Data Analysis on Deaths During the COVID Pandemic

Posted on Feb 7, 2021
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Data Analysis on Deaths During the COVID Pandemic

Introduction

During the past year, the world has been beset by a pandemic going by many names: Coronavirus, COVID-19, SARS-COV-2, et al. Data shows that this pandemic has been the most virulent disease since the Spanish Flu of the early 1900s.

It is said that to defeat one's opponent, one must know the enemy. A good approach is to understand the trend analysis and correlation of the pandemic’s metrics. The goal of this research is to evaluate the trajectory of deaths due to COVID based on positive test cases. By observing a fall in the rate of positive cases, we can infer that deaths will decline after a lag.

In order to understand the trends, I built an application that visualizes the daily trends of the disease on a state-by-state basis and on the United States as a whole.

Data Sources

Data about the disease was obtained from covidtracking.com, which is curated by the Atlantic Monthly Group. Each state reports on a daily basis its positive and negative test cases as well as deaths due to COVID. It should be noted that the original data set came from healthdata.gov; however, covidtracking.com was found to have better data on testing as well as cleaner, better “scrubbed” data since the Atlantic Monthly Group manually monitors the data collection process and checks for any anomalies.

Estimated population data for 2020 was taken from Wikipedia.

Data Calculations

Figures for the United States were calculated as the sum of the 50 states’ figures. Other territories’ data were ignored for this study.

Population data was merged with the COVID data set to arrive at per capita figures. For example, New Cases per Capita was calculated by taking the daily change in total positive cases and dividing by the estimated 2020 population. The importance is to normalize the figures across states with different populations to achieve better comparability. Furthermore, 7-day rolling averages were calculated to remove noise in the data.

Positivity rates were calculated as the number of positive cases on a given day divided by the number of tests given on that day.

Analysis of Positivity Rates vs. Deaths

By inspecting the graph of positivity rates and deaths over time, one immediately notices that there were a number of cycles where the rates rise and subsequently fall.

Data Analysis on Deaths During the COVID Pandemic

Likewise, the number of new deaths per day also exhibits peaks and troughs over time.

Data Analysis on Deaths During the COVID Pandemic

We can superimpose these two graphs  on each other. There is a noticeable simularity between the ebbs  and flows.

In order to establish the presence of a correlated behavior of two time series, I made use of the cross-correlation function defined as:

where X̄ and Ȳ are the means and SDX and SDY are the standard deviations of the respective time series.

When the statistic is greater than the 5% confidence level, we can accept the Alternative Hypothesis that there is a lagged relationship between the two time series.

Looking at the graph of the cross-correlation function for the positivity rates and deaths, we notice that there is a significant probability for a lag between zero and approximately 45 days.

In order  to estimate the strongest lag, we leverage the partial cross-correlation function as coded below in R.

pacf(ts(cbind(dx, dy)), lag.max=45)

where dx = positivity rate and dy = number of deaths. For greater insight into the partial autocorrelation function (which is used here in the code), please see https://en.wikipedia.org/wiki/Partial_autocorrelation_function. Usually, the pacf is run against the same time series; however, in this case, we use two different time series to create the partial cross-correlation function.

The result of the function is graphed below.

From this graph, we notice a significant positive lag at 29 days. Graphing the Positivity Rate with a 29-day lag against the number of deaths, we see a very close correlation in the rising and falling of both curves.

Conclusion

The death rate lags the positivity rate. Since the positivity rate has started to decline, we should see a decline in the number of new deaths within approximately 29 days later.

This is expected since there is a natural delay between the time a person tests positive and a possible adverse outcome.

This decline will occur in the absence of any other factors such as vaccinations and seasonal changes.

About This Research

Analysis for this research was performed using R and can be seen online at https://dpwasserman.shinyapps.io/covid_analysis.

The code for the application is housed at https://www.github.com/DPWasserman/covid_analysis.

Graphs were produced through R Studio and Microsoft Excel.

About Author

David Wasserman

Data science professional with a background in finance and credit risk. Has the rare combination of technical skills and business acumen.
View all posts by David Wasserman >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI