China AI for Predicting Pandemics and Global Health Emergencies Market - Forecasts From 2025 To 2030

China AI for Predicting Pandemics and Global Health Emergencies Market is poised to grow from USD 374.982 million in 2025 to USD 763.66459 million by 2030, witnessing a CAGR of 15.29%.

China AI for Predicting Pandemics and Global Health Emergencies Market Key Highlights

The Chinese AI for Predicting Pandemics and Global Health Emergencies market represents a convergence of significant state-led policy drivers and rapidly advancing domestic technological capabilities. The national imperative to enhance public health surveillance and crisis response, stemming from recent global health events, has positioned artificial intelligence from a supplementary tool to a foundational technology within the public health infrastructure. This integration is manifest in the deployment of sophisticated AI platforms by government agencies and healthcare institutions for applications ranging from disease outbreak prediction to resource allocation optimization. The market's structural evolution is fundamentally tied to the national strategy of leveraging digital intelligence, underscoring the shift towards data-driven governance models to manage future health risks, thereby creating a sustained and high-priority demand environment for verifiable, high-accuracy AI solutions.

China AI for Predicting Pandemics and Global Health Emergencies Market Analysis

Growth Drivers

• The central government’s adoption of the AI+ Initiative and the concomitant push for smart city development functions as the primary catalyst, creating immediate demand for AI platforms. This macro-policy directly mandates the integration of AI capabilities into public infrastructure, driving procurement for systems that can analyze large-scale, heterogeneous data—including electronic health records, genomic sequences, and population mobility patterns—for real-time disease surveillance and modeling. Furthermore, the demonstrated efficacy of AI in processing vast diagnostic and epidemiological data during recent health crises established a performance benchmark, compelling public health agencies and hospitals to procure robust AI systems to meet new operational standards for early warning and rapid response.

Challenges and Opportunities

The primary challenge constraining market expansion is the persistent issue of data interoperability and governance. Inconsistent data reporting, data silos across disparate provincial and municipal systems, and complex privacy regulations hinder the seamless, real-time data integration essential for highly accurate pandemic modeling. This friction reduces the immediate operational value of advanced AI solutions, consequently dampening demand velocity. Conversely, a significant opportunity resides in the rapidly advancing maturity of domestic foundational models, such as large language models (LLMs). Companies that successfully develop vertical, medical-specific LLMs, integrating them with existing proprietary medical knowledge graphs, can offer high-precision, customized decision-support systems that address the core problem of model interpretability and clinical relevance, thereby unlocking substantial new demand from sophisticated end-users.

Supply Chain Analysis

The market's supply chain is fundamentally structured around talent and data. Key production hubs are concentrated in major technology centers like Beijing, Shenzhen, and Shanghai, where access to elite AI researchers, deep learning engineers, and data scientists is maximized. The logistical complexity lies not in physical movement but in data acquisition and purification. Providers are highly dependent on cooperation agreements with hospitals and government agencies to access large, anonymized, and high-quality datasets, which are the 'raw material' for model training. This dependence creates an oligopolistic complexity where a few key data-rich entities—the hospitals and public health agencies—dictate data flow, making the supply of high-fidelity training data the critical and most fragile link in the value chain.

Government Regulations

In-Depth Segment Analysis

By Application: Outbreak Prediction & Detection

The Outbreak Prediction & Detection segment is defined by the critical demand for proactive, rather than purely reactive, public health tools. This segment's growth is driven by the post-pandemic mandate to achieve pre-symptomatic and pre-diagnostic detection of emerging threats. AI models leverage advanced machine learning techniques, specifically deep learning and Natural Language Processing (NLP), to analyze diverse, unconventional data streams: atypical hospital admissions, over-the-counter drug sales, social media chatter, and genomic sequencing data from environmental samples. The growth driver is the inability of traditional epidemiological surveillance methods to process this volume and variety of data in real-time. Public health agencies invest in these systems because they promise to reduce the time lag between initial infection and large-scale public health response from weeks to mere days, directly translating into reduced economic disruption and mortality, thus making it a core procurement priority.

By End-User: Government & Public Health Agencies

The Government & Public Health Agencies segment represents the largest and most strategically important demand source. This segment’s growth is fundamentally driven by its non-negotiable legal and political responsibility to maintain public safety and national health security. Unlike commercial end-users, their procurement decisions are not solely based on return on investment but on disaster preparedness and risk mitigation. Their core demand is for national or regional-scale platforms capable of aggregating data from disparate sources (hospitals, labs, provincial health departments) for a unified operational view. This necessitates the acquisition of robust, secure, and scalable AI-driven decision-support systems for resource allocation, policy simulation, and real-time intervention coordination. Government investment in these systems—often through multi-year infrastructure projects—acts as the foundational demand stabilizing the entire market ecosystem.

Competitive Environment and Analysis

The Chinese AI for Predicting Pandemics and Global Health Emergencies market is dominated by a few well-capitalized domestic technology giants and specialized medical AI firms, leveraging deep relationships with major domestic hospitals and government bodies. Competition centers on demonstrating superior model accuracy and, crucially, the ability to securely integrate disparate data silos across provincial boundaries. The competitive edge is increasingly determined by the strength of proprietary medical knowledge graphs and the successful integration of general-purpose LLMs into vertical healthcare applications.

Company Profiles

Yidu Tech (02158.HK): Yidu Tech's strategic positioning is rooted in its proprietary core algorithm engine, YiduCore. This technology has processed billions of authorized medical records, building a highly comprehensive disease knowledge graph. Their strategy focuses on empowering users to build independent intelligent applications via an AI middleware platform. The company's key product is the use of its large language model, integrated with YiduCore, to enhance doctors’ efficiency in clinical trial recommendations and intelligent medical record generation. This positions them as a critical enabler of data-driven research and clinical decision support, both of which are foundational to pandemic-preparedness efforts.

Ping An Smart Healthcare (Ping An Health - 1833.HK): Ping An Smart Healthcare, leveraging the wider Ping An Group's massive retail customer base and proprietary cloud infrastructure, is positioned as a comprehensive digital health service provider. Their key strategy involves integrating AI across their full-stack healthcare ecosystem, including their family doctor services and senior care concierge. The company has developed Ping An Medical Master, a large multi-modal model based on extensive medical databases and consultation records. The platform's AI-assisted inquiry and consultation capabilities, which have managed millions of daily consultation requests, directly serve the public health imperative by providing front-line triage and consultation services, significantly reducing the burden on physical healthcare facilities during health emergencies.

Recent Market Developments

• February 2025: Yidu Tech announced that it had combined its proprietary YiduCore with the domestic large language model DeepSeek. This strategic combination was integrated into Yidu Tech's AI middleware platform, linking it directly with high-quality hospital big data platforms. The collaboration focused on creating a smarter AI diagnostic assistant covering core clinical scenarios such as precise consultation Q&A and intelligent medical record generation, marking a major capacity addition in vertical, high-precision medical AI.
• June 2024: Yidu Tech announced that its large model algorithm and large model service algorithm successfully passed the algorithm registration of the Cyberspace Administration of China. This milestone is a critical prerequisite for commercial deployment, validating the safety and compliance of its generative AI technology, YiduCore, for use in the highly sensitive healthcare sector. This regulatory approval accelerates the integration of their medical vertical large language model with hospital data platforms to create intelligent diagnostic assistants.

China AI for Predicting Pandemics and Global Health Emergencies Market Segmentation

• BY COMPONENT
  • Software
  • Services
  • Hardware
• BY DEPLOYMENT MODE
  • Cloud-based
  • On-Premises
• BY APPLICATION
  • Outbreak Prediction & Detection
  • Disease Surveillance
  • Contract Tracing
  • Risk Assessment
  • Health Trend Forecasting
  • Public Health Resource Allocation
  • Others
• BY END-USER
  • Government & Public Health Agencies
  • Hospitals & Clinics
  • Research Institutions
  • Pharmaceuticals and Biotech Companies
  • Academic Institutions
  • Others