Artificial Intelligence is being widely used in the field of epidemiology to prevent, control, surveil and predict the spread of diseases. With new outbreaks such as the current outbreak of coronavirus, AI is making the most impact. We know that even before the news of Corona virus outbreak went public, a Canadian-based artificial intelligence (AI) based company, BlueDot already had raised an early alarm about the mystery virus. It specializes in infectious disease surveillance. Artificial Intelligence has come a long way in epidemic prediction and understanding it’s patterns. Not only that, by analyzing the global airline ticketing data, the company also predicted that the disease might spread to places such as Tokyo, Bangkok, Seoul and Taipei. During the 2018 Nipah virus outbreak in Kerala, AI helped in tracking the specific bat species that were the carriers.
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AI for Virus Detection and Epidemic Prediction
Firstly, AI and ML techniques can identify any disease outbreak even before they actually do. For example, BlueDot used an AI-driven algorithm that can scan though foreign language news reports, plant and animal disease networks and other official sources to avoid danger zones.
For emergency management, physicians can put early warning mechanisms for cardiac arrest, strokes, sepsis and other critical conditions.
ML was used to train model using NLP to track anthrax outbreak in Mongolia. After the automated data-sifting is done, analysts take over. The final analysis after checking from a scientific view is sent to government or clients.
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AI-Assisted Research in Development of Treatment and Vaccines
Not only for Corona virus, AI had also contributed during the outbreak of Zika virus too. Machine learning models can help identify the carriers of the virus. Globally, only two species of primates were confirmed to be carriers of the virus. The researchers tried augmenting a limited corpus. One by multiple imputations, and other by MICE. Multiple imputations can replace missing data in two ways. Either by substituted values that reflect uncertainty around the true values, or by Bayesian multi-label machine learning. Secondly, is the MICE a.k.a. multiply imputed chained equations, an algorithmic system. This MICE system can sift through data sets for biological and ecological trait relationships among organisms which infers missing information from those relationships.
In the past few years, AI has helped researchers to find either cures, or specific patterns of any disease, or treatments. Since these viruses can mutate, thus it is very crucial to understand these changes, hence eliminating it at its bud.
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The fact that researchers were able to produce new gene sequencing technique is an achievement in itself. This shows that even with meager data available, AI can trace patterns and send alerts before it takes a huge turn. Thus, in this way artificial intelligence can successfully help in epidemic prediction.
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