covid-19 modeling and monitoring
In March 2020, the COVID-19 pandemic brought the world to a halt to stop the spread of the disease. As global data collection was well organized and through John’s Hopkins University COVID-19 dashboard it became immediately apparent that the state of the art Data Science techniques were not up to the challenge for this data. While many mathematical models such as the Susceptible-Infected-Recovered models had been widely studied in the literature from a theoretical perspective, none had been used in a pandemic situation. The lab was one of the first to develop a methodology that utilized mathematical models for epidemiology in conjunction with the Bayesian framework to determine the effect of various government interventions on the disease dynamics. This work was published before a vaccine was available, in addition the framework was extended into a monitoring tool that signals when a change to the disease dynamics occurs. When the vaccine became available the tools were extended to study the efficacy of the vaccine at the population level.
The data visualization was created by graphic designer and VCUarts Qatar Alumni Hannah Fakhri.