The Star Group CEO David Karwacki is calling on USDA and CFIA to use the technology underlying social media platforms like Google, teamed with big data and rapid microbiome monitoring technology to more accurately predict food pathogen outbreaks. The reduction of mass recalls and quickly detecting the source will strengthen consumer trust in fresh produce.
‘By collaborating with platforms like Google, CFIA and USDA could more accurately and rapidly predict the source of E. coli or Salmonella outbreaks. Millions of dollars in food waste would be saved if predictive algorithms were used to detect the outbreaks at a micro level quickly and accurately,’ said Karwacki.
As consumers query words like stomach flu or diarrhea on Google, data algorithms can predict trends and isolate illnesses quickly and geo targeted. By limiting the scope of recalls to geographic hot spots instead of nation wide recalls, food borne illness would decrease and effective food safety protocols would be enhanced.
Google and Harvard University have been collaborating to beta test machine learning in epidemiology for real time detection of foodborne illness. The cities of Chicago and Las Vegas have been testing sites for digital health epidemiology algorithms like finder. These systems have been used on poultry and to detect restaurant violators of food safety protocols.
‘What the food industry desperately needs is the ability to rapidly detect disease causing microbes throughout the food system,’ stated Trevor Charles, CSO of Metagenom Bio and Director of Waterloo Centre for Microbial Research.
