figsize=(25,10)) plt.subplots_adjust(hspace=0.5)
for n, bucket in enumerate(['all_ages_adj', 'age_below_50', 'age_above_50', '12-17','18-29', '30-49', '50-64', '65-79', '80+']):    i, j = np.divmod(n, 3)
   subset = "Age_group == @bucket and outcome == @outcome and Vaccine_product == 'all_types'"    df.query(subset)[cols].plot(ax = axes[i, j], title = bucket)
plt.savefig(outcome + "_count_by_age_group.jpg")

# In[8]:

outcome = "death" cols = ["Unvaccinated_with_outcome", "Vaccinated_with_outcome", "Total_with_outcome"]
fig, axes = plt.subplots(3,3, figsize=(25,10)) plt.subplots_adjust(hspace=0.5)
for n, bucket in enumerate(['all_ages_adj', 'age_below_50', 'age_above_50', '12-17','18-29', '30-49', '50-64', '65-79', '80+']):    i, j = np.divmod(n, 3)
   subset = "Age_group == @bucket and outcome == @outcome and Vaccine_product == 'all_types'"    df.query(subset)[cols].plot(ax = axes[i, j], title = bucket)
plt.savefig(outcome + "_count_by_age_group.jpg")

# In[9]:

df["protection_rate"] = 1 - df["vaxx_outcome_rate"] / df["unvaxx_outcome_rate"]

# In[10]:

outcome = "case" cols = ["protection_rate"]
fig, axes = plt.subplots(3,3, figsize=(25,10)) plt.subplots_adjust(hspace=0.5)
for n, bucket in enumerate(['all_ages_adj', 'age_below_50', 'age_above_50', '12-17','18-29', '30-49', '50-64', '65-79', '80+']):    i, j = np.divmod(n, 3)
   subset = "Age_group == @bucket and outcome == @outcome"    df.query(subset)[cols].plot(ax = axes[i, j], title = bucket)
plt.savefig(outcome + "_protection_rate_by_age_group.jpg")

# In[11]:

outcome = "death" cols = ["protection_rate"]
fig, axes = plt.subplots(3,3, figsize=(25,10)) plt.subplots_adjust(hspace=0.5)
for n, bucket in enumerate(['all_ages_adj', 'age_below_50', 'age_above_50', '12-17','18-29', '30-49', '50-64', '65-79', '80+']):    i, j = np.divmod(n, 3)
   subset = "Age_group == @bucket and outcome == @outcome"    df.query(subset)[cols].plot(ax = axes[i, j], title = bucket)
plt.savefig(outcome + "_protection_rate_by_age_group.jpg")

# In[12]:

df["proportion"] = df["Vaccinated_with_outcome"] / (df["Unvaccinated_with_outcome"] + df["Vaccinated_with_outcome"])

# In[13]:

outcome = "case" cols = ["proportion"]
fig, axes = plt.subplots(3,3, figsize=(25,10)) plt.subplots_adjust(hspace=0.5)
for n, bucket in enumerate(['all_ages_adj', 'age_below_50', 'age_above_50', '12-17','18-29', '30-49', '50-64', '65-79', '80+']):    i, j = np.divmod(n, 3)
   subset = "Age_group == @bucket and outcome == @outcome"    df.query(subset)[cols].plot(ax = axes[i, j], title = bucket)
plt.savefig(outcome + "_proportion_by_age_group.jpg")

# In[14]:

outcome = "death" cols = ["proportion"]
fig, axes = plt.subplots(3,3, figsize=(25,10)) plt.subplots_adjust(hspace=0.5)
for n, bucket in enumerate(['all_ages_adj', 'age_below_50', 'age_above_50', '12-17','18-29', '30-49', '50-64', '65-79', '80+']):    i, j = np.divmod(n, 3)
   subset = "Age_group == @bucket and outcome == @outcome"    df.query(subset)[cols].plot(ax = axes[i, j], title = bucket)
plt.savefig(outcome + "_proportion_by_age_group.jpg")

# In[15]:

fig, axes = plt.subplots(2,3, figsize=(25,10)) plt.subplots_adjust(hspace=0.5) buckets = ["age_above_50", "age_below_50"] buckets = ['12-17','18-29', '30-49', '50-64', '65-79', '80+']
for i, col in enumerate(['case', 'death']):    for j, value in enumerate([total_outcome_col, vaxx_outcome_col, unvaxx_outcome_col]):        counts = df.query("outcome == @col").reset_index()             .pivot(index="MMWR_week", columns = "Age_group", values = value)        pct = counts.div(counts["all_ages_adj"], axis=0).drop(columns="all_ages_adj").reset_index()        subset = ["MMWR_week"] + buckets        pct[subset].plot(x = "MMWR_week", stacked=True, kind = 'bar', ax = axes[i, j], title = value)

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