Estimating COVID-19 Case Severity in each US County

Goal: Estimate what proportion of COVID-19 infections would require hospitalization in each US county based on reported COVID-19 severity by demographics and data on US county demographics.

1. Load Data

Conditional Fatality Rates by Age

These numbers are based on data from the Chinese CDC (http://weekly.chinacdc.cn/en/article/id/e53946e2-c6c4-41e9-9a9b-fea8db1a8f51).

I assume we can use these rates to estimate a lower bound of US hospitalizations by comorbidity and an upper bound for the number of fatalities.

cond_fatality_rates = fread('data/ccdc_rates_v5.csv')
cond_fatality_rates  %>% kable() %>% kable_styling()

age_decade	case_fatality_rate
0-9	0.000
10-19	0.002
20-29	0.002
30-39	0.002
40-49	0.004
50-59	0.013
60-69	0.036
70-79	0.080
80+	0.148

Conversion Factors between Fatalities to Severe, Critical, and Overall Cases

Using conversion factors from CCDC data for now. We do not have them conditional on age or comorbidities.

ccdc_ratios = fread('data/ccdc_ratios.csv')
ccdc_ratios

##              variable    value
## 1:   severe_per_death 6.029326
## 2: critical_per_death 2.040078

County Age Distribution

Data source:

Census County Population by Characteristics: 2010-2018 (https://www.census.gov/data/datasets/time-series/demo/popest/2010s-counties-detail.html?)

Using 2018 population estimates.

age_buckets_by_county = fread('data/county_age_populations_v5.csv')
age_buckets_by_county[CTYNAME == "Santa Clara County"]  %>% kable() %>% kable_styling()

FIPS	STNAME	CTYNAME	age_decade	pop
6085	California	Santa Clara County	0-9	231605
6085	California	Santa Clara County	10-19	237135
6085	California	Santa Clara County	20-29	278072
6085	California	Santa Clara County	30-39	302185
6085	California	Santa Clara County	40-49	270163
6085	California	Santa Clara County	50-59	252777
6085	California	Santa Clara County	60-69	186657
6085	California	Santa Clara County	70-79	109920
6085	California	Santa Clara County	80+	69056

2. Estimating COVID-19 Case Severity in each County

Steps to get simple estimate of severe cases:

Join the county population numbers with CCDC conditional fatality rates by age bucket.
Multiply the fatality rate by the CCDC ratios to get severe and critical case rates by county and age bracket.
Report the aggregated severity rates for each county.

Step 1: Join the county population numbers with CCDC conditional fatality rates by age bucket.

merged_pop_fat_rate = merge(
  age_buckets_by_county[, .(FIPS, State = STNAME, County = CTYNAME, age_decade, population_in_age_group = pop)],
  cond_fatality_rates,
  by = 'age_decade')

merged_pop_fat_rate[County == "Santa Clara County"]  %>% kable(digits=4) %>% kable_styling()

age_decade	FIPS	State	County	population_in_age_group	case_fatality_rate
0-9	6085	California	Santa Clara County	231605	0.000
10-19	6085	California	Santa Clara County	237135	0.002
20-29	6085	California	Santa Clara County	278072	0.002
30-39	6085	California	Santa Clara County	302185	0.002
40-49	6085	California	Santa Clara County	270163	0.004
50-59	6085	California	Santa Clara County	252777	0.013
60-69	6085	California	Santa Clara County	186657	0.036
70-79	6085	California	Santa Clara County	109920	0.080
80+	6085	California	Santa Clara County	69056	0.148

Step 2: Multiply the fatality rate by the CCDC ratios to get severe and critical case rates.

severe_per_death = ccdc_ratios[variable == 'severe_per_death', value][1]
critical_per_death = ccdc_ratios[variable == 'critical_per_death', value][1]

merged_pop_fat_rate[,`:=`(
  severe_cases_rate = severe_per_death*case_fatality_rate,
  critical_case_rate = critical_per_death*case_fatality_rate,
  hospitalizations_per_case = (severe_per_death+critical_per_death)*case_fatality_rate
)]

case_severity_rates = merged_pop_fat_rate[, 
  .(FIPS, State, County, age_decade, population_in_age_group, hospitalizations_per_case, severe_cases_rate, critical_case_rate, case_fatality_rate)]

case_severity_rates[County == "Santa Clara County"]  %>% kable(digits=3) %>% kable_styling()

FIPS	State	County	age_decade	population_in_age_group	hospitalizations_per_case	severe_cases_rate	critical_case_rate	case_fatality_rate
6085	California	Santa Clara County	0-9	231605	0.000	0.000	0.000	0.000
6085	California	Santa Clara County	10-19	237135	0.016	0.012	0.004	0.002
6085	California	Santa Clara County	20-29	278072	0.016	0.012	0.004	0.002
6085	California	Santa Clara County	30-39	302185	0.016	0.012	0.004	0.002
6085	California	Santa Clara County	40-49	270163	0.032	0.024	0.008	0.004
6085	California	Santa Clara County	50-59	252777	0.105	0.078	0.027	0.013
6085	California	Santa Clara County	60-69	186657	0.290	0.217	0.073	0.036
6085	California	Santa Clara County	70-79	109920	0.646	0.482	0.163	0.080
6085	California	Santa Clara County	80+	69056	1.194	0.892	0.302	0.148

Step 3: Report the aggregated severity rates for each county.

county_case_severity_rates = case_severity_rates[,.(
  hospitalizations_per_case = weighted.mean(hospitalizations_per_case, population_in_age_group),
  severe_cases_rate = weighted.mean(severe_cases_rate, population_in_age_group),
  critical_case_rate = weighted.mean(critical_case_rate, population_in_age_group),
  case_fatality_rate = weighted.mean(case_fatality_rate, population_in_age_group)
  ), by = c('FIPS', 'State', 'County')]

county_case_severity_rates[County == "Santa Clara County"] %>% kable(digits=3) %>% kable_styling()

FIPS	State	County	hospitalizations_per_case	severe_cases_rate	critical_case_rate	case_fatality_rate
6085	California	Santa Clara County	0.132	0.099	0.033	0.016

fwrite(county_case_severity_rates, 'data/county_case_severity_rates_v5.csv')

county_case_severity_rates[order(hospitalizations_per_case, decreasing = TRUE)][1:100] %>%
   kable(digits=3) %>% kable_styling()

FIPS	State	County	hospitalizations_per_case	severe_cases_rate	critical_case_rate	case_fatality_rate
15005	Hawaii	Kalawao County	0.421	0.315	0.107	0.052
12119	Florida	Sumter County	0.418	0.313	0.106	0.052
35021	New Mexico	Harding County	0.332	0.248	0.084	0.041
12015	Florida	Charlotte County	0.327	0.244	0.083	0.041
4012	Arizona	La Paz County	0.324	0.242	0.082	0.040
35003	New Mexico	Catron County	0.319	0.238	0.081	0.040
12115	Florida	Sarasota County	0.308	0.230	0.078	0.038
51103	Virginia	Lancaster County	0.304	0.227	0.077	0.038
12055	Florida	Highlands County	0.299	0.223	0.076	0.037
35051	New Mexico	Sierra County	0.296	0.221	0.075	0.037
51133	Virginia	Northumberland County	0.296	0.221	0.075	0.037
48299	Texas	Llano County	0.296	0.221	0.075	0.037
38051	North Dakota	McIntosh County	0.296	0.221	0.075	0.037
12017	Florida	Citrus County	0.296	0.221	0.075	0.037
38083	North Dakota	Sheridan County	0.295	0.220	0.075	0.037
26001	Michigan	Alcona County	0.295	0.220	0.074	0.037
26131	Michigan	Ontonagon County	0.294	0.219	0.074	0.036
32009	Nevada	Esmeralda County	0.291	0.218	0.074	0.036
41069	Oregon	Wheeler County	0.288	0.215	0.073	0.036
31103	Nebraska	Keya Paha County	0.287	0.215	0.073	0.036
26083	Michigan	Keweenaw County	0.287	0.214	0.072	0.036
53031	Washington	Jefferson County	0.284	0.212	0.072	0.035
48243	Texas	Jeff Davis County	0.282	0.210	0.071	0.035
51091	Virginia	Highland County	0.281	0.210	0.071	0.035
13281	Georgia	Towns County	0.280	0.209	0.071	0.035
12061	Florida	Indian River County	0.278	0.208	0.070	0.034
46021	South Dakota	Campbell County	0.275	0.205	0.069	0.034
12021	Florida	Collier County	0.274	0.204	0.069	0.034
41015	Oregon	Curry County	0.272	0.204	0.069	0.034
48327	Texas	Menard County	0.272	0.203	0.069	0.034
31091	Nebraska	Hooker County	0.270	0.202	0.068	0.034
45065	South Carolina	McCormick County	0.270	0.202	0.068	0.033
12085	Florida	Martin County	0.270	0.202	0.068	0.033
27001	Minnesota	Aitkin County	0.269	0.201	0.068	0.033
26143	Michigan	Roscommon County	0.266	0.199	0.067	0.033
55051	Wisconsin	Iron County	0.266	0.199	0.067	0.033
53055	Washington	San Juan County	0.266	0.198	0.067	0.033
29085	Missouri	Hickory County	0.265	0.198	0.067	0.033
13291	Georgia	Union County	0.265	0.198	0.067	0.033
37149	North Carolina	Polk County	0.265	0.198	0.067	0.033
31071	Nebraska	Garfield County	0.265	0.198	0.067	0.033
26119	Michigan	Montmorency County	0.265	0.198	0.067	0.033
20089	Kansas	Jewell County	0.264	0.197	0.067	0.033
26071	Michigan	Iron County	0.262	0.196	0.066	0.033
30079	Montana	Prairie County	0.262	0.196	0.066	0.032
51115	Virginia	Mathews County	0.262	0.196	0.066	0.032
26141	Michigan	Presque Isle County	0.261	0.195	0.066	0.032
20039	Kansas	Decatur County	0.261	0.195	0.066	0.032
31015	Nebraska	Boyd County	0.261	0.195	0.066	0.032
51119	Virginia	Middlesex County	0.260	0.194	0.066	0.032
5005	Arkansas	Baxter County	0.260	0.194	0.066	0.032
13239	Georgia	Quitman County	0.259	0.194	0.066	0.032
8055	Colorado	Huerfano County	0.258	0.193	0.065	0.032
53069	Washington	Wahkiakum County	0.257	0.192	0.065	0.032
46089	South Dakota	McPherson County	0.257	0.192	0.065	0.032
38039	North Dakota	Griggs County	0.257	0.192	0.065	0.032
25001	Massachusetts	Barnstable County	0.257	0.192	0.065	0.032
37175	North Carolina	Transylvania County	0.257	0.192	0.065	0.032
55125	Wisconsin	Vilas County	0.256	0.192	0.065	0.032
31133	Nebraska	Pawnee County	0.256	0.191	0.065	0.032
20065	Kansas	Graham County	0.256	0.191	0.065	0.032
26089	Michigan	Leelanau County	0.255	0.191	0.065	0.032
36041	New York	Hamilton County	0.255	0.190	0.064	0.032
4025	Arizona	Yavapai County	0.254	0.190	0.064	0.032
38029	North Dakota	Emmons County	0.254	0.190	0.064	0.032
20049	Kansas	Elk County	0.254	0.190	0.064	0.031
38001	North Dakota	Adams County	0.254	0.190	0.064	0.031
31165	Nebraska	Sioux County	0.253	0.189	0.064	0.031
41023	Oregon	Grant County	0.253	0.189	0.064	0.031
48263	Texas	Kent County	0.253	0.189	0.064	0.031
31085	Nebraska	Hayes County	0.253	0.189	0.064	0.031
20157	Kansas	Republic County	0.252	0.189	0.064	0.031
38103	North Dakota	Wells County	0.252	0.188	0.064	0.031
48403	Texas	Sabine County	0.252	0.188	0.064	0.031
46107	South Dakota	Potter County	0.252	0.188	0.064	0.031
29015	Missouri	Benton County	0.252	0.188	0.064	0.031
48171	Texas	Gillespie County	0.252	0.188	0.064	0.031
20183	Kansas	Smith County	0.251	0.188	0.064	0.031
30103	Montana	Treasure County	0.251	0.188	0.063	0.031
38037	North Dakota	Grant County	0.251	0.188	0.063	0.031
27073	Minnesota	Lac qui Parle County	0.250	0.187	0.063	0.031
24041	Maryland	Talbot County	0.250	0.187	0.063	0.031
12035	Florida	Flagler County	0.250	0.187	0.063	0.031
32029	Nevada	Storey County	0.249	0.186	0.063	0.031
4015	Arizona	Mohave County	0.249	0.186	0.063	0.031
29209	Missouri	Stone County	0.249	0.186	0.063	0.031
38045	North Dakota	LaMoure County	0.248	0.186	0.063	0.031
31069	Nebraska	Garden County	0.248	0.186	0.063	0.031
26069	Michigan	Iosco County	0.248	0.186	0.063	0.031
47035	Tennessee	Cumberland County	0.248	0.186	0.063	0.031
42113	Pennsylvania	Sullivan County	0.248	0.185	0.063	0.031
16059	Idaho	Lemhi County	0.248	0.185	0.063	0.031
30039	Montana	Granite County	0.248	0.185	0.063	0.031
30089	Montana	Sanders County	0.247	0.185	0.063	0.031
37043	North Carolina	Clay County	0.247	0.185	0.063	0.031
6091	California	Sierra County	0.247	0.185	0.062	0.031
48267	Texas	Kimble County	0.247	0.185	0.062	0.031
48385	Texas	Real County	0.246	0.184	0.062	0.031
41021	Oregon	Gilliam County	0.246	0.184	0.062	0.030
46069	South Dakota	Hyde County	0.246	0.184	0.062	0.030