How data analytics can slow down the spread of COVID-19

With the rapid spread of the novel Coronavirus throughout the world, researchers and scientists are racing against time to find breakthroughs that can help us contain the global outbreak. Although new information is trickling in, we still need to find many insights to battle this widespread disease.

Data analytics can be our weapon to emerge from these uncertain times. Machine learning and data analytics approaches can help with the drug development process, provide insights into current antivirals, give accurate predictions of infection rates, faster screening of patients, identification of infection hotspots and the development of a vaccine.

Analytics is also an effective way to combat the proliferation of misinformation and inaccurate data which is perhaps even more dangerous than the pandemic. Even after the global pandemic subsides, data analytics can be useful in helping us deal with the social, economic and political aftermath.

Whether you have an analytics background or plan to pursue a career in this field, it can be worthwhile to learn more about the ways your skills can be put to use by your company in these unprecedented times. Read ahead to find out more about the role of data analytics in these troubled times.

Identifying important trends and correlations in data

As there is no treatment or cure for Coronavirus as of now, preventive measures are the best line of defence. Data analytics has been particularly helpful by identifying correlations and patterns between different factors and determinants from massive amounts of patient data which can determine risk factors.

Analytics has also been helpful in recommending strategies for disease quarantine and protection.

Analysing the GIS data

Global Information Systems or GIS has proved to be an important tool in retrieving data that can determine the spread of the disease in different countries. Data analytics tools like data mining and predictive analytics can help analyse the GIS data to aid the discovery of treatment patterns and clinical outcomes of drug trials. Evaluating the GIS data can also help experts identify the causal factors behind the outbreak.

Predicting protein structures to facilitate vaccine development

Analytic approaches such as deep learning and machine learning can also be extended to study and predict the shape of protein molecules that contribute to the virulence of the coronavirus.

Machine learning can help in recreating the molecular interactions at the cellular level which can bring us a step closer to the development of vaccines for COVID-19. Machine learning and analytics can also determine the efficacy of present anti-viral drugs and vaccines to diminish the effects of the pandemic.

Forecasting infection rates to plan responses

Data analytics approaches can contribute to the prediction of the criticality in patients with severe COVID-19 based on age and other risk factors. This can give governments insight into the measures needed to upgrade medical equipment such as ventilators. Data-based modelling and analytics can also forecast the infection rates in different regions in the future - highlighting infection hotspots that need extra attention.

The monumental task of analyzing huge amounts of data is on analytics companies and world governments. Technical universities can also pitch in to improve the speed and efficiency of the analysis and to provide fresh perspectives.

The ongoing lockdown is the perfect time to pursue a data analytics course and take advantage of the immense need of qualified analysts. If you are searching for suitable courses, the Diploma in Data Analytics Co-op course offered by Toronto School of Management (TSoM) is a great choice. The one-year program will help you gain the necessary skills and expertise to make data-backed business decisions.

Click here to get more details on the course.