Influenza

In response to the 2009 H1N1 pandemic, the Early Warning Infectious Disease Surveillance Program Office of Binational Border Health in the California Department of Public Health sought to strengthen outpatient ILI surveillance along the CA/BC border by creating the first binational influenza surveillance network in the region. The establishment of this network was crucial for enhancing cross-border situational awareness of influenza activity, especially in a region characterized by high levels of population mobility.

Objective

To enhance cross-border surveillance for Influenza-Like-Illness (ILI) in the California/Baja California (CA/BC) border region through the formation of a border binational surveillance network.

Syndromic surveillance has been widely used in influenza surveillance worldwide. However, despite the potential benefits created by the large volume of data, biases due to the changes in healthcare seeking behavior and physicians’ reporting behavior, as well as the background noise caused by seasonal flu epidemics, contribute to the complexity of the surveillance system and may limit its utility as a tool for early detection. Since most current analysis methods are developed for outbreak detection, there are few tools to characterize influenza surveillance data for situational awareness purposes in a quantitative manner. Hong Kong Centre for Health Protection has a comprehensive influenza surveillance system based on healthcare providers, laboratories, schools, daycare centers and residential care homes for the elderly. Hong Kong usually experiences a summer peak in July and August, which potentially doubles the data volume and constitutes a natural experiment to assess the effect of school-age children in the influenza transmission dynamics. The richness of the available data and the unique epidemiological characteristics make Hong Kong an ideal study object to develop and evaluate our model.

Objective

Our goal is to develop a statistical model for characterizing influenza surveillance systems that will be helpful in interpreting multiple streams of influenza surveillance data in future outbreaks.

Each year, influenza affects approximately 5-20% of the United States population causing over 200,000 hospitalizations and 3,000 – 49,000 death. As a key point of entry to the health care system, EDs are responsible for the initial management and treatment of a substantial proportion of these influenza patients, thus directly impacting overall public health. As the front line of influenza diagnosis and treatment, ED providers may benefit from real-time easily shared influenza surveillance information.

Objective

To evaluate the utility and acceptability of a real-time cloud based influenza surveillance tool amongst emergency department (ED) providers.

Previous studies have demonstrated the benefit of laboratory surveillance and its capability to accurately detect influenza outbreaks earlier than syndromic surveillance. Current laboratory surveillance has an approximate 2-week lag due to laboratory test turn–around time and data collection. In order to provide real-time access to aggregated test results, we utilized direct cloud connectivity with a rapid PCR-based influenza test, Xpert Flu, to centrally consolidate test results along with GIS data. On-site, type-specific results were available to physicians and uploaded for public health awareness within 100 minutes of patient nasopharyngeal swab.

Objective

To demonstrate the feasibility and validity of a novel electronic surveillance system utilizing a cloud-based interface that consolidates laboratory test results and geographical information in real-time.

A seroprevalence survey carried out in four counties in the Tampa Bay area of Florida (Hillsborough, Pinellas, Manatee and Pasco) provided an estimate of cumulative incidence of infection due to the 2009 influenza A (H1N1) as of the end of that year’s pandemic. During the pandemic, high-level decison-makers wanted timely, credible forecasts as to the likely near-term course of the pandemic. The cumulative percentage of people who will be infected by the end of the epidemic can be estimated from the intrinsic reproductive number of the viral strain, its R0 , which can be measured early in the epidemic. If the current cumulative number of infections can be estimated, then one can determine what fraction of the eventual total number of infected people have already been infected.

Objective

To estimate the number of infections due to the novel 2009 influenza A/H1N1 virus corresponding to each ED visit for ILI in a four-county area of Florida. Knowing such ratios, one could (in future similar situations) estimate the cumulative number of infections due to a novel influenza virus in a population.

The purpose of this work was to develop a novel method of estimating the amount of influenza-like illness (ILI) in a population, in near-real time, by using a source of information that is completely open to the public and free to access. We investigated the usefulness of data gathered from Wikipedia to estimate the prevalence of ILI in the United States, using data from the Centers for Disease Control and Prevention (CDC) as well as Google Flu Trends.

Introduction

Each year, there are an estimated 250,000–500,000 deaths worldwide that are attributed to seasonal influenza, with anywhere between 3,000–50,000 deaths occurring in the United States of America (US). In the US, the Centers for Disease Control and Prevention (CDC) continuously monitors the level of influenza-like illness (ILI) circulating in the population. While the CDC ILI data is considered to be a useful indicator of influenza activity, its availability has a known lag-time of between 7–14 days. To appropriately distribute vaccines, staff, and other healthcare commodities, it is critical to have up-to-date information about the prevalence of ILI in a population. To this end, we have created a method of estimating current ILI activity in the US by gathering information on the number of times particular Wikipedia articles have been viewed. Not only is the information held within Wikipedia articles very useful on its own, but statistics and trends surrounding the amount of usage of particular articles, frequency of article edits, region specific statistics, and countless other factors make the Wikipedia environment an area of interest for researchers. Furthermore, Wikipedia makes all of this information public and freely available, greatly increasing and expediting any potential research studies that aim to make use of their data.

To date, avian influenza virus (AIV) is an unpredictable pathogen affecting both animals, birds and people. The regular emergence of new strains and variants with different properties and pathogenicities requires additional monitoring and careful research of those viruses. It is known that wild birds— especially waterfowl and shorebirds— are the main and primary reservoir of AIV in nature which makes epizootological monitoring of populations of these birds necessary.

Objective

To carry out monitoring studies of circulation of the AIV subtypes H5 and H7 in wild waterfowl and shorebirds around the Azov-Black Sea in Ukraine

The Centers for Disease Control and Prevention (CDC) recommends implementing early targeted school closures as one of the front-line interventions to slow progression of a severe influenza pandemic before appropriate vaccine becomes available. However, prolonged school closures may impose unintended economic and social costs and consequences to students’ families. These costs and consequences have not been carefully evaluated. To better understand this unintended impact, we conducted five investigations of unplanned school closures lasting >=4 school days implemented for various reasons from August 2012 through May 2013. Each closure was investigated separately as a public health evaluation. School closures implemented for reasons other than pandemic influenza may serve as a proxy to pandemic-related closures. Our findings can inform updates to CDC’s pandemic preparedness guidance.