Infectious Disease

Coordinated proactive school closures can help to reduce disease transmission in communities during an influenza pandemic; however, limited information is available about effects of school closures during influenza-like illness (ILI) outbreaks. A rural school district (District A) in Kentucky was closed during January 29-February 1, 2013, in response to an increase in ILI-related student absenteeism.

Objective

We investigated effects of this closure by comparing self-reported illness among household members of students enrolled in District A with 2 adjacent districts (Districts B and C) that remained open during that period.

New York State has implemented a statewide Electronic Clinical Laboratory Reporting System (ECLRS) to which laboratories can electronically submit test results for reportable conditions. The Communicable Disease Electronic Surveillance System (CDESS) was used by 57 Local Health Departments (LHDs) to transfer ECLRS information and initiate investigations. Currently over 98% of licensed clinical labs are reporting via ECLRS. Positive laboratory test results are required to confirm over 80% of communicable diseases and they are often the first indication of a disease. Early detection of disease outbreaks is important for timely implementation of disease prevention and control measures. The space-time permutation scan statistic only requires disease counts, event date and disease location, which are collected from ECLRS and can be used to detect potential disease outbreaks by identifying spatial-temporal lab report clusters.

Objective

This abstract explains how the space-time permutation scan statistic only requires disease counts, event date and disease location, which are collected from ECLRS and can be used to detect potential disease outbreaks by identifying spatial-temporal lab report clusters.

Los Alamos National Laboratory (LANL) was tasked with developing methods to determine the relevance of data streams for an integrated global biosurveillance system. We used a novel method of evaluating the effectiveness of data streams called the 'surveillance window'. We defined a surveillance window as the brief period of time when information gathered can be used to assist decision makers in effectively responding to an impending outbreak. Information obtained for data streams beyond this window is deemed to have limited use.

Objective

The goal of this project was to provide an approach and evaluation of data stream utility for integrated, global disease surveillance. This effort is part of a larger project which is developing tools to aid decision-makers with timely information to predict, prepare for, and mitigate the spread of disease.

The Houston Department of Health Department of Health and Human Services (HDHHS) monitors emergency departments (ED) chief complaints across the Houston metropolitan area, Harris County, and the surrounding jurisdictions by Real-time Outbreak Disease Surveillance (RODS). The influenza-like illnesses (ILI) data is collected by sentinel surveillance provider network of 12 physicians and RODS, an electronic syndromic surveillance database consisting of about 30 EDs in metropolitan Houston. Previous research indicates that there is a relationship between new HIV diagnoses and neighborhood poverty. However, there is limited research on health disparity to investigate the association between influenza-like illnesses (ILI) and social determinants of health (SDH), such as poverty.

Objective

To investigate the association between social determinants of health and influenza-like illnesses in Houston/Harris County and to identify neighborhoods for targeted surveillance or interventions.

Methicillin-resistant Staphylococcus aureus (MRSA) has since become a major cause of illness and death in our healthcare setting. Risk factors for HA-MRSA include hospitalization, older age, invasive devices, and residence in long-term care facility, including exposure to antimicrobial agents. HA-MRSA isolates are often resistant to several antimicrobial drug classes in addition to beta-lactams. The CA-MRSA infections usually affects young, healthy persons and associated with sharing towels or athletic equipment, participating in contact sports, living in unsanitary and crowded areas, using illegal intravenous drugs.

Objective

To properly ascertain and assess the indicators responsible for the rising cases of Methicillin-resistant Staphlococcus aureus strains as it concerns community and nosocomial cases.

Absenteeism has great advantages in promoting the early detection of epidemics. School absenteeism surveillance could timely detect the aggregations of absentees in time and space, so as to provide effective early warning and prevention and control of infectious diseases outbreaks in schools. Since April 1, 2012, an integrated syndromic surveillance system (ISSC) has been implemented in rural Hubei Province, China. With school absence data, finding the optimal model and related appropriate parameters for early warning of epidemics is necessary and practical.

Objective

To explore the optimal model and its related parameters via EWMA and CUSUM (C1, C2, C3) models in school absenteeism surveillance for early detection of infectious disease outbreaks in rural China.

Interactive tools for visualization of disease outbreaks has been improving markedly in the past few years. With the flagships Google Flutrends1 and HealthMap2 providing prime examples. These tools provide interactive access to the general public concerning the current state-of-affairs for disease outbreaks generally and specifically for influenza. For example, while browsing HealthMap I learned of a case of tuberculosis on my campus, Iowa State University. While extremely sophisticated, these tools do not utilize modern statistical algorithms for detection or forecasting. In addition, the development cost and perhaps the maintenance cost is not trivial. We aim to build a similar visualization tool that incorporates modern algorithms for detection and forecasting but has low development and maintenance cost. Due to the low cost this tool is appropriate for quick deployment in developing countries for emerging outbreaks as well as public health agencies with declining operating budgets.

Objective

To build a zero-cost tool for disease outbreak visualization, detection, and forecasting incorporating modern tools.

Researchers have developed varied methods for forecasting influenza activity using surveillance data with predictive models, but real-world applications in public health programs are rare. To inform consideration of whether and how public health practice should incorporate influenza forecasting, we conducted a systematic review of these methods.

Objective

To assess studies of epidemiological forecasting models for human influenza activity.