From leadership research to district action: Artificial intelligence-enabled early warning systems for public health preparedness: perspectives of senior public health leaders in a Small Island Developing State

AI-assisted editorial implementation guide. Proposed district actions are separate from the original authors’ findings and recommendations. Independent peer review of this guide is not asserted.

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Evidence and transfer

Education-related source: verify which participants, institutions and outcomes were actually studied before district adoption. Source publication type: brief-report. License and identity checks do not establish study quality or causality.

Original authors’ abstract, reproduced under the source license:

Abstract Purpose Artificial intelligence-enabled early warning systems (AI-EWS) are increasingly recognised as tools for strengthening public health preparedness and education in climate-vulnerable settings. However, limited empirical evidence exists on how public health leaders perceive their use in practice. This study aimed to examine the perspectives of senior public health leaders, specifically County Medical Officers of Health (CMOHs), on AI-EWS in Trinidad and Tobago. Materials and methods An exploratory descriptive study was conducted using a structured survey of County Medical Officers of Health, with six of nine CMOHs completing the questionnaire (response rate = 66.7%). The survey assessed familiarity, perceived usefulness, system priorities, institutional readiness, and implementation barriers. Data were analysed descriptively using frequencies and proportions, with open-ended responses summarised using inductive thematic categorisation. Results All respondents (6/6) identified infectious diseases and flooding as priority applications for AI-EWS, while three (3/6) identified heat-related risks. Key system features, including dashboards (5/6), integration with emergency services (5/6), and automated alerts (3/6), were widely perceived as useful. Equity was prioritised by all respondents (6/6), particularly for underserved populations. However, barriers were also reported, including budget constraints (5/6), limited technical capacity (3/6), and data challenges (3/6). Conclusion AI-EWS are perceived as valuable tools for supporting public health decision-making, coordination, and professional learning in SIDS contexts. However, successful implementation will require strengthening infrastructure, workforce capacity, governance frameworks, and equitable system design. These findings provide early empirical insight to inform the responsible integration of AI-enabled systems into public health education and preparedness. Keywords: artificial intelligence, climate and health education, early warning systems, health system preparedness and response, leadership perspectives, public health education and health promotion, Small Island Developing States

Study methods

The study was conducted in Trinidad and Tobago, a twin-island, Small Island Developing State in the Caribbean. The national public health system is governed by the Ministry of Health, with healthcare delivery managed through Regional Health Authorities (RHAs). County Medical Officers of Health (CMOHs) serve as senior public health leaders within this system and are responsible for disease surveillance, outbreak response coordination, health promotion, and community-level public health decision-making. In Trinidad and Tobago, there are a total of 9 CMOHs across both islands.

Limitations

This study has several strengths. It provides an important empirical insight from a highly specialised leadership group directly involved in public health decision-making. By targeting all CMOHs within Trinidad and Tobago, the study captures perspectives from key actors responsible for translating surveillance data into policy and operational action.

A 90-day district inquiry

Days 1–15 · Appraise. Read the source methods, recruitment, comparator, outcomes and limitations. Record how the study setting differs from your district and which claim the design can support.
Days 16–30 · Design. Choose one recurring leadership practice described in the source. Map its participants, incentives and constraints to a district setting, then design a small reversible staff pilot with an accountable owner. Agree participation, privacy protections, baseline and stopping rules before starting.
Days 31–60 · Observe. Collect the pre-agreed measures, keep a log of implementation changes and invite confidential voluntary feedback. Preserve existing support for staff and learners.
Days 61–90 · Decide. Record implementation fidelity, staff review time, task quality and unintended burdens against a baseline. Use a comparison team where feasible and document alternative explanations before expanding. Make a dated decision to stop, revise or extend the inquiry.

Board decision brief

Present the local problem, source setting, evidence limits, intervention, responsible owner, total cost, measured changes and uncertainty. Separate staff perceptions, implementation measures and student learning outcomes.

Attribution and reuse

Letetia Addison; Shalini Pooransingh; Loren De Freitas. Artificial intelligence-enabled early warning systems for public health preparedness: perspectives of senior public health leaders in a Small Island Developing State. Frontiers in Public Health (2026-08-13). https://doi.org/10.3389/fpubh.2026.1931303

Prepared 2026-11-07. Source authorship and DOI are preserved. The abstract and source passages are attributed extracts; this editorial guide is offered under the same CC BY 4.0 license.