A recent article, published in Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, has explored the role of big data in propelling our understanding of COVID-19.
SARS-CoV-2 was first identified in December 2019 in Wuhan, China. Since then, due to its high transmissibility, it has rapidly spread across the whole world, resulting in a global pandemic. Researchers have scrambled to understand this virus at both the genomic and molecular level in order to strategically plan effective treatment and control.
To date, no targeted medicine or vaccine is available for COVID-19. Consequently, international collaborations have attempted to use advanced technologies, such as big data and analytics, to fight against the pandemic. A notable example is the use of big data analytics and cell phone tracking in Taiwan. In this case, big data analytics was successfully used to control the spread of the virus.
Advanced technologies not only help with identifying signs and symptoms of the disease and tracking the virus, they also help monitor the availability of hospital resources. The ongoing pandemic has also seen unprecedented levels of collaboration. Many organisations and consortiums have been sharing datasets and analytical tools to gain a better understanding of this disease.
Artificial intelligence (AI), as seen prior to the pandemic, is showing great promise in accelerating new drug discovery processes and repurposing of existing drugs. Throughout the pandemic, several research labs and data centres have employed AI to help find an effective treatment or vaccine.
Using big data
Data is being produced an unparallel rate. With the increasing data volume and advancements in technology, the era of big data has come into focus. Millions of people across the world are being impacted by the virus. This in turn has generated huge amounts of data. Analysing each dataset and establishing a solution to control the pandemic is difficult. Big data has the potential to analyse datasets and determine patterns that can be used to track, predict, diagnose and prognose the virus. Here is a summary of how big data will play a role in the fight against COVID-19:
- Tracking and Prediction: Outbreak analytics combines all data including positive cases, deaths, recoveries, contacts of positive cases, population flow, travel history and population densities. Researchers use AI and machine learning to process data in order to predict disease infection rates and their effects. Various countries are harnessing big data to speed up the surveillance of infected populations. Examples include the use of big data analytics and AI in forms of security cameras or smartphone data.
- Diagnosis: A reliable and timely diagnosis of COVID-19 infection is vital. While RT-PCR is the gold-standard for detection, employment of smart devices in combination with an AI framework has proven a simple and cost-effective measure. AI technologies have also assisted in detecting COVID-19 from medical images, i.e. X-ray images and CT scans.
- Prognosis: There is currently no effective treatment available for COVID-19. Big data has the potential to help identify potential therapeutics and candidate vaccines. The use of machine learning could also help identify treatments for repurposing.
Big data and AI show great promise in battling against COVID-19 and other similar pandemics that may come. Nonetheless, processing of big data using AI systems is still in its infancy. The lack of standard datasets is a major challenge in applying big data platforms. As a result, the government and healthcare organisations need to work together to generate high-quality and big datasets. Another challenge is concerns about privacy and security of personal data. To address this, several technologies have become available, such as blockchain and federated learning, that take care of privacy and security concerns. Most importantly, centralised collection of COVID-19 patient data will prove highly beneficial in the future. It can help improve AI and ML research in the field of predictive, diagnostic and therapeutic strategies, not only for COVID-19 but for other similar future pandemics.
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