Background: The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) is a secure web-based tool that enables health care practitioners to monitor health indicators of public health importance for the detection and tracking of disease outbreaks, consequences of severe weather, and other events of concern.
Lewis Sheri
Electronic disease surveillance systems can be extremely valuable tools; however, a critical step in system implementation is collection of data. Without accurate and complete data, statistical anomalies that are detected hold little meaning. Many people who have established successful surveillance systems acknowledge the initial data collection process to be one of the most challenging aspects of system implementation. These challenges manifest from varying degrees of economical, infrastructural, environmental, cultural, and political factors. Although some factors are not controllable, selecting a suitable collection framework can mitigate many of these obstacles. JHU/APL, with support from the Armed Forces Health Surveillance Center, has developed a suite of tools, Suite for Automated Global bioSurveillance, that is adaptable for a particular deployment’s environment and takes the above factors into account. These subsystems span communication systems such as telephone lines, mobile devices, internet applications, and desktop solutions - each has compelling advantages and disadvantages depending on the environment in which they are deployed. When these subsystems are appropriately configured and implemented, the data are collected with more accuracy and timeliness.
Objective
This paper describes the common challenges of data collection and presents a variety of adaptable frameworks that succeed in overcoming obstacles in applications of public health and electronic disease surveillance systems and/or processes, particularly in resource-limited settings.
Traditional public health practice has relied on public health surveillance of disease to detect outbreaks in an effort to mitigate their effects. Often the earlier an outbreak is detected, the greater the mitigation of its effects. The logical extension of this relationship is to predict outbreaks before they occur. A predictive model for an emerging infectious disease would forecast, when and where an outbreak of a given disease will occur, well before its emergence. This is a challenging task and truly predictive models for emerging infectious diseases and is still in their infancy.
Objective
This paper addresses the problem of predicting outbreaks of diseases of military importance in a chosen region of the world, one to several months in advance.
OSS is rapidly becoming part of more public health applications. Mobile health (mHealth) initiatives and the need for electronic processes to support healthcare (eHealth) provide particularly good examples of government use of open source software. The growth of global and national mHealth and eHealth needs has spurred innovation in software development. In resource limited areas that do not have the infrastructure for sophisticated computing tools but where cellular technology is prevalent, mHealth solutions are able to move such communities into the digital age. Monetary costs of licensing and maintaining proprietary software systems have been common challenges to these end users, but OSS helps solve these problems. OSS has already been used to further certain global public health initiatives, but more needs to be done. For instance, the passage of the World Health Organization (WHO) International Health Regulations (IHR) in 2005 required member countries to implement certain core public health capacities by June 2012. The adoption more broadly of OSS has the potential to improve the efficiency of IHR implementation, and therefore global public health initiatives in general, because it provides a free, modifiable software option which can be altered to meet specific requirements.
Objective
Provide an overview of common open source software (OSS) licenses used in public health applications, and discuss how OSS can help improve global public health security.
Influenza epidemics occur seasonally but with spatiotemporal variations in peak incidence. Many modeling studies examine transmission dynamics [1], but relatively few have examined spatiotemporal prediction of future outbreaks [2]. Bootsma et al [3] examined past influenza epidemics and found that the timing of public health interventions strongly affected the morbidity and mortality. Being able to predict when and where high influenza incidence levels will occur before they happen would provide additional lead time for public health professionals to plan mitigation strategies. These predictions are especially valuable to them when the positive predictive value is high and subsequently false positives are infrequent.
Objective
Advanced techniques in data mining and integrating evidence from multiple sources are used to predict levels of influenza incidence several weeks in advance and display results on a map in order to help public health professionals prepare mitigation measures.
Dengue fever is endemic in over 100 countries and there are an estimated 50 - 100 million cases annually. There is no vaccine for dengue fever yet, and the mortality rate of the severe form of the disease, dengue hemorrhagic fever, ranges from 10-20% but may be greater than 40% if dengue shock occurs. A predictive method for dengue fever would forecast when and where an outbreak will occur before its emergence. This is a challenging task and truly predictive models for emerging infectious diseases are still in their infancy.
Objective
This paper addresses the problem of predicting high incidence rates of dengue fever in Peru several weeks in advance.
Recent events have focused on the role of emerging and re-emerging diseases not only as a significant public health threat but also as a serious threat to the economy and security of nations. The lead time to detect and contain a novel emerging disease or events with public health importance has become much shorter, making developing countries particularly vulnerable to both natural and man-made threats. There is a need to develop disease surveillance systems flexible enough to adapt to the local existing infrastructure of developing countries but which will still be able to provide valid alerts and early detection of significant public health threats.
Objective
To determine system usefulness of the ESSENCE Desktop Edition in detecting increases in the number of dengue cases in the Philippines.
A pandemic caused by influenza A/H5N1 or another novel strain could kill millions of people and devastate economies worldwide. Recent computer simulations suggest that an emerging influenza pandemic might be contained in Southeast Asia through rapid detection, antiviral distribution, and other interventions [1]. To facilitate containment, the World Health Organization (WHO) has established large, global antiviral stockpiles and called on countries to develop rapid pandemic detection and response protocols [2]. However, developing countries in Southeast Asia would face significant challenges in containing an emerging pandemic. Limited surveillance coverage and diagnostic capabilities; poor communication and transportation infrastructure; and lack of resources to investigate outbreaks could cause critical delays in pandemic recognition. Wealthy countries have committed substantial funds to improve pandemic detection and response in developing countries, but tools to guide system planning, evaluation, and enhancement in such places are lacking.
Objective
We propose a framework for evaluating the ability of syndromic, laboratory-based, and other public health surveillance systems to contain an emerging influenza pandemic influenza in developing countries, and apply the framework to systems in Laos.
Dengue fever is a major cause of morbidity and mortality in the Republic of the Philippines (RP) and across the world. Early identification of geographic outbreaks can help target intervention campaigns and mitigate the severity of outbreaks. Electronic disease surveillance can improve early identification but, in most dengue endemic areas data pre-existing digital data are not available for such systems. Data must be collected and digitized specifically for electronic disease surveillance. Twitter, however, is heavily used in these areas; for example, the RP is among the top 20 producers of tweets in the world. If social media could be used as a surrogate data source for electronic disease surveillance, it would provide an inexpensive pre-digitized data source for resource-limited countries. This study investigates whether Twitter extracts can be used effectively as a surrogate data source to monitor changes in the temporal trend of dengue fever in Cebu City and the National Capitol Region surrounding Manila (NCR) in the RP.
Objective:
To determine whether Twitter data contains information on dengue-like illness and whether the temporal trend of such data correlates with the incidence dengue or dengue-like illness as identified by city and national health authorities.
A U.S. Department of Defense program is underway to assess health surveillance in resource-poor settings and to evaluate the Early Warning Outbreak Reporting System. This program has included several information-gathering trips, including a trip to Lao PDR in September, 2006.
Objective
This modeling effort will provide guidance for policy and planning decisions in developing countries in the event of an acute respiratory illness epidemic, particularly an outbreak with pandemic potential.
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