Pandemic Data Modeling and Visualization

About me!

Hello Everyone! My name is Mugdha S. Varpe!
I'm a Computer Science Master's student at Rochester Institute of Technology.
This Web App is been hosted as a part of my Final year Master's Capstone project.
As we all know millions and millions of lives are affected due to this pandemic, this application is my small attempt to spread awareness regarding the spread of the Covid19 virus and the various parameters that are triggering this spread.
I would like to thank Norberto Vergani, for developing his mathematical model, and making his work publically available to others, such as myself. I am grateful to Dr. Christopher T. Field, a member of the Nasty Covid-19 team, for his valuable guidance and for helping me understand the Nasty COVID-19 model.I would especially like to thank Dr. Thomas B. Kinsman for his continuous guidance and support throughout this project. He provided constant encouragement to be my best self, and contribute to the betterment of society.

What's this application about?

Understanding the spread of epidemics is cumbersome. The issue is that those who understand the mathematics of exponential growth are not necessarily those who make tactical decisions related to the spread of a pandemic. Mathematical models are available which both help describe and understand past events, and help predict future trends. Written in the language of mathematics, these models can accurately communicate the impact of different parameter choices for society, but the language of mathematics is not accessible to everyone. To help a broader audience understand the spread of disease, an interactive visualization was developed for use over the Internet. Using this tool, decision-makers such as County Executives and County Health Directors can change parameters and see projected estimates immediately. The tool generates visualizations to help communicate trends graphically to a more general audience, an audience with a less technical background. This tool implements a sophisticated model of Norberto H. Vergani. A spectrum of computing technologies was utilized to create a graphical user interface for Norberto H. Vergani’s model of the spread of COVID-19. Internally this model involves Markov chain Monte Carlo simulations. Development and deployment required the integration of front-end and back-end technologies including Python, HTML-5, Flask, JavaScript, Ajax, and chart.js. This application can be used for educating all levels of the public. It is hoped that this work helps fight the current pandemic and save lives.

Getting familiar with the vocabulary

Graph Parameter Description
Number of contacts of an apparently healthy person(Nc) Number of daily contacts a healthy or apparently healthy person has with other individuals
Probability of Contagion between a healthy and infected person(Pc) Probability that an healthy individual will be infected by coming in contact with and infected person.
Vaccine Daily Dose Available Vaccination rate (Number of doses available per day)
Active Cases Number of people infected at a given time
Total Cases Sum of all infected individuals (past+present)
Total Deaths Sum of people loosing their life over the period of time
Total Recovered Sum of people who are recovered from the disease
Hospital Occupation Total number of people who are hospitalized
Intensive Care Occupation Number of people in Intensive care units of hospitals
Apparent Total Cases in the first 100 Days Sum of "known" number of people infected at any given time
Apparent infected in the first 100 Days Number of people infected and show symptoms, are in isolation
Daily infected by asymptomatic Number of people infected daily due to asymptomatic people
Daily infected by future symptomatic Number of people infected daily due to the people who will develop symptoms in the future.
Daily infected by patients Number of people infected daily due the people who are already infected and symptomatic
Total of Daily infected Sum of daily infected people due to symptomatic, asymptomatic and patients