Smart Analysis and Decision-Making for Emerging Infectious Diseases

Avian Influenza, Chikungunya Fever, COVID-19, Dengue Fever, Emerging Infectious Diseases, Influenza, Mpox (Monkeypox)

Emerging Infectious Diseases, Cluster Randomized Controlled Trial, Artificial Intelligence, Machine Learning, Risk Assessment, Early Warning, Public Health Emergency

Emerging infectious diseases, such as COVID-19, mpox, and dengue fever, are characterized by rapid transmission, wide impact, and high uncertainty, posing ongoing threats to global public health. While China achieved significant success in COVID-19 control, the response also revealed key challenges, including fragmented information, delayed risk perception, experience-dependent assessment, and inefficiencies in complex decision-making. This study aims to establish a smart technology system covering the full chain of "risk perception-situational assessment-intelligent decision-making-comprehensive evaluation." Specific objectives include: Constructing a global disease burden database and knowledge graph for emerging infectious diseases; Developing early risk assessment models covering the full transmission spectrum (cross-species, imported, and local outbreak); Building an AI-driven collective intelligence decision-support tool for epidemic control; Developing precise intervention frameworks and comprehensive evaluation indicators for key populations (e.g., elderly, students); Integrating the above technologies into a multi-agent toolkit and evaluating its effectiveness through a cluster randomized controlled trial involving at least 36 district/county-level CDC clusters across five provinces/municipalities (Guangdong, Zhejiang, Hubei, Sichuan, and Shanghai). Three eligible CDC staff members will be enrolled from each participating cluster, including one CDC director or relevant leader responsible for infectious disease prevention and control and two professional staff members engaged in acute infectious disease surveillance, risk assessment, decision-making, or emergency response. Accordingly, at least 108 participants will be enrolled, and the final anticipated enrollment will be determined by the total number of participating clusters. The intervention group will use the smart toolkit alongside routine practices, while the control group will follow routine practices only. The primary outcome is response time for epidemic assessment and decision-making (hours from risk perception to decision completion). Secondary outcomes include epidemic control effectiveness, user satisfaction, and socioeconomic benefits. The intervention period is 3 months, starting around July 2026 and ending in December 2027. This study has been approved by the Peking University Biomedical Ethics Committee. The study does not involve individual patient data; all data are aggregated at the district/county level from CDC sources or publicly available data. Anonymous questionnaires do not collect any personal identifiable information.

This is a multicenter, cluster-randomized controlled trial (cRCT) with a single-blind design (blinding of statisticians). The study will be conducted across five provinces/municipalities: Guangdong, Zhejiang, Hubei, Sichuan, and Shanghai. At least 36 district/county-level Centers for Disease Control and Prevention (CDCs) will be selected as study clusters and allocated in an approximately 1:1 ratio to either the intervention group or the control group. Three eligible CDC staff members will be enrolled from each participating cluster, including one CDC director or relevant leader responsible for infectious disease prevention and control and two professional staff members engaged in acute infectious disease surveillance, risk assessment, decision-making, or emergency response. Randomization Procedure: For the four provinces (Zhejiang, Guangdong, Hubei, and Sichuan), participating district/county-level CDC clusters will be stratified within each province according to socioeconomic level (high, medium, and low), as applicable, and randomly allocated within strata to the intervention or control group. For Shanghai municipality, participating district-level CDC clusters will be stratified by urban functional zone (central urban vs. new/suburban districts) and randomly allocated within strata. Cluster allocation will be maintained in an approximately 1:1 ratio. Intervention: The intervention group will use a multi-agent integrated toolkit (including data-knowledge agent, assessment agent, decision agent, and evaluation agent) to assist with epidemic risk perception, situational assessment, and emergency decision-making, in addition to routine practices. The control group will follow routine practices only. Follow-up Plan: The intervention period is 3 months, timed to coincide with peak seasons for specific infectious diseases (winter/spring for respiratory infections; summer/autumn for vector-borne diseases like dengue). Follow-up assessments will occur every 3 months, with the endpoint defined as the conclusion of an emerging infectious disease event. Sample Size: Using PASS software (two-sided α=0.05, Power=80%, ICC=0.05, CV=0.5, standard deviation=12 hours, and average cluster size m=3), and assuming a 30% reduction in response time in the intervention group, a minimum of 32 evaluable clusters, corresponding to 16 clusters per group and 96 evaluable participants, is required. Because only 3 participants will be enrolled from each cluster and an individual attrition rate of approximately 10% is anticipated, at least 36 clusters and at least 108 participants will be recruited. The final anticipated enrollment will equal three times the actual number of participating clusters. Data Management: Dual independent data entry will be performed. Data will be stored on Peking University's encrypted servers, with backups on the university cloud platform and offline encrypted hard drives (AES-256 encryption). All data will be physically destroyed after the retention period. Missing Data: Analysis will follow the intention-to-treat (ITT) principle. Missing primary outcome data will be handled using the last observation carried forward (LOCF) method. Safety Evaluation: Adverse events include headache and absenteeism, classified using a five-level attribution scale (definitely, probably, possibly, probably not, definitely not related), with the first three categories counted as adverse reaction rates. Any serious adverse event must be reported immediately to the sponsor and/or ethics committee. Early Termination: The study may be terminated early under the following conditions: (1) identification of serious safety issues; (2) the toolkit proves ineffective or futile; (3) major protocol flaws or implementation deviations; (4) request by the applicant or administrative authority.

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