Frontiers in public health · 2026
Artificial intelligence-enabled early warning systems for public health preparedness: perspectives of senior public health leaders in a Small Island Developing State.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Addison Letetia, Pooransingh Shalini, De Freitas Loren
Аннотация
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.
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.