Detecting and Predicting Emerging Disease in Poultry With the Implementation of New Technologies and Big Data: A Focus on Avian Influenza Virus

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.

Review: Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data

“…As with all models, with DD models one must be cautious of biases introduced via skewed training data (e.g. to favour other characteristics, such as chicken age or background noise, Astill et al, 2018). Data-driven models are datahungry, requiring large data sets for training and evaluation purposes to rule out biases, noise and data imbalance.…”

Emerging coronavirus diseases and future perspectives

“…In era of the technology, chemical sensors, microelectronic designs, and artificial intelligence are contributing towards prevention, diagnosis and management of animals ailments. Technological advancements such as wearable technologies and biosensors make it possible to manage the health status of livestock and birds [43][44][45]. In addition to this precision livestock forming technology could also help to manage and monitor animal's status health through providing a continuous picture of health in real time and enabling fast mediations that benefit the safety of both animals and consumers [46,47].…”

COVID-19 Pandemic: Group Testing

“…However, theoretically, a single Illumina sequencer can, for example, cover the SARS-CoV-2 genome 12 billion times in a 24h run 6 ; hundreds of thousands if not millions of samples could be tested in a single spot. That could sound like overestimation, neglecting several practical limitations, but feasible proposals with impressive capacity offerings exist 7 . Released the protocols for a massively parallel COVID-19 diagnostic assay enabling simultaneous testing of 19,200 patient samples.…”