Modeling and Forecasting Influenza-like Illness (ILI) in Houston, Texas Using Three Surveillance Data Capture Mechanisms
نویسندگان
چکیده
Objective The objective was to forecast and validate prediction estimates of influenza activity in Houston, TX using four years of historical influenza-like illness (ILI) from three surveillance data capture mechanisms. Background Using novel surveillance methods and historical data to estimate future trends of influenza-like illness can lead to early detection of influenza activity increases and decreases. Anticipating surges gives public health professionals more time to prepare and increase prevention efforts. Methods Data was obtained from three surveillance systems, Flu Near You, ILINet, and hospital emergency center (EC) visits, with diverse data capture mechanisms. Autoregressive integrated moving average (ARIMA) models were fitted to data from each source for week 27 of 2012 through week 26 of 2016 and used to forecast influenza-like activity for the subsequent 10 weeks. Estimates were then compared to actual ILI percentages for the same period. Results Forecasted estimates had wide confidence intervals that crossed zero. The forecasted trend direction differed by data source, resulting in lack of consensus about future influenza activity. ILINet forecasted estimates and actual percentages had the least differences. ILINet performed best when forecasting influenza activity in Houston, TX. Conclusion Though the three forecasted estimates did not agree on the trend directions, and thus, were considered imprecise predictors of long-term ILI activity based on existing data, pooling predictions and careful interpretations may be helpful for short term intervention efforts. Further work is needed to improve forecast accuracy considering the promise forecasting holds for seasonal influenza prevention and control, and pandemic preparedness.
منابع مشابه
Utility of Outpatient Syndromic Data for Monitoring Influenza-like Illness
Introduction The North Dakota Department of Health (NDDoH) collects outpatient ILI data through North Dakota Influenza-like Illness Network (ND ILINet), providing situational awareness regarding the percent of visits for ILI at sentinel sites across the state. Because of increased clinic staff time devoted to electronic health initiatives and an expanding population, we have found sentinel site...
متن کاملForecasting Influenza Epidemics from Multi-Stream Surveillance Data in a Subtropical City of China
BACKGROUND Influenza has been associated with heavy burden of mortality and morbidity in subtropical regions. However, timely forecast of influenza epidemic in these regions has been hindered by unclear seasonality of influenza viruses. In this study, we developed a forecasting model by integrating multiple sentinel surveillance data to predict influenza epidemics in a subtropical city Shenzhen...
متن کاملAttempted early detection of influenza A (H1N1) pandemic with surveillance data of influenza‐like illness and unexplained pneumonia
BACKGROUND To collect disease information and provide data for early detection of epidemics, two surveillance systems were established for influenza-like illness (ILI) and unexplained pneumonia (UP) in Wuxi, People's Republic of China. OBJECTIVES The current study aims to describe the performance of these surveillance systems during 2004-2009 and to evaluate the value of surveillance data in ...
متن کاملارزشیابی عملکرد الگوریتم میانگین متحرک وزن داده شده نمایی در شناسایی طغیان های آنفلونزا در ایران
Background: Timely response to influenza outbreaks using Influenza like illness (ILI) data is one of the most important priorities for public health authorities. The aim of this study was to evaluate the performance of the Exponentially Weighted Moving Average (EWMA) for timely detection of influenza outbreaks in Iran using simulated approaches from January 2010 to December 2015. Methods: Simu...
متن کاملAssessing the use of hospital staff influenza-like absence (ILA) for enhancing hospital preparedness and national surveillance
BACKGROUND Early warning and robust estimation of influenza burden are critical to inform hospital preparedness and operational, treatment, and vaccination policies. Methods to enhance influenza-like illness (ILI) surveillance are regularly reviewed. We investigated the use of hospital staff 'influenza-like absences' (hospital staff-ILA), i.e. absence attributed to colds and influenza, to impro...
متن کامل