Using Big Data to Battle Coronavirus: Real-Life examples and Recommendations

Real-world examples of using Big Data to overcome COVID-19

  1. Using mobile geolocation data to create COVID-19 spread heatmaps. Mobile carriers like Vodafone track the location of their customers to create heatmaps of the populace relocations. This helps identify potential outbreaks before they occur and take preventive measures, as well as monitoring the efficiency of lockdowns.
  2. Providing real-time updates on the pandemic spreading. Gisanddata platform aggregates Big Data from a variety of sources to provide real-time updates on the situation, both globally and per country.
  3. Adopting the “remote work” approach. The businesses need to continue working despite lockdowns and their reliance on cloud computing helps them overcome the challenges of working from home. When Big Data, as well as any other business-critical information, is processed in the cloud — there is no need for an expensive on-prem data center and the businesses can operate through the pandemic.
  4. Analyzing the updated customer behavior patterns. Lockdowns changed the patterns of our lives and businesses have to react to it to adjust their marketing strategies, supply chains, and customer support approaches. Using Big Data analytics helps understand the changing needs of your audience faster and react accordingly.
  5. Establishing foundations for efficient work of geographically dispersed teams. While many organizations prefer to work from the office, geographically dispersed teams will be a viable approach to workforce organization in the future. By using cloud computing to support remote work businesses lay the foundation for long-term post-pandemic sustainable growth. It is also invaluable for medics and scientists who can now better combine their efforts. This will undoubtedly lead to a boost in the efficiency of healthcare and pharmaceutical research in the future.

Concerns and Recommendations

  • Using blockchain to ensure patient data security and privacy. The blockchain ledger is essentially a distributed, immutable, and decentralized database, where the validity of data is ensured by the consensus of the majority of the network nodes. Blockchain is already actively used in multiple healthcare solutions and platforms across the globe. Storing patient data in blockchain ledgers can ensure its safe and appropriate usage.
  • Using federated learning to speed up ML models training. AI algorithms are traditionally trained in the cloud and require huge computing resources to handle large centralized and labeled datasets. Federated learning (FL) uses distributed networks (like millions of apps on smartphones South Koreans use to inform the authorities of the results of self-health checks).
  • This way, the apps train primary modes using the data set on the patient’s phone, but instead of sending all the data to the central server, they send only the trained model. The central server in the cloud aggregates all the individual models and forms a new version of the central model, which is then sent back to the users. This way, ML model training takes much less time and resources.
  • Using incentives to improve the quality of Big Data analytics. As we mentioned before, the accuracy of the Big Data analysis depends greatly on the volume and accuracy of the model’s training data set. However, the size of COVID-19 data sets available to healthcare and government organizations is minuscule in comparison to the data stored by individuals, businesses, and organizations. If the majority of the population and companies are to be incentivized to share this data, this would dramatically improve the relevance and accuracy of COVID-19-related Big Data analytics.



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