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

野生动物疾病在物种灭绝、病原体向人类扩散（人畜共患病）方面发挥作用，并可能威胁粮食安全（例如野生渔业）。然而，对野生动物传染病的系统监测极为稀少，英国的例外情况包括关键的人畜共患病原体，例如鸟类中的禽流感和獾中的牛分枝杆菌（牛结核病）。对大多数野生动物来说，疾病未被发现，但了解野生动物疾病发生的地点、时间和原因对于保护生物多样性、农业资源以及动物和公众健康至关重要。然而，人工智能（AI）可以提供一种填补知识空白的新工具。许多图片在社交媒体上公开分享，并没有特别的科学意图，但这些图片可以成为重要生态数据的丰富来源。野生动物疾病的存在就是这样一个例子。许多疾病都有可检测的、可见的症状，例如皮肤损伤和溃疡。提交给社交媒体的图片数量众多，通常拍摄于广泛的地理范围和时间段。事实上，拥有120亿全球用户网络的社交媒体反映了四分之一人口所观察到的现实（以图像形式）。社交媒体网站“Flickr”和“Facebook”已经积累了大约1000亿张图片，每天上传的新图片超过1亿张。为了利用这一巨大的信息潜力，我们将整理一个来自社交媒体的大型图像数据库，并使用计算机视觉（一种人工智能（AI））来检测这些图像中的疾病。我们将使用野生鱼类作为试验平台，因为垂钓者群体（估计有290万垂钓者）有强烈的将捕获物拍照并发布到网上的文化，这提供了很高的监测潜力。野生渔业是一个重要的物种群体，代表着一个重要的收入来源，仅在英国，每年在娱乐和创造就业机会方面的价值就超过10亿英镑。因此，鱼类健康状况不佳对垂钓、经济和保护构成威胁。我们的试点工作表明，计算机视觉可以高效地从图像中识别鱼类常见疾病的症状。由于许多图像都是用具有gps功能的设备拍摄的，我们可以从这些图像中提取时间和日期，从而使我们能够深入了解疾病热点发生的地点和时间。一旦我们掌握了疾病发生的地点和时间的数据，我们就可以评估疾病发生的原因。了解疾病发生的原因对管理至关重要。我们将使用统计模型来评估导致鱼类患病的因素（温度、水流、污染），从而使我们能够更好地控制疾病。最终，这种研究方法可以在全球范围内扩大到不同野生动物宿主物种的一系列疾病。这些数据将为了解传染病动态提供重要的见解，并可能为人畜共患病对人类的溢出潜力提供早期预警。

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.