Integrated spatiotemporal surveillance system: Data, Analysis and Visualization

Most surveillance methods in the literature focus on temporal aberration detections with data aggregated to certain geographical boundaries. SaTScan has been widely used for spatiotemporal aberration detection due to its user friendly software interface. However, the software is limited to spatial scan statistics and suffers from location imprecision and heterogeneity of population. R Surveillance has a collection of spatiotemporal methods that focus more on research instead of surveillance.

 Objective

July 07, 2017

Integrated surveillance: Joint modeling of rodent and human tularemia cases in Finland

An increasing number of geo-coded information streams are available with possible use in disease surveillance applications. In this setting, multivariate modeling of health and non-health data allows assessment of concurrent patterns among data streams and conditioning on one another. Therefore it is appropriate to consider the analysis of their spatial distributions together. Specifically for vector-borne diseases, knowledge of spatial and temporal patterns of vector distribution could inform incidence in humans.

July 07, 2017

Modeling spatial and temporal variability by Bayesian multilevel model

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.

July 16, 2017

Socio-environmental and measurement factors drive variation in influenza-like illness

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.

August 10, 2017

Using a Bayesian Method to Assess Google, Twitter, and Wikipedia for ILI Surveillance

Traditional influenza surveillance relies on reports of influenzalike illness (ILI) by healthcare providers, capturing individuals who seek medical care and missing those who may search, post, and tweet about their illnesses instead. Existing research has shown some promise of using data from Google, Twitter, and Wikipedia for influenza surveillance, but with conflicting findings, studies have only evaluated these web-based sources individually or dually without comparing all three of them1-5.

August 22, 2017

Value of evidence from syndromic surveillance with delayed reporting

Taking into account reporting delays in surveillance systems is not methodologically trivial. Consequently, most use the date of the reception of data, rather than the (often unknown) date of the health event itself. The main drawback of this approach is the resulting reduction in sensitivity and specificity1. Combining syndromic data from multiple data streams (most health events may leave a “signature” in multiple data sources) may be performed in a Bayesian framework where the result is presented in the form of a posterior probability for a disease2.

August 26, 2017

Detecting Overlapping Outbreaks of Influenza

Influenza is a contagious disease that causes epidemics in many parts of the world. The World Health Organization estimates that influenza causes three to five million severe illnesses each year and 250,000-500,000 deaths. Predicting and characterizing outbreaks of influenza is an important public health problem and significant progress has been made in predicting single outbreaks. However, multiple temporally overlapping outbreaks are also common. These may be caused by different subtypes or outbreaks in multiple demographic groups.

September 20, 2017

Assessing the distribution and drivers of vaccine hesitancy using medical claims data

In the United States, surveillance of vaccine uptake for childhood infections is limited in scope and spatial resolution. The National Immunization Survey (NIS) - the gold standard tool for monitoring vaccine uptake among children aged 19-35 months - is typically constrained to producing coarse state-level estimates. In recent years, vaccine hesitancy (i.e., a desire to delay or refuse vaccination, despite availability of vaccination services) has resurged in the United States, challenging the maintenance of herd immunity.

June 03, 2017

Bayesian Surveillance for the Detection of Small Area Health Anomalies

The surveillance task when faced with small area health data is more complex than in the time domain alone. Both changes in time and space must be considered. Such questions as ‘where will the infection spread to next?’ and, ‘when will the infection arrive here’, or ‘when do we see the end of the epidemic?’ are all spatially specific questions that are commonly of concern for both the public and public health agencies.  Hence both spatial and temporal dimensions of the surveillance task must be considered.

March 14, 2017

A Bayesian Hierarchical Model for Estimating Influenza Epidemic Severity

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.

August 07, 2017

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