Livestock Disease Surveillance & Modelling

Disease prevention and control measures to safeguard India’s livestock sector

The economic impact of diseases like Foot-and-Mouth disease (FMD) and Lumpy Skin Disease Virus (LSDV) extends beyond immediate production losses, encompassing trade, livestock-related industries, and long-term stability. Prevention and disease control measures are crucial to mitigate risks and protect the health of livestock as well as the sector. With a data-driven approach, ARTPARK is supporting the Empowered Committee for Animal Health (ECAH) to adopt evidence-based policy making.

The primary objective is to inform strategies to control livestock diseases – starting with FMD, across various national programs.



Understanding the Problem

The Indian livestock sector is a net exporter and accounts for 4.9% of the country’s total GDP. It contributes about 26% to the income of the poorest farmers (<0.01 ha landholding) and is an important component of subsistence farming. While the sector has grown significantly over the past decades, productivity continues to be a challenge. The prevalence of livestock diseases result in higher animal mortality, morbidity, infertility, reduction in quality of the hide and skin, and reduction in draught power. ICAR has estimated the economic loss due to FMD alone at rupees 20,000 crores per year. Farmers encounter economic losses throughout the entire lifecycle of the animal, making it a high-risk sector. As a result, these diseases have limited the entry of Indian products into international markets.

Designing the Solution 

Smallholder farmers in the livestock trade stand to gain significantly from reduced foot-and-mouth disease (FMD) outbreaks. Regular vaccinations are crucial for controlling FMD, and we're working to optimise vaccination strategies to strengthen national control efforts.

Veterinary officials and animal husbandry departments implement these strategies, which may include targeted coverage in high-risk areas, prioritising regions based on outbreak history, and seasonal adjustments to vaccination frequency. Risk-based vaccination schedules and coordinated cross-border campaigns are also considered. Ring vaccination, where animals surrounding an outbreak are rapidly vaccinated to create a buffer zone, is another key tactic in containment efforts.

To inform these strategies, we're leveraging existing surveillance and monitoring data from multiple sources. These include the National Institute of Veterinary Epidemiology and Disease Informatics (NIVEDI), the National Institute on Foot and Mouth Disease (NIFMD), the Department of Animal Husbandry and Dairying (DAHD), and the Information Network for Animal Productivity and Health (INAPH).

This comprehensive dataset enables insights into disease patterns, vaccination efficacy and effectiveness, and outbreak risks. Our goal is to create coherent, accessible data systems that support research, innovation, and data-driven policy decisions in FMD control. The project's findings will be used to inform government-level policy decisions, ultimately strengthening national FMD control and elimination efforts.

Key Features

1. Effective utilisation of modelling across different susceptible species:

  • Cattle and buffalo: Modelling transmission of FMD at different spatial levels to test the impact of various vaccination strategies.

  • Pigs, goats and sheep: Modelling mixed-species interaction.

2. Applications for disease elimination strategies in India

  • Zoning: Using models to define and validate FMD-free zones for targeted disease elimination. 

  • Resource allocation: Prioritising vaccine distribution based on outbreak risk.

  • Surveillance: Sampling strategies for early detection in high-risk areas.

  • Exports: Strengthening disease control measures to improve the sector's biosecurity, thereby promoting exports of animal products in the International market.

3. Mechanistic models being used for simulation and scenario analysis for FMD

  • Compartmental models with pulsed vaccination: Simulating disease transmission and population-level protection against FMD, accounting for periodic vaccination campaigns

  • Metapopulation models: Analysing disease spread across interconnected subpopulations, such as different villages or farming communities, to understand spatial dynamics of FMD transmission

Technical Aspects

Data Standardisation: Converting diverse data sources into a consistent format. This includes using standard column names, region IDs, region names, and metadata to ensure that all data adheres to a common structure.

  1. Data Harmonisation: This process may involve adjusting for discrepancies in data collection methods, as well as changes in methodologies and criteria. For example, harmonisation might address variations in antibody titre thresholds used to classify animals as protected, ensuring that results from different surveys or time periods can be meaningfully compared.

  2. Disease modelling and scenario analysis: Employs mathematical simulations to predict the spread of FMD and assess the impact of various interventions. By incorporating data such as livestock population density, movement patterns, and historical disease prevalence, these models provide insights into potential outbreak scenarios.

  3. Toolkit development: We are developing user-friendly toolkits that include web-based analytical tools, standardised data collection protocols, and clear guidelines for interpreting results. These tools ensure that stakeholders, regardless of their location or technical expertise, can access and utilise the resources needed for disease control. This promotes collaboration and consistency in the application of disease management strategies.

Implementation & Deployment

Comprehensive data explorations have been conducted to identify key data requirements and address existing gaps. A tool has been developed to prioritise districts for FMD vaccination, ensuring that resources are allocated where they are most needed. Additionally, we have established an initial data pipeline for high-priority datasets.

Team

Jagadish Midthala

Adish Illikkal

Partners

Next
Next

MIDAS