Cause of tremendous growing worldwide population becomes more mobile and urbanized, Infectious diseases epidemic and their associated problems may threat the global humn life and economy.
To minimize the social and economic damage caused by infectious diseases, the public health communities need to be in the position to anticipate the spatial and temporal evolution of epidemics and evaluate the potential impact of available containment and prevention strategies.
We conducted the simulation for estimating the Spread of Covid-19 virus January of 2020.
We used Bio-Infomatics technology with various bigdata(world popiulation, world transportation, demographics, …etc)
The objetcts of Pandemic Simulation using Bio-Infomatics is evaluting the evaluate the potential impact of available containment and preparing the prevention strategies.
We estimated that the early of Covid-19 virus spreads at January eventually would be pandemic before April and We signalled for our Koren goveriment and Korean Society.
Our Korean healthcare system could prepared the flatten the curve strategy from our news of simulations
the flatten the curve is created by a more gradual increase in the number of cases per day and a more gradual decrease. Over a long period of time the number of people infected might be around the same, but the difference is the number of cases that occur each day.
Korean government effectively are controlling the Covid-19 Pandemic without any lock down and minimize the infectious cases.
Our Simulation tool is Gleam(Open Platform) and STEM(Open Source)
Two software is based on Bio-Infomatics
that combines real-world data on populations and human mobility with elaborate stochastic models of disease transmission to deliver analytic and forecasting power to address the challenges faced in developing intervention strategies that minimize the impact of potentially devastating epidemics.
What was the hardest part of this project?
Bigdata Analytics like Bio-Infomatics need the various and humongous data from gloabl demograpphics to health care data from all over the world.
Tremendous computing power, well trained data anlayst and effective simulation model based on maschine learning must be needed.
We conducted the simulation on Gleam Europe cloud computing and proofing with Infectious disease scientists.
Bigdata like gloabl demograpphics and transportation data was extracted the global nonprofit organization such as UN, OECD.
What can others learn from this project?
Modern journalist have to learn the knowledge of Bigdata Analytics.
We think that Bigdata Analytics is the powerful weapon of data journalism.
Predictive analytics and journalism is more omportant that descriptive journalism.
Data journalism have to help the preparation against the various global sychronized issues for human society.