Abstract
Future demands for food will place agricultural systems under pressure to increase production. Poultry is accepted as a good source of protein and the poultry industry will be forced to intensify production in many countries, leading to greater numbers of farms that house birds at elevated densities. Increasing farmed poultry can facilitate enhanced transmission of infectious pathogens among birds, such as avian influenza virus among others, which have the potential to induce widespread mortality in poultry and cause considerable economic losses. Additionally, the capability of some emerging poultry pathogens to cause zoonotic human infection will be increased as greater numbers of poultry operations could increase human contact with poultry pathogens. In order to combat the increased risk of spread of infectious disease in poultry due to intensified systems of production, rapid detection and diagnosis is paramount. In this review, multiple technologies that can facilitate accurate and rapid detection and diagnosis of poultry diseases are highlighted from the literature, with a focus on technologies developed specifically for avian influenza virus diagnosis. Rapid detection and diagnostic technologies allow for responses to be made sooner when disease is detected, decreasing further bird transmission and associated costs. Additionally, systems of rapid disease detection produce data that can be utilized in decision support systems that can predict when and where disease is likely to emerge in poultry. Other sources of data can be included in predictive models, and in this review two highly relevant sources, internet based-data and environmental data, are discussed. Additionally, big data and big data analytics, which will be required in order to integrate voluminous and variable data into predictive models that function in near real-time are also highlighted. Implementing new technologies in the commercial setting will be faced with many challenges, as will designing and operating predictive models for poultry disease emergence. The associated challenges are summarized in this review. Intensified systems of poultry production will require new technologies for detection and diagnosis of infectious disease. This review sets out to summarize them, while providing advantages and limitations of different types of technologies being researched.
Highlights
Global population growth, along with rising affluence in Asia, are driving up our total demand for food, and the amount of protein required to feed all of humanity [1, 2]
Big data and big data analytics will be highlighted in the context of designing predictive models for disease emergence in poultry, in addition to data-driven decision support systems that enhance decision making pertaining to management practices when faced with the threat of emerging disease
Poultry farms will be forced to become larger in size with greater numbers of birds
Summary
Along with rising affluence in Asia, are driving up our total demand for food, and the amount of protein required to feed all of humanity [1, 2]. A concern is raised in the literature that intensive systems of livestock production may be more vulnerable to outbreaks of disease in both farmed animals and in human populations [4, 5] Despite such worries, technological advancements that make it possible for farmers to manage the health status of more birds with less resources are a current source of research and development. The ability to contain infectious disease on poultry farms could benefit immensely from systems that first can rapidly detect unhealthy or sick birds, and secondly devices that can accurately and rapidly determine the causative agent that led to disease Devices that make this a reality are a current source of research, and comprise a variety of different technologies, including multiple types of biosensors and rapid-assays, real-time poultry analysis tools that utilize audio or visual components to assess poultry health, and wearable sensors that can transmit and analyze data about the health of poultry. Big data and big data analytics will be highlighted in the context of designing predictive models for disease emergence in poultry, in addition to data-driven decision support systems that enhance decision making pertaining to management practices when faced with the threat of emerging disease
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