Julia Lang - Enabling Mathematical Modeling

 In 2016, I started using Julia Lang based on its open source availability and the extensive published conference videos posted on YouTube.  I liked Julia for several reasons: (1) linking to C, C++, FORTRAN, Python libraries, (2) GPU interface support, (3) REPL, (4) parallel compute interfaces and (5) differential equations. Differential equations are at the core of most domains, enabling compact mathematical equations relating subsystems to each other. 

There are many programming language utilized for a variety of tasks including web services, databases, operating systems, distributed systems, file systems, etc.

There are very few programming languages dedicated to ease scientific and engineering modeling and exploration. With ease means that first level programming entities such as matrices and vectors. Famous proprietary examples include Mathematica,  Macsyma, Matlab.

Julia provides a shareable, non-proprietary implementation:

https://juliapackages.com/p/diffeqtutorials

The URL above contains several examples related to epidemiology models and a video introduction. 

SIR Simulation is also available; our current pandemic evolution has become complex due to several unknown factors; it is likely that more compartments / states and transitions are needed to capture the full dynamics.

SIR Simulation in Julia

S - Susceptible

I - Infected 

R - Removed or Recovered

Additional states suggestion:

Q - Quarantine 

D - Deceased rather than removed

W - Weakened Immune system due to hard recovery, return to Susceptible pool

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