“That was then, this is now” - improved nowcasting tools for public health surveillance
PHE ePoster Library. Morbey R. 09/13/16; 138037; 53
Dr. Roger Morbey
Dr. Roger Morbey
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Abstract IntroductionEarly detection of public health threats requires identifying unusual activity within seasonal trends in near real-time. Traditionally, public health surveillance systems have compared the most recently available data (often not contemporaneous) with previous years to quantify significant changes in community morbidity. Since the introduction of syndromic surveillance, big data sets are monitored in near real-time to infer current trends in community incidence. In order to identify unusual trends, we are developing 'nowcasting' tools to compare expected trends, in the absence of emerging threats, with current activity.MethodsNegative binominal regression was used to model daily syndromic surveillance data e.g. GP consultations, allowing for day of week and holiday effects. To account for step changes in data caused by changes in healthcare system working practices or public health interventions, confounding variables were included. Finally, regression models were smoothed and used to provide short term forecasts of expected trends.ResultsThe regression models developed have now replaced traditional surveillance baselines (based on simple averages of historical data) in the syndromic surveillance systems used in PHE. They are now being presented in weekly bulletins for 53 syndromes across four syndromic surveillance systems. The improved models capture seasonal trends and expected changes in levels of activity, compared to previous years.ConclusionsDaily nowcasts of health data provide context for epidemiologists to make decisions about seasonal disease activity and emerging public health threats. They show whether current activity is consistent with what's expected, given all available information, and highlight when trends diverge from expectations.
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