Time series modeling for syndromic surveillance

BACKGROUND: Emergency department (ED) based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED) visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates. METHODS: Using time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA) residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks. RESULTS: Models were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity. CONCLUSIONS: Time series methods applied to historical ED utilization data are an important tool for syndromic surveillance. Accurate forecasting of emergency department total utilization as well as the rates of particular syndromes is possible. The multiple models in the system account for both long-term and recent trends, and an integrated alarms strategy combining these two perspectives may provide a more complete picture to public health authorities. The systematic methodology described here can be generalized to other healthcare settings to develop automated surveillance systems capable of detecting anomalies in disease patterns and healthcare utilization

Assessing the utility of public health surveillance using specificity, sensitivity, and lives saved

In modern surveillance of public health, data may be reported in a timely fashion, and include spatial data on cases in addition to the time of their occurrence. This has lead to many recent developments in statistical methods to detect events of public health importance. However, there has been relatively little work into methods to identify how to compare such methods. One powerful rationale for performing surveillance is earlier detection of events of public health significance; previous evaluation tools have focused on metrics which include the timeliness of detection in addition to sensitivity and specificity. However, such metrics have not accounted for the number of persons affected by the events. We re-examine the rationale for this surveillance and conclude that earlier detection is preferred because it can prevent additional morbidity and mortality. Based on this observation, we propose evaluating the number of cases prevented by each detection method, and include this information in assessing the value of different detection methods. Using this approach incorporates more information about the events and the detection and provides a sound basis for making decisions about which detection methods to employ

Sentinel surveillance system for early outbreak detection in Madagascar

<p>Abstract</p> <p>Background</p> <p>Following the outbreak of chikungunya in the Indian Ocean, the Ministry of Health directed the necessary development of an early outbreak detection system. A disease surveillance team including the Institut Pasteur in Madagascar (IPM) was organized to establish a sentinel syndromic-based surveillance system. The system, which was set up in March 2007, transmits patient data on a daily basis from the various voluntary general practitioners throughout the six provinces of the country to the IPM. We describe the challenges and steps involved in developing a sentinel surveillance system and the well-timed information it provides for improving public health decision-making.</p> <p>Methods</p> <p>Surveillance was based on data collected from sentinel general practitioners (SGP). The SGPs report the sex, age, visit date and time, and symptoms of each new patient weekly, using forms addressed to the management team. However, the system is original in that SGPs also report data at least once a day, from Monday to Friday (number of fever cases, rapid test confirmed malaria, influenza, arboviral syndromes or diarrhoeal disease), by cellular telephone (encrypted message SMS). Information can also be validated by the management team, by mobile phone. This data transmission costs 120 ariary per day, less than US$1 per month.</p> <p>Results</p> <p>In 2008, the sentinel surveillance system included 13 health centers, and identified 5 outbreaks. Of the 218,849 visits to SGPs, 12.2% were related to fever syndromes. Of these 26,669 fever cases, 12.3% were related to Dengue-like fever, 11.1% to Influenza-like illness and 9.7% to malaria cases confirmed by a specific rapid diagnostic test.</p> <p>Conclusion</p> <p>The sentinel surveillance system represents the first nationwide real-time-like surveillance system ever established in Madagascar. Our findings should encourage other African countries to develop their own syndromic surveillance systems.</p> <p>Prompt detection of an outbreak of infectious disease may lead to control measures that limit its impact and help prevent future outbreaks.</p

Visualized exploratory spatiotemporal analysis of hand-foot-mouth disease in southern China

Objectives: In epidemiological research, major studies have focused on theoretical models; however, few methods of visual analysis have been used to display the patterns of disease distribution.Design: For this study, a method combining the space-time cube (STC) with space-time scan statistics (STSS) was used to analyze the pattern of incidence of hand-foot-mouth disease (HFMD) in Guangdong Province from May 2008 to March 2009. In this research, STC was used to display the spatiotemporal pattern of incidence of HFMD, and STSS were used to detect the local aggregations of the disease.Setting: The hand-foot-mouth disease data were obtained from Guangdong Province from May 2008 to March 2009, with a total of 68,130 cases.Results: The STC analysis revealed a differential pattern of HFMD incidence among different months and cities and also showed that the population density and average precipitation are correlated with the incidence of HFMD. The STSS analysis revealed that the most likely aggregation includes the Shenzhen, Foshan and Dongguan populations, which are the most developed regions in Guangdong Province.Conclusion: Both STC and STSS are efficient tools for the exploratory data analysis of disease transmission. STC clearly displays the spatiotemporal patterns of disease. Using the maximum likelihood ratio, the STSS model precisely locates the most likely aggregation

Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts

<p>Abstract</p> <p>Background</p> <p>Public health surveillance is the monitoring of data to detect and quantify unusual health events. Monitoring pre-diagnostic data, such as emergency department (ED) patient chief complaints, enables rapid detection of disease outbreaks. There are many sources of variation in such data; statistical methods need to accurately model them as a basis for timely and accurate disease outbreak methods.</p> <p>Methods</p> <p>Our new methods for modeling daily chief complaint counts are based on a seasonal-trend decomposition procedure based on loess (STL) and were developed using data from the 76 EDs of the Indiana surveillance program from 2004 to 2008. Square root counts are decomposed into inter-annual, yearly-seasonal, day-of-the-week, and random-error components. Using this decomposition method, we develop a new synoptic-scale (days to weeks) outbreak detection method and carry out a simulation study to compare detection performance to four well-known methods for nine outbreak scenarios.</p> <p>Result</p> <p>The components of the STL decomposition reveal insights into the variability of the Indiana ED data. Day-of-the-week components tend to peak Sunday or Monday, fall steadily to a minimum Thursday or Friday, and then rise to the peak. Yearly-seasonal components show seasonal influenza, some with bimodal peaks.</p> <p>Some inter-annual components increase slightly due to increasing patient populations. A new outbreak detection method based on the decomposition modeling performs well with 90 days or more of data. Control limits were set empirically so that all methods had a specificity of 97%. STL had the largest sensitivity in all nine outbreak scenarios. The STL method also exhibited a well-behaved false positive rate when run on the data with no outbreaks injected.</p> <p>Conclusion</p> <p>The STL decomposition method for chief complaint counts leads to a rapid and accurate detection method for disease outbreaks, and requires only 90 days of historical data to be put into operation. The visualization tools that accompany the decomposition and outbreak methods provide much insight into patterns in the data, which is useful for surveillance operations.</p

Biosurveillance: Detecting, Tracking, and Mitigating the Effects of Natural Disease and Bioterrorism

Encyclopedia of Operations Research and the Management Sciences, Cochran, J.J. (ed.), John Wiley & Sons Ltd.The article of record as published may be located at http://dx.doi.org/10.1002/9780470400531Biosurveillance is the regular collection, analysis, and interpretation of health and health related data for indicators of diseases and other outbreaks by public health organizations. Motivated by the threat of bioterrorism, biosurviellance systems are being developed and implemented around the world. The goal of these systems has been expanded to include both early event detection and situational awareness, so that the focus is not simply on detection, but also on response and consequence management. Whether they rae useful for detecting bioterrorism or not, there seems to be consensus that these biosurveillance systems are likely to be useful for detecting bioterrorism or not, there seems to be consensus that these biosurveillance systems are likely to be useful for detecting and responding to naural disease outbreaks such as seasonal and pandemic flu, and thus they have potential to significantly advance and modernize the practice of public health surveillance