How data analytics and artificial intelligence shaped the response to Covid-19

Orchestrating a cohesive response to Covid-19

Disease forecasting models, built on complex algorithms consistently aided in decision making which influenced public bodies in enhancing testing capabilities, creating quarantine zones, and imposing lockdowns to flatten the curve. The multiple disease waves predicted by these models have been largely proven to be accurate and gave forewarnings on the possible grim situations if there is any complacency in adhering to protocols and preparedness.

Streamlining healthcare infrastructure

AI and ML have made a positive impact in the area of Health Information Systems. These models helped generate customized messaging, in multiple languages and formats, for different groups of stakeholders. AI and ML-powered a host of applications providing streamlined information to the public, including information pertaining to PPE kits, over-the-counter medications, emergency services, and hospital and vaccine availability, among others. Advanced AI/ML capabilities have helped policymakers spread important messages quickly and effectively. Citizens significantly benefitted from this speedy and customized guidance.

Expanding healthcare capabilities with new analytics models

Clear trends have emerged in terms of AI for healthcare during the pandemic and stakeholders continue to find new, effective use cases as we speak. Even at a broad systemic level, we have seen greater AI adoption during the pandemic with the open-sourcing of large amounts of data. AI-embedded tools have enabled a generation of novel datasets by scraping through web information and general social media platforms. Feature engineering of new and novel parameters provided higher accuracy and continue to build innovative models. As a result, a large number of crowd-sourced models have emerged, helping detect anomalies in official data and news. These datasets are used to create forecasting models across a host of critical use-cases, sometimes with greater accuracy than those previously in use.

Driving continuous improvement in post-pandemic healthcare

Technology has proven its strength in supporting critical life-saving systems and processes. Even after the pandemic is behind us, healthcare organizations will encourage the usage of external data and internal data to increase the reliability of prediction models. The increasingly higher acceptance of social listening will help track the next pandemic much faster. Smart NLP tools will be explored further to match patterns from the humongous archive of past research data which will accelerate preventative and prescriptive responses to disease management. With the emergence of health technology companies capturing the most granular patient data, conducive data sharing platforms needs to be provided, as this digital dust can be the next game-changer from the public health management perspective. This data can be mined to infer disease lifecycle, the population at risk, demographic influence, etc. Workplaces will install IoT sensors for monitoring employees’ general health and behavior to recognize any unwell staff. We believe that the potential of AI goes way beyond curbing and abating disasters. It will soon pave the way for robust and effective systems that prevent healthcare disasters.

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Affine is a provider of analytics solutions, working with global organizations solving their strategic and day to day business problems www.affineanalytics.com