BackgroundIn mid-2015, the United States’ Pandemic Prediction and Forecasting Science and Technical Working Group of the National Science and Technology Council, Food and Agriculture Organization Emergency Prevention Systems, and Kenya Meteorological Department issued an alert predicting a high possibility of El-Niño rainfall and Rift Valley Fever (RVF) epidemic in Eastern Africa.Methodology/Principal findingsIn response to the alert, the Kenya Directorate of Veterinary Services (KDVS) carried out an enhanced syndromic surveillance system between November 2015 and February 2016, targeting 22 RVF high-risk counties in the country as identified previously through risk mapping. The surveillance collected data on RVF-associated syndromes in cattle, sheep, goats, and camels from >1100 farmers through 66 surveillance officers. During the 14-week surveillance period, the KDVS received 10,958 reports from participating farmers and surveillance officers, of which 362 (3.3%) had at least one syndrome. The reported syndromes included 196 (54.1%) deaths in young livestock, 133 (36.7%) abortions, and 33 (9.1%) hemorrhagic diseases, with most occurring in November and December, the period of heaviest rainfall. Of the 69 herds that met the suspect RVF herd definition (abortion in flooded area), 24 (34.8%) were defined as probable (abortions, mortalities in the young ones, and/or hemorrhagic signs) but none were confirmed.Conclusion/SignificanceThis surveillance activity served as an early warning system that could detect RVF disease in animals before spillover to humans. It was also an excellent pilot for designing and implementing syndromic surveillance in animals in the country, which is now being rolled out using a mobile phone-based data reporting technology as part of the global health security system.

Reducing the burden of emerging and endemic infectious diseases on commercial livestock production systems will require the development of innovative technology platforms that enable information from diverse animal health resources to be collected, analyzed, and communicated in near real-time. In this paper, we review recent initiatives to leverage data routinely observed by farmers, production managers, veterinary practitioners, diagnostic laboratories, regulatory officials, and slaughterhouse inspectors for disease surveillance purposes. The most commonly identified challenges were (1) the lack of standardized systems for recording essential data elements within and between surveillance data streams, (2) the additional time required to collect data elements that are not routinely recorded by participants, (3) the concern over the sharing and use of business sensitive information with regulatory authorities and other data analysts, (4) the difficulty in developing sustainable incentives to maintain long-term program participation, and (5) the limitations in current methods for analyzing and reporting animal health information in a manner that facilitates actionable response. With the significant recent advances in information science, there are many opportunities to develop more sophisticated systems that meet national disease surveillance objectives, while still providing participants with valuable tools and feedback to manage routine animal health concerns.

The objective of the current study was to update parameterization of mathematical simulation models for foot-and-mouth disease (FMD) spread in cattle utilizing recent knowledge of FMD virus (FMDV) pathogenesis and infection dynamics to estimate the duration of distinct phases of FMD. Specifically, the durations of incubation, latent, and infectious periods were estimated for 3 serotypes (O, Asia1, and A) of FMDV, individually and collectively (pan-serotypic). Animal-level data were used in Accelerated Failure Time (AFT) models to estimate the duration of the defined phases of infection, while also investigating the influence of factors related to the experimental design (exposure methods) and virus serotype on disease progression. Substantial influences upon the estimated duration of distinct phases of FMD included the quantity of viral shedding used as a proxy for the onset of infectiousness, virus serotypes, and experimental exposure methods. The use of detection of any viral RNA in nasal secretions as a proxy of infectiousness lengthened the total infectious period compared to use of threshold-based detection. Additionally, the experimental system used to infect the animals also had significant effects on the duration of distinct phases of disease. Overall, the mean [95% Confidence Interval (CI)] durations of pan-serotype disease phases in cattle were estimated to be: incubation phase = 3.6 days (2.7–4.8), latent phase = 1.5 days (1.1–2.1), subclinical infectious phase = 2.2 days (1.5–3.5), clinical infectious phase = 8.5 days (6.2–11.6), and total infectious phase = 10.8 days (8.2–14.2). This study highlights the importance of identifying appropriate proxy measures to define the onset and duration of infectiousness in FMDV-infected cattle in the absence of actual transmission data. Additionally, it is demonstrated herein that factors associated with experimental design, such as virus exposure methods, may significantly affect disease progression in individual animals and should be considered when data is extrapolated from experimental studies. Given limitations in experimental data availability, pan-serotypic parameters which include all routes of exposure and a threshold-defined onset of infectiousness may be the most robust parameters for exploratory disease spread modeling approaches, when information on the specific virus of interest is not available.

Functional responses describe how changing resource availability affects consumer resource use, thus providing a mechanistic approach to prediction of the invasibility and potential damage of invasive alien species (IAS). However, functional responses can be context dependent, varying with resource characteristics and availability, consumer attributes, and environmental variables. Identifying context dependencies can allow invasion and damage risk to be predicted across different ecoregions. Understanding how ecological factors shape the functional response in agro‐ecosystems can improve predictions of hotspots of highest impact and inform strategies to mitigate damage across locations with varying crop types and availability. We linked heterogeneous movement data across different agro‐ecosystems to predict ecologically driven variability in the functional responses. We applied our approach to wild pigs (Sus scrofa), one of the most successful and detrimental IAS worldwide where agricultural resource depredation is an important driver of spread and establishment. We used continental‐scale movement data within agro‐ecosystems to quantify the functional response of agricultural resources relative to availability of crops and natural forage. We hypothesized that wild pigs would selectively use crops more often when natural forage resources were low. We also examined how individual attributes such as sex, crop type, and resource stimulus such as distance to crops altered the magnitude of the functional response. There was a strong agricultural functional response where crop use was an accelerating function of crop availability at low density (Type III) and was highly context dependent. As hypothesized, there was a reduced response of crop use with increasing crop availability when non‐agricultural resources were more available, emphasizing that crop damage levels are likely to be highly heterogeneous depending on surrounding natural resources and temporal availability of crops. We found significant effects of crop type and sex, with males spending 20% more time and visiting crops 58% more often than females, and both sexes showing different functional responses depending on crop type. Our application demonstrates how commonly collected animal movement data can be used to understand context dependencies in resource use to improve our understanding of pest foraging behavior, with implications for prioritizing spatiotemporal hotspots of potential economic loss in agro‐ecosystems.

The ability to rapidly detect and report infectious diseases of domestic animals and wildlife is paramount to reducing the size and duration of an outbreak. There is currently a need in the United States livestock industry for a centralized animal disease surveillance platform, capable of collecting, integrating, and analyzing multiple data streams with dissemination to end-users. Such a system would be disease agnostic and establish baseline information on animal health and disease prevalence; it would alert health officials to anomalies potentially indicative of emerging and/or transboundary disease outbreaks, changes in the status of endemic disease, or detection of other causative agents (eg, toxins). As a part of its mission to accelerate and develop countermeasures against the introduction of emerging and/or transboundary animal diseases into the United States, the Department of Homeland Security is leading and investing in the development of an enhanced passive surveillance platform capable of establishing animal health baselines over time and alerting health officials to potential infectious disease outbreaks or other health anomalies earlier, allowing for more rapid response, improved animal health, and increased economic security.

Information technologies are rapidly advancing the way in which animal health data and information are collected, analysed and shared in order to support animal health management, disease surveillance and response, and decision-making. However, the full potential of these technologies for early detection and response to natural or intentional disease events has not been fully realised in animal health at the global level. This paper discusses advances made in these technologies and examples of how they have been applied in animal health for near real-time data collection and analysis. The technologies reviewed include: i) mobile health (mHealth) technologies, wireless sensors and biosensors for remote data collection; ii) crowdsourced and Internet-based data collection; and iii) electronic health (eHealth) technologies for data integration and analysis. Experiences of implementing these technologies, and challenges with their use, are also discussed so as to provide recommendations on their future application in animal health. The world is ripe with opportunities to develop and enhance mHealth and eHealth technologies that are cost effective and capable of near real-time data collection and analysis. Such technologies have been shown to be valuable and capable of being implemented in both developing and developed countries, and ultimately will strengthen disease surveillance and reporting across the globe. International mechanisms and data standards are needed to facilitate the sharing and analysis of animal and human health data between countries. Identifying ways in which animal and human health data collection and analysis can be better integrated within a 'One Health' approach will enhance the coordination and capability of disease detection and response at the human-animal interface.

Background To improve early detection of emerging infectious diseases in sub-Saharan Africa (SSA), many of them zoonotic, numerous electronic animal disease-reporting systems have been piloted but not implemented because of cost, lack of user friendliness, and data insecurity. In Kenya, we developed and rolled out an open-source mobile phone-based domestic and wild animal disease reporting system and collected data over two years to investigate its robustness and ability to track disease trends. Methods The Kenya Animal Biosurveillance System (KABS) application was built on the Java® platform, freely downloadable for android compatible mobile phones, and supported by web-based account management, form editing and data monitoring. The application was integrated into the surveillance systems of Kenya’s domestic and wild animal sectors by adopting their existing data collection tools, and targeting disease syndromes prioritized by national, regional and international animal and human health agencies. Smartphone-owning government and private domestic and wild animal health officers were recruited and trained on the application, and reports received and analyzed by Kenya Directorate of Veterinary Services. The KABS application performed automatic basic analyses (frequencies, spatial distribution), which were immediately relayed to reporting officers as feedback. Results Of 697 trained domestic animal officers, 662 (95%) downloaded the application, and >72% of them started reporting using the application within three months. Introduction of the application resulted in 2- to 14-fold increase in number of disease reports when compared to the previous year (relative risk = 14, CI 13.8–14.2, p<0.001), and reports were more widely distributed. Among domestic animals, food animals (cattle, sheep, goats, camels, and chicken) accounted for >90% of the reports, with respiratory, gastrointestinal and skin diseases constituting >85% of the reports. Herbivore wildlife (zebra, buffalo, elephant, giraffe, antelopes) accounted for >60% of the wildlife disease reports, followed by carnivores (lions, cheetah, hyenas, jackals, and wild dogs). Deaths, traumatic injuries, and skin diseases were most reported in wildlife. Conclusions This open-source system was user friendly and secure, ideal for rolling out in other countries in SSA to improve disease reporting and enhance preparedness for epidemics of zoonotic diseases.

Development of a foot-and-mouth disease (FMD) carrier state following FMD virus (FMDV) infection is a well-established phenomenon in cattle. However, the proportion of cattle likely to become carriers and the duration of the carrier state at a herd or population-level are incompletely understood. The objective of this study was to examine the epidemiologic and economic impacts of vaccination-to-live strategy in a disease-free region or country. We developed and simulated scenarios of FMD spread and control in the US livestock population, which included depopulation for a limited period, followed by a vaccinate-to-live strategy with strong biosecurity and movement restrictions. Six scenarios of FMD spread and control were simulated in the InterSpread Plus (ISP) modeling tool. Data on the number of infected and depopulated cattle (by operation types) from ISP model runs were used to estimate the monthly number of infected but not depopulated (potential carrier) cattle after the infection. Using available literature data on the FMD carrier state, we estimated the monthly proportion of carrier cattle (from infected but not depopulated cattle) over time following infection. Among the simulated scenarios, the median (25th, 75th percentile) number of infected cattle ranged from 43,217 (42,819, 55,274) head to 148,907 (75,819, 205,350) head, and the epidemic duration ranged from 20 (11, 30) to 76 (38, 136) days. In general, larger outbreaks occurred when depopulation was carried out through longer periods, and the onset of the vaccination was late (p &gt; 0.05). The estimated proportion of surviving cattle, which were infected and not depopulated and had the potential to become persistently infected ranged from 14 to 35% of total infected cattle. Production losses in beef and dairy sectors were higher when outbreaks started in multiple states simultaneously, but production losses were small compared to trade losses and consumer avoidance losses. These results can be used to inform the consideration of a vaccinate-to-live strategy for FMD outbreaks and the development of appropriate post-outbreak management strategies. Furthermore, this output will enable a more detailed examination of the epidemiologic and economic implications of allowing convalescent cattle to survive and remain in production chains after FMD outbreaks in FMD-free regions.