Naumova Elena

Influenza viral infection is contentious, has a short incubation period, yet preventable if multiple barriers are employed. At some extend school holidays and travel restrictions serve as a socially accepted control measure. A study of a spatiotemporal spread of influenza among school-aged children in Belgium illustrated that changes in mixing patterns are responsible for altering disease seasonality3. Stochastic numerical simulations suggested that weekends and holidays can delay disease seasonal peaks, mitigate the spread of infection, and slow down the epidemic by periodically dampening transmission. While Christmas holidays had the largest impact on transmission, other school breaks may also help in reducing an epidemic size. Contrary to events reducing social mixing, sporting events and mass gatherings facilitate the spread of infections. A study on county-level vital statistics of the US from 1974-2009 showed that Super Bowl social mixing affects influenza dissemination by decreasing mortality rates in older adults in Bowl-participating counties. The effect is most pronounced for highly virulent influenza strains and when the Super Bowl occurs closer to the influenza seasonal peak. Simulation studies exploring how social mixing affects influenza spread demonstrated that impact of the public gathering on prevalence of influenza depends on time proximity to epidemic peak. While the effects of holidays and social events on seasonal influenza have been explored in surveillance time series and agent-based modeling studies, the understanding of the differential effects across age groups is incomplete.

Objective: In the presented study, we examined the impact of school holidays (Autumn, Winter, Summer, and Spring Breaks) and social events (Super Bowl, NBA Finals, World Series, and Black Friday) for five age groups (<4, 5-24, 25-44, 45-64, >65 years) on four health outcomes of influenza (total tested, all influenza positives, positives for influenza A, and B) in Milwaukee, WI, in 2004-2009 using routine surveillance.

Influenza is a significant public health problems in the US leading to over one million hospitalizations in the elderly population (age 65 and over) annually. While influenza preparedness is an important public health issue, previous research has not provided comprehensive analysis of season-by-season timing and geographic shift of influenza in the elderly population. These findings fail to document the intricacies of each unique influenza season, which would benefit influenza preparedness and intervention. The annual harmonic regression model fits each season of disease incidence characterized by its own unique curve. Using this model, characteristics of the seasonal curve for each state and each season can be compared. We hypothesize that travelling waves of influenza in the 48 contiguous states differ dramatically in each influenza season.

Objective

In surveillance it is imperative that we know when and where a disease first begins. The objective of this study was to examine trends in traveling waves of influenza in the US elderly population. Preparedness for influenza is an important yet difficult public health goal due to variability in annual strains, timing, and shift of the influenza virus. In order to better prepare for influenza epidemics, it is important to assess seasonal variation across individual influenza seasons on a state-by-state basis. This approach will lead to effective interventions especially for susceptible populations such as the elderly.

One of the most important goals of disease surveillance is to identify the "what" and "when" of an epidemic. Influenza surveillance is made difficult by inconsistent laboratory testing, deficiencies in testing techniques, and coding subjectivity in hospital records. We hypothesized that respiratory diseases other than influenza may serve as a useful proxy for this infection in pediatric populations, due to similarities in the seasonal characteristics of these illnesses.

The objective of this communication is to demonstrate an approach for modeling time-distributed effects of exposures to cases of infection which can be utilized in syndromic surveillance systems for characterizing, detecting, and forecasting a potential outbreak.

In the last decade, time series analysis has become one of the most important tools of surveillance systems. Understanding the nature of temporal fluctuations is essential for successful development of outbreak detection algorithms, aberration assessment, and to control for seasonal variations. Typically, in applying the time series methods to health outcomes collected over an extended period of time it is assumed that population profiles remain constant. In practice, such assumptions have been rarely tested. At best, the temporal analysis is performed using stratification by age or other discriminating factors if heterogeneity is suspected. Any community can experience population changes in various forms. Long-term trends of inflow/outflow migration and rapid transient fluctuations associated with specific events are typical examples of changes in population profile. Seasonality, as an intrinsic property of infectious diseases manifestation in a community, is typically attributed to periodic changes in transmissibility of pathogens. To some extent, seasonal fluctuations in the incidence of infectious diseases could also be associated with the changes in population profiles. The ability to detect and describe such changes would provide valuable clues into seasonally changing factors associated with an infection.

Objective

The objective of this communication is two-fold: 1) to introduce an analytical approach for assessing temporal changes in the surveillance reporting with respect to population profile; and 2) to demonstrate the utility of this method using laboratory-confirmed cases for four reportable enteric infections (cryptosporidiosis, giardiasis, shigellosis, and salmonellosis) recorded by the Massachusetts Department of Public Health over the last 12 years. This new approach for assessing seasonal changes is based on comparison of gender-specific single-year age distributions, which constitute population profiles.

Accurate and precise estimation of disease rates for a given population during a specified time frame is a major concern for public health practitioners and researchers in biosurveillance. Many diseases follow distinct patterns; incidence and prevalence of many diseases increase approximately exponentially with age, including many cancers, respiratory infections, and gastroenteritis. With increasing demographic information available in biosurveillance systems leading to more complex and comprehensive disease databases, seeking concise and informative summary measures of disease burden over space and time is becoming more critical for public health surveillance. In this paper we present two summary measures of disease burden in the elderly that simultaneously reflect disease dynamics and population characteristics.

Objective

To better estimate disease burden in the elderly population we illustrate an approach—the Slope Intercept Modeling for Population Linear Estimation (SIMPLE) method—that summarizes age-specific disease rates in the 65+ population using the observed exponential increase in disease rates with age in this dynamic and rapidly growing population subgroup.