Seasonality Assessment for Biosurveillance Systems

Naumova, Elena N.; MacNeill, Ian B.

doi:10.1007/978-0-8176-4542-7_28

Cited by 36 publications

(52 citation statements)

References 2 publications

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“…We expanded the utility of this model by estimating the essential parameters using the δ-method [30]. Specifically, we estimated peak timing, the week at which disease incidence was highest, and absolute intensity, the difference between the maximum seasonal incidence and minimum seasonal incidence [29].…”

Section: Methodsmentioning

confidence: 99%

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Seasonal Synchronization of Influenza in the United States Older Adult Population

Wenger

Naumova

2010

PLoS ONE

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BackgroundIn temperate regions, influenza epidemics occur annually with the highest activity occurring during the winter months. While seasonal dynamics of the influenza virus, such as time of onset and circulating strains, are well documented by the Centers for Disease Control and Prevention Influenza Surveillance System, an accurate prediction of timing, magnitude, and composition of circulating strains of seasonal influenza remains elusive. To facilitate public health preparedness for seasonal influenza and to obtain better insights into the spatiotemporal behavior of emerging strains, it is important to develop measurable characteristics of seasonal oscillation and to quantify the relationships between those parameters on a spatial scale. The objectives of our research were to examine the seasonality of influenza on a national and state level as well as the relationship between peak timing and intensity of influenza in the United States older adult population.Methodology/Principal FindingsA total of 248,889 hospitalization records were extracted from the Centers for Medicare and Medicaid Services for the influenza seasons 1991–2004. Harmonic regression models were used to quantify the peak timing and absolute intensity for each of the 48 contiguous states and Washington, DC. We found that individual influenza seasons showed spatial synchrony with consistent late or early timing occurring across all 48 states during each influenza season in comparison to the overall average. On a national level, seasons that had an earlier peak also had higher rates of influenza (rs = −0.5). We demonstrated a spatial trend in peak timing of influenza; western states such as Nevada, Utah, and California peaked earlier and New England States such as Rhode Island, Maine, and New Hampshire peaked later.Conclusions/SignificanceOur findings suggest that a systematic description of influenza seasonal patterns is a valuable tool for disease surveillance and can facilitate strategies for prevention of severe disease in the vulnerable, older adult population.

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Section: Methodsmentioning

confidence: 99%

“…In all three models, represents the intercept of the yearly epidemic curve or a baseline level; and are the respective coefficients of the harmonic; and ω = 1/ M , where M is the length of one cycle (52.25 weeks) [30]. The estimation of peak week and intensity along with their standard deviations are shown in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Seasonal Synchronization of Influenza in the United States Older Adult Population

Wenger

Naumova

2010

PLoS ONE

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“…Mathematical details are available elsewhere. 8 Data analyses were performed with S-Plus 8.0 (TIBCO Software Inc., Somerville, MA). The statistical significance level was set at P<.05.…”

Section: Methodsmentioning

confidence: 99%

Hospitalization of the Elderly in the United States for Nonspecific Gastrointestinal Diseases: A Search for Etiological Clues

et al. 2011

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The frequency of hospitalization among the elderly in the United States caused by gastrointestinal diseases between 1991 and 2004 increased dramatically, especially hospitalization of elderly individuals with nonspecific diagnoses. We analyzed 6 640 304 gastrointestinal disease-associated hospitalization records in this 14-year period by comparing the peak times of nonspecific gastrointestinal diseases with those of specific diseases. We found that most nonspecific gastrointestinal diseases peak concurrently with viral enteritis, suggesting a lack of diagnostic testing for viruses, which may adversely affect the efficiency of prevention, surveillance, and treatment efforts.

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“…We adapted an analytic framework by Naumova et al . that takes a parametric modeling approach to seasonality using Poisson linear regression and incorporates harmonic wave functions and meteorological data as the predictor variable171819. Modern surveillance systems are primed to expand the conventional tools available to better define seasonal patterns and consistently estimate seasonal peak timing.…”

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confidence: 99%

A comparative analysis of three vector-borne diseases across Australia using seasonal and meteorological models

Stratton

Ehrlich

Mor

et al. 2017

Sci Rep

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Ross River virus (RRV), Barmah Forest virus (BFV), and dengue are three common mosquito-borne diseases in Australia that display notable seasonal patterns. Although all three diseases have been modeled on localized scales, no previous study has used harmonic models to compare seasonality of mosquito-borne diseases on a continent-wide scale. We fit Poisson harmonic regression models to surveillance data on RRV, BFV, and dengue (from 1993, 1995 and 1991, respectively, through 2015) incorporating seasonal, trend, and climate (temperature and rainfall) parameters. The models captured an average of 50–65% variability of the data. Disease incidence for all three diseases generally peaked in January or February, but peak timing was most variable for dengue. The most significant predictor parameters were trend and inter-annual periodicity for BFV, intra-annual periodicity for RRV, and trend for dengue. We found that a Temperature Suitability Index (TSI), designed to reclassify climate data relative to optimal conditions for vector establishment, could be applied to this context. Finally, we extrapolated our models to estimate the impact of a false-positive BFV epidemic in 2013. Creating these models and comparing variations in periodicities may provide insight into historical outbreaks as well as future patterns of mosquito-borne diseases.

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Seasonality Assessment for Biosurveillance Systems

Cited by 36 publications

References 2 publications

Seasonal Synchronization of Influenza in the United States Older Adult Population

Seasonal Synchronization of Influenza in the United States Older Adult Population

Hospitalization of the Elderly in the United States for Nonspecific Gastrointestinal Diseases: A Search for Etiological Clues

A comparative analysis of three vector-borne diseases across Australia using seasonal and meteorological models

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