AI: A Pandemic Forecaster

AI, or Artificial Intelligence, is a powerful tool in today’s world. How is it helpful to us though? This is a question asked by many since in today’s world everyone has a pros and cons list for everything they come across. So, if we start from the basics that is, what is Artificial Intelligence (AI)? The answer to this is in computer science; artificial intelligence is a subfield that focuses on creating intelligent computers that can analyze data, draw conclusions, and forecast future events correctly. Developing computer-operated machines that can perform activities that normally require human intelligence is the goal of artificial intelligence (AI), which collaborates with computer science. With the increasing usage of data and processing power, the world is developing quickly in artificial intelligence (AI). The intelligence exhibited by machines, or artificial intelligence (AI), has produced previously unheard-of results in a variety of fields and is predicted to augment the GDP by roughly 13 trillion dollars by 2030.

Artificial intelligence (AI) is gradually permeating many parts of daily life. One of the key goals of modern healthcare is to develop towards personalized medicine, which might be considerably improved by AI. Pandemics are a global threat, primarily to healthcare but also to other industries. The occurrence of worldwide pandemics has underscored the necessity of looking into health issues and developing response plans faster than the spread of infectious diseases. Pandemics of infectious diseases like COVID-19, Zika, SARS, Ebola, and Middle East respiratory syndrome occurred in the twenty-first century.

Any pandemic’s management, containment, and prevention depend heavily on an early diagnosis. Studying the literature indicated that the widespread use of smartphones and social media since the previous outbreak, along with artificial intelligence (AI) and machine learning (ML) analysis techniques, are crucial weapons in the pandemic’s arsenal. The fast advancement of computer software, hardware, and mathematics has led to the development of artificial intelligence (AI), which is the broad category of methods that let computers think and function similarly to the human brain in order to aid in decision-making. Of the various subfields of artificial intelligence, machine learning (ML) and deep learning (DL) are the two most prominent. A subset of artificial intelligence called machine learning (ML) can learn from experience automatically and make necessary improvements without needing to be explicitly programmed. The computer creates a model or logic from the data set, and the result is machine learning (ML). ML-based techniques can be used to build a vast and complex amount of data. Larger data sets become available over time, which aids in improving algorithms for use in applications and raising the accuracy of their output. A few decades ago, the constraints of data storage and computational power made this impractical. These days, with cloud servers and the current computer capacity, we can manage any size of data. These factors have made the development of ML/AI applications more popular. These methods have been widely applied to forecasting and the identification of epidemic trends.

With every successful artificial intelligence (AI) application in the healthcare industry, machine learning (ML) is one of the most cutting-edge analytical approaches available. ML automates the execution of rules using algorithms. Mathematical models capable of tracking the spread of new infections and automating these surveillance techniques are urgently needed for online real-time decision-making. We would be able to forecast illness behavior by creating, gathering, and evaluating data on the affected individuals, such as patient specifics, their mobility throughout the community, and public health information. By combining this data with AI, machine learning estimates of the disease’s potential spread location and timing may be made. These areas could receive advance notice so they can make the required preparations. Developing prediction models without any prior information or established methods is the core competence of machine learning. Lots of data are used to create complex patterns. Combining these prediction models helps with clinical diagnosis. In COVID-19, the increasing accessibility of multiple types of data makes it favorable to apply AI techniques to help us to prevail over the pandemic. Researchers used deep learning (DL), a subset of machine learning, to tackle the COVID-19 epidemic. Additionally, it is utilized for pandemic forecasting and prediction, medication repurposing, screening, diagnosis, and categorization.

Here’s an example of how an AI platform predicted a very recent pandemic by a random observation: The first indication of an epidemic was a coincidental finding by the artificial intelligence (AI) platform BlueDot, which observed a clustering of an uncommon respiratory condition resembling pneumonia in China in December 2019 and January 2020. It had identified the early signs of the 2019 Coronavirus Disease (COVID-19), which was brought on by a brand-new coronavirus. This was nine days prior to the formal declaration from the World Health Organization (WHO) warning everyone about the severe acute respiratory syndrome virus (SARS-CoV-2) as a new threat. AI’s ability to identify the early signs of the SARS-CoV-2 pandemic serves as an illustration of the potential applications of AI in healthcare and the prospect of pandemic-proofing the globe in the future. Growing surveillance for diseases proactively tracking and monitoring the increasing number of illness cases requires AI-based tools, which are necessary for pandemic prediction. Real-time population demographic surveillance would be carried out via such systems. They would have to filter information on a person’s health history, employment history, and socioeconomic characteristics for various areas, ethnic groups, and sizable portions of the population. This would establish a connection between the illness and its potential causes. This will make it possible for us to identify the initial location where patients get in touch with a positive case. Transparency and internationally open data-sharing regulations would be necessary for this.

AI-based systems that forecast disease patterns locally and worldwide may provide early warning of impending pandemics and enable preparations. This is just the basic idea on how AI helps in pandemic forecasting and control. Few demerits of AI can be that the majority of these AI-powered products are developed and used in wealthy nations. Implementing such applications may be difficult for less developed nations owing to a lack of funding, a lack of technological know-how, and inadequate infrastructure, which makes it difficult to manage and control the pandemic. Identifying Covid-19 instances, quickly reallocating the hospital’s limited resources for both infected and non-infected patients, and comprehending how the populace behaves in opposition to the limitation measures are a few examples. There is a dire need for better AI foundation to forecast upcoming pandemics, and history is proof that until there is life present on this globe, various outbreaks will be occurring, so we need to be prepared, and AI tools will play a key role in this development.