Why the Next Pandemic Could Start in Animals.

COVID-19 wasn’t the first, and it won’t be the last. Many of the world’s deadly outbreaks, including Ebola, SARS, and now the growing threat of avian flu (H5N1), began when viruses jumped from animals to humans. According to the Pan American Health Organization (PAHO), these zoonotic diseases account for more than 60% of emerging infectious diseases globally. Health experts have recently warned that the risk of emerging pathogens to humans is rising sharply due to increasing human-wildlife interaction, intensive farming, deforestation, and climate change. Yet despite this looming threat, traditional disease surveillance methods often react too late, costing lives and devastating economies. Artificial intelligence (AI) can meet the urgent need for faster, smarter, and more innovative solutions for pandemic prevention, due to the redefined monitoring and response to infectious disease threats. AI offers more profound insights and faster analysis, therefore significantly transforming public health surveillance.

AI can achieve this in many ways, including but not limited to:

• Real-time Monitoring: AI algorithms can scan thousands of sources, news articles, scientific reports, medical records, and even social media chatter, to detect early warning signals of outbreaks. This real-time data collection enables health authorities to respond swiftly, often before traditional surveillance systems detect the problem.
• Predictive Modeling: AI can analyze environmental, demographic, and mobility data to forecast where outbreaks are most likely to occur through machine learning models. Both governments and organizations can allocate resources proactively and prioritize surveillance in high-risk regions using these predictive models.
• Genomic Analysis: AI systems can rapidly detect and process genomic sequencing data to identify genetic mutations in viruses and bacteria. This will enhance the overall pathogen monitoring due to the control of the disease spread and vaccination strategies.
• Environmental Monitoring: By leveraging satellite imagery combined with AI analytics, researchers can monitor ecological changes like deforestation, wildlife migrations, and urban expansion, all factors that heighten the risk of zoonotic spillovers. Remote sensing technologies enable early identification of potential hotspots long before an outbreak reaches humans. Integrating AI into health surveillance represents a giant leap forward, making pandemic prevention faster, more intelligent, and more predictive.

Real-World Examples of AI Catching Threats Early Several AI-powered systems have already proven their value by detecting threats before traditional public health agencies could respond.

• BlueDot: In late December 2019, the Toronto-based company BlueDot used AI COVID-19 detection algorithms to flag unusual pneumonia cases in Wuhan, China. BlueDot’s system analyzed airline ticketing data, news reports, and official statements, predicting the virus’s global spread days before the WHO issued a formal warning.
• HealthMap: Developed at Boston Children’s Hospital, HealthMap scours online sources, news, blogs, and health reports to track global disease outbreaks in real-time. HealthMap provided crucial early insights during the H1N1 flu pandemic and continues to track emerging threats like dengue fever and Ebola.
• EcoHealth Alliance: Where the subsequent zoonotic spillover might occur is an essential issue that the EcoHealth Alliance can predict by combining field research with AI-powered data analytics.

Microsoft’s AI for Earth initiative supports them, integrating ecological, animal health, and environmental data to anticipate future pandemic hotspots.

These examples show that AI early warning systems are no longer theoretical; they are actively reshaping how we detect and prevent infectious disease threats worldwide.

The Future

Ultimately, harnessing the power of AI to combat zoonotic diseases holds promise for improving global health outcomes, reducing morbidity and mortality, and preventing future pandemics.

Considering that low- and middle-income countries are often the frontlines for emerging zoonotic threats, investing in accessible AI tools for these countries is essential to strengthening their surveillance capabilities, which benefits global health security.

Through better collaboration among statistical modelers and empiricists, future development of zoonotic risk technologies can iteratively validate or falsify model predictions, helping to improve the accuracy and applicability of predictive models over time. While some modelling publications may recommend further characterization of specific viruses, this will be unlikely to occur without active partnership between modellers and experimentalists, given that the priority is often placed on known and recurrent threats