#join to add the selected column to match up the rows recode the dates
#recode data by quarter $ rate
#inner join to match all ca_vax_rates_grouped rows to sim_ca_la_incidence
#ready to join all recoding steps are done
sim_ca_la_vax<- inner_join(x = sim_ca_la_incidence, y=ca_vax_rates_grouped,
by = c("county", "age_cat", "quarter"))
#sim_ca_la_vax
#str(sim_ca_la_vax)
#geographic strata(s) of interest
bay_counties <-c("Alameda", "Contra Costa", "Marin", "Napa","San Francisco",
"San Mateo", "Santa Clara", "Solano","Sonoma")
sim_ca_la_vax_bay<- sim_ca_la_vax %>%
filter(county %in% bay_counties)
#sim_ca_la_vax_bay
#str(sim_ca_la_vax_bay)Milestone 4 Group 12
Project Milestone # 4
Specific Expectations for Milestone #3:
Recoding Column Names, Values, Formats For Flu Severity Datas
Covid Vaccination Rate Metrics & Summarize
Joining data-sets
After the calculation of flu incidence per 1000 in pediatric population in the Bay Area for the flu season (Q4 2022 and Q1 2023), and the calculation of vaccination coverage per county, the cleaned sim_ca_la_incidence and ca_vax_rates_grouped were joined.
Vistualizations
Chart: Flu incidence rate in pediatric population of the Bay Area during the Flu season of 2022 and 2023
# Barchart comparing incidence rate during Q4 2022 and Q1 2023
flu_season_incidence <- sim_ca_la_vax_bay %>%
mutate(quarter = case_when(quarter == "2022-10-01" ~ "Q4 2022",
quarter == "2023-01-01" ~ "Q1 2023")) %>%
arrange(quarter) %>% select(quarter, county, incidence_per_1000)
ggplot(flu_season_incidence, aes(x = county, y = incidence_per_1000, fill = quarter)) +
geom_bar(aes(fill = quarter), stat = "identity",
position = position_dodge(width = 0.5)) +
labs(x = "Bay Area Counties", y = "Cases per 1,000", fill = "Quarter",
title = "Bay Area Flu Incidence Rate in Pediatric Population", subtitle = "October 2022 - March 2023, 0 -17 years old") +
theme(axis.text.x = element_text(size = 7, angle = 45))
Interpretation: The bar chart above, shows an increase of approximately 3 times of Flu cases during the months on January, February and March of 2023 in the pediatric population of the Bay Area Counties compared to the Q4 (Oct-Nov-Dec) of 2022.
# Summary table for flu incidence, morbidity and cases for the Bay Area during flu season compared to vaccination rate.
flu_summary_vax_tbl <- kable(sim_ca_summary, format = "html",
col.names = c("County", "Population",
"New Infections Q4 2022",
"New Infections Q1 2023",
"Iincidence Rate / 1000 Q4 2022",
"Incidence Rate / 1000 Q1 2023",
"Severity Incidence Q4 2022",
"Severity Incidence Q1 2023",
"COVID-19 vaccination coverage % Q4 2022",
"COVID-19 vaccination coverage % Q1 2023"),
caption = "Flu Season Incidence and Morbidity in Pediatric Population in the
Bay Area, Q4 2022 and Q1 2023 and COVID-19 vaccination coverage",
digits = 1) %>%
kable_styling(bootstrap_options = "condensed", full_width = FALSE) %>%
column_spec(column = 1, bold = TRUE)
flu_summary_vax_tbl| County | Population | New Infections Q4 2022 | New Infections Q1 2023 | Iincidence Rate / 1000 Q4 2022 | Incidence Rate / 1000 Q1 2023 | Severity Incidence Q4 2022 | Severity Incidence Q1 2023 | COVID-19 vaccination coverage % Q4 2022 | COVID-19 vaccination coverage % Q1 2023 |
|---|---|---|---|---|---|---|---|---|---|
| Alameda | 338015 | 21974 | 76557 | 65.0 | 226.5 | 0.7 | 0.9 | 61.7 | 62.8 |
| Contra Costa | 238272 | 17621 | 55459 | 74.0 | 232.8 | 0.5 | 0.9 | 60.7 | 61.5 |
| Marin | 45611 | 2861 | 10563 | 62.7 | 231.6 | 0.3 | 0.7 | 76.7 | 77.9 |
| Napa | 26401 | 1734 | 6288 | 65.7 | 238.2 | 1.1 | 1.7 | 52.5 | 53.0 |
| San Francisco | 137000 | 9649 | 30942 | 70.4 | 225.9 | 0.7 | 0.9 | 56.5 | 57.6 |
| San Mateo | 155335 | 12028 | 36082 | 77.4 | 232.3 | 1.0 | 0.9 | 62.7 | 64.0 |
| Santa Clara | 420021 | 30297 | 97677 | 72.1 | 232.6 | 0.4 | 0.9 | 57.4 | 58.4 |
| Solano | 98272 | 6283 | 23904 | 63.9 | 243.2 | 0.3 | 1.1 | 37.3 | 37.6 |
| Sonoma | 87496 | 6255 | 20456 | 71.5 | 233.8 | 0.3 | 0.6 | 52.3 | 52.9 |
Interpretation: The table above, compares the new cases, incidence rate, severity incidence and COVID-19 vaccination coverage between Q4 2022 and Q1 2023 in the Bay Area counties in pediatric population. It can be observed that there is an increase of the cases from Q4 to Q1 of 3 times approximately. However, there is not a significant difference in morbidity over the observed period.
sim_bay_q4_incd <-sim_ca_la_vax_bay %>%
arrange(quarter) %>%
filter(quarter == "2022-10-01") %>%
select(county,incidence_per_1000,prop_fully_vax)
q4_incd_tbl <- kable(sim_bay_q4_incd , format = "html",
col.names = c("County","Flu Incidence Rate",
"COVID-19 Vaccination Rate"),
caption="COVID-19 Vaccination Rate/100 & Flu Incidence Rate/1000, \
Bay Area, October - December 2022, Ages 0-17",
digits = 1) %>%
kable_styling(bootstrap_options = "condensed", full_width = FALSE) %>%
column_spec(column = 1, bold = TRUE)
q4_incd_tbl| County | Flu Incidence Rate | COVID-19 Vaccination Rate |
|---|---|---|
| Alameda | 65.0 | 61.7 |
| Contra Costa | 74.0 | 60.7 |
| Marin | 62.7 | 76.7 |
| Napa | 65.7 | 52.5 |
| San Francisco | 70.4 | 56.5 |
| San Mateo | 77.4 | 62.7 |
| Santa Clara | 72.1 | 57.4 |
| Solano | 63.9 | 37.3 |
| Sonoma | 71.5 | 52.3 |
q4_incd_plot<-ggplot(sim_bay_q4_incd, aes(x= prop_fully_vax ,
y= incidence_per_1000)) +
geom_point(aes(color=county), size =3) +
theme_bw() +
xlab("COVID-19 Vaccination Rate per 100") +
ylab("Flu Incidence Rate per 1000") +
ggtitle("COVID-19 Vaccination Rate & Flu Incidence Rate",
subtitle ="Bay Area, October - December 2022, Ages 0-17")
q4_incd_plot
Interpretation: The table and plot above show the incidence rate of flu in pediatric population in the Q4 of 2022 compared to COVID-19 vaccination coverage in the Bay Area counties showing that San Mateo county has the highest incidence rate with 77.4 cases per 1,000 and Marin county has the lowest incidence rate of 62.7 cases per 1000 and has te highest coverage of COVID-19 vaccination rate.
sim_bay_q4_sev <-sim_ca_la_vax_bay %>%
arrange(quarter) %>%
filter(quarter == "2022-10-01") %>%
select(county, severity_incidence, prop_fully_vax)
q4_sev_tbl <- kable(sim_bay_q4_sev, format = "html",
col.names = c("County","Flu Severity Incidence",
"COVID-19 Vaccination Rate"),
caption="COVID-19 Vaccination Rate/100 & Flu Severity Incidence/1000 \
Bay Area October - December 2022, Ages 0-17",
digits = 1) %>%
kable_styling(bootstrap_options = "condensed", full_width = FALSE) %>%
column_spec(column = 1, bold = TRUE)
q4_sev_tbl| County | Flu Severity Incidence | COVID-19 Vaccination Rate |
|---|---|---|
| Alameda | 0.7 | 61.7 |
| Contra Costa | 0.5 | 60.7 |
| Marin | 0.3 | 76.7 |
| Napa | 1.1 | 52.5 |
| San Francisco | 0.7 | 56.5 |
| San Mateo | 1.0 | 62.7 |
| Santa Clara | 0.4 | 57.4 |
| Solano | 0.3 | 37.3 |
| Sonoma | 0.3 | 52.3 |
q4_sev_plot<-ggplot(sim_bay_q4_sev, aes(x=prop_fully_vax ,
y= severity_incidence)) +
geom_point(aes(color=county), size=3) +
theme_bw() +
xlab("COVID-19 Vaccination Rate per 100") +
ylab("Flu severity Incidence per 1000") +
ggtitle("COVID-19 Vaccination Rate & Flu Severity Incidence",
subtitle = "Bay Area, October - December 2022, Ages 0-17")
q4_sev_plot
Interpretation: The table and plot above compares the flu severity in pediatric population during Q4 (Oct-Nov-Dec) 2022 and COVID-19 vaccination in the Bay Area counties. The severity incidence for the Bay Area was 0.60 per 1000, being Napa county with the highest morbidity. COVID-19 vaccination coverage during the Q4 of 2022 was 58% for the Bay Area estimated population, where the lowest coverage was in Solano county.
sim_bay_q1_incd <-sim_ca_la_vax_bay %>%
arrange(quarter) %>%
filter(quarter == "2023-01-01") %>%
select(county, incidence_per_1000, prop_fully_vax)
q1_incd_tbl <- kable(sim_bay_q1_incd , format = "html",
col.names = c("County","Flu Incidence Rate",
"COVID-19 Vaccination Rate"),
caption ="COVID-19 Vaccination Rate/100 & Flu Incidence Rate/1000 \
Bay Area, January - March 2023, Ages 0-17",
digits = 1) %>%
kable_styling(bootstrap_options = "condensed", full_width = FALSE) %>%
column_spec(column = 1, bold = TRUE)
q1_incd_tbl| County | Flu Incidence Rate | COVID-19 Vaccination Rate |
|---|---|---|
| Alameda | 226.5 | 62.8 |
| Contra Costa | 232.8 | 61.5 |
| Marin | 231.6 | 77.9 |
| Napa | 238.2 | 53.0 |
| San Francisco | 225.9 | 57.6 |
| San Mateo | 232.3 | 64.0 |
| Santa Clara | 232.6 | 58.4 |
| Solano | 243.2 | 37.6 |
| Sonoma | 233.8 | 52.9 |
q1_incd_plot<-ggplot(sim_bay_q1_incd, aes(x= prop_fully_vax,
y= incidence_per_1000)) +
geom_point(aes(color=county), size =3) +
theme_bw() +
xlab("COVID-19 Vaccination Rate per 100") +
ylab("Flu incidence rate per 1000") +
ggtitle("COVID-19 Vaccination Rate & Flu Incidence Rate",
subtitle ="Bay Area, January - March 2023, Ages 0-17")
q1_incd_plot
Interpretation: The table and plot above, shows the flu incidence rate per 1000 in pediatric population in the Bay Area counties during the Q1 of 2023 and COVID-19 vaccination rate. For the Q1 of 2023 the flu incidence was 233 cases per 1000 in the Bay Area counties in pediatric population, where San Francisco county had the lowest incidence with 225 cases per 1000 and Solano county with the highest incidence of 243 cases per 1000. The highest COVID-19 vaccination coverage was of 77% of fully vaccinated in Marin county and the lowest vaccination rate was in Solano County with the highest incidence rate of flu.
sim_bay_q1_sev <-sim_ca_la_vax_bay %>%
arrange(quarter) %>%
filter(quarter == "2023-01-01") %>%
select(county,severity_incidence, prop_fully_vax)
q1_sev_tbl <- kable(sim_bay_q1_sev , format = "html",
col.names = c("County","Flu Severity Incidence",
"COVID-19 Vaccination Rate"),
caption="COVID-19 Vaccination Rate/100 & Flu Severity Incidence/1000 \
Bay Area, January - March 2023, Ages 0-17",
digits = 1) %>%
kable_styling(bootstrap_options = "condensed", full_width = FALSE) %>%
column_spec(column = 1, bold = TRUE)
q1_sev_tbl| County | Flu Severity Incidence | COVID-19 Vaccination Rate |
|---|---|---|
| Alameda | 0.9 | 62.8 |
| Contra Costa | 0.9 | 61.5 |
| Marin | 0.7 | 77.9 |
| Napa | 1.7 | 53.0 |
| San Francisco | 0.9 | 57.6 |
| San Mateo | 0.9 | 64.0 |
| Santa Clara | 0.9 | 58.4 |
| Solano | 1.1 | 37.6 |
| Sonoma | 0.6 | 52.9 |
q1_sev_plot<- ggplot(sim_bay_q1_sev, aes(x= prop_fully_vax,
y= severity_incidence)) +
geom_point(aes(color=county), size =3) +
theme_bw() +
xlab("COVID-19 Vaccination Rate per 100") +
ylab("Flu Severity Incidence per 1000") +
ggtitle("COVID-19 Vaccination Rate & Flu Severity Incidence",
subtitle ="Bay Area, January - March 2023, Ages 0-17")
q1_sev_plot
Interpretation: The table and plot above shows the Flu severity incidence in pediatric population in the Bay Area counties compared to COVID-19 Vaccination rate during the Q1 of 2023. The severity incidence for the Bay Area was 0.95 per 1000, being Napa county with the highest morbidity and Sonoma with the lowest severity incidence. The highest COVID-19 vaccination coverage was of 77% of fully vaccinated in Marin county and the lowest vaccination rate was in Solano County with the highest incidence rate of flu.
Conclusion: Addressing the Research Question
There was an increase of 3.4 times in the flu cases despite the same proportion of coverage of COVID-19 vaccination in the Bay Area.
There is no relation between COVID-19 vaccination and flu cases in the Bay Area counties in pediatric population.
Even though the exponential increase of cases between the Q4 of 2022 to the Q1 of 2023, the incidence of severe cases was similar.
Higher efforts to offer flu vaccination coverage in pediatric population of the Bay Area should be performed.
Our summary of findings indicated that NO correlations between COVID vaccination rates and flu rates/severity,in bay area counties in pediatric population. We are keeping in mind that correlation is not causation other conditions need to apply.