Real Time Surveillance and Early Warning Systems

Spatial and Temporal Distribution and the Implementation of a Near Real-Time Surveillance System for Infectious Hematopoietic Necrosis Virus (IHNV) Infection in California


Aquaculture is rapidly growing in economic importance in the USA, where California (CA) is considered as an emergent leader aquaculture industry that worth an estimated $110 million [1]. Primary aquaculture species in the state are rainbow trout and various salmon species for which endemic infectious hematopoietic necrosis virus (IHNV), an acute systemic disease [2] and notifiable for the World Animal Health Organization (OIE), that causes high mortality and significant economic and social losses in hatchery stocks and wild salmonids in fresh waters [3]. IHNV spread in these species has been facilitated by a limited knowledge on the virus distribution, poor knowledge on risk factors, and the lack of an adequate surveillance system. In line with the priorities of the United States Aquatic Animal Health Plan, and with CFAH high priority issues, this project will address a key problem of national importance by analyzing historical monitoring data and by developing a prototype for a near real-time surveillance system for IHNV in CA. During the project, monitoring data that is available and has been systematically compiled and organized during the past 20 years will be gathered with host and hatchery data, and selected environmental factors including water temperature, salinity, distance to urban settings, etc. Such database will be used to 1) describe the spatial and temporal patterns of IHNV detection in California, 2) to quantify the nature and extent to which environmental factors may be associated with the presence of the virus, and 3) to generate a series of IHNV risk maps varying in space and time using a user-friendly web-based platform

AoC – Development and Maintenance of the ASF News Web-Service


The aim is to develop an intelligence technology system refer to as “ASF News”. We will develop the infrastructure and interface to capture, validate, store and provide in near real-time official and unofficial information, news and notifications about African Swine Fever reported in more than 10 languages. During the time of this collaboration we will complete the ASF News infrastructure design, implementation and beta-testing. Following, we will provide real-time information as well as daily reports with qualitative and quantitative details contained in ASF News such as source, type and number of news, space-time evolution of notifications, etc. This information will be stored in a ad hoc database and will be accessible anytime.

Linking veterinary diagnostic laboratory submissions to spatiotemporal mapping tools: the future of disease management, control and elimination

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The objectives of this project are to integrate, further develop, and use a number of existing diagnostic and epidemiologic information management technologies to create a functional, scalable, and universally applicable swine diagnostic information network and disease management tool that links participating veterinary diagnostic laboratory (VDL) submissions and corresponding Porcine Epidemic Diarrhea Virus (PEDV) test results and interpreted PEDV health status of the site to a spatiotemporal disease management tool (Disease BioPortal) for use in national, area-regional, veterinary clinic, or production system specific PEDV monitoring and control initiatives. Initially, while the more complex programming work is being completed to adapt the existing software for use in the proposed network and to establish the necessary connectivity amongst its parts; we will use PEDV diagnostic data derived via conventional VDL information management system query algorithms of participating VDL submissions to demonstrate the utility of the tool to monitor and further study the emergence of PEDV in the US swine industry. Participating VDL submissions must i) indicate their willingness to share and release their case (farm site) specific PEDV diagnostic results for use in further epidemiologic study and monitoring of the spread of PEDV throughout the US over the course of the project period and ii) provide the premise identification number, sample type, and type of farm site (Breeding Herd or Growing Pig) on the VDL submission form. These data will be used to map site-specific PEDV positive test results and PEDV phylogenetic summaries over the course of the 12-month project. This PEDV diagnostic information will be uploaded into the Secure Animal Health Diagnostic Database (SAHDD) for case-level interpretation, and subsequently downloaded into Disease Bioportal for further bioinformatic analyses and viewing by permissioned users. Collaborative efforts will be made to ensure the animal health information

management network being assembled in this project are synergistic and fully capable of delivering information into a program disease management tool (i.e., AgConnect) being developed by The Center for Foreign Animal and Zoonotic Disease Defense (FAZD). Permissioned users from Centers for Epidemiology and Animal Health (CEAH) and FAZD will be given full access to the PEDV diagnostic results and Disease BioPortal output from participating VDL submissions for further epidemiologic study. Participating veterinary diagnostic laboratories will be given permissioned access to the Disease Bioportal output to share with their stakeholders contributing to the data. The network and area regional disease management tools being created in this project will be betatested and used to support the complete diagnostic information management and reporting needs of the PEDV area-regional control project underway in Southeast Iowa (i.e., including maintaining the current PEDV status

of all premises participating in the project). Once fully developed and beta-tested, the network and disease management tool created will be available for use across any number of regions, states, diagnostic laboratories, veterinary practices, and/or production systems. Although PEDV is focus of this project; the network and technologies being developed are being strategically designed to be broadly applicable, scalable, and readily adaptable for use in managing other domestic diseases of high consequence. Similarly, the core elements of this project are well-aligned to help advance US foreign animal disease preparedness and risk-based continuity of

business programs being developed for the US Pork Industry. The Secure Pork Supply Plan is being established to maintain the safe movement of pigs that have been determined to be free of infection during the face of a reportable or program disease outbreak in the US.

Network analysis to identify important factors for managing zones


This WP will describe spatial and temporal dynamics of fish transportation and characterize the contact network patterns among the fish holdings based on the transportations, characterize infection-inducing contact patterns, identify the highly connected sites, and elucidate implications of the contact pattern on controlling disease spread.

Social network analysis (SNA) will be the approach used to characterize the contact pattern on the basis of fish transportation, and then identify highly connected sites and areas to be targeted for surveillance and control programs. The degree centrality (number of incoming and outgoing contacts that a site has) and closeness centrality (how closely connected each site is to all other sites within the network) will be estimated for each site in the contact network (Koschutzki et al., 2005; Dube et al 2009; Martinez-Lopez et al., 2009b). The centrality measures will subsequently be used to identify the sites at potential highest risk of receiving and/or transmitting infections within the network, and which sites, areas and time periods may play a key role for disease introduction and spread in Norwegian fish farming industry.

The working hypothesis is that “central” Norwegian fish farms (i.e., farms with high values for centrality measures) and network structure (i.e., relationship between different farms or groups of farms) have a strong influence on the vulnerability or risk on introduction and spread of diseases.

Additionally, we will use exponential random graph models (ERGM) to identify which node attributes and network structural properties influence the formation of an observed contact or, in other words, what is the probability that a movement between two sites occur given the properties and characteristics of those sites. This is useful for prediction of “future” contact patterns and ultimately, will allow the better prevention and control of diseases and the implementation of risk-reduction measures on a farming site.”

SOW: Web-based System for Surveillance of Infectious Animal Disease in the European Union


Goal: To develop a web-based system for surveillance of infectious animal disease in Europe
Specific objectives

  1. To collaborate with AHVLA staff on maintaining the utility of Bioportal for application to transboundary or notifiable disease events in poultry and wild birds in real time including exercises with data supplied by AHVLA.
  2. To respond on an emergency basis to support the EURL and Member States in the event of an avian influenza outbreak.
  3. The specific objectives, scope, progress and contributions of the work by CADMS staff to be discussed and agreed at monthly teleconferences with AHVLA.
  4. To contribute to training and development opportunities of EU National Reference Laboratory (NRL) staff and EURL staff as agreed with AHVLA.

Collaborative Research Agreement between Iowa State University and University of California, Davis


The Center for Animal Disease Modeling and Surveillance (CADMS) at the University of California Davis (Principal Investigator, Dr. Beatriz-Martinez Lopez) proposes to work collaboratively with and provide epidemiological and information management development and support services to the Iowa State University Veterinary Diagnostic Laboratory (Principal Investigator, Dr. Rodger Main) in effort to further develop and expand the capabilities and use of Disease BioPortal® across a broad-array of applied epidemiologically focused veterinary diagnostic and production animal medicine applications. 
Objectives of Work:

  1. Continue to advance the functionality and capabilities of a suite of complementary web-based tools (collectively referred to as the Animal Health Information Management Network, see Figure 1.) that links veterinary diagnostic laboratory submissions, corresponding test results, attending veterinarian insight, and an interpreted health status of farm sites via the Animal Health Monitoring and Evaluation System (AHMES, Iowa State University) to a spatiotemporal disease management tool (Disease BioPortal®, University of California - Davis) for use in area-regional, veterinary clinic, or production system animal health monitoring and control initiatives.  
  2. Complete the development and provide the technical support and database administrative access necessary for the Iowa State University Veterinary Diagnostic Laboratory to use Disease BioPortal® (University of California-Davis) for creating web-based aggregate summary analysis of the national, state, or regional trends being observed in the diagnostic case submissions being processed at the Iowa State University Veterinary Diagnostic Laboratory as well for client-specific spatiotemporal bioinformatic analysis and study. 

  3. Continue to advance the functionality and connectivity involving other ISU VDL associated data aggregation tools (or efforts) and Disease BioPortal®.

  4. Maintain and protect the confidentiality, security, and integrity of the ISU VDL associated data under this agreement on an isolated database server that contains only information pertaining to ISU VDL associated data (and backed-up) at the University of California-Davis.

Workshop: Next generation of real-time surveillance and risk assessment platforms


Workshop intended to provide hands-on-practice using the Disease BioPortal platform (, which is an on-line, user-friendly, tool for the advanced integration, visualization and analysis of genomic, epidemiologic and environmental data to support real-time decision making.

Development of an early-warning system based on real-time risk assessment for the prevention and rapid control of Avian Influenza in California Poultry industry


The recent cases of highly pathogenic avian influenza (HPAI) in a commertial Turkey flock in Stanislaus country (H5N8, Jan 2015) and a commertial poultry flock (broiler chickens and ducks) in Kings county (H5N8, Feb 2015) highlights the urgent need to develop and implement solutions to protect California poultry operations (PO) against avian influenza (AI) outbreaks. The unique peculiarities of the different types of PO coexisting in California (CA) (i.e., organic vs commercial, backyard flocks, live bird markets, etc.) pose a challenge on the early detection and control of diseases such as AI which cost producers and the US millions of dollars. Mapping the occurrence of AI in wild birds and the presence of environmental and anthropogenic factors for AI occurrence has been proven useful to identify high-risk areas for poultry exposure to AI virus in countries such as China or Thailand (Gilbert et al., 2008a; Fang et al., 2013a; Gilbert et al., 2014); however, the awareness of the producers and the implementation of appropriate biosecurity and management practices on farm are key to prevent and mitigate the consequences of an AI outbreak. The aim of this project is to pilot the development an innovative early-warning system based on scientific-based risk maps, real-time notifications, on-farm risk assessments and educational tools for better prevention and control of AI outbreaks in CA. First, we will generate high-resolution AI risk maps and identify environmental, climatic and anthropogenic factors associated with AI occurrence in CA using maximum entropy ecological niche modeling. Those methods will be integrated into a web-based, dynamic, platform with capabilities to send automatic notifications to producers if changes of AI risk are detected at local or state level. Second, a self-assessment tool will allow producers to quantify the specific risk of AI virus exposure in their operations at any time given their specific location, biosecurity and management practices. Finally, we will implement workshops to increase awareness, training and responsiveness of both small-scale and large-scale producers about biosecurity practices and early detection of AI. Results of this project will built capacity, increase awareness and provide updated risk-base estimates to better prevent, detect and control AI outbreaks in CA.

Development of an Early-Warning System Based on Real-Time Risk Assessment, Producers Self-Assessment of Biosecurity Practices for the Prevention, Early Detection and Rapid Control of AI Outbreaks in the CA Poultry Industry.


The goal of this interdisciplinary, multi-institution, research-extension project within the "Critical Agricultural Research and Extension (CARE)" priority is to develop an innovative early-warning system for better prevention and control of Avian Influenza (AI) outbreaks in US poultry industry. The specific objectives are: i) produce accurate, continuosly updated, high-resolution AI risk maps and identify key factors (e.g., environmental, climatic and anthropogenic factors) associated with AI occurrence in US, ii) integrate those risk maps into a web-based platform for easy visualization and with capabilities to send automatic notifications to producers if changes of AI risk are detected at local or regional level, iii) develop a self-assessment tool where producers can quantify the risk of AIV exposure for their operations at any time given their specific location, biosecurity and management practices, iv) design, implement and test the value of outreach activities, workshops and interactive educational tools to increase awareness, training and responsiveness of small-scale and large scale producers about biosecurity practices and early detection of AI. To accomplish those goals high resolution risk maps will be produced using the cutting-edge method of maximum entropy ecological niche modeling. Data and methods will be integrated into a user-friendly web-based and mobile “app” interface to facilitate the long-term access, visualization, analysis and communication of the AI risk to producers and to provide customized recommendations and educational tools for implementing risk-mitigation measures. This work will provide valuable knowledge and operational tools for poultry producers and other stakeholders to better prevent and control AI outbreaks in the US.

Network Analysis of Livestock Movements (Including Poultry and Hatching Eggs) Introduced in California from 2004 to 2014: Implications for Potential Introduction and Spread of Infectious Diseases


Transmission of infectious diseases mostly occurs due to contacts among infected and susceptible individuals. Therefore, the characterization of the contact patterns among individuals is a prerequisite to better understand and even predict the spread of diseases in a population. California is one of the most important states for livestock production and trade, particularly cattle and poultry. The evaluation of the livestock movements being introduced into the state, would be useful to identify highly connected areas and premises in which risk-based surveillance and outreach activities could be allocated to better prevent the risk of introduction and spread of diseases into the state. To the best of our knowledge, very few studies have been published characterizing the livestock trade network in the US and none in California, despite the potential to provide valuable insights for better prevention, tracing and control of infectious diseases.. In this project we aim to characterize the nature, extent and temporal-spatial patterns of the livestock movements (including beef, dairy, sheep, goat, swine, horses, poultry, hatching eggs and rabbits) being introduced in California as a preliminary but fundamental step to better understand livestock trade dynamics and evaluate their association with the potential risk of introduction of diseases into the State. For such purpose, we will use social network analysis (SNA) and geo-statistical analysis (scan statistics) similar to previous studies conducted by PI. The use of SNA alone or in combination with other methods in veterinary medicine is becoming highly popular due to the multiple advantages that SNA offers to handle and analyze the intrinsically complex contact information to support policies.

Spatio-temporal dynamics of condemnation cases in cattle slaughter plants in California and other US states from 2004-2015


The meat and cattle industry is the largest segment of U.S. agriculture. In 2015 the US commercial slaughter 28.74 million head with the commercial carcass weight of 23.69 billion pounds (National Cattlemen’s Beef Association). Based on the data obtained from USDA in 2015, 141,450 carcasses were condemned in the US which is approximately 0.5% of the total cattle carcasses produced in the US. Beef price in 2015 was $6.29/lb (National Cattlemen’s Beef Association) thus the condemned carcass in the slaughter plants roughly cost $0.81 billion (0.5% x 23.69 billion lb x $6.29/lb) to the US producers. California (CA) holds one of the most important cattle industries in the US. A total of 21.3% of all condemnation cases from 2005-2015 in slaughter plants in US occurred in CA (USDA), which corresponds to approximately to $1,38 billion (307,966 heads condemned x 714 lb/carcass x $6.29/lb) in the total period and $0.18 billion when considering only 2015. A better knowledge of the spatio-temporal distribution and potential reasons of carcasses condemnation will allow to identify areas where management practices should be improved to reduce the economic impact related with carcass condemnations. Moreover, diagnostic cases could be used to identify areas with higher than expected condemnation cases that could be associated to inappropriate management or for syndromic surveillance of emerging and new diseases. To the best of our knowledge, very few studies, if any, have assessed the spatial and temporal dynamics and the economic impact associated with beef/carcass condemnations or quantified the value that the use of post-mortem information may have for syndromic surveillance in the US. There is also a need to identify reasons for condemnation cases in CA and nationwide that may be increasing/emerging and identify clusters or “hot spot” areas where condemnations are more frequent and abundant. This study will provide the foundations to inform the design of future studies to identify the main on-farm management practices and other factors that are responsible for carcass condemnations in the US, which is highly needed1. This research-extension and multi-state study aims, first, to describe and compare the spatial and temporal trends of the reported cattle diagnostic cases in slaughter plants in CA with those in the US and identify temporal and spatial patterns of condemned cases and their associated economic impact. The second aim will be to develop an extension component that include the creation of a dynamic website and an on-line learning tool to increase awareness of produces about main reasons of carcass condemnation by slaughter plant, type of cattle and state and recommendations to prevent them.