In gabrielburcea/cvindia: Analysis of symptoms

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  echo = FALSE,
  warning = FALSE,
  message = FALSE
)

library(magrittr)
library(tidyverse)

library(tidyverse)
#counting symptoms without severity 
data_categ_nosev <- read_csv("/Users/gabrielburcea/Rprojects/stats_data_whole/data_categ_nosev.csv")

I. Descriptive analytics

Exploratory questions of interest are: 1. How does symptom profile map to mild/moderate/severe? 2. Does symptom profile differ by underlying co-morbidity?

Answering to first question: 1. How does symptom profile map to mild/moderate/severe?

We choose to look at the most common symptoms according to UK guidelines in patients who reported that they had a Covid 19 positive test. We look at these symptoms according to whether they were reported as mild/moderate/severe

symtoms_covid_in_resp <- cvindia::symptom_profile_covid_tested(data = data_categ_nosev, start_date = as.Date("2020-04-01", format = "%Y-%m-%d"), 
                                           end_date = as.Date("2020-09-01", format = "%Y-%m-%d"), plot_chart = TRUE)
symtoms_covid_in_resp

Cough, followed by muscle ache are the most symptoms in patient who declared they had a Covid-19 positive test. Loss of smell and shorthness of breath are next. Yet, as observed there are different levels of severity, were mild form of symptoms are reported.

symtoms_covid_in_resp <- cvindia::symptom_profile_covid_tested(data = data_categ_nosev, start_date = as.Date("2020-04-01", format = "%Y-%m-%d"), 
                                           end_date = as.Date("2020-09-01", format = "%Y-%m-%d"), plot_chart = FALSE)

sympt_cov_in_resp_n <- dplyr::top_n(symtoms_covid_in_resp, 10)

knitr::kable(dplyr::top_n(sympt_cov_in_resp_n, 10))

The next barchart takes into accound all other symptoms for the purpose of comparison. However, fatigue seems to have the highest countS, followed by the most common symptoms of Covid-19.

all_syptpms_frequency <- cvindia::symptom_profile_frequency_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", format = "%Y-%m-%d"), 
                                            end_date = as.Date("2020-05-09", format = "%Y-%m-%d"),
                                            plot_chart = TRUE)

all_syptpms_frequency

Table bellow shows the first ten most occurring symptoms taking into account the level of severity. However, the most common symptoms expressed in level of severity are Cough as a mild for, shortness of breath, fatigue, muscle ache and headache, all in a mild form.

all_syptpms_numbers <- cvindia::symptom_profile_frequency_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", format = "%Y-%m-%d"), 
                                            end_date = as.Date("2020-05-09", format = "%Y-%m-%d"),
                                            plot_chart = FALSE)

all_sympt_numb <- dplyr::top_n(all_syptpms_numbers, 10)


ten_most_occuring_symptoms <- dplyr::top_n(all_sympt_numb, 10) %>% filter(Event != "No")

ten_most_occuring_symptoms

show_symptoms  <- cvindia::symptom_profile_showing_symptom(data = data_categ_nosev, start_date = as.Date("2020-04-09", format = "%Y-%m-%d"), 
                                           end_date = as.Date("2020-05-09", format = "%Y-%m-%d"),
                                           plot_chart = TRUE) 

show_symptoms

As a conclusion we may pressume a symptom trajectory in covid-19 positive tested. Mild sore throat which then progresses to a cough, and shortness of breath?

Does symptom profile differ by underlying co-morbidity?

The bar chart bellow shows symptom across co-morbidity groups. By observing the obesity and hypertensive patients, although we see a similar pattern in symptom manifestation, there are slight differences when it comes to sputum and sore throat. In hypertensive respondents sore throat is more prominent than in obese respondents, which are experiencing more sputum. This is not the same with respondent with asthma, which are experiencing sputum and shortness of breath, where sore throat comes on the fourth place, after muscle ache.

comorb_cov_sympt <- cvindia::comorbidities_symptoms(data = data_categ_nosev, start_date = as.Date("2020-04-09"), 
                                                    end_date = as.Date("2020-05-09"), plot_chart = TRUE)


comorb_cov_sympt

comorb_cov_sympt_num <- cvindia::comorbidities_symptoms(data = data_categ_nosev, start_date = as.Date("2020-04-09"), 
                                                    end_date = as.Date("2020-05-09"), plot_chart = FALSE)


comorb_cov_sympt_num


#knitr::kable(dplyr::top_n(comorb_cov_sympt_num,10))

hypertension_p <- cvindia::hypertension_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", tz = "Europe/London"), end_date = as.Date("2020-08-20"), plot_chart = TRUE)

hypertension_p

obesity_p <- cvindia::obesity_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", tz = "Europe/London"), end_date = as.Date("2020-08-20"), plot_chart = TRUE)

obesity_p

obesity_n <- cvindia::obesity_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", tz = "Europe/London"), end_date = as.Date("2020-08-20"), plot_chart = FALSE)

obesity_n

asthma_p <- cvindia::asthma_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", tz = "Europe/London"), end_date = as.Date("2020-08-20"), plot_chart = TRUE)

asthma_p

asthma_n <- cvindia::asthma_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", tz = "Europe/London"), end_date = as.Date("2020-08-20"), plot_chart = FALSE)

asthma_n

lung_p <- cvindia::lung_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", tz = "Europe/London"), end_date = as.Date("2020-08-20"), plot_chart = TRUE)

lung_p

lung_n <- cvindia::lung_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", tz = "Europe/London"), end_date = as.Date("2020-08-20"), plot_chart = FALSE)

lung_n

kidney_p <- cvindia::kidney_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", tz = "Europe/London"), end_date = as.Date("2020-08-20"), plot_chart = TRUE)

kidney_p

kidney_n <- cvindia::kidney_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", tz = "Europe/London"), end_date = as.Date("2020-08-20"), plot_chart = FALSE)

kidney_n

heart_p <- cvindia::heart_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", tz = "Europe/London"), end_date = as.Date("2020-08-20"), plot_chart = TRUE)

heart_p

heart_n <- cvindia::heart_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", tz = "Europe/London"), end_date = as.Date("2020-08-20"), plot_chart = FALSE)

heart_n

diabetes_one_p <- cvindia::diabetes_one_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", tz = "Europe/London"), end_date = as.Date("2020-08-20"), plot_chart = TRUE)

diabetes_one_p

diabetes_one_n <- cvindia::diabetes_one_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", tz = "Europe/London"), end_date = as.Date("2020-08-20"), plot_chart = FALSE)

diabetes_one_n

diabetes_two_p <- cvindia::diabetes_two_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", tz = "Europe/London"), end_date = as.Date("2020-08-20"), plot_chart = TRUE)

diabetes_two_p

diabetes_two_n <- cvindia::diabetes_two_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", tz = "Europe/London"), end_date = as.Date("2020-08-20"), plot_chart = FALSE)

diabetes_two_n

liver_p <- cvindia::liver_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", tz = "Europe/London"), end_date = as.Date("2020-08-20"), plot_chart = TRUE)

liver_p

liver_n <- cvindia::liver_plot(data = data_categ_nosev, start_date = as.Date("2020-04-09", tz = "Europe/London"), end_date = as.Date("2020-08-20"), plot_chart = FALSE)

liver_n