• Prediction

As the global human population grows and continues to interact with animals, other opportunities for the viruses that originate in animals could make the jump from humans and spread. India has seen this in recent years, from the recent SARS and MERS viruses to new firms of the flu and even in the 2018 Ebola crisis in West Africa, where it was founded and discovered that the Ebola outbreak resulted from a toddler interacting with bats in a tree stump.

Government and public health officials can use this data to be proactive and take steps to prevent these kinds of outbreaks – or at a minimum, do a better job preparing for them.

• Detection

When any of the unknown viruses make the jump to humans, time becomes a precious resource. The quicker a disease outbreak is detected, the sooner action can be taken to stop the spread and effectively treat the infected population. AI can help here, as well.

• Response

After a disease event is identified, making informed decisions in a timely manner is critical to limiting the impact. AI can integrate travel, population, and disease data to predict where and how quickly a disease might spread.

Additionally, using AI to predict disease spread, it can improve the application of current treatment and accelerate the time it takes to develop new treatments. Radiologists are also using the AI deep learning – machine learning systems that learn from experience with large data sets, to make better treatment decisions based on medical imaging.

• Recovery

Once an outbreak is contained or has ended, governments and global health organizations must make decisions about how to prevent or limit outbreaks in the future. Machine Learning can be used here too by simulating different outcomes to test and validate policies, public health initiatives and response plans.

Moreover, AI permits policy makers and health leaders to conduct a host of “what-if” analyses that will enable them to make data-driven decisions that have an increased likelihood of being effective.