A decision support framework for prediction of avian influenza.

For years, avian influenza has influenced economies and human health around the world. The emergence and spread of avian influenza virus have been uncertain and sudden. The virus is likely to spread through several pathways such as poultry transportation and wild bird migration. The complicated and global spread of avian influenza calls for surveillance tools for timely and reliable prediction of disease events. These tools can increase situational awareness and lead to faster reaction to events. Here, we aimed to design and evaluate a decision support framework that aids decision makers by answering their questions regarding the future risk of events at various geographical scales. Risk patterns were driven from pre-built components and combined in a knowledge base. Subsequently, questions were answered by direct queries on the knowledge base or through a built-in algorithm. The evaluation of the system in detecting events resulted in average sensitivity and specificity of 69.70% and 85.50%, respectively. The presented framework here can support health care authorities by providing them with an opportunity for early control of emergency situations.

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

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.

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

[This corrects the article DOI: 10.3389/fvets.2018.00263.].

Cytokine gene expression in chicken cecal tonsils following treatment with probiotics and Salmonella infection.

Probiotics are currently employed for control of pathogens and enhancement of immune response in chickens. In this study, we investigated the underlying immunological mechanisms of the action of probiotics against colonization of the chicken intestine by Salmonella enterica subsp. enterica serovar Typhimurium (Salmonella serovar Typhimurium). Birds received probiotics by oral gavage on day 1 of age and, subsequently, received Salmonella serovar Typhimurium on day 2 of age. Cecal tonsils were removed on days 1, 3 and 5 post-infection (p.i.), RNA was extracted and subjected to real-time quantitative RT-PCR for measurement of interleukin (IL)-6, IL-10, IL-12 and interferon (IFN)-gamma gene expression. There was no significant difference in IL-6 and IL-10 gene expression in cecal tonsils of chickens belonging to various treatment groups. Salmonella serovar Typhimurium infection resulted in a significant increase in IL-12 expression in cecal tonsils on days 1 and 5p.i. However, when chickens were treated with probiotics prior to experimental infection with Salmonella, the level of IL-12 expression was similar to that observed in uninfected control chickens. Treatment of birds with probiotics resulted in a significant decrease in IFN-gamma gene expression in cecal tonsils of chickens infected with Salmonella compared to the Salmonella-infected birds not treated with probiotics. These findings reveal that repression of IL-12 and IFN-gamma expression is associated with probiotic-mediated reduction in intestinal colonization with Salmonella serovar Typhimurium.