Zika Virus Infections

Christopher Capozzola
Posted on Feb 6, 2017

About the Zika Virus

The Zika virus is a mosquito-borne virus carried by the Aedes type of mosquitoes. The virus was first isolated in 1947 from the Zika Forest of Uganda, where it inherits it's name. The infection is very similar to the dengue fever, having symptoms such as fever, rash, joint pain, as well as conjunctivitis. Contracting the the infection during pregnancy has the potential to cause a birth defect of the brain called microcephaly and other severe fetal brain defects.

Data Acquisition and Cleansing

The data used in this visualization project comes from two main sources:

  1. A data set from Kaggle website on Zika infections provided by the CDC - https://www.kaggle.com/cdc/zika-virus-epidemic
  2. Shape files for mapping the data to administrative areas found was found on - www.gadm.org

The data set for the Zika virus information includes the following fields:

  • report_date - The report date is the date that the report was published. The date should be specified in standard ISO format (YYYY-MM-DD).
  • location - A location is specified for each observation following the specific names specified in the country place name database. This may be any place with a 'location_type' as listed below, e.g. city, state, country, etc. It should be specified at up to three hierarchical levels in the following format: [country]-[state/province]-[county/municipality/city], always beginning with the country name. If the data is for a particular city, e.g. Salvador, it should be specified: Brazil-Bahia-Salvador.
  • location_type - A location code is included indicating: city, district, municipality, county, state, province, or country. If there is need for an additional 'location_type', open an Issue to create a new 'location_type'.
  • data_field - The data field is a short description of what data is represented in the row and is related to a specific definition defined by the report from which it comes.
  • data_field_code - This code is defined in the country data guide. It includes a two letter country code (ISO-3166 alpha-2, list), followed by a 4-digit number corresponding to a specific report type and data type.
  • time_period - Optional. If the data pertains to a specific period of time, for example an epidemiological week, that number should be indicated here and the type of time period in the 'time_period_type', otherwise it should be NA.
  • time_period_type - Required only if 'time_period' is specified. Types will also be specified in the country data guide. Otherwise should be NA.
  • value - The observation indicated for the specific 'report_date', 'location', 'data_field' and when appropriate, 'time_period'.
  • unit - The unit of measurement for the 'data_field'. This should conform to the 'data_field' unit options as described in the country-specific data guide.

Once the data was pulled into R, it was necessary to clean the dates, breakout data into probable and confirmed cases, as well as separate the location field into administrative districts so that it could be merged with the locations in pulled from www.gadm.org shape files by country.

Below is the code used for this process:


Data Visualization

After merging the data to the shape files, I was then able to create a small app to visualize the Zika virus data set by country and by confirmed or probable cases.

Screen Shot 2017-02-05 at 9.20.12 PM Screen Shot 2017-02-05 at 9.19.54 PM Screen Shot 2017-02-05 at 9.19.26 PM

About Author

Christopher Capozzola

Christopher Capozzola

Christopher is a passionate analyst with a certification as a Data Scientist and extensive background in mathematics. He has years of experience in research, analytics, and modeling. He leveraged his experience at NYC Data Science Academy to enrich...
View all posts by Christopher Capozzola >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 airbnb Alex Baransky alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus API Application artist aws 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 Bundles California Cancer Research capstone Career Career Day citibike clustering Coding Course Demo Course Report D3.js data Data Analyst data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization Deep Learning Demo Day Discount dplyr employer networking feature engineering Finance Financial Data Science Flask gbm Get Hired ggplot2 googleVis Hadoop higgs boson Hiring hiring partner events Hiring Partners Industry Experts Instructor Blog Instructor Interview Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter lasso regression Lead Data Scienctist Lead Data Scientist leaflet linear regression Logistic Regression machine learning Maps matplotlib Medical Research Meet the team meetup Networking neural network Neural networks New Courses nlp NYC NYC Data Science nyc data science academy NYC Open Data NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time Portfolio Development prediction Prework Programming PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R 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 Selenium sentiment analysis Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau team TensorFlow Testimonial tf-idf Top Data Science Bootcamp twitter visualization web scraping Weekend Course What to expect word cloud word2vec XGBoost yelp