Forewarning System for Reducing Impact of Epidemics

A warning system for recognising and identifying potential Epidemics or Pandemics using News Article.

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Inspiration

_ "News article in early December and January headlined a possible epidemic in China and contrary to popular thought today, the international community as a whole tended to ignore it, the low early numbers would soon balloon to become the largest pandemic of the 21st century." _

What it does

This detector parses through news articles across Google and helps predict possible and emerging pandemics, so prompt and appropriate action can be taken. Visit Site (In-Development) at https://cocky-beaver-ae5834.netlify.app/

How we built it

Using Google Search Engine to search for specific keywords to sort latest news article, followed by web-scraping and data extraction using Natural Language Extraction and SpaCy in a dedicated Python notebook to extract important features such as Cause, Location and People Infected and displaying the cumulative results on a User Friendly interface. Web parsing is done using Beautiful Soup and Requests Library.

Challenges we ran into

1) Parsing Random Website (HTML Format Unknown)
2) Extracting maximum information
3) Reducing redundancy in output response

Accomplishments that we're proud of

1) Successfully implemented Random HTML Parsing using Natural Language Processing.
2) Data Extraction in ordered manner

What we learned

We learnt to successfully implement a data parser and Natural Language Processor from SpaCy to analyze HTML data of News site to extract necessary information. Beginner level knowledge in Python library pycountry, SpaCy, nltk, requests.
Using Geopandas and Folium for interactive Maps.

What's next for Forewarning System for Reducing Impact of Epidemics

1) Integration with a website
2) Enhanced data extraction
3) Better Accessibility
4) Fully Automatic (currently semi-automatic operation)

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