Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses. Issue 6 (19th August 2019)

• Record Type: Journal Article
• Authors: Walsh, Daniel P.; Ma, Ting Fung; Ip, Hon S.; Zhu, Jun
• Is Part Of: Transboundary and emerging diseases. Volume 66:Issue 6(2019)
• Page Start: 2537
• Page End: 2545
• Publication Date: 2019-08-19
• Subjects: artificial intelligence -- influenza A -- machine learning -- surveillance -- virus isolation -- waterfowl; Veterinary medicine -- Periodicals; 636.089
• DOI: 10.1111/tbed.13318
• Languages: English
• ISSNs: 1865-1674

Abstract

Abstract: Influenza A viruses are one of the most significant viral groups globally with substantial impacts on human, domestic animal and wildlife health. Wild birds are the natural reservoirs for these viruses, and active surveillance within wild bird populations provides critical information about viral evolution forming the basis of risk assessments and countermeasure development. Unfortunately, active surveillance programs are often resource‐intensive, and thus, enhancing programs for increased efficiency is paramount. Machine learning, a branch of artificial intelligence applications, provides statistical learning procedures that can be used to gain novel insights into disease surveillance systems. We use a form of machine learning, gradient boosted trees, to estimate the probability of isolating avian influenza viruses (AIV) from wild bird samples collected during surveillance for AIVs from 2006 to 2011 in the United States. We examined several predictive features including age, sex, bird type, geographic location and matrix gene rRT‐PCR results. Our final model had high predictive power and only included geographic location and rRT‐PCR results as important predictors. The highest predicted viral isolation probability was for samples collected from the north‐central states and the south‐eastern region of Alaska. Lower rRT‐PCR Ct‐values are associated with increased likelihood of AIV isolation, and the model estimated 16% probability of isolating AIV from samples declared negative (i.e., ≥35 Ct‐value) using the rRT‐PCR screening test and standard protocols. Our model can be used to prioritize previously collected samples for isolation and rapidly evaluate AIV surveillance designs to maximize the probability of viral isolation given limited resources and laboratory capacity.