COVID-19 Tracking Data API and Data Anomalies (No Correlations? Cases to Hospitalizations Increases)

Is there a correlation between Positive cases and Hospitalizations? Below is the API for python access, open to all who desire to filter the data. I want to just give easy access to all the beginner students data scientists out there, such as myself..Explore and Discover: **My Apologies It says High Def, but does not look High Def on video here**

Code: import matplotlib.pyplot as plt import pandas as pd from scipy import stats import statsmodels.api as sm import requests import time from IPython.display import clear_output response = requests.get(“https://covidtracking.com/api/v1/us/daily.csv”) covid = response.content ccc = open(“daily.csv”,”wb”) ccc.write(covid) ccc.close() df = pd.read_csv(“daily.csv”, index_col = ‘date’) df.head() data = df[[‘positiveIncrease’,’hospitalizedIncrease’]] dataT = df[[‘positiveIncrease’,’hospitalizedIncrease’,’hospitalizedCurrently’]] dataD = df[[‘hospitalizedIncrease’,’deathIncrease’]] dataT.head(20) plt.figure(figsize=(20,10)) Y = data[‘positiveIncrease’] X = data[‘hospitalizedIncrease’] plt.scatter(X,Y) plt.ylabel(“Tested Positive Increase”) plt.xlabel(“Hospitalization Increase”) plt.show() Y1 = sm.add_constant(Y) reg = sm.OLS(X, Y1).fit() reg.summary() data.plot(y=[‘hospitalizedIncrease’,’positiveIncrease’],xticks=data.index[0:len(data):30], rot=90, figsize=(20,10) ) for x in range(len(data)): plt.figure(figsize=(20,10)) plt.xticks( data.index.values[0:len(data):30], rotation = 90, fontsize=20 ) plt.plot(data.tail(x))

COVID-19 Infection and Mortality Rate Questions

COVID-19 Infection and Mortality Rate Questions

Person-to-person transmission of SARS-CoV-2 occurred between two people with prolonged, unprotected exposure while the first patient was symptomatic. Despite active monitoring and testing of 372 contacts of both cases, no further transmission was detected

The RKI added: ‘We don’t consider post-mortem tests to be a decisive factor.

‘We work on the principle that patients are tested before they die.’

But this means that if a person dies in quarantine at home and does not go to hospital, there is a high chance they will not be included in the statistics, as Giovanni Maga of Italy’s National Research Council pointed out in an interview with Euronews

First known person-to-person transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the USA. The Lancet, 2020; DOI: 10.1016/S0140-6736(20)30607-3

https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30607-3/fulltext

COVID-19 and the Risk to Health Care Workers: A Case Report

Published: Ann Intern Med. 2020. DOI: 10.7326/L20-0175

https://annals.org/aim/fullarticle/2763329/covid-19-risk-health-care-workers-case-report

cov19, covid19, sarscov2, SARS-CoV-2 test, SARS-CoV-2, infection, risk, contagion, pathogenicity, pathogen, mortality, diagnosis, data collection, covid-19, statistics, mutagenesis, data, protection, Health care workers, person-to-person transmission, transmission

DNA discovered not to be a good predictor of health

 

DNA discovered not to be a good predictor of health

“Simply put, DNA is not your destiny, and SNPs are duds for disease prediction,” said David Wishart, professor in the University of Alberta’s Department of Biological Sciences and the Department of Computing Science and co-author on the study. “The vast majority of diseases, including many cancers, diabetes, and Alzheimer’s disease, have a genetic contribution of 5 to 10 per cent at best.”

#DNA #HEALTH #Disease

Jonas Patron, Arnau Serra-Cayuela, Beomsoo Han, Carin Li, David Scott Wishart. Assessing the performance of genome-wide association studies for predicting disease risk. PLOS ONE, 2019; 14 (12): e0220215 DOI: 10.1371/journal.pone.0220215

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0220215