Detecting early signals of COVID-19 outbreaks in 2020 in small areas by monitoring healthcare utilisation databases: first lessons learned from the Italian Alert_CoV project

Abstract

Background During the COVID-19 pandemic, large-scale diagnostic testing and contact tracing have proven insufficient to promptly monitor the spread of infections. Aim To develop and retrospectively evaluate a system identifying aberrations in the use of selected healthcare services to timely detect COVID-19 outbreaks in small areas. Methods Data were retrieved from the healthcare utilisation (HCU) databases of the Lombardy Region, Italy. We identified eight services suggesting a respiratory infection (syndromic proxies). Count time series reporting the weekly occurrence of each proxy from 2015 to 2020 were generated considering small administrative areas (i.e. census units of Cremona and Mantua provinces). The ability to uncover aberrations during 2020 was tested for two algorithms: the improved Farrington algorithm and the generalised likelihood ratio-based procedure for negative binomial counts. To evaluate these algorithms’ performance in detecting outbreaks earlier than the standard surveillance, confirmed outbreaks, defined according to the weekly number of confirmed COVID-19 cases, were used as reference. Performances were assessed separately for the first and second semester of the year. Proxies positively impacting performance were identified. Results We estimated that 70% of outbreaks could be detected early using the proposed approach, with a corresponding false positive rate of ca 20%. Performance did not substantially differ either between algorithms or semesters. The best proxies included emergency calls for respiratory or infectious disease causes and emergency room visits. Conclusion Implementing HCU-based monitoring systems in small areas deserves further investigations as it could facilitate the containment of COVID-19 and other unknown infectious diseases in the future.

Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends

“…In conclusion, using Google Trends to identify control chart-based outliers for non-pathognomonic symptoms such as fever, cough, and sore throat has high predictive power for anticipating COVID-19 epidemic waves 7–8 weeks ahead of the official reports in Lombardy. If combined with other syndromic sources like those of data from healthcare utilisation ( 8 ) and emergency visits ( 7 ), data from Google Trends searches may serve as a useful infodemiological tool for anticipating an impending outbreak, which can provide valuable buffer time to allocate the necessary supplies and personnel to hospitals expecting a surge in COVID-19 patients. Upon verification by prospective research comparing model performance in different regions of Italy, public health organisations are encouraged to take advantage of this free forecasting system to anticipate and effectively manage COVID-19 outbreaks throughout Italy.…”

“…Syndromic surveillance is an emerging approach in this field, defined as the ongoing systematic collection, analysis, and interpretation of "syndrome" specific data for early detection of public health threats (5). Syndromic surveillance systems seek to use existing data in real-time to provide immediate analysis and feedback to policymakers (6)(7)(8). Technologies using social media, search queries, and other internet resources are novel and inexpensive approaches for detecting and tracking emerging diseases.…”