Influenza

The past decade has witnessed rapid development and implementation of numerous syndromic and other advanced surveillance systems to supplement traditional laboratory testing to identify the presence of novel influenza strains and track the impact on local populations. While much of the development and widespread implementation of these systems had been supported by public health preparedness funding, the loss of these monies has greatly constrained the ability of public health agencies to staff and maintain these systems. The periodic appearance of novel flu viruses, such as H3N2v, requires agencies to carefully choose which systems will provide the most cost-effective data to support their public health practice.

Objective

This project was organized to facilitate discussions on whether successful novel flu surveillance can be conducted by jurisdictions with limited resources. The discussions will focus on gathering opinions regarding the best combination of surveillance systems to quickly and efficiently identify the presence of influenza A (H3N2)v and other novel influenza viruses in circulation.

National borders do not prevent the transmission of pathogens and associated vectors among border populations. The Naval Health Research Center (NHRC) has collaborated with the Mexican Secretariat of Health, the U. S. Department of State’s Biosecurity Engagement Program (BEP) and the U. S. Centers for Disease Control and Prevention (CDC) in concert with local health officials to conduct ILI surveillance (since 2004) and SARI surveillance (since 2009) in the border region.

Objective

To identify the pathogens responsible for influenza-like illness (ILI) and severe acute respiratory illness (SARI) along the U.S.-Mexico border region in San Diego and Imperial Counties, CA and Pima County, AZ.

The emergence of new influenza strains including H1N1, H5N1, H3N2v as well as other respiratory pathogens such as SARS, along with generally weak information about household and community transmission of influenza, enforce the need for augmented influenza surveillance. At the same time, Internet penetration and access has grown, with 82% of American adults using the Internet, enabling transfer and communication of information that can be collected and aggregated in near real-time. Surveillance targeted towards influenza in other countries, and towards malaria in India, has previously been executed with good user engagement. In this study, we created an online participatory influenza surveillance tool in the United States, called Flu Near You.

Objective

To develop a participatory system for monitoring the activity of influenza-like-illness among the United States general population.

So as to develop more effective countermeasures against influenza, timely and precise information about influenza activity at schools, kindergartens, and nursery schools may be helpful. At the Infectious Diseases Surveillance Center of the National Institute of Infectious Diseases, a School Absenteeism Surveillance System (SASSy) has been in operation since 2009. SASSy monitors the activity of varicella, mumps, mycoplasma pneumonia, pharyngoconjunctival fever, hand-foot-mouth disease, influenza, and many other infectious diseases in schools. In 2010, SASSy was extended to the Nursery School Absenteeism Surveillance System (NSASSy). These systems record the number of absentees due to infectious diseases in each class of all grades of schools every day. As a powerful countermeasure to the pandemic flu of 2009, SASSy was activated in 9 prefectures, in which included more than 6000 schools, and it is gradually being adopted in other prefectures. As of February 2012, 18 prefectures and 4 big cities, which together comprised 15,700 schools (about 35% of all schools in Japan), utilized SASSy. NSASSy is used in more than 4100 nursery schools, which is about 18% of all nursery schools in Japan. Some studies of similar systems were performed in the UK (1), Hong Kong (2), and the USA (3,4), examined surveillance systems for monitoring infectious disease incidence, but the systems to construct in those studies do not operate nationwide like SASSy or NSASSy, and they cannot provide influenza incidence rates in children.

Objective: 

So far, it is difficult to show the incidence rate of influenza in the official sentinel surveillance in Japan. Hence we construct the system which record infectious diseases at schools, kindergartens, and nursery schools, and then can show the accurate incidence rate of influenza in children by age/grade.

Telephone triage is a relatively new data source available to biosurveillance systems.1-2Because early detection and warning is a high priority, many biosurveillance systems have begun to collect and analyze data from non-traditional sources [absenteeism records, overthe-counter drug sales, electronic laboratory reporting, internet searches (e.g. Google Flu Trends) and TT]. These sources may provide disease activity alerts earlier than conventional sources. Little is known about whether VA telephone program influenza data correlates with established influenza biosurveillance.

Objective:

To evaluate the utility and timeliness of telephone triage (TT) for influenza surveillance in the Department of Veterans Affairs (VA).

Antimicrobial prescriptions are a new data source available to the Veterans Health Administration (VHA) biosurveillance program. Little is known about whether antiviral or antibacterial prescription data correlates with influenza ICD-9-CM coded encounters. We therefore evaluated the utility and timeliness of antiviral and antibacterial utilization for influenza surveillance.

Early detection of influenza outbreaks is critical to public health officials. Case detection is the foundation for outbreak detection. Previous study by Elkin el al. demonstrated that using individual emergency department (ED) reports can better detect influenza cases than using chief complaints. Our recent study using ED reports processed by Bayesian networks (using expert constructed network structure) showed high detection accuracy on detection of influenza cases.

Objective

Compare 7 machine learning algorithms with an expert constructed Bayesian network on detection of patients with influenza syndrome.

Objective:

To assess effectiveness of the influenza vaccine among US military dependents and US-Mexico Border populations during the 2011-12 influenza season.

Introduction:

As a result of antigenic drift of the influenza viruses, the composition of the influenza vaccine is updated yearly to match circulating strains. Consequently, there is need to assess the effectiveness of the influenza vaccine (VE) on a yearly basis. Ongoing febrile respiratory illness (FRI) surveillance captures data and specimens that are leveraged to estimate influenza VE on an annual basis.