Influenza-Like-Illness (ILI)

Traditional infectious disease epidemiology is built on the foundation of high quality and high accuracy data on disease and behavior. Digital infectious disease epidemiology, on the other hand, uses existing digital traces, re-purposing them to identify patterns in health-related processes. Medical claims are an emerging digital data source in surveillance; they capture patient-level data across an entire population of healthcare seekers, and have the benefits of medical accuracy through physician diagnoses, and fine spatial and temporal resolution in near real-time. Our work harnesses the large volume and high specificity of diagnosis codes in medical claims to improve our understanding of the mechanisms driving spatial variation in reported influenza activity each year. The mechanisms hypothesized to drive these patterns are as varied as: environmental factors affecting transmission or virus survival, travel flows between different populations, population age structure, and socioeconomic factors linked to healthcare access and quality of life. Beyond process mechanisms, the nature of surveillance data collection may affect our interpretation of spatial epidemiological patterns, particularly since influenza is a non-reportable disease with non-specific symptoms ranging from asymptomatic to severe. Considering the ways in which medical claims are generated, biases may arise from healthcare-seeking behavior, insurance coverage, and medical claims database coverage in study populations.

Objective

To assess the use of medical claims records for surveillance and epidemiological inference through a case study that examines how ecological and social determinants and measurement error contribute to spatial heterogeneity in reports of influenza-like illness across the United States.

The primary goal of syndromic surveillance is early recognition of disease trends, in order to identify and control infectious disease outbreaks, such as influenza. For surveillance of influenza-like illness (ILI), public health departments receive data from multiple sources with varying degrees of patient acuity, including outpatient clinics and emergency departments. However, the lack of standardization of these data sources may lead to varying baseline levels of ILI activity within a local area.

Objective

To examine the baseline influenza-like illness (ILI) rates in the emergency departments (ED) of a large academic medical center (AMC), community hospital (CH), and neighboring adult and pediatric primary care clinics.

The early detection of outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. In order to achieve this goal, the primary foundation is using those big surveillance data for understanding and controlling the spatiotemporal variability of disease through populations. Typically, public health’s surveillance system would generate data with the big data characteristics of high volume, velocity, and variety. One common question of big data analysis is most of the data have the multilevel or hierarchy structure, in other word the big data are non-independent. Traditional multilevel or hierarchical model can only deal with 2 or 3 hierarchical data structure, which bound health big data further research for modeling, forecast and early-warning in the public health surveillance, in particular involving complex spatial and temporal variability of Infectious Diseases in the reality. 

Objective

The purpose of this article was to quantitative analyses the spatial variability and temporal variability of influenza like illness (ILI) by a three-level Poisson model, which means to explain the spatial and temporal level effects by introducing the random effects. 

Public Health England (PHE) uses syndromic surveillance systems to monitor for seasonal increases in respiratory illness. Respiratory illnesses create a considerable burden on health care services and therefore identifying the timing and intensity of peaks of activity is important for public health decision-making. Furthermore, identifying the incidence of specific respiratory pathogens circulating in the community is essential for targeting public health interventions e.g. vaccination. Syndromic surveillance can provide early warning of increases, but cannot explicitly identify the pathogens responsible for such increases.

PHE uses a range of general and specific respiratory syndromic indicators in their syndromic surveillance systems, e.g. “all respiratory disease”, “influenza-like illness”, “bronchitis” and “cough”. Previous research has shown that “influenza-like illness” is associated with influenza circulating in the community1 whilst “cough” and “bronchitis” syndromic indicators in children under 5 are associated with respiratory syncytial virus (RSV)2, 3. However, the relative burden of other pathogens, e.g. rhinovirus and parainfluenza is less well understood. We have sought to further understand the relationship between specific pathogens and syndromic indicators and to improve estimates of disease burden. Therefore, we modelled the association between pathogen incidence, using laboratory reports and health care presentations, using syndromic data. 

Objective

To improve understanding of the relative burden of different causative respiratory pathogens on respiratory syndromic indicators monitored using syndromic surveillance systems in England. 

Traditionally, public health surveillance departments collect, analyze, interpret, and package information into static surveillance reports for distribution to stakeholders. This resource-intensive production and dissemination process has major shortcomings that impede end users from optimally utilizing this information for public health action. Often, by the time traditional reports are ready for dissemination they are outdated. Information can be difficult to find in long static reports and there is no capability to interact with the data by users. Instead, ad hoc data requests are made, resulting in inefficiencies and delays.

Use of electronic dashboards for surveillance reporting is not new. Many public health departments have worked with information technology (IT) contractors to develop such technically sophisticated products requiring IT expertise. The technology and tools now exist to equip the public health workforce to develop in-house surveillance dashboards, which allow for unprecedented speed, flexibility, and cost savings while meeting the needs of stakeholders. At Alberta Health Services (AHS), in-house, end-to-end dashboard development infrastructure has been established that provides epidemiologists and data analysts full capabilities for effective and timely reporting of surveillance information. 

Objective

To address the limitations of traditional static surveillance reporting by developing in-house infrastructure to create and maintain interactive surveillance dashboards. 

Since the emergence of avian influenza A(H7N9) virus in 2013, extensive surveillances have been established to monitor the human infection and environmental contamination with avian influenza virus in southern China. At the end of 2015, human infection with influenza A(H5N6) virus was identified in Shenzhen for the first time through these surveillances. These surveillances include severe pneumonia screening, influenza like illness (ILI) surveillance, follow-up on close contact of the confirmed case, serological survey among poultry workers, environment surveillance in poultry market.

Objective

To determine avian influenza A(H5N6) virus infection in human and environment using extensive surveillances. To evaluate the prevalence of H5N6 infection among high risk population. 

To describe the results of the new organization of influenza surveillance in France, based on a regional approach. This regional multi-source approach has been made possible by the sharing of data visualizations and statistical results through a web application. This application helped detecting early the epidemic start and allowed a reactive communication with the regional health authorities in charge of the organization of health care, the management and the setting up of the appropriate preventive measures.