Group15 proposal

Overview

Since December 2019, new cases of coronary pneumonia have been discovered in Wuhan, and have spread to all over China and even other countries. As of 2019-03-01, a total of 79,971 cases have been diagnosed and a total of 2,873 deaths have been confirmed in China. The situation in the rest of the world is not optimistic, 7,277 cases have been confirmed and total 18 deaths occurred in other countries. Its wide spread and rapid speed are alarming. The 2019 nCov has caused a certain impact on the economy of China and even countries around the world, even has been taken seriously by WHO. In order to study trends in number of diagnoses, number of suspected patients, and deaths over time for different areas, we use the 2019 nCov data for further analysis and visualisation.

Project Objectives

We plan to use the 2019 nCov data to: 1)Analyze trends in cumulative cure rates and mortality across the country and provinces. 2)Analyze trends in new diagnoses and mortality rates across the country and provinces. 3)Using multiple measurement methods to display epidemic data maps-based on nuclear probability density, based on clustering,etc.

Data Source

Since the outbreak of the new coronavirus pneumonia, many agencies / portals (Clove Garden, Tencent, Sina, ...) have provided pages for "real-time tracking of the epidemic", which mainly displays the number and area of suspected, confirmed, cured, and dead cases distributed, the data used in this project is collected from the "Clove Garden "website.

The data is from the website below. It includes Province name(Chinese and English), City name(Chinese and English), zipcode, province confirmed count, province suspected count, province cured count, province dead count, city confirmed count, city suspected count, city cured count, city dead count and update time.

https://ncov.dxy.cn/ncovh5/view/pneumonia

Methodology

(1)Data Exploration and Visualization: We will make full use of the dataset to visualize the data in different ways. Maps is the most intuitive way to show the spread and control of the epidemic. Line charts are the best way to show data trends and forecast data. (2)Clustering: Cluster the city and pations according to the daily addtion rate, cured rate and dead rate. (3)Time Series Analytics: Based on the current data to predict the future trend for every single city.

Tools&Packages

Tools: Tableau; R-shiny Packages:(Excel)