Introduction
Zoonotic diseases are certain infectious diseases that jump from vertebrate animals to humans (1). Sometimes, this infection spread is limited from animals to humans only and do not spread further from humans to humans unlike ebola where infections spread from humans to non-infected humans and is termed as reverse zoonosis (2).
These diseases account for more than 60% infectious disease spread globally. Certain traditional models are available which detect these outbreaks only after they occur. However, digital twin approach predicts zoonotic diseases before spillover actually happens. Digital twins are virtual replicas of biological systems including cells, tissues, organs, systems and a complete organism as well.
. It continuously updates with a real world data. It has been in use in healthcare for predicting simulation studies for certain drug discovery models, and when applied to one health approach, digital twins of animals, eco system and humans can be integrated and correlated together for better outcomes in controlling these zoonotic diseases (4).
How digital twins can predict zoonosis?
1. Animal health monitoring
Various wearable devices can be used to monitor temperature, movement and feeding behavior of livestock animals. This generated data can be fed to animal digital twins for continuous adaptability. Based on this data, these digital twins can predict certain anomalies and can predict certain infections. Prior prediction of such infections can be used to prevent outbreaks of these infections and their zoonosis (3).
2. Environmental factors
Ecosystem digital twins can also be used for this one health approach. Certain fluctuations in climate, migration patterns and land-use data can be incorporated into these digital twins, which thus predict effects of environmental stressors on survival and spread of pathogens (5).
3. Human models
AI based digital twin models can simulate exposure scenarios to predict what sort of populations are at higher risk of spillover. These human based digital twins includes data on the basis of their genetics, lifestyle, medication history, immunity and travel plans to such areas (4).
Advantages over traditional models
1. It is a predictive model. Which means it predicts outbreak before it actually happens.
2. Traditional models work on one size fits all mode but digital twins can simulate individual level susceptibility and can be personalized.
3. During epidemics, economies suffer huge losses to research. Digital twins can be very cost-effective models before actually working on the in vivo models that can be an extra burden on research department of the country.
Case Scenarios (Hypothetical Applications)
Avian Influenza: Digital twins of poultry farms connected with human worker twins can predict the probability of avian flu transmission during seasonal migrations.
Nipah Virus: Bat migration patterns modeled through ecosystem twins may forecast spillover zones into pig farms and nearby human communities.
Climate-Driven Malaria Expansion: Environmental twins simulating rainfall and temperature changes can anticipate mosquito-borne zoonotic malaria spread to new geographies.
Challenges and future directions
However, there are certain challenges faced by this technology and need provisions to improve its adaptability. This technology requires harmonization of human, veterinary and environmental data for integration. There are certain concerns about human data privacy and raise ethical questions about data ownership and surveillance. Countries which have high zoonotic burden lacks infrastructure for adaptability of this technology. Moreover, this technology cannot work without complete validation with real world situations.
Therefore, interconnected zoonotic monitoring system must be implemented across the globe for global networking. Real-time prediction platforms such as AI-augmented one health dashboards should be accessible to policy makers.
Conclusion
The fusion of One Health principles and digital twin technology marks a paradigm shift in zoonotic disease prediction. Instead of waiting for the next pandemic, humanity can leverage AI-powered virtual replicas to foresee and prevent cross-species transmission events. While challenges remain, digital twin-enabled One Health systems hold the promise of transforming global preparedness into a predictive shield against emerging zoonosis.
References
1. Bag, A. K., & Sengupta, D. (2024). Computational frameworks for zoonotic disease control in Society 5.0: opportunities, challenges and future research directions. AI & SOCIETY, 1-30.
2. Devaux, C. A., Mediannikov, O., Medkour, H., & Raoult, D. (2019). Infectious disease risk across the growing human-non human primate interface: a review of the evidence. Frontiers in public health, 7, 305.
3. Ding, L., Zhang, C., Yue, Y., Yao, C., Li, Z., Hu, Y., & Li, Q. (2025). Wearable Sensors-Based Intelligent Sensing and Application of Animal Behaviors: A Comprehensive Review. Sensors, 25(14), 4515.
4. Folasole, A. (2023). Data analytics and predictive modelling approaches for identifying emerging zoonotic infectious diseases: surveillance techniques, prediction accuracy, and public health implications. Int J Eng Technol Res Manag, 7(12), 292.
5. Sahu, Y., & Upadhyay, V. K. (2024). Environmental application of digital twins: A review. Environmental Risk and Resilience in the Changing World: Integrated Geospatial AI and Multidimensional Approach, 287-295.