Infectious Disease

Data obtained through public health surveillance systems are used to detect and locate clusters of cases of diseases in space-time, which may indicate the occurrence of an outbreak or an epidemic. We present a methodology based on adaptive likelihood ratios to compare the null hypothesis (no outbreaks) against the alternative hypothesis (presence of an emerging disease cluster).

Objective

Disease surveillance is based on methodologies to detect outbreaks as soon as possible, given an acceptable false alarm rate. We present an adaptive likelihood ratio method based on the properties of the martingale structure which allows the determination of an upper limit for the false alarm rate.

A devastating cholera outbreak began in Haiti in 2010. Sequencing of Vibrio cholerae isolates showed that the epidemic was likely the result of the introduction of cholera from a distant geographic source. The same strain of cholera was detected in other countries within 100 days. The unique instigation and geographic spread of this epidemic highlight the need for improvements in timely global outbreak surveillance. Novel information sources have been shown to provide early information about public health events and disease epidemiology. Particularly, volume of Internet metrics such as web searches or micro-blogs have been shown to be a good corollary for public health events. In this study, we evaluate geographic trends in online social media following an infectious disease outbreak to determine whether this may enable prediction of secondary outbreak locations.

Objective

To evaluate the association between and develop a risk model relating geographic trends of social media and spread of an infectious disease outbreak.

Informal surveillance systems like HealthMap are effective at the early detection of outbreaks. However, reliance on informal sources such as news media makes the efficiency of these systems vulnerable to newsroom constraints, namely high-profile disease events drawing reporting resources at the expense of other potential outbreaks and diminished staff over weekends and holidays. To our knowledge, this effect on informal or syndromic surveillance systems has yet to be studied.

Objective

Reporting about large public health events may reduce effective disease surveillance by syndromic or informal surveillance systems. The goal is to determine to what extent this problem exists and characterize situations in which it is likely to occur.

Taiwan had established a nation-wide emergency department (ED)-based syndromic surveillance system since 2004, with a mean detection sensitivity of 0.67 in 2004-06 [1]. However, this system may not represent the true epidemic situation of infectious disease in community, particularly those who don't seek medical care [2]. Moreover, the epidemiological settings, sources of the infection and social network all together may still facilitate the transmissions. These rooted problems cannot be rapidly solved.

Objective

This study has two specific aims:

(1) to establish a web-based, public-access infectious disease reporting system (www.eid.url.tw), using newly designed public syndrome groups and based on computational and participatory epidemiology

(2) to evaluate this system by comparing the epidemiological patterns with national-wide electronic health-database and traditional passive surveillance systems from Taiwan-CDC.

Disease screening facilitates the reduction of disease prevalence in two ways: (1) by preventing transmission and (2) allowing for treatment of infected individuals. Hospitals choosing an optimal screening level must weigh the benefits of decreased prevalence against the costs of screening and subsequent treatment. If screening decisions are made by multiple decision units (DU, e.g., hospital wards), they must consider the disease prevalence among admissions to their unit. Thus, the screening decisions made by one DU directly affect the disease prevalence of the other units when patients are shared. Because of this interdependent relationship, one DU may have an incentive to "free-ride" off the screening decisions of others as the disease prevalence declines. On the other hand, DUs may find it futile to invest in screening if they admit a large number of infected patients from neighbors who fail to screen properly. This problem is important in determining the optimal level of unit autonomy, since increasing a unit's level of autonomy in screening effectively increases the total number of DUs.

Objective

To analyze optimal disease screening in strategic multi-unit settings, and determine how the level of unit autonomy may effect screening decisions.

Argus is an event-based, multi-lingual surveillance system which captures and analyzes information from publicly available Internet media. Argus produces reports that summarize and contextualize indications and warning (I&W) of emerging threats, and makes these reports available to the system's users. The significance of the Escherichia coli (EHEC) outbreak analyzed here lies primarily in the fact that it raised epidemiological questions and public health infrastructure concerns that have yet to be resolved, and required the development of new resources for detecting and responding to newly-emerging epidemics.

Objective

To demonstrate how event-based biosurveillance, using direct and indirect I&W of disease, provides early warning and situational awareness of the emergence of infectious diseases that have the potential to cause social disruption and negatively impact public health infrastructure, trade, and the economy. Specifically, tracking of I&W during the 2011 enterohaemorrhagic EHEC O104:H4 outbreak in Germany and Europe was selected to illustrate this methodology.

There are currently no federal laws mandating the reporting of infectious diseases to public health authorities. Reporting requirements reside at the state level and such laws do not apply to federal agencies including the VA. Heretofore, VA's reporting of infectious diseases to public health authorities has been strictly voluntary, and has been accomplished via traditional methods (phone, mail, and fax) that are highly prone to human error, create a significant administrative burden, and do not adequately safeguard the privacy of Veterans' data. Previously, without a reporting mandate applicable to VA facilities, public health authorities have had an incomplete picture of the VA contribution to the overall infectious disease burden existing in the larger population. Moreover, at a national level, the VA has not had the ability to monitor the prevalence of the various infectious diseases within its own 151 hospitals and 827 community-based outpatient clinics. Nor has the VA been able to meet the spirit of the Health Information Technology for Clinical and Economic Health Act's Meaningful Use requirements, mandating electronic exchange of information.

Objective

In June 2013, in anticipation of the passage of proposed federal legislation (S 875 and HR 1792), the Department of Veterans Affairs (VA) issued a Directive requiring mandatory reporting of infectious diseases to various public health authorities (VHA Directive 2013-008). In terms of implementation strategies, the ideal is to build on an existing technology, optimize the quality and completeness of reporting, and minimize additional work burdens on VA staff.

This abstract describes an Electronic Surveillance System for Infectious Disease Outbreaks used by all federal levels in Germany and comments on timelyness and comprehensiveness of informations about outbreak settings and infection sources.

As part of the epidemiological investigation of an outbreak of Q fever in a factory in Scotland, we aimed to utilise a spatial scan statistic to aid in identification of areas associated with increased relative risk of infection.