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Topic: Artificial Intelligence (AI), COVID-19, and Health Blog Brand: Techland Region: Africa, and Americas Tags: Ebola, North America, United States, and Zika Virus Melding Global Health and AI for National Security April 13, 2026 By: Mark P. Lagon, and Maureen Lewis
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To prevent the next pandemic, the United States should invest in AI-driven global disease surveillance.
The United States is racing to deploy artificial intelligence (AI) across finance, defense, and social media. Yet, AI remains underutilized where it matters urgently for American safety: preventing the next pandemic. While billions flow into tools that optimize markets and consumer behavior, comparatively little is invested in global disease surveillance systems that could stop outbreaks before they reach US shores. That imbalance isn’t just a missed opportunity. It is a national security vulnerability.
Outbreaks continue to emerge—from new strains of influenza to Ebola and Zika. Governments routinely commit vast resources to guarding against geopolitical and technological threats, while investing relatively little in pandemic preparedness. Neglecting disease surveillance abroad is cost-shifting, transferring risk from potential prevention budgets to later, far more expensive emergency response and economic recovery. The US administration’s America First Global Health Strategy explicitly framed global health security as essential “to make America safer, stronger, and more prosperous.” This principle should guide AI investment today.
Pandemics as National Security Risks
Pandemics are not only public health crises; they are systemic shocks to national security and economic stability. American businesses depend on healthy workers, resilient supply chains, and predictable markets, all of which are disrupted by infectious disease outbreaks. COVID-19, which killed over one million Americans, made this painfully clear. No amount of personal wealth or corporate resilience alone can contain biological risk to the American public at large without preparation.
Current surveillance approaches leave Americans and other populations vulnerable due to massive data gaps. One reason AI remains underutilized for pandemic prevention is that much of the world remains invisible to it. Most medical and epidemiological data used to train AI systems come from high-income countries, even though many outbreaks originate or accelerate elsewhere. Private healthcare providers in low- and middle-income countries (LMICs) already generate clinical information, but without secure systems to integrate that data into universal surveillance platforms, its public-health value is lost.
The result is global data asymmetry: advanced analytics become highly accurate in some settings, while surveillance gaps persist in others. Pathogens don’t respect borders. Gaps in surveillance abroad translate directly into delayed detection, slower response, and higher risk at home.
Global Health Data Gaps
Yet the technology to close those gaps already exists. Globally, AI spending was projected to reach $1.5 trillion in 2025, demonstrating that capital isn’t the constraint. Within the United States, however, healthcare AI spending reached only $1.4 billion last year, and even that figure is misleading. Private and commercial healthcare AI investment is heavily skewed toward within-system efficiency, rather than population-level disease surveillance. Public health surveillance infrastructure—built on clinical data foundations—remains comparatively underfunded and fragmented, particularly across international and inter-agency levels (such as between national security and health agencies).
The core problem is not a lack of technology or funds. It’s the failure to deploy and integrate AI-enabled tools where early outbreak detections around the world can prevent risk to American lives.
AI-Enabled Disease Surveillance in Practice
Some countries are already demonstrating what AI-enabled surveillance can achieve. During the COVID-19 pandemic, Rwanda used AI to protect frontline health workers and improve disease surveillance—from robots taking vitals and delivering food and medicines, to a chatbot giving guidance to 580,000 citizens from December 2020 through July 2022. It tackled its 2024 Marburg virus outbreak with machine learning predictive analytics, AI-driven diagnostics, and robots decontaminating medical units. Rwanda’s effective use of AI to contain deadly outbreaks demonstrates the promise of a comprehensive global surveillance system.
Regional initiatives show similar potential. The Africa Centers for Disease Control and Prevention (Africa CDC) operates the Integrated Disease Surveillance and Response (IDSR) system across 55 member states, with real-time data sharing protocols strengthened since COVID-19. Africa CDC and IDSR were initially supported by the United States, demonstrating how targeted investments can catalyze large-scale disease detection systems. A more comprehensive surveillance architecture that fully leverages AI and integrates public institutions, private-sector capabilities, and trusted local partners will bolster early warning and response.
Scaling AI Surveillance Through Global Health Infrastructure and Governance
This approach isn’t hypothetical. Institutions such as the Global Fund to Fight AIDS, Tuberculosis and Malaria have already demonstrated what is possible by supporting national health information systems like DHIS2 and developing interoperable reporting platforms, including its Aggregate Data Exchange (ADEx). By integrating data from public and private providers, these systems have strengthened cross-border outbreak detection and response in LMICs. But these models remain underfunded and unevenly deployed, exposing rather than insulating the US public from pandemic risk.
The United States should also invest in the digital infrastructure that makes effective AI possible in LMICs: reliable internet connectivity, cloud computing capacity, and interoperable national health data systems. These investments are not charitable giveaways; they are strategic force multipliers that reduce blind spots and strengthen global detection networks that protect US citizens.
Finally, AI development must be aligned with the realities of global public health needs. The datasets used to train AI models should be highly varied, representative, and transparent. Moreover, institutions like the Global Fund, with experience delivering innovation at scale and coordinating across the private sector and community stakeholders, should have a seat at the table in AI governance discussions.
Building Global Health Data Systems: Infrastructure, Governance, and AI Integration
Artificial intelligence is often framed as a race for economic or military dominance. But one of its most consequential tests may be whether it helps prevent the next global health catastrophe before it begins. Pandemics that can overwhelm even the most powerful nations are inevitable. Yet, they are also predictable—and often preventable. That’s one reason China and Russia are ramping up health surveillance spending and access to data in Africa and Asia.
Investing in global disease surveillance isn’t generosity. It is self-defense. It is American lives saved.
The COVID-19 pandemic cost the US economy enormously. A 2024 report from the Heritage Foundation’s nonpartisan commission uncovered at least $18 trillion in economic costs to the US caused by the COVID-19 pandemic. US health spending to counter COVID-19 totaled $4.6 trillion. Spending even a small fraction of that amount to harness AI for disease outbreak surveillance is an insurance policy that citizens, consumers, and workers cannot afford to neglect.
About the Authors: Mark P. Lagon and Maureen Lewis
Mark P. Lagon is chief policy officer at Friends of the Global Fight Against AIDS, Tuberculosis and Malaria and adjunct professor in the Masters of Science in Foreign Service (MSFS) program at Georgetown University. He was a centennial fellow in the SFS 2016-2017. Previously, he was president of Freedom House. From 2010-2014, he was the global politics and security chair at the Master of Science in Foreign Service (MSFS) program at Georgetown University. In the same period, he was an adjunct senior fellow for human rights at the Council on Foreign Relations.
Maureen Lewis is studying for an MD at the Keck School of Medicine of the University of Southern California. She previously served as a global health policy research fellow at Friends of the Global Fight Against AIDS, Tuberculosis and Malaria. She holds a BA in molecular and cell biology from UC Berkeley.
The post Melding Global Health and AI for National Security appeared first on The National Interest.
Источник: nationalinterest.org