ArtiFISHal Intelligence: merging technology and ecology to understand infectious disease dynamics

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Wildlife diseases play a role in species extinctions, spillover of pathogens to humans (zoonoses), and can threaten food security (e.g., wild fisheries). Systematic monitoring of infectious diseases in wildlife, however, is extremely sparse, exceptions in the UK include key zoonotic pathogens, e.g., avian influenza in birds and Mycobacterium bovis (bovine TB) in badgers, Meles meles. For the majority of wildlife, disease goes undetected, yet understanding where, when and why wildlife disease occurs is essential for safeguarding biodiversity, agricultural resources as well as animal and public health. Artificial Intelligence (AI), however, could offer a new tool by which to fill the knowledge gap. Many images are shared publicly on social media with no particular scientific intent, but those images can be a rich source of important ecological data. The presence of wildlife disease is one such example. Many diseases have detectable, visible symptoms, for example, skin lesions and ulcers. Images submitted to social media are numerous and are often taken across broad geographical ranges and time periods. Indeed, social media with its user network of >2 billion worldwide reflects the observed reality (in image format) of a quarter of the human population. Social media sites 'Flickr' and 'Facebook' have amassed an estimated 100 billion images alone, with over 100 million new images uploaded every day. To harness this huge potential for information we will collate a large database of images from social media and use computer vision, a form of artificial intelligence (AI) to detect disease within those images. We will use wild fish as our test bed because the angling community (an estimated 2.9 million anglers), have a strong culture of photographing their catches and posting them online, which provides high monitoring potential. Wild fisheries are an important group of species that represent a significant income generator, worth over £1billion annually in the UK alone in recreation and job creation. Poor fish health therefore represents an angling, economic and conservation threat. Our pilot work has shown that computer vision can identify symptoms of common diseases of fish from images with high efficacy. Because many images are taken with GPS-enabled devices we can extract time and date from those images, allowing us to gain insight into where and when hotspots of disease occur. Once we have data on where and when disease occurs, we can then assess why disease occurs. Understanding why disease occurs is essential to management. We will use statistical models to assess which factors underlie disease presence on fish (temperature, water flow, pollution), allowing us to facilitate better control of diseases. Ultimately this research approach could be widened to a range of diseases in different wildlife host species at a global level. Such data would provide important insight into infectious disease dynamics and potentially provide early warnings for zoonotic spillover potential to humans.