The Potential for Big Data in Animal Disease Surveillance in Ireland

The evolution of Kenya's animal health surveillance system and its potential for efficient detection of zoonoses

Introduction

Animal health surveillance systems in Kenya have undergone significant changes and faced various challenges throughout the years.

Methods

In this article, we present a comprehensive overview of the Kenya animal health surveillance system (1944 to 2024), based on a review of archived documents, a scoping literature review, and an examination of past surveillance assessments and evaluation reports.

Results

The review of archived documents revealed key historical events that have shaped the surveillance system. These include the establishment of the Directorate of Veterinary Services in 1895, advancements in livestock farming, the implementation of mandatory disease control interventions in 1944, the growth of veterinary services from a section to a ministry in 1954, the disruption caused by the Mau Mau insurrection from 1952 to 1954, which led to the temporary halt of agriculture in certain regions until 1955, the transition of veterinary clinical services from public to private, and the progressive privatization plan for veterinary services starting in 1976. Additionally, we highlight the development of electronic surveillance from 2003 to 2024. The scoping literature review, assessments and evaluation reports uncovered several strengths and weaknesses of the surveillance system. Among the strengths are a robust legislative framework, the adoption of technology in surveillance practices, the existence of a formal intersectoral coordination platform, the implementation of syndromic, sentinel, and community-based surveillance methods, and the presence of a feedback mechanism. On the other hand, the system's weaknesses include the inadequate implementation of strategies and enforcement of laws, the lack of standard case definitions for priority diseases, underutilization of laboratory services, the absence of formal mechanisms for data sharing across sectors, insufficient resources for surveillance and response, limited integration of surveillance and laboratory systems, inadequate involvement of private actors and communities in disease surveillance, and the absence of a direct supervisory role between the national and county veterinary services.

Discussion and recommendations

To establish an effective early warning system, we propose the integration of surveillance systems and the establishment of formal data sharing mechanisms. Furthermore, we recommend enhancing technological advancements and adopting artificial intelligence in surveillance practices, as well as implementing risk-based surveillance to optimize the allocation of surveillance resources.

Spatio-temporal evaluation of social media as a tool for livestock disease surveillance

Recent outbreaks of Avian Influenza across Europe have highlighted the potential for syndromic surveillance systems that consider other modes of data, namely social media. This study investigates the feasibility of using social media, primarily Twitter, to monitor illness outbreaks such as avian flu. Using temporal, geographical, and correlation analyses, we investigated the association between avian influenza tweets and officially verified cases in the United Kingdom in 2021 and 2022. Pearson correlation coefficient, bivariate Moran's I analysis and time series analysis, were among the methodologies used. The findings show a weak, statistically insignificant relationship between the number of tweets and confirmed cases in a temporal context, implying that relying simply on social media data for surveillance may be insufficient. The spatial analysis provided insights into the overlaps between confirmed cases and tweet locations, shedding light on regionally targeted interventions during outbreaks. Although social media can be useful for understanding public sentiment and concerns during outbreaks, it must be combined with traditional surveillance methods and official data sources for a more accurate and comprehensive approach. Improved data mining techniques and real-time analysis can improve outbreak detection and response even further. This study underscores the need of having a strong surveillance system in place to properly monitor and manage disease outbreaks and protect public health.

Regional Differences in and Influencing Factors of Animal Epidemic Risk in China

Based on data from three major pig diseases, this study calculated the animal disease epidemic index of 31 provinces and autonomous regions in mainland China. We adopted the Gini coefficient to investigate the interregional differences in animal disease epidemic risk and used the Shapley value decomposition method to illustrate the contribution of influencing factors. The results showed that the Gini coefficient remains above 0.60, indicating significant interregional differences in mainland China. Animal breeding level, ecological environment, and animal disease prevention and control contribute most to the interregional differences in animal epidemic risk. The results imply that reducing sewage discharge, increasing pig production, and changing the breeding style from free-range to large-scale farming are measures that may help improve disease prevention and control. This study has implications for providing theoretical references for preventing and controlling animal epidemics and for improving public health governance.

Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives-a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA)

Objectives

This manuscript reviews the current state of veterinary medical electronic health records and the ability to aggregate and analyze large datasets from multiple organizations and clinics. We also review analytical techniques as well as research efforts into veterinary informatics with a focus on applications relevant to human and animal medicine. Our goal is to provide references and context for these resources so that researchers can identify resources of interest and translational opportunities to advance the field.

Methods and Results

This review covers various methods of veterinary informatics including natural language processing and machine learning techniques in brief and various ongoing and future projects. After detailing techniques and sources of data, we describe some of the challenges and opportunities within veterinary informatics as well as providing reviews of common One Health techniques and specific applications that affect both humans and animals.

Discussion

Current limitations in the field of veterinary informatics include limited sources of training data for developing machine learning and artificial intelligence algorithms, siloed data between academic institutions, corporate institutions, and many small private practices, and inconsistent data formats that make many integration problems difficult. Despite those limitations, there have been significant advancements in the field in the last few years and continued development of a few, key, large data resources that are available for interested clinicians and researchers. These real-world use cases and applications show current and significant future potential as veterinary informatics grows in importance. Veterinary informatics can forge new possibilities within veterinary medicine and between veterinary medicine, human medicine, and One Health initiatives.

A Transdisciplinary Approach Supporting the Implementation of a Big Data Project in Livestock Production: An Example From the Swiss Pig Production Industry

Big Data approaches offer potential benefits for improving animal health, but they have not been broadly implemented in livestock production systems. Privacy issues, the large number of stakeholders, and the competitive environment all make data sharing, and integration a challenge in livestock production systems. The Swiss pig production industry illustrates these and other Big Data issues. It is a highly decentralized and fragmented complex network made up of a large number of small independent actors collecting a large amount of heterogeneous data. Transdisciplinary approaches hold promise for overcoming some of the barriers to implementing Big Data approaches in livestock production systems. The purpose of our paper is to describe the use of a transdisciplinary approach in a Big Data research project in the Swiss pig industry. We provide a brief overview of the research project named “Pig Data,” describing the structure of the project, the tools developed for collaboration and knowledge transfer, the data received, and some of the challenges. Our experience provides insight and direction for researchers looking to use similar approaches in livestock production system research.