The public health war you’re forgetting


Amid the coronavirus crisis we have forgotten our old friend. Antimicrobial resistance is one of the greatest threats to global health – killing an estimated 700,000 people every year.

A World Health Organization report outlined that in a post-antibiotic age where common infections can kill. Generating a time of crisis for developing countries. What do the statistics say?

By 2030 antimicrobial resistance severity has been predicted to double. It is forecasted to double again by 2050, with approximately 10,000,000 deaths every year.

Are there any solutions on the horizon?

Fortunately, artificial intelligence approaches have been advancing the front of the antimicrobial resistance war across different aspects.

Machine-learning has allowed identification of novel compounds and genetic signatures involved in antimicrobial resistance.  

Identifying antibiotics such as carbapenem resistance with a 88–99% accuracy and other antibiotics (isoniazid, kanamycin, ofloxacin, rifampicin, and streptomycin) with a 71-88% accuracy.

Identified mutation-mapping to identify signatures of antimicrobial resistance evolution 13 antibiotics.

Identifying 33 genes known to that grant resistance in Mycobacterium tuberculosis, a pathogenic bacterium that is a serious human threat exhibiting complex evolution of antimicrobial resistance.

AI has been doing more than addressing this issue from a treatment standpoint – machine-learning has been used to identify the strongest determinant of antibiotics and antimicrobial resistance. 

The team further identified Individuals lacking higher education and without opportunities to obtain primary information should be considered a target population for effective interventions.



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