RESUMO
OBJECTIVES: This study aims to create an enhanced EPIRISK tool in order to correctly predict COVID-19 severity in various countries. The original EPIRISK tool was developed in 2018 to predict the epidemic risk and prioritise response. The tool was validated against nine historical outbreaks prior to 2020. However, it rated many high-income countries that had poor performance during the COVID-19 pandemic as having lower epidemic risk. STUDY DESIGN: This study was designed to modify EPIRISK by reparameterizing risk factors and validate the enhanced tool against different outbreaks, including COVID-19. METHODS: We identified three factors that could be indicators of poor performance witnessed in some high-income countries: leadership, culture and universal health coverage. By adding these parameters to EPIRISK, we created a series of models for the calibration and validation. These were tested against non-COVID outbreaks in nine countries and COVID-19 outbreaks in seven countries to identify the best-fit model. The COVID-19 severity was determined by the global incidence and mortality, which were equally divided into four levels. RESULTS: The enhanced EPIRISK tool has 17 parameters, including seven disease-related and 10 country-related factors, with an algorithm developed for risk level classification. It correctly predicted the risk levels of COVID-19 for all seven countries and all nine historical outbreaks. CONCLUSIONS: The enhanced EPIRSIK is a multifactorial tool that can be widely used in global infectious disease outbreaks for rapid epidemic risk analysis, assisting first responders, government and public health professionals with early epidemic preparedness and prioritising response to infectious disease outbreaks.
RESUMO
OBJECTIVES: The EPIWATCH artificial intelligence (AI) system scans open-source data using automated technology and can be used to detect early warnings of infectious disease outbreaks. In May 2022, a multicountry outbreak of Mpox in non-endemic countries was confirmed by the World Health Organization. This study aimed to identify signals of fever and rash-like illness using EPIWATCH and, if detected, determine if they represented potential Mpox outbreaks. STUDY DESIGN: The EPIWATCH AI system was used to detect global signals for syndromes of rash and fever that may have represented a missed diagnosis of Mpox from 1 month prior to the initial case confirmation in the United Kingdom (7 May 2022) to 2 months following. METHODS: Articles were extracted from EPIWATCH and underwent review. A descriptive epidemiologic analysis was conducted to identify reports pertaining to each rash-like illness, locations of each outbreak and report publication dates for the entries from 2022, with 2021 as a control surveillance period. RESULTS: Reports of rash-like illnesses in 2022 between 1 April and 11 July (n = 656 reports) were higher than in the same period in 2021 (n = 75 reports). The data showed an increase in reports from July 2021 to July 2022, and the Mann-Kendall trend test showed a significant upward trend (P = 0.015). The most frequently reported illness was hand-foot-and-mouth disease, and the country with the most reports was India. CONCLUSIONS: Vast open-source data can be parsed using AI in systems such as EPIWATCH to assist in the early detection of disease outbreaks and monitor global trends.
