A much of flooding news regarding COVI-19 virus spreading sparked me to ask the very naïve research question: When will this nightmare be ended, and have we handled well so far ? As a data scientist mainly working for government-funded research projects, I’ve insisted an importance of open source project, living-lab movement, and data-driven policy. However, facing a wholly new panic called corona virus, I must confess that I’ve been neglected in exploring real-world questions and spent much of time in submitting and revising old-fashioned-nobody-care manuscripts.
Related Work
This work is especially owe to several seminal work of a novelist (Isaac Asimov) from his book Foundation (https://en.wikipedia.org/wiki/Foundation_series), and an artist (Daniel Shiffman) from his book of Nature of code (https://natureofcode.com/), a scientist (Anthony Giddens) from his theory of Structuration(https://en.wikipedia.org/wiki/Structuration_theory) and much of efforts of software developers in open source society (https://github.com/d3/d3-force). There have been extant work on virus simulation in Academy. One of notable works is Thomas Woolley’s project on diffusion or dead (https://www.cardiff.ac.uk/news/view/825352-diffusion-of-the-dead-using-mathematics-to-escape-a-zombie-apocalypse) which especially inspired for me to teach University students about the random-walk and Monte Carlo simulation in java-script with aid of Kahn Academy. Author wish this work would contribute to much of extant wisdom of various discipline above mentioned.
Model
This study investigates how the trend of virus spread will be affected by force, society and policy within network under the view of time and space as follows:
In this model, people’s movements are viewed as positions of nodes of world network. Their positions are initially determined by law of nature (force) and social scheme (society). As for force, I assume every nodes of network interacts with each other with attraction or repulsion like the way force does in the nature. As for society, I assume nodes belong to a certain party such as nation or political party, but furthermore, nodes interact with each other who belong to different parties. People with same party are possibility positioned close. As for policy, I incorporate the governmental action as isolating (blocking) some of people. Turning to virus movement, it is featured by statistics with known numbers – cure rate, host-killing rate, and with unknown numbers – spreading rate in terms of the social links or spatial position. Both of people’s movement and virus spreading, both time and space have unique and important roles in that virus move to spatially-closely positioned host (person) and move to timely-next target (person).
https://github.com/wildcat842/NARA_Force_Virus_Block_Simulation
Living platform for NLP Data Science 