Can AI Forecast the Timeline of The Next Covid Outbreak?

Artificial intelligence forecasts the timeline of the next pandemic before it starts

The World Health Organization (WHO) defines zoonosis as the transmission of infectious diseases from wild animals to humans, which can be fatal enough to cause pandemics such as the Covid outbreak. In this article, we have discussed the capabilities of artificial intelligence in forecasting and spotting of next pandemic such as covid. the timeline of the next covid outbreak. Read to know more.

The primary causes of transmission are human encroachment on animal habitats, climate change, and increased movement of people, animals, and animal products as a result of international trade. According to the Global Virome Project, approximately 1.7 million animal viruses are known to cause infections in birds and mammals.

 Scientists believe that nearly half of those viruses can infect humans. Understanding these has now become critical to preventing pandemics or, at the very least, being better prepared if one occurs.

This type of research is a massive undertaking that has resulted in the creation of a new discipline in which machine learning (ML) and statistical models are used to forecast the emergence of various diseases, likely geographical hotspots, animal hosts, and viruses that are most likely to affect humans. Scientists who support this technology are confident that the findings will guide the development of medicines and vaccines, as well as assist everyone involved in accurately studying, observing, and forecasting situations.

Naturally, not all researchers share this viewpoint. Many people doubt that predictive technology can keep up with the rapidly changing virome or the scale of what exists at any given time. True, data and artificial intelligence (AI) models are constantly improving, but for such tools to be truly predictive of future pandemics, efforts must include a global network of researchers.

AI spotted the first signs of Covid-19

Bluedot, based in Canada, was among the first to recognise the emergence of the Covid-19 pandemic and raise the alarm. It employs an artificial intelligence-based algorithm that continuously searches global data to predict the next outbreak of an infectious disease. The Boston Children's Hospital's HealthMap algorithm detected the first signs of Covid-19 as well. The same was true for the Mayo Clinic's Coronavirus Map Tracking Tool.

Natural-language processing (NLP) algorithms in rapid development monitor global healthcare reports and news outlets in multiple languages, flagging mentions of diseases such as Covid-19 or endemic ones such as tuberculosis or HIV. Data on air travel is also monitored to assess the risks of spread. During Covid-19, social media was discovered to be a very reliable source of data. Data scientists at the University of Colorado, Boulder, used machine learning and a short-term forecasting model to analyse large datasets gathered from popular online platforms and compared the results to insights obtained from more traditional mobile device location data. The technology recognised specific keywords and gathered relevant data as people travelled during the pandemic or recovered and talked about their Covid experience.

When mask-wearing policies, lockdowns, and travel restrictions changed in 2021, this model was found to be far more accurate than other models.

Early buzz but not much later

However, after the initial buzz, AI was powerless. When applied to real-world clinical settings, almost all AI models performed poorly. Deep-learning models were found to be ineffective when applied to CT scans and chest x-rays. One of the most likely causes for these failures is that the AI models were working on a real pandemic for the first time. Imperfect datasets, human errors, automated discrimination, and complex global conditions were identified as the primary roadblocks.