Matogen Applied Insights (MAI) assisted with automating parameters for an extensive COVID modelling endeavour.
The pandemic’s impact on mining
The commodities market is no stranger to external shocks and already early on in the pandemic, analysts were scrambling to quantify how COVID-19 would affect mining operations globally. A data science company specialising in health care modelling was contracted by an international mining conglomerate to construct a compartment model to track the Covid 19 epidemic’s impact on their operations worldwide.
COVID modelling parameter automation
The compartment model’s parameters were fitted by choosing reasonable values and running an extensive grid search around these values on a cluster. However, given that the model could accommodate up to 60 parameters, the computationally costly process proved problematic to repeat as new data became available.
Matogen Applied Insights (MAI) was approached to assist to automate and simplify the fitting of the model. Parameter fitting was made less complex by implementing a gradient descent with multiple live points, after a few rounds of which, a Markov Chain Monte Carlo (MCMC) was run on each live point. The best results were used as summary statistics for the parameter space.
The algorithms were implemented in an Approximate Bayesian Computation framework and parallelized in an R package to run on an Azure server. This removed the need to manually fit the model and run an expensive grid search, saving many person-hours and giving better results.
“Matogen AI’s interdisciplinary team has a lot of experience applying advanced statistics and machine learning for predictive modelling, but they also recognise the power of human intelligence so they are about the synergy of human and machine intelligence. I have been working very closely with the team over the past six months and they are truly an amazing group of people. Not only do they produce top quality results in a very time efficient way, they also operate from the core values of integrity, respect, sustainability and social impact.”