One of the critical pain point in COVID-19 pandemic is the lack of data on patient symptoms due to which there is no possibility of creating reliable hypotheses about which geographical regions are more susceptible to COVID-19 spread. In the light of above mentioned, a team of researchers at NUST are conducting an analysis on crowd sourced data to enable COVID-19 potential spread and management assessment within Pakistan.
To the best of our knowledge, this is the first project of its kind in Pakistan with the overarching goal of enabling NIH and concerned departments in identifying regions where probability of COVID-19 spread is higher from analytics on the collected questionnaires. Not only will the data be useful for performing predictive analytics and temporal trend analysis, it can also be made available so other researchers can use it for their analysis.
The dashboard contains 4 different visualizations:
District wise symptoms score calculated using a domain expert recommended weightage formula from the data entered by public.
District wise isolation score calculated from the data entered in the questionnaire regarding how well public is maintaining social distancing.
A district wise heatmap containing total cases, recoveries and deaths all-in-one from case 1 till date.
A timeline of how district-wise COVID-19 spread took place in Pakistan from case 1 till date.
Dr. Umair Hashmi, Assistant Professor at SEECS, NUST
Syed Ali Hasnain, CEME Alumnus and PhD Student Texas A&M, USA
Syed Shaharyaar Hussain, junior year student of Bachelor's of Electrical Engineering at SEECS, NUST
Muhammad Uzair Khattak, junior year student of Bachelor's of Electrical Engineering at SEECS, NUST