SYNDROMIC SURVEILLANCE FOR THE EARLY DETECTION OF INFLUENZA OUTBREAKS

Syndromic surveillance is a new mechanism utilized to detect naturally occurring and bioterroristic outbreaks. The public health significance is its potential to alert public health to outbreaks earlier and allow a timelier public health response. It involves monitoring data that can be collected in near real-time to find anomalous data. Syndromic surveillance includes school and work absenteeism, over-the-counter drug sales, and hospital admissions data to name a few. This study is an assessment of an extension of the use of syndromic surveillance as an improvement to the traditional method to detect more routine public health problems, specifically, the detection of influenza outbreaks. The assessment involves the prediction of outbreaks in four areas during the period October 15, 2003 to March 31, 2004. The four areas studied included Allegheny County, Pennsylvania, Jefferson County, Kentucky, Los Angeles County, California, and Salt Lake County, Utah. Two aspects of community activity were used as the method for syndromic surveillance, over-the-counter pharmaceutical sales and hospital chief complaints. The over-the-counter sales encompassed a panel of six items including anti-diarrheal medication, anti-fever adult medication, anti-fever pediatric medication, cough and cold products, electrolytes, and thermometers. Additionally, two of the seven hospital chief complaints used in the RODS open source paradigm were monitored. These were constitutional and respiratory chief complaints. Application of standard statistical algorithms showed that the system was able to identify unusual activity several weeks prior to the time when the local health departments were able to identify an outbreak using the standard methods. The largest improvement in detection using syndromic surveillance occurred in Los Angeles where the outbreak was detected 52 days before the Centers for Disease Control had declared widespread activity for the state. In each county over-the-counter sales detected the outbreak sooner then hospital chief complaints, but the hospital chief complaints detect the outbreaks consistently across the various algorithms. More conclusive evidence regarding the possible improvement in outbreak detection with syndromic surveillance can be obtained once a longer time frame has passed to allow more historical data to accumulate. Conducting additional studies on influenza outbreaks in other jurisdictions would also be useful assessments

Automated real time constant-specificity surveillance for disease outbreaks

<p>Abstract</p> <p>Background</p> <p>For real time surveillance, detection of abnormal disease patterns is based on a difference between patterns observed, and those predicted by models of historical data. The usefulness of outbreak detection strategies depends on their specificity; the false alarm rate affects the interpretation of alarms.</p> <p>Results</p> <p>We evaluate the specificity of five traditional models: autoregressive, Serfling, trimmed seasonal, wavelet-based, and generalized linear. We apply each to 12 years of emergency department visits for respiratory infection syndromes at a pediatric hospital, finding that the specificity of the five models was almost always a non-constant function of the day of the week, month, and year of the study (<it>p </it>< 0.05). We develop an outbreak detection method, called the expectation-variance model, based on generalized additive modeling to achieve a constant specificity by accounting for not only the expected number of visits, but also the variance of the number of visits. The expectation-variance model achieves constant specificity on all three time scales, as well as earlier detection and improved sensitivity compared to traditional methods in most circumstances.</p> <p>Conclusion</p> <p>Modeling the variance of visit patterns enables real-time detection with known, constant specificity at all times. With constant specificity, public health practitioners can better interpret the alarms and better evaluate the cost-effectiveness of surveillance systems.</p

A Wavelet Analysis Approach for Categorizing Air Traffic Behavior

In this paper two frequency domain techniques are applied to air traffic analysis. The Continuous Wavelet Transform (CWT), like the Fourier Transform, is shown to identify changes in historical traffic patterns caused by Traffic Management Initiatives (TMIs) and weather with the added benefit of detecting when in time those changes take place. Next, with the expectation that it could detect anomalies in the network and indicate the extent to which they affect traffic flows, the Spectral Graph Wavelet Transform (SGWT) is applied to a center based graph model of air traffic. When applied to simulations based on historical flight plans, it identified the traffic flows between centers that have the greatest impact on either neighboring flows, or flows between centers many centers away. Like the CWT, however, it can be difficult to interpret SGWT results and relate them to simulations where major TMIs are implemented, and more research may be warranted in this area. These frequency analysis techniques can detect off-nominal air traffic behavior, but due to the nature of air traffic time series data, so far they prove difficult to apply in a way that provides significant insight or specific identification of traffic patterns

Synchrony of clinical and laboratory surveillance for influenza in Hong Kong

Background: Consultation rates of influenza-like illness (ILI) in an outpatient setting have been regarded as a good indicator of influenza virus activity in the community. As ILI-like symptoms may be caused by etiologies other than influenza, and influenza virus activity in the tropics and subtropics is less predictable than in temperate regions, the correlation between of ILI and influenza virus activity in tropical and subtropical regions is less well defined. Methodology and Principal Findings: In this study, we used wavelet analysis to investigate the relationship between seasonality of influenza virus activity and consultation rates of ILI reported separately by General Out-patient Clinics (GOPC) and General Practitioners (GP). During the periods 1998-2000 and 2002-2003, influenza virus activity exhibited both annual and semiannual cycles, with one peak in the winter and another in late spring or early summer. But during 2001 and 2004-2006, only annual cycles could be clearly identified. ILI consultation rates in both GOPC and GP settings share a similar non-stationary seasonal pattern. We found high coherence between ILI in GOPC and influenza virus activity for the annual cycle but this was only significant (P<0.05) during the periods 1998-1999 and 2002-2006. For the semiannual cycle high coherence (p<0.05) was also found significant during the period 1998-1999 and year 2003 when two peaks of influenza were evident. Similarly, ILI in GP setting is also associated with influenza virus activity for both the annual and semiannual cycles. On average, oscillation of ILI in GP and of ILI in GOPC preceded influenza virus isolation by approximately four and two weeks, respectively. Conclusions: Our findings suggest that consultation rates of ILI precede the oscillations of laboratory surveillance by at least two weeks and can be used as a predictor for influenza epidemics in Hong Kong. The validity of our model for other tropical regions needs to be explored. © 2008 Yang et al.published_or_final_versio

Multiyear Climate Variability and Dengue—El Niño Southern Oscillation, Weather, and Dengue Incidence in Puerto Rico, Mexico, and Thailand: A Longitudinal Data Analysis

Michael Johansson and colleagues use wavelet analysis to show that there is limited evidence for a multiyear relationship between climate and dengue incidence in Puerto Rico, Mexico, and Thailand

A non-stationary relationship between global climate phenomena and human plague incidence in Madagascar

Background Plague, a zoonosis caused by Yersinia pestis, is found in Asia and the Americas, but predominantly in Africa, with the island of Madagascar reporting almost one third of human cases worldwide. Plague's occurrence is affected by local climate factors which in turn are influenced by large-scale climate phenomena such as the El Niño Southern Oscillation (ENSO). The effects of ENSO on regional climate are often enhanced or reduced by a second large-scale climate phenomenon, the Indian Ocean Dipole (IOD). It is known that ENSO and the IOD interact as drivers of disease. Yet the impacts of these phenomena in driving plague dynamics via their effect on regional climate, and specifically contributing to the foci of transmission on Madagascar, are unknown. Here we present the first analysis of the effects of ENSO and IOD on plague in Madagascar. Methodology/principal findings We use a forty-eight year monthly time-series of reported human plague cases from 1960 to 2008. Using wavelet analysis, we show that over the last fifty years there have been complex non-stationary associations between ENSO/IOD and the dynamics of plague in Madagascar. We demonstrate that ENSO and IOD influence temperature in Madagascar and that temperature and plague cycles are associated. The effects on plague appear to be mediated more by temperature, but precipitation also undoubtedly influences plague in Madagascar. Our results confirm a relationship between plague anomalies and an increase in the intensity of ENSO events and precipitation. Conclusions/significance This work widens the understanding of how climate factors acting over different temporal scales can combine to drive local disease dynamics. Given the association of increasing ENSO strength and plague anomalies in Madagascar it may in future be possible to forecast plague outbreaks in Madagascar. The study gives insight into the complex and changing relationship between climate factors and plague in Madagascar

Regional-scale climate-variability synchrony of cholera epidemics in West Africa

BACKGROUND: The relationship between cholera and climate was explored in Africa, the continent with the most reported cases, by analyzing monthly 20-year cholera time series for five coastal adjoining West African countries: Côte d'Ivoire, Ghana, Togo, Benin and Nigeria. METHODS: We used wavelet analyses and derived methods because these are useful mathematical tools to provide information on the evolution of the periodic component over time and allow quantification of non-stationary associations between time series. RESULTS: The temporal variability of cholera incidence exhibits an interannual component, and a significant synchrony in cholera epidemics is highlighted at the end of the 1980's. This observed synchrony across countries, even if transient through time, is also coherent with both the local variability of rainfall and the global climate variability quantified by the Indian Oscillation Index. CONCLUSION: Results of this study suggest that large and regional scale climate variability influence both the temporal dynamics and the spatial synchrony of cholera epidemics in human populations in the Gulf of Guinea, as has been described for two other tropical regions of the world, western South America and Bangladesh