Influenza

This paper presents an investigation using data mining techniques to model patterns of influenza from positive case demographics, symptoms and laboratory tests.

To estimate diagnostic demand in case of pandemic threat using trends in respiratory syndromes (as input for a laboratory preparedness program).

This paper examines the continued usefulness, through the 2005-06 influenza season, of a hospital admissions-based syndromic surveillance system as a supplement to laboratory and clinical influenza surveillance in preparation for pandemic influenza.

If the next influenza pandemic emerges in Southeast Asia, the identification of early detection strategies in this region could enable public health officials to respond rapidly. Accurate, real-time influenza surveillance is therefore crucial. Novel approaches to the monitoring of infectious disease, especially respiratory disease, are increasingly under evaluation in an effort to avoid the cost- and timeintensive nature of active surveillance, as well as the processing time lag of traditional passive surveillance. In response to these issues, we have developed an indications and warning (I&W) taxonomy of pandemic influenza based on social disruption indicators reported in news media.

Objective

Our aim is to analyze news media for I&W of influenza to determine if the signals they create differ significantly between seasonal and pandemic influenza years.

We sought to evaluate the validity of pneumonia and influenza hospitalizations (PI) data gathered by our biosurveillance system.

Emergency Department surveillance methods currently rely on identification of acute illness by tracking chief complaint or ICD9 discharge codes. Newer generation electronic medical records are now capturing additional  information such as vital signs. These data have the potential for identifying disease syndromes earlier than the traditional methods.

Objective

This paper describes the temporal relationship between numbers of cases of fever, recorded as discrete vital sign data in an electronic medical record, and ICD9 Influenza Like Illnesses in the Emergency Department at the University of Wisconsin Hospital.

A Quest Diagnostics Incorporated – CDC collaboration in 2000  pioneered  exploration  of  test  ordering data to enhance infectious diseasessurveillance1. This  year’s  unexpected shortage of vaccine and reports of human illness caused by avian influenza  A  (H5N1)  in  Asia2  heightened concern about  influenza and focused attention on moving toward more complete, real time surveillance. We extended our previous collaboration to explore the use of  the Quest Diagnostics Corporate Informatics Data Warehouse (QIDW) as a tool for surveillance of influenza.

Objective

To explore the potential of a large commercial data warehouse for influenza surveillance.

The  ability  to  accurately  predict  influenza  infection  by  symptoms  and  local  epidemiology  prior  to  lab  confirmation  warrants  further  study  and  is  particular  concern as the threat of pandemic flu heightens.  Antiviral drugs are effective when given within 48 hours of  symptom  onset,  but  this  usually  precludes  culture  confirmation. Further,  rapid  tests  can  be  clinically  helpful   but   lack   the   sensitivity   of   viral   culture. Hence,  ILI  symptoms  are  a  potentially  important  covariate  in  the  early  diagnosis  of  flu. However,  gaps  remain  in  several  areas  of  flu  symptom  research,  including  knowledge  of  potential  differences  between  symptoms  of  Influenza  A  and  of  Influenza  B  [1]. Therefore,  an  examination  of  symptoms  generally  associated  with  Influenza  infection  was  begun,  as  well  as  an  examination  of  symptoms  specifically  associated with Flu A and Flu B. An additional focus in  this  study  was  to  evaluate  the  performance  of  the  current  ILI  case  definition  used  by  the  DoD  flu  program.  This definition is useful to identify individuals who  are  likely  to  be  infected  with  influenza,  as  the  ability  to  capture  and  characterize  novel  strains  of  influenza is an important component to this program. Better yields of influenza mean less time and money spent processing negative specimens.

Objective

This study describes clinical symptoms reported in conjunction with influenza, non-influenza respiratory viruses, and negative viral cultures from the Department of Defense (DoD) Global Influenza Surveillance Program; influenza-like illness (ILI) case questionnaires were linked to corresponding laboratory specimen results for the 2005-06 influenza season for analysis.

An important goal of influenza surveillance is to provide public health decisionmakers with timely estimates of the severity of community-wide influenza. One potential indicator is the number of influenza hospitalizations. In New York City methods for estimating influenza hospitalizations include asking hospitals to self-report, sending field staff to review medical records, and analyzing electronic hospital discharge data available months after influenza season is over. Given the limitations of each of these approaches, we evaluated whether electronic ED data, received daily for syndromic surveillance, could be used to monitor hospitalizations during influenza  epidemics.

Objective

To evaluate whether trends in influenza hospitalizations can be monitored using ED syndromic surveillance data.

The 2003/04 influenza season included a more pathogenetic organism and had an earlier onset. There were noticeably more deaths in otherwise healthy children than in previous seasons. Following this season, States were asked by the Centers for Disease Control and Prevention to increase their surveillance efforts for influenza illness.

Objective 

This paper describes data that was available in Ohio for analysis and considered valuable to determine the occurrence of influenza-like illness (ILI). These data sources were studied to determine their value to ILI surveillance and to develop an improved method of establishing influenza activity levels.