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Time Series Analysis of Infectious Disease Mortality in Ukraine (1965-2015)

Monitoring of long-term infectious disease mortality trends is of great value to national public health systems both in estimation of the efficacy of preventive programs, and in development of the new strategies of preventive measures. In the developed countries, there are a number of studies with long-term time series of infectious disease mortality analysis in epidemiological and historical aspects. Our research was based on the work by Armstrong GL, Conn LA and Pinner RW, 1999. Literature review revealed that such analysis has been never carried out in Ukraine up to now.

June 18, 2019

Using real-time syndromic surveillance to monitor the health effects of air pollution

The negative effect of air pollution on human health is well documented illustrating increased risk of respiratory, cardiac and other health conditions. Currently, during air pollution episodes Public Health England (PHE) syndromic surveillance systems provide a near real-time analysis of the health impact of poor air quality.

January 21, 2018

Modeling of Hepatitis B Epidemic Process by the Risk Factors Analysis

Viral hepatitis is a global public health problem affecting millions of people every year, causing disability and death [1].The hepatitis B virus (HBV) is transmitted through the contact with the blood or other body fluids of an infected person. For formulating evidence-based policy of Public Health and data for action we should know about main ways of transmission HBV and population group with high risk of infection.

Objective

To develop model to study risk factors for hepatitis B (HB) and to identify the main causes affecting the incidence of HB.

April 28, 2019

Changes in the Spatial Distribution of Syphilis

Public health officials and epidemiologists have been attempting to eradicate syphilis for decades, but national incidence rates are again on the rise.

May 02, 2019

Real-time Adaptive Monitoring of Vital Signs for Clinical Alarm Preemption

Cardiovascular event prediction has long been of interest in the practice of intensive care. It has been approached using signal-processing of vital signs [1-4], including the use of graphical models [3,4]. Our approach is novel in making data segmentation as well as hidden state segmentation an unsupervised process, and in simultaneously tracking evolution of multiple vital signs. The proposed models are adaptable to the individual patient's vitals online and in real time, without requiring patient-specific training data if the patient-specific feedback signal is available.

May 02, 2019

Optimal sequential management decisions for measles outbreaks

Optimal sequential management of disease outbreaks has been shown to dramatically improve the realized outbreak costs when the number of newly infected and recovered individuals is assumed to be known (1,2). This assumption has been relaxed so that infected and recovered individuals are sampled and therefore the rate of information gain about the infectiousness and morbidity of a particular outbreak is proportional to the sampling rate (3). We study the effect of no recovered sampling and signal delay, features common to surveillance systems, on the costs associated with an outbreak.

May 02, 2019

Clustering of U.S. Cities Based on Mortality from Influenza and Pneumonia

Influenza is a major cause of mortality. In developed countries, mortality is at its highest during winter months, not only as a result of deaths from influenza and pneumonia but also as a result of deaths attributed to other diseases (e.g. cardiovascular disease). Understandably, much of the surveillance of influenza follows predefined geographic regions (e.g. census regions or state boundaries). However, the spread of influenza and its resulting mortality does not respect such boundaries.

 

Objective

May 02, 2019

Comparing Methods for Sentinel Surveillance Site Placement

Influenza-like illness (ILI) data is collected via an Influenza Sentinel Provider Surveillance Network at the state level. Because participation is voluntary, locations of the sentinel providers may not reflect optimal geographic placement. This study analyzes two different geographic placement schemes - a maximal coverage model (MCM) and a K-median model, two location-allocation models commonly used in geographic information systems. The MCM chooses sites in areas with the densest population.

May 02, 2019

Analysis of Zero-Inflated and Overdispersed Time Series: An Application to Syphilis Surveillance in the United States

Time series data involving counts are frequently encountered in many biomedical and public health applications. For example, in disease surveillance, the occurrence of rare infections over time is often monitored by public health officials, and the time series data collected can be used for the purpose of monitoring changes in disease activity. For rare diseases with low infection rates, the observed counts typically contain a high frequency of zeros (zero-inflated), but the counts can also be very large (overdispersed) during an outbreak period.

May 02, 2019

An automated influenza-like-illness reporting system using freetext emergency department reports

Current methods for influenza surveillance include laboratory confirmed case reporting, sentinel physician reporting of Influenza-Like-Illness (ILI) and chief-complaint monitoring from emergency departments (EDs).

The current methods for monitoring influenza have drawbacks. Testing for the presence of the influenza virus is costly and delayed. Specific, sentinel physician reporting is subject to incomplete, delayed reporting. Chief complaint (CC) based surveillance is limited in that a patient’s chief complaint will not contain all signs and symptoms of a patient.

June 10, 2019

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