Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses

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.

Artificial intelligence: Potential tool to subside SARS-CoV-2 pandemic

“…Humans and animals are vulnerable to viruses such as Influenza, SARS and current COVID viruses due to their transmitive nature [ 12 , 13 ]. In most cases wild birds are the source of these viruses and an efficient surveillance system within the bird population helps control the spread of these viruses to humans.…”

“…Despite numerous types of vaccines claiming to subside COVID-19, none have been declared to consist of certain effectiveness by the Food and Drug Administration, which indicates the urgency of the situation and the need for an efficient strategy to overcome this current issue. With this global pandemic impacting our society for the past two years, a more efficient and impactful solution could be derived from the various data that has been collected, not only from the patients and statistics regarding COVID-19 such as age, symptoms and location, but the amassed medical data gathered before the pandemic [ 12 ]. AI is one of the methods that could be employed in order to accumulate the data and output processed data which could be used to benefit countless situations.…”

Regional distribution of non-human H7N9 avian influenza virus detections in China and construction of a predictive model

“…For example, Gilbert et al (9) constructed a spatial econometric model to predict the risk of H7N9 avian influenza infection in live poultry markets in Asia, Young et al (33) constructed a niche model to predict the prevalence of H5N1 and H9N2 avian influenza in poultry flocks, and Li et al (16) developed a Bayesian inference system to predict the infection rate of H7N9 also in poultry flocks. Qiang and Kou (23) used the wavelet packet decomposition (WPD) method to predict the transmission of avian influenza virus between humans and various poultry species, Walsh et al (28) adopted the gradient-boosted tree to build a model for predicting the probability of isolating avian influenza virus from wild bird samples, and lastly, Yang et al (31) predicted the spread of H9N2 avian influenza with a system dynamics model. Although these previous studies achieved good results, most of them adopted the single-model prediction method.…”

“…Qiang and Kou ( 23 ) used the wavelet packet decomposition (WPD) method to predict the transmission of avian influenza virus between humans and various poultry species, Walsh et al . ( 28 ) adopted the gradient-boosted tree to build a model for predicting the probability of isolating avian influenza virus from wild bird samples, and lastly, Yang et al . ( 31 ) predicted the spread of H9N2 avian influenza with a system dynamics model.…”

The Constant Threat of Zoonotic and Vector-Borne Emerging Tropical Diseases: Living on the Edge

“…Many tools have been deployed to counteract emerging infectious diseases. Amongst these are active surveillance (some supported by artificial intelligence) (140)(141)(142), leading to the rapid identification of novel pathogens by genome sequencing and phylogenetic tracing studies (36,105,107,(143)(144)(145)(146) based on computing methods to predict possible interspecies barriers spillover between humans and animals (147). Coupling biotechnological approaches with social sciences-the holistic understanding of humans and their interactions in the disease ecosystems-is also a critical element needed when studying emerging infectious diseases (148,149).…”