Intense stress can severely degrade one's ability to process and utilize new kinds of information.1 This psychological phenomena may partially explain why epidemiologist are challenged to communicate and establish the value of SyS information with emergency management professionals (EMPs). Despite the timely and useful insights that SyS data and methods can provide, it is very difficult to convey what these data are when EMPs and epidemiologists are working to make intense, highly-scrutinized and high-consequence emergency decisions.
This report is designed to aid state, territorial, tribal, and local public health leaders as they improve their capacity to achieve situational awareness during a public health emergency. We intend this report to serve as a concise reference work public health leaders can use to help design and manage biosurveillance systems to be used during an anticipated public health emergency.
Disaster epidemiology (i.e., applied epidemiology in disaster settings) presents a source of reliable and actionable information for decision-makers and stakeholders in the disaster management cycle. However, epidemiological methods have yet to be routinely integrated into disaster response and fully communicated to response leaders.
In general, data from public health surveillance can be used for short- and long-term planning and response through retrospective data analysis of trends over time or specific events. Combining health outcome data (e.g., hospitalizations or deaths) with environmental and socio-demographic information also provides a more complete picture of most vulnerable populations. Using syndromic surveillance systems for climate and health surveillance offers the unique opportunity to help quantify and track in near-real time the burden of disease from climate and weather impacts.
This Primer, published by the Network for Public Health Law on Friday, September 8, 2017, provides a visual snapshot and a timeline on state and federal emergency declarations in response to Hurricane Harvey and Irma.
Pacific Northwest National Laboratory (PNNL), on behalf the Defense Threat Reduction Agency (DTRA; project number CB10190), hosts an annual intern- based web app development contest. Previous competitions have focused on mobile biosurveillance applications. The 2016 competition pivoted away from biosurveillance to focus on addressing challenges within the field of chemical surveillance and increasing public health chemical situational awareness. The result of the app will be integrated within the DTRA BSVE.
Following Hurricane Superstorm Sandy, the New Jersey Department of Health (NJDOH) developed indicators to enhance syndromic surveillance for extreme weather events in EpiCenter, an online system that collects and analyzes real-time chief complaint emergency department (ED) data and classifies each visit by indicator or syndrome.
Previous research identifies social media as an informal source of near-real time health data that may add value to disease surveillance systems by providing broader access to health data across hard-toreach populations. This indirect health monitoring may improve public health professionals’ ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. The Philippines consists of over 7,000 islands and is prone to meteorological (storms), hydrological (floods), and geophysical disasters (earthquakes and volcanoes).
Hurricane Sandy hit New York City (NYC) on October 29, 2012. Before and after the storm, 73 temporary evacuation shelters were established. The total census of these shelters peaked at approximately 6,800 individuals. Concern about the spread of communicable diseases in shelters prompted the NYC Department of Health and Mental Hygiene (DOHMH) to rapidly develop a surveillance system to report communicable diseases and emergency department transports from shelters. We describe the implementation of this system.
Infectious disease outbreaks during crises can be controlled by detecting epidemics at their earliest possible stages through cost effective and time efficient data analytical approaches. The slow or non reporting is a real gap in existing reporting systems that delays in receiving the disease alerts and outbreaks, and hence delays in response causing high burden of morbidity and mortality, especially during crises situation.
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