The covid-19 virus is a type of coronavirus. It has been linked to severe acute respiratory syndrome (SARS). The covid-19 virus can be contracted through contact with saliva or mucous from an infected person. Symptoms include fever, cough, sore throat, headache, muscle aches, and fatigue. There are several problems related to the Covid-19 pandemic which can be solved using machine learning/data science techniques. In this blog post, we will look into some of these Covid-19 use cases which can be solved using machine learning classification and clustering techniques.
One of the datasets available for studying Covid-19 is GISAID data (https://www.gisaid.org/) that represents million viral genomes (virome) sequences of COVID-19 or more precisely SARS-CoV-2. Genomic data has a high volume, as the SARS-CoV-2 virome has around 30000 nucleotide base-pairs, and there are more than 2.5 million such sequences available in GISAID alone. In March 2020, when COVID-19 was declared a pandemic by the world health organization (WHO), there were a few thousand sequences available. GISAID collects sequences from all over the world, they come from heterogeneous sources of sequencing technologies and centers, leading to multiple levels of veracity.
The genomic sequence of a virus encodes all of its functions such as virulence and transmissibility. It is variation in this genomic sequence itself which defines the different variants of SARS-CoV-2 such as Alpha, Delta, and Gamma.
The following is the list of Covid-19 datasets publicly available:
The following represents few machine learning use cases which can help deal with Covid-19 pandemic:
When training classification/clustering models on genomic sequence data, feature selection, and feature extraction are key as the number of sequences is so huge in numbers. Supervised and unsupervised feature selection/extraction methods such as ridge regression, lasso regression, and principal component analysis (PCA) could prove to be very helpful resulting in improving the overall predictive performance of the models. However, given the data volume is quite large, one can also try kernel methods for identifying important features although it also has its downsides.
Covid-19 is a pandemic that has been identified by the WHO as of March 2020. Covid-19 can be difficult to identify because it doesn’t always fit into the traditional classification system for viruses, but machine learning and data science techniques like clustering and classification models are helping Covid-19 experts make sense of Covid-19 genomic sequence data. This post will be updated from time to time with more Covid-19 machine learning use cases. If you want to learn more about different techniques, please feel free to reach out.
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