I noticed that traditional methods to predict a disease outbreak was by performing sentiment analysis on Twitter posts and Google Search terms. Unfortunately, these methods were inadequate, as Twitter and Google is not popular in all countries. So, I created a system to model and predict outbreaks without the need for social media.
The system was able to update the probabilities of a virus from spreading from A to B in real time, and I plan to release it to the public next year. I also used Machine Learning and Deep Learning to predict larger long-term virus trends with Google Trends, and this acted as a validator for the MSIRD model.
-
Mosquito Distribution Formulation a. Temperature, Environmental Factors b. Living Standards
-
Basic Markov Modelling a. Growth and Decay b. New Route Derivation - Encoding and Decoding graphs
-
MSIR Model a. Matrix block concentation b. States and average states c. [S->I], [S->S], [S->R], [I->R], [I->I], [R->R] blocks d. Markov Updating
-
Demonstration a. 9 squares b. 49 squares
-
MSIRD Model a. Revised Formulas b. Deaths incoporation i. [S->D], [I->D], [R->D]