Artificial Intelligence framework for prediction of Infection severity

Infectious disease forecasting aims to predict characteristics of both seasonal pandemics and future pandemics. Accurate and timely infectious disease forecasts could aid public health responses by informing key preparation and mitigation efforts. 
The predictive model learns from historical data to help predict who will develop acute respiratory distress syndrome,a severe outcome of Covid-19.

Despite the advances in medicine, infectious diseases are leading causes of death worldwide,
especially in low income countries. With the advent of mathematical tools, scientists are now able 
to better predict epidemics, understand the specificity of each pathogen, and identify potential targets for drug development. Artificial intelligence and its components have been widely publicized for 
their ability to better diagnose certain types of cancer from imaging data. It is to cover series of applications selectively chosen as to how Artificial intelligence is moving in the field of infectious disease further.

Artificial intelligence and machine learning can assist with infectious disease prevention, 
management and surveillance. With the capability to analyze huge amounts of Data, including 
medical information, human behavior patterns, and environmental conditions big data tools can be invaluable in dealing with deadly outbreaks.          
 
The first AI supported entity to sound the alarm about the novel Coronavirus was  Blue Dot, a 
Canadian health monitoring platform. It uses algorithms to conduct automated infectious disease surveillance, predict the emergence and spread of diseases, and give advance warnings about danger zones. The Blue Dot platform uses natural language processing and machine learning to analyze 
around one lakh new stories, animal and plant disease reports in 65 languages every day, working around the clock. After AI conducts the data sifting, epidemiologists check the conclusions before sending out reports to government agencies, businesses and public health experts.           

Once scientists detect new diseases, next step is to figure out whee they are heading next. AI can 
assist in that phase too. For instance, analysis of Airline ticketing Data and flight paths accurately predicted that Covid 19 would jump from Wuhan to Bangkok, Seoul, Taipei and Tokyo. Other 
example that help the Doctors detect the spread of a disease as new patients enter the hospital and 
rapid diagnoses, aided by machine learning that enable effective isolation and quarantine efforts. 

Once AI predicts where a disease will travel next, officials can set up travel check points and call 
for a ramp-up of hospital resources in the right places. A combination of AI with simulation models 
can assess the effectiveness of various policy responses. Additionally, AI algorithms can determine what populations would best benefit from public health communications to prevent and slow disease.

Over the past couple of decades, 75% of infectious diseases have been Zoonotic, i.e. capable of spreading from animals to humans. By the time, Scientists identified host species, the disease had already leaped to humans, leaving mitigation as the only response. AI is now used to identify the ecological and epidemiological patterns that lead up to epidemics to predict outbreaks before they happen. Mathematical models and machine learning can analyze huge amounts of Data to get clues about the next likely source of infection.       

The expanded use of AI and machine learning has enabled to prevent or better manage outbreaks 
of infectious diseases resulting in reduced harm and disruption. As such, AI can keep an eye on 
other possible outbreaks around the world, so we will be prepared to respond, if needed.    

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