Influenza

The test-negative design is a variation of the case-control study, in which patients are enrolled in outpatient clinics (and/or hospitals) based on a clinical case definition such as influenza-like illness (ILI). Patients are then tested for influenza virus, and VE is estimated from the odds ratio comparing the odds of vaccination among patients testing positive for influenza versus those testing negative, adjusting for potential confounding factors. The design leverages existing disease surveillance networks and as a result, studies using it are increasingly being reported.

Objective

We aimed to describe the theoretical basis and the potential applications of the test-negative design for estimating influenza vaccination effectiveness in sentinel influenza surveillance.

Every year, circulating influenza viruses generate a significant number of deaths. During the 2009 pandemic influenza A(H1N1), a national non mandatory surveillance system of severe influenza cases admitted to intensive care units(ICU) was set up in France. This surveillance is regionally driven by the regional offices (CIRE) of Santé publique France, the French Public Health Agency. This report provides epidemiologic analysis of the recorded data since the implementation of surveillance in the Centre-Val de Loire region over seasons 2009-10 to 2015-16 in regard of influenza epidemics dynamics.

Objective

The study aimed at: i) analyses the regional characteristics and risk factors of severe influenza, taking into account dominant circulating virus(es) ii) estimate the regional completeness of the surveillance system.

Ukraine’s ability to respond to the spread of viruses that cause pandemics and reduce economic losses from influenza, can be strengthened only in the presence of a developed surveillance network including the monitoring of virus circulation in humans. Specialists of Dnipropetrovsk Oblast have great experience in virological surveillance on the circulation of influenza virus A/California/H1N1 and timely determination of the etiology of outbreaks caused by the virus.

As part of this surveillance study for Avian Influenza both active and passive surveillance samples were tested using PCR and also utilized to validate the LAMP method. Active surveillance samples include pathological material and tracheal and cloacal swabs from ill poultry, which were subsequently assessed for avian influenza during diagnosis, and birds collected by hunters. Passive surveillance included environmental samples such as sand and bird faeces. Active surveillance samples were taken mostly from poultry farms across Ukraine, where infected birds are required to be diagnosed by State Scientific Research Institute of Laboratory Diagnostics and Veterinary Sanitary Expertise (SSRILDVSE) by Ukraine Law. Passive surveillance samples were taken primarily during the annual bird migration season. Development of simple, sensitive, and cheap methods for diagnostics of avian influenza is a very important task for practical veterinary medicine. LAMP is one of such methods. The technique is based on isothermal amplification of nucleic acids. It does not require special conditions and equipment (PCR cyclers), therefore it is cheaper in comparison with PCR. Accurate diagnosis is necessary for determining the risk associated with avian influenza in Ukraine and along the Dnipro River during the migratory season.

Objective

The performance of comparative analysis of sensitivity and results of detection of avian influenza virus by real time polymerase chain reaction (PCR-RT) and loop-mediated isothermal amplification of the nucleic acids (LAMP) was the main goal of the study.

Assigning causes of deaths to seasonal infectious diseases is difficult in part due to laboratory testing prior to death being uncommon. Since influenza (and other common respiratory pathogens) are therefore notoriously underreported as a (contributing) cause of death in deathcause statistics modeling studies are commonly used to estimate the impact of influenza on mortality.

Objective

To estimate mortality attributable to influenza adjusted for other common respiratory pathogens, baseline seasonal trends and extreme temperatures.

We describe an automated system that can detect multiple outbreaks of infectious diseases from emergency department reports. A case detection system obtains data from electronic medical records, extracts features using natural language processing, then infers a probability distribution over the diseases each patient may have. Then, a multiple outbreak detection system (MODS) searches for models of multiple outbreaks to explain the data. MODS detects outbreaks of influenza and non-influenza influenza-like illnesses (NI-ILI).

Timely monitoring and prediction of the trajectory of seasonal influenza epidemics allows hospitals and medical centers to prepare for, and provide better service to, patients with influenza. The CDC’s ILINet system collects data on influenza-like illnesses from over 3,300 health care providers, and uses this data to produce accurate indicators of current influenza epidemic severity. However, ILINet indicators are typically reported at a lag of 1-2 weeks. Another source of severity data, Google Flu Trends, is calculated by aggregating Google searches for certain influenza related terms. Google Flu Trends data is provided in near-real time, but is a less direct measurement of severity than ILINet indicators, and is likely to suffer from bias. We create a hierarchical model to estimate epidemic severity for the 2014 - 2015 epidemic season which incorporates current and historical data from both ILINet and Google Flu Trends, allowing our model to benefit both from the recency of Google Flu Trends data and the accuracy of ILINet data.

Objective

To use multiple data sources of influenza epidemic severity to inform a model which can estimate and forecast severity for the current influenza epidemic season by accounting for the bias from each source.

School-based influenza surveillance has been considered for real-time monitoring of influenza, as children 5-17 years old play an important role in community-level transmission.

Objective

To determine if all-cause and cause-specific school absences improve predictions of virologically confirmed influenza in the community.

The mortality monitoring system (initiated in 2009 during the influenza A(H1N1) pandemic) is a collaboration between the Centre for Infectious Disease Control (CIb) and Statistics Netherlands. The system monitors nation-wide reported number of deaths (population size 2014: 16.8 million) from all causes, as cause of death information is not available real-time. Data is received from Statistics Netherlands by weekly emails. 

Objective

Weekly numbers of deaths are monitored to increase the capacity to deal with both expected and unusual (disease) events such as pandemic influenza, other infections and non-infectious incidents. The monitoring information can potentially be used to detect, track and estimate the impact of an outbreak or incident on all-cause mortality.