Outbreak Detection

The outbreak of the Ebola Virus Disease (EVD) in Africa in 2014 presented a major threat and concern across the world, spreading to two other continents (Europe and North America). Though the epidemic is on a downward trend, there is a need to evaluate the performance of the systems in place to detect and control such outbreaks and determine the need for improvement in countries affected.

With its first traceable case reported to have been in Guinea, the outbreak spread to Nigeria through an air traveler from Liberia which led to an outbreak in the country that luckily, was quickly contained. This imported case was initially managed at a private health facility (PHF) eventually leading to 20 cases and eight deaths, four of which were health workers from the initial managing PHF. Despite effort to contact the authorities about the suspected imported case by the PHF, it reportedly took some time before the health authorities could be reached and action at control instituted. This might suggest an inefficiency of the IDSR system which was previously adopted by Nigeria as a means of implementing the International Health Regulation (IHR) of 1969. The IHR is a set of regulations that the World Health Assembly uses to implement its constitutional responsibility to prevent the international spread of diseases.

Hemorrhagic fevers like EVD ought to be reported immediately upon suspicion to the health authorities but the delay despite effort suggests this system is not efficient. This is important as PHFs are noted to attend to over 60% of the Nigerian population. Thus, it is important to carry out an assessment of the IDSR system in PHFs to forestall a repeat episode and limit the impact of outbreak of infectious diseases in future.

Objective

To investigate the compliance of private health facilities to the integrated disease surveillance and response (IDSR) system in Nigeria.

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.

Objective

We apply an empirical Bayesian framework to perform change point analysis on multiple cattle mortality data streams, accounting for delayed reporting of syndromes.

The Florida Department of Health (DOH) utilizes the Electronic Surveillance System for the Early Notification of Community Based Epidemics (ESSENCE-FL) as its statewide syndromic surveillance system. ESSENCE-FL comprises of chief complaint data from 231 of 240 EDs, representing 96 percent of the total number of EDs in Florida. Historically, syndromic surveillance has categorized patient chief complaint data into syndromes for the purpose of disease surveillance or outbreak detection. Triage notes are much longer freetext, pre-diagnostic data that capture the presenting symptoms and complaints of a patient.

Objective

This study assesses the utilization of triage notes from emergency departments (EDs) and urgent care centers (UCCs) for active case finding in ESSENCE-FL during the Zika response.

Neill’s fast subset scan2 detects significant spatial patterns of disease by efficiently maximizing a log-likelihood ratio statistic over subsets of locations, but may result in patterns that are not spatially compact. The penalized fast subset scan (PFSS)3 provides a flexible framework for adding soft constraints to the fast subset scan, rewarding or penalizing inclusion of individual points into a cluster with additive point-specific penalty terms. We propose the support vector subset scan (SVSS), a novel method that iteratively assigns penalties according to distance from the separating hyperplane learned by a kernel support vector machine (SVM). SVSS efficiently detects disease clusters that are geometrically compact and irregular.

Objective

We present the support vector subset scan (SVSS), a new method for detecting localized and irregularly shaped patterns in spatial data. SVSS integrates the penalized fast subset scan3 with a kernel support vector machine classifier to accurately detect disease clusters that are compact and irregular in shape.

The Biosurveillance Ecosystem (BSVE) is a biological and chemical threat surveillance system sponsored by the Defense Threat Reduction Agency (DTRA). BSVE is intended to be user-friendly, multi-agency, cooperative, modular and threat agnostic platform for biosurveillance [2]. In BSVE, a web-based workbench presents the analyst with applications (apps) developed by various DTRAfunded researchers, which are deployed on-demand in the cloud (e.g., Amazon Web Services). These apps aim to address emerging needs and refine capabilities to enable early warning of chemical and biological threats for multiple users across local, state, and federal agencies. Soda Pop is an app developed by Pacific Northwest National Laboratory (PNNL) to meet the current needs of the BSVE for early warning and detection of disease outbreaks. Aimed for use by a diverse set of analysts, the application is agnostic to data source and spatial scale enabling it to be generalizable across many diseases and locations. To achieve this, we placed a particular emphasis on clustering and alerting of disease signals within Soda Pop without strong prior assumptions on the nature of observed diseased counts.

Objective

To introduce Soda Pop, an R/Shiny application designed to be a disease agnostic time-series clustering, alarming, and forecasting tool to assist in disease surveillance “triage, analysis and reporting” workflows within the Biosurveillance Ecosystem (BSVE). In this poster, we highlight the new capabilities that are brought to the BSVE by Soda Pop with an emphasis on the impact of metholodogical decisions.

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).

Drug overdoses are an increasingly serious problem in the United States and worldwide. The CDC estimates that 47,055 drug overdose deaths occurred in the United States in 2014, 61% of which involved opioids (including heroin, pain relievers such as oxycodone, and synthetics).1 Overdose deaths involving opioids increased 3-fold from 2000 to 2014.1 These statistics motivate public health to identify emerging trends in overdoses, including geographic, demographic, and behavioral patterns (e.g., which combinations of drugs are involved). Early detection can inform prevention and response efforts, as well as quantifying the effects of drug legislation and other policy changes.

The fast subset scan2 detects significant spatial patterns of disease by efficiently maximizing a log-likelihood ratio statistic over subsets of data points, and has recently been extended to multidimensional data (MD-Scan).3 While MD-Scan is a potentially useful tool for drug overdose surveillance, the high dimensionality and sparsity of the data requires a new approach to estimate and represent baselines (expected counts), maintaining both accuracy and efficient computation when searching over subsets. 

Objective

We present the multidimensional tensor scan (MDTS), a new method for identifying emerging patterns in multidimensional spatio-temporal data, and demonstrate the utility of this approach for discovering emerging geographic, demographic, and behavioral trends in fatal drug overdoses. 

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.