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Background:  The early identification of outbreaks of both known and novel influenza-like illnesses (ILIs) is an important public health problem., Objective:  This study aimed to describe the design and testing of a tool that detects and tracks outbreaks of both known and novel ILIs, such as the SARS-CoV-2 worldwide pandemic, accurately and early., Methods:  This paper describes the ILI Tracker algorithm that first models the daily occurrence of a set of known ILIs in hospital emergency departments in a monitored region using findings extracted from patient care reports using natural language processing. We then show how the algorithm can be extended to detect and track the presence of an unmodeled disease that may represent a novel disease outbreak., Results:  We include results based on modeling diseases like influenza, respiratory syncytial virus, human metapneumovirus, and parainfluenza for 5 emergency departments in Allegheny County, Pennsylvania, from June 1, 2014, to May 31, 2015. We also include the results of detecting the outbreak of an unmodeled disease, which in retrospect was very likely an outbreak of the enterovirus D68 (EV-D68)., Conclusions:  The results reported in this paper provide support that ILI Tracker was able to track well the incidence of 4 modeled influenza-like diseases over a 1-year period, relative to laboratory-confirmed cases, and it was computationally efficient in doing so. The system was also able to detect a likely novel outbreak of EV-D68 early in an outbreak that occurred in Allegheny County in 2014 as well as clinically characterize that outbreak disease accurately., (©John M Aronis, Ye Ye, Jessi Espino, Harry Hochheiser, Marian G Michaels, Gregory F Cooper. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 13.08.2024.)

Pathogens threaten human lives and disrupt economies around the world. This has been clearly illustrated by the current COVID-19 pandemic and outbreaks in livestock and food crops. To manage pathogen emergence and spread, cooperative engagement programs develop and strengthen biosafety, biosecurity, and biosurveillance capabilities among local researchers to detect pathogens. In this case study, we describe the efforts of a collaboration between the Los Alamos National Laboratory and the Uganda Virus Research Institute, the primary viral diagnostic laboratory in Uganda, to implement and ensure the sustainability of sequencing for biosurveillance. We describe the process of establishing this capability along with the lessons learned from both sides of the partnership to inform future cooperative engagement efforts in low- and middle-income countries. We found that by strengthening sequencing capabilities at the Uganda Virus Research Institute before the COVID-19 pandemic, the institute was able to successfully sequence SARS-CoV-2 samples and provide data to the scientific community. We highlight the need to strengthen and sustain capabilities through in-country training, collaborative research projects, and trust.

Biosurveillance for rapid detection of epidemics of diseases is a challenging area of endeavor in many respects. Hence, this area is in need of development of methodology and opens to novel methods of detection. In this study, a new simple statistical early outbreak detection approach is proposed to detect outbreaks of diseases in real time. The new approach is called LWMAT since it is based on linearly weighted moving average. Furthermore, it does not require a long baseline and partly takes into account of likely features of biosurveillance data such as nonstationary and overdispersion to some extent. Moreover, this newly proposed method is easily adapted to automated use in public health surveillance systems to monitor simultaneously large number time series of indicators associated with the relevant diseases. To compare the performance of the new method with those of some well-known outbreak detection methods, semisynthetic data with outbreaks of various magnitudes and durations are simulated by considering the weekly number of outpatient visits for influenza-like illness for the influenza seasons 2014-2015 through 2017-2018 at Centers for Disease Control and Prevention (CDC) in the United States. Under the conditions of the simulation studies, Serfling regression and Farrington flexible seem to be preferable methods for monitoring the weekly influenza data at CDC in terms of early identification of influenza outbreaks with a high probability. In addition, the newly proposed LWMAT-type methods appear to be promising and useful methods in the case of small magnitude outbreaks with a short duration., (© 2019 John Wiley & Sons, Ltd.)

Laws are fundamental tools that regulate and manage various issues to protect the rights of the people in a society. Legislation on disease surveillance enables agencies to regulate and manage public health, including preventing the spread of infectious diseases. We assessed the Infectious Disease Prevention and Control Act of Korea (IDPCA) through the lens of biosurveillance to understand its effectiveness in protecting public health. In addition, the relevant legislation and regulations of the United States and the World Health Organization were examined. The evaluation concludes that the current IDPCA is limited in terms of providing guidance for early detection of and response to hazards using integrated data and an information-sharing system. Further revision of the laws is needed to enable early detection and warning of potential threats to public health.

Most emerging or re-emerging infections are vector-borne or zoonotic and can be disseminated worldwide by infected humans or animals. They are a major public health problem and cause a great impact on economy. Zoonotic outbreaks began to be characterized in the 90s, after the creation of Europol and the FBI. Such investigations are carried by forensic pathologists and other specialists to determine whether an outbreak is natural or deliberate. This review will discuss ten zoonotic outbreaks nonrelated to wars focusing on forensic management. In conclusion, some points should be highlighted in the management of a zoonotic outbreak: (i) its diagnosis and detection by forensic pathologists and the coordination of efforts between other specialists are key factors; (ii) communication guidelines and an efficient healthcare system are crucial for any emergency response; (iii) biosafety of all specialists involved must be guaranteed., (© 2019 American Academy of Forensic Sciences.)

Background: Internet access and usage has changed how people seek and report health information. Meanwhile,infectious diseases continue to threaten humanity. The analysis of Big Data, or vast digital data, presents an opportunity to improve disease surveillance and epidemic intelligence. Epidemic intelligence contains two components: indicator based and event-based. A relatively new surveillance type has emerged called event-based Internet biosurveillance systems. These systems use information on events impacting health from Internet sources, such as social media or news aggregates. These systems circumvent the limitations of traditional reporting systems by being inexpensive, transparent, and flexible. Yet, innovations and the functionality of these systems can change rapidly., Aim: To update the current state of knowledge on event-based Internet biosurveillance systems by identifying all systems, including current functionality, with hopes to aid decision makers with whether to incorporate new methods into comprehensive programmes of surveillance., Methods: A systematic review was performed through PubMed, Scopus, and Google Scholar databases, while also including grey literature and other publication types., Results: 50 event-based Internet systems were identified, including an extraction of 15 attributes for each system, described in 99 articles. Each system uses different innovative technology and data sources to gather data, process, and disseminate data to detect infectious disease outbreaks., Conclusions: The review emphasises the importance of using both formal and informal sources for timely and accurate infectious disease outbreak surveillance, cataloguing all event-based Internet biosurveillance systems. By doing so, future researchers will be able to use this review as a library for referencing systems, with hopes of learning, building, and expanding Internet-based surveillance systems. Event-based Internet biosurveillance should act as an extension of traditional systems, to be utilised as an additional, supplemental data source to have a more comprehensive estimate of disease burden., (Copyright © 2017 Elsevier B.V. All rights reserved.)

We describe the implementation of an automated infectious disease surveillance system that uses data collected from 210 microbiologic laboratories throughout the Provence-Alpes-Côte d'Azur region in France. Each week, these facilities report bacterial species that have been isolated from patients in their area. An alarm is triggered whenever the case count for a bacterial species infection exceeds 2 SDs of the historical mean for that species at the participating laboratory. At its inception in July 2013, the system monitored 611 bacterial species. During July 1, 2013-March 20, 2016, weekly analyses of incoming surveillance data generated 34 alarms signaling possible infectious disease outbreaks; after investigation, 14 (41%) of these alarms resulted in health alerts declared by the regional health authority. We are currently improving the system by developing an Internet-based surveillance platform and extending our surveillance to include more laboratories in the region.

The Global Health Security Agenda (GHSA) was launched in February 2014 to bring countries with limited capacity into compliance with the International Health Regulations (IHR) (2005). Recent international public health events, such as the appearance of Middle Eastern respiratory syndrome coronavirus and the reappearance of Ebola in West Africa, have highlighted the importance of early detection of disease events and the interconnectedness of countries. Surveillance systems that allow early detection and recognition of signal events, a public health infrastructure that allows rapid notification and information sharing within countries and across borders, a trained epidemiologic workforce, and a laboratory network that can respond appropriately and rapidly are emerging as critical components of an early warning and response system. This article focuses on 3 aspects of the GHSA that will lead to improved capacities for the detection and response to outbreaks as required by the IHR: (1) early detection and reporting of events, (2) laboratory capacity, and (3) a trained epidemiologic workforce.

Background: Pathogen metadata includes information about where and when a pathogen was collected and the type of environment it came from. Along with genomic nucleotide sequence data, this metadata is growing rapidly and becoming a valuable resource not only for research but for biosurveillance and public health. However, current freely available tools for analyzing this data are geared towards bioinformaticians and/or do not provide summaries and visualizations needed to readily interpret results., Results: We designed a platform to easily access and summarize data about pathogen samples. The software includes a PostgreSQL database that captures metadata useful for disease outbreak investigations, and scripts for downloading and parsing data from NCBI BioSample and BioProject into the database. The software provides a user interface to query metadata and obtain standardized results in an exportable, tab-delimited format. To visually summarize results, the user interface provides a 2D histogram for user-selected metadata types and mapping of geolocated entries. The software is built on the LabKey data platform, an open-source data management platform, which enables developers to add functionalities. We demonstrate the use of the software in querying for a pathogen serovar and for genome sequence identifiers., Conclusions: This software enables users to create a local database for pathogen metadata, populate it with data from NCBI, easily query the data, and obtain visual summaries. Some of the components, such as the database, are modular and can be incorporated into other data platforms. The source code is freely available for download at https://github.com/wchangmitre/bioattribution .

National syndromic surveillance systems require optimal anomaly detection methods. For method performance comparison, we injected multi-day signals stochastically drawn from lognormal distributions into time series of aggregated daily visit counts from the U.S. Centers for Disease Control and Prevention's BioSense syndromic surveillance system. The time series corresponded to three different syndrome groups: rash, upper respiratory infection, and gastrointestinal illness. We included a sample of facilities with data reported every day and with median daily syndromic counts ⩾1 over the entire study period. We compared anomaly detection methods of five control chart adaptations, a linear regression model and a Poisson regression model. We assessed sensitivity and timeliness of these methods for detection of multi-day signals. At a daily background alert rate of 1% and 2%, the sensitivities and timeliness ranged from 24 to 77% and 3.3 to 6.1days, respectively. The overall sensitivity and timeliness increased substantially after stratification by weekday versus weekend and holiday. Adjusting the baseline syndromic count by the total number of facility visits gave consistently improved sensitivity and timeliness without stratification, but it provided better performance when combined with stratification. The daily syndrome/total-visit proportion method did not improve the performance. In general, alerting based on linear regression outperformed control chart based methods. A Poisson regression model obtained the best sensitivity in the series with high-count data., (Published by Elsevier Inc.)

The Global Health Security Agenda's objectives contain components that could help health departments address emerging public health challenges that threaten the population. As part of the agenda, partner countries with advanced public health systems will support the development of infrastructure in stakeholder health departments. To facilitate this process and augment local programs, state and local health departments may want to include concepts of health security in their public health preparedness offices in order to simultaneously build capacity. Health security programs developed by public health departments should complete projects that are closely aligned with the objectives outlined in the global agenda and that facilitate the completion of current preparedness grant requirements. This article identifies objectives and proposes tactical local projects that run parallel to the 9 primary objectives of the Global Health Security Agenda. Executing concurrent projects at the international and local levels in preparedness offices will accelerate the completion of these objectives and help prevent disease epidemics, detect health threats, and respond to public health emergencies. Additionally, future funding tied or related to health security may become more accessible to state and local health departments that have achieved these objectives.

The biosurveillance capabilities needed to rapidly detect and characterize emerging biological threats are an essential part of the Global Health Security Agenda (GHSA). The analyses of the global public health system's functioning during the 2009 H1N1 pandemic suggest that while capacities such as those identified in the GHSA are essential building blocks, the global biosurveillance system must possess 3 critical capabilities: (1) the ability to detect outbreaks and determine whether they are of significant global concern, (2) the ability to describe the epidemiologic characteristics of the pathogen responsible, and (3) the ability to track the pathogen's spread through national populations and around the world and to measure the impact of control strategies. The GHSA capacities-laboratory and diagnostic capacity, reporting networks, and so on-were essential in 2009 and surely will be in future events. But the 2009 H1N1 experience reminds us that it is not just detection but epidemiologic characterization that is necessary. Similarly, real-time biosurveillance systems are important, but as the 2009 H1N1 experience shows, they may contain inaccurate information about epidemiologic risks. Rather, the ability of scientists in Mexico, the United States, and other countries to make sense of the emerging laboratory and epidemiologic information that was critical-an example of global social capital-enabled an effective global response. Thus, to ensure that it is meeting its goals, the GHSA must track capabilities as well as capacities.

Disease outbreaks of international public health importance continue to occur regularly; detecting and tracking significant new public health threats in countries that cannot or might not report such events to the global health community is a challenge. The Centers for Disease Control and Prevention's (CDC) Global Disease Detection (GDD) Operations Center, established in early 2007, monitors infectious and non-infectious public health events to identify new or unexplained global public health threats and better position CDC to respond, if public health assistance is requested or required. At any one time, the GDD Operations Center actively monitors approximately 30-40 such public health threats; here we provide our perspective on five of the top global infectious disease threats that we were watching in 2012: 1 avian influenza A (H5N1), 2 cholera, 3 wild poliovirus, 4 enterovirus-71, and 5 extensively drug-resistant tuberculosis11†Current address: Division of Integrated Biosurveillance, Armed Forces Health Surveillance Center, US Department of Defense, Silver Spring, MD, USA.

In recent years, improved biosurveillance has become a bipartisan national security priority. As has been pointed out by the National Biosurveillance Advisory Subcommittee and others, building a national biosurveillance enterprise requires having strong biosurveillance systems at the state and local levels, and additional policies are needed to strengthen their biosurveillance capabilities. Because of the foundational role that state and local health departments play in biosurveillance, we sought to determine to what extent state and local health departments have the right capabilities in place to provide the information needed to detect and manage an epidemic or public health emergency-both for state and local outbreak management and for reporting to federal agencies during national public health crises. We also sought to identify those policies or actions that would improve state and local biosurveillance and make recommendations to federal policymakers who are interested in improving national biosurveillance capabilities., (© Mary Ann Liebert, Inc.)

Novel H1N1 influenza spread rapidly around the world in spring 2009. Few places were as widely affected as the New York metropolitan area. Emergency departments (EDs) in the region experienced daily visit increases in 2 distinct temporal peaks, with means of 36.8% and 60.7% over baseline in April and May, respectively, and became, in a sense, the "canary in the coal mine" for the rest of the country as we braced ourselves for resurgent spread in the fall. Biosurveillance efforts by public health agencies can lead to earlier detection, potentially forestalling spread of outbreaks and leading to better situational awareness by frontline medical staff and public health workers as they respond to a crisis, but biosurveillance has traditionally relied on manual reporting by hospital administrators when they are least able: in the midst of a public health crisis. This article explores the use of health information exchange networks, which enable the secure flow of clinical data among otherwise unaffiliated providers across entire regions for the purposes of clinical care, as a tool for automated biosurveillance reporting. Additionally, this article uses a health information exchange to assess H1N1's effect on ED visit rates and discusses preparedness recommendations and lessons learned from the spring 2009 H1N1 experience across 11 geographically distinct EDs in New York City that participate in the health information exchange., (Copyright (c) 2009 American College of Emergency Physicians. Published by Mosby, Inc. All rights reserved.)

The disease early warning system (DEWS) was introduced in the immediate aftermath of the 2005 earthquake in Pakistan, with the objective to undertake prompt investigation and mitigation of disease outbreaks. The DEWS network was replicated successfully during subsequent flood and earthquake disasters as well as during the 2008-09 internally displaced persons' crisis. DEWS-generated alerts, prompt investigations and timely responses had an effective contribution to the control of epidemics. Through DEWS, 1360 reported alerts during 2005-09 averted the risk of disease outbreaks through pre-emptive necessary measures, while the 187 confirmed outbreaks were effectively controlled. In the aftermath of the disasters, DEWS technology also facilitated the development of a disease-surveillance system that became an integral part of the district health system. This study aims to report the DEWS success and substantiate its lead role as a priority emergency health response intervention.

This paper investigates Bayesian modeling of unknown causes of events in the context of disease-outbreak detection. We introduce a Bayesian approach that models and detects both (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities and (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively non-informative prior probabilities. We report the results of simulation experiments which support that this modeling method can improve the detection of new disease outbreaks in a population. A key contribution of this paper is that it introduces a Bayesian approach for jointly modeling both known and unknown causes of events. Such modeling has broad applicability in medical informatics, where the space of known causes of outcomes of interest is seldom complete.

Disease surveillance systems provide early warnings of disease outbreaks before they become public health emergencies. However, pandemics containment would be challenging due to the complex immunity landscape created by multiple variants. Genomic surveillance is critical for detecting novel variants with diverse characteristics and importation/emergence times. Yet, a systematic study incorporating genomic monitoring, situation assessment, and intervention strategies is lacking in the literature. We formulate an integrated computational modeling framework to study a realistic course of action based on sequencing, analysis, and response. We study the effects of the second variant's importation time, its infectiousness advantage and, its cross-infection on the novel variant's detection time, and the resulting intervention scenarios to contain epidemics driven by two-variants dynamics. Our results illustrate the limitation in the intervention's effectiveness due to the variants' competing dynamics and provide the following insights: i) There is a set of importation times that yields the worst detection time for the second variant, which depends on the first variant's basic reproductive number; ii) When the second variant is imported relatively early with respect to the first variant, the cross-infection level does not impact the detection time of the second variant. We found that depending on the target metric, the best outcomes are attained under different interventions' regimes. Our results emphasize the importance of sustained enforcement of Non-Pharmaceutical Interventions on preventing epidemic resurgence due to importation/emergence of novel variants. We also discuss how our methods can be used to study when a novel variant emerges within a population. [ABSTRACT FROM AUTHOR]

Advancements in data analytics and the proliferation of the Internet of Things (IoT) have opened new frontiers in disease surveillance and early outbreak detection. In this paper, we present a comprehensive framework that integrates IoT-driven predictive data analytics with a secure blockchain network to revolutionize the early warning of disease outbreaks. Our system model comprises edge devices equipped with sensors for data collection and processing, coupled with a blockchain network ensuring data integrity and transparency. Within this framework, we focus on the pivotal role of a Support Vector Machine (SVM) for disease outbreak prediction, showcasing its exceptional accuracy and performance. Through extensive experimentation and comparative analysis, we demonstrate that the SVM, when embedded in our IoT ecosystem, excels in predicting disease outbreaks, outperforming other machine learning models. This approach not only enhances the timeliness and precision of outbreak detection but also facilitates informed decision-making and resource allocation. Furthermore, our system model's integration with blockchain technology ensures the secure storage and validation of prediction results, bolstering the trustworthiness of collected data. This research represents a significant leap forward in proactive disease management and public health, offering a blueprint for future endeavors in epidemiology and healthcare. It underscores the transformative potential of IoT-driven predictive analytics in safeguarding global health and well-being. [ABSTRACT FROM AUTHOR]

This study used the Baidu Index to analyze searches using discriminatory language toward the source of the outbreak during the initial stages of the COVID-19 pandemic. The study found a strong correlation between official intervention and a reduction in searches using discriminatory language. Following the official naming of the virus bt the government, the general public understood that searches using discriminatory language should be avoided, and search behavior dramatically changed. Meanwhile, this study also found that official intervention also overcame the effects of local socioeconomic factors. Regions with higher levels of socioeconomic development and higher population density had a stronger discriminatory language in internet searches, while the opposite was the case for regions with lower CPI and unemployment rates. However, after the official intervention, these factors no longer had a significant effect. It is recommended that officials worldwide provide early official media guidance during infectious disease outbreaks to reduce the use of discriminatory language and ensure the delivery of transparent and effective information to the public. [ABSTRACT FROM AUTHOR]

Lebanon has been one of the most affected countries by the Syrian humanitarian crisis. The national communicable disease surveillance was enhanced to detect outbreaks among Syrians. In this study, we aim to describe the findings of the communicable disease surveillance among Syrians in Lebanon, compare it to residents' data, and describe the implemented surveillance activities between 2013 and 2019. During the study period, data on communicable diseases was mainly collected through the routine national surveillance system and an enhanced syndromic surveillance system. Predefined case definitions and standard operating procedures were in place. Data collection included both case-based and disease-specific reporting forms. Descriptive data and incidence rates were generated. Information was disseminated through weekly reports. Activities were conducted in close collaboration with different partners. The most commonly reported diseases were: viral hepatitis A, cutaneous leishmaniasis, mumps, and measles. Hepatitis A incidence increased in 2013 and 2014 among Syrians as well as residents. For leishmaniasis, the incidence increased only among Syrians in 2013 and decreased after that. An outbreak of mumps was reported among Syrians between 2014 and 2016, with a peak in 2015 concomitant with a national outbreak. Outbreaks of measles were reported among Syrians and residents in 2013, 2018, and 2019. The infrastructure of the well-implemented surveillance system in Lebanon has been utilized to monitor the health status of Syrians in Lebanon, early detect communicable diseases among this population, and guide needed preventive and control measures. This highlights the importance of having a flexible surveillance system that can be adapted to emergencies and the importance of sharing results with involved partners. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)/coronavirus disease 2019 (COVID-19) pandemic has amplified the role of hospitals in infectious disease response and capacity building. In efforts to respond to the growing volume of cases, hospitals have become a microcosm for US pandemic response. The COVID-19 outbreak has highlighted that hospital preparedness for biological events, such as a pandemic, are often inadequate and dependent on leadership investment in biopreparedness. This article discusses the proactive decision, before COVID-19, that a Phoenix-based hospital system made to invest in high-consequence disease (HCD) preparedness. Within these efforts, a gap analysis was performed, which led to creation of an HCD subcommittee and corresponding efforts to address vulnerabilities and opportunities for improvement. From establishing enhanced personal protective equipment (PPE) and infectious disease training for frontline staff, to building an outbreak tracking mechanism for travel alerts within the electronic medical record, the HCD efforts of this hospital system created a stronger foundation to respond to biological events like the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Quantitative assessments of when infectious disease outbreaks end are crucial, as resources targeted towards outbreak responses typically remain in place until outbreaks are declared over. Recent improvements and innovations in mathematical approaches for determining when outbreaks end provide public health authorities with more confidence when making end-of-outbreak declarations. Although quantitative analyses of outbreaks have a long history, more complex mathematical and statistical methodologies for analysing outbreak data were developed early in the 20th century and continue to be refined. Historically, such methodologies focused primarily on factors affecting the early and middle phases of an outbreak, with less attention given to determining how and when outbreaks end. This review discusses mathematical modelling methods from the last 20 years that have been developed for determining the ends of infectious disease outbreaks, and considers factors that affect the accuracy of such determinations. When disease surveillance systems provide timely and representative data to inform models, the timings of end-of-outbreak declarations can be fine-tuned to allow outbreaks to be declared over quickly and with a low risk of being incorrect. Premature declarations that outbreaks are over can undermine earlier achievements in disease control and may result in a resurgence of cases, but unnecessary delays in declaring outbreaks over can cause significant economic and social harm. Appropriate declarations that balance the benefits of relaxing control measures against the risk of a surge in cases allow public health resources to be conserved (and economic and social pressures to be reduced) while limiting the potential for additional transmission. [ABSTRACT FROM AUTHOR]

With growing concerns about international spread of disease and expanding use of early disease detection surveillance methods, the field of syndromic surveillance has received increased attention over the last decade. The purpose of this article is to clarify the various meanings that have been assigned to the term syndromic surveillance and to propose a refined categorization of the characteristics of these systems. Existing literature and conference proceedings were examined on syndromic surveillance from 1998 to 2010, focusing on low- and middle-income settings. Based on the 36 unique definitions of syndromic surveillance found in the literature, five commonly accepted principles of syndromic surveillance systems were identified, as well as two fundamental categories: specific and non-specific disease detection. Ultimately, the proposed categorization of syndromic surveillance distinguishes between systems that focus on detecting defined syndromes or outcomes of interest and those that aim to uncover non-specific trends that suggest an outbreak may be occurring. By providing an accurate and comprehensive picture of this field's capabilities, and differentiating among system types, a unified understanding of the syndromic surveillance field can be developed, encouraging the adoption, investment in, and implementation of these systems in settings that need bolstered surveillance capacity, particularly low- and middle-income countries.

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With growing concerns about international spread of disease and expanding use of early disease detection surveillance methods, the field of syndromic surveillance has received increased attention over the last decade. The purpose of this article is to clarify the various meanings that have been assigned to the term syndromic surveillance and to propose a refined categorization of the characteristics of these systems. Existing literature and conference proceedings were examined on syndromic surveillance from 1998 to 2010, focusing on low- and middle-income settings. Based on the 36 unique definitions of syndromic surveillance found in the literature, five commonly accepted principles of syndromic surveillance systems were identified, as well as two fundamental categories: specific and non-specific disease detection. Ultimately, the proposed categorization of syndromic surveillance distinguishes between systems that focus on detecting defined syndromes or outcomes of interest and those that aim to uncover non-specific trends that suggest an outbreak may be occurring. By providing an accurate and comprehensive picture of this field’s capabilities, and differentiating among system types, a unified understanding of the syndromic surveillance field can be developed, encouraging the adoption, investment in, and implementation of these systems in settings that need bolstered surveillance capacity, particularly low- and middle-income countries.

A strategic multilateral dialogue related to biosecurity risks in Southeast Asia, established in 2014, now includes participants from Singapore, Malaysia, Indonesia, Thailand, Philippines, and the United States. This dialogue is conducted at the nonministerial level, enabling participants to engage without the constraints of operating in their official capacities. Participants reflect on mechanisms to detect, mitigate, and respond to biosecurity risks and highlight biosecurity issues for national leadership. Participants have also identified factors to improve regional and global biosecurity, including improved engagement and collaboration across relevant ministries and agencies, sustainable funding for biosecurity programs, enhanced information sharing for communicable diseases, and increased engagement in international biosecurity forums. [ABSTRACT FROM AUTHOR]

Determining optimal surveillance networks for an emerging pathogen is difficult since it is not known beforehand what the characteristics of a pathogen will be or where it will emerge. The resources for surveillance of infectious diseases in animals and wildlife are often limited and mathematical modeling can play a supporting role in examining a wide range of scenarios of pathogen spread. We demonstrate how a hierarchy of mathematical and statistical tools can be used in surveillance planning help guide successful surveillance and mitigation policies for a wide range of zoonotic pathogens. The model forecasts can help clarify the complexities of potential scenarios, and optimize biosurveillance programs for rapidly detecting infectious diseases. Using the highly pathogenic zoonotic H5N1 avian influenza 2006-2007 epidemic in Nigeria as an example, we determined the risk for infection for localized areas in an outbreak and designed biosurveillance stations that are effective for different pathogen strains and a range of possible outbreak locations. We created a general multi-scale, multi-host stochastic SEIR epidemiological network model, with both short and long-range movement, to simulate the spread of an infectious disease through Nigerian human, poultry, backyard duck, and wild bird populations. We chose parameter ranges specific to avian influenza (but not to a particular strain) and used a Latin hypercube sample experimental design to investigate epidemic predictions in a thousand simulations. We ranked the risk of local regions by the number of times they became infected in the ensemble of simulations. These spatial statistics were then complied into a potential risk map of infection. Finally, we validated the results with a known outbreak, using spatial analysis of all the simulation runs to show the progression matched closely with the observed location of the farms infected in the 2006-2007 epidemic. [ABSTRACT FROM AUTHOR]

Abstract: We consider the problem of disease surveillance, early detection, and the requirement for rapid response. If it is important to implement a certain intervention before a threshold number of individuals become infected, then we must determine the expected time (after detection) at which this will occur. We define this as the critical reaction time. Suppose a disease outbreak within a closed population N may be detected when a certain number I 0 individuals are infected. Now assume that a specific intervention should be applied before this number increases to a level I 1 > I 0. The first time at which I 1 is reached is called the critical reaction time. We derive simple asymptotic formulae for the expectation, variance and full distribution of the reaction time T(I 1, I 0, N) in the framework of a stochastic SI (susceptible/infected) model. Asymptotically these results are in agreement with the mean-field model and Gaussian approximations. Further corrections to these approximations are obtained that give a better fit to the tails of the distribution of reaction times, which are important for the modelling of extreme events. [Copyright &y& Elsevier]

A cornerstone of effective disease surveillance programs comprises the early identification of infectious threats and the subsequent rapid response to prevent further spread. Effectively identifying, tracking and responding to these threats is often difficult and requires international cooperation due to the rapidity with which diseases cross national borders and spread throughout the global community as a result of travel and migration by humans and animals. From Oct.1, 2008 to Sept. 30, 2009, the United States Department of Defense's (DoD) Armed Forces Health Surveillance Center Global Emerging Infections Surveillance and Response System (AFHSC-GEIS) identified 76 outbreaks in 53 countries. Emerging infectious disease outbreaks were identified by the global network and included a wide spectrum of support activities in collaboration with host country partners, several of which were in direct support of the World Health Organization's (WHO) International Health Regulations (IHR) (2005). The network also supported military forces around the world affected by the novel influenza A/H1N1 pandemic of 2009. With IHR (2005) as the guiding framework for action, the AFHSC-GEIS network of international partners and overseas research laboratories continues to develop into a far-reaching system for identifying, analyzing and responding to emerging disease threats. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: Early detection of infectious disease outbreaks is crucial to protecting the public health of a society. Online news articles provide timely information on disease outbreaks worldwide. In this study, we investigated automated detection of articles relevant to disease outbreaks using machine learning classifiers. In a real-life setting, it is expensive to prepare a training data set for classifiers, which usually consists of manually labeled relevant and irrelevant articles. To mitigate this challenge, we examined the use of randomly sampled unlabeled articles as well as labeled relevant articles. Methods: Naïve Bayes and Support Vector Machine (SVM) classifiers were trained on 149 relevant and 149 or more randomly sampled unlabeled articles. Diverse classifiers were trained by varying the number of sampled unlabeled articles and also the number of word features. The trained classifiers were applied to 15 thousand articles published over 15 days. Top-ranked articles from each classifier were pooled and the resulting set of 1337 articles was reviewed by an expert analyst to evaluate the classifiers. Results: Daily averages of areas under ROC curves (AUCs) over the 15-day evaluation period were 0.841 and 0.836, respectively, for the naïve Bayes and SVM classifier. We referenced a database of disease outbreak reports to confirm that this evaluation data set resulted from the pooling method indeed covered incidents recorded in the database during the evaluation period. Conclusions: The proposed text classification framework utilizing randomly sampled unlabeled articles can facilitate a cost-effective approach to training machine learning classifiers in a real-life Internet-based biosurveillance project. We plan to examine this framework further using larger data sets and using articles in non-English languages. [Copyright &y& Elsevier]

The article presents an analysis on Bayesian anomaly approach prior to disease outbreak detection to provide a subsequent probability of the outbreak. The study describes the algorithm in disease-outbreak setting and the semi-informative prior used in the study was based on the baseline prior. Results indicate that the performance of the Bayesian method is better than the control chart method when baseline periods are employed in estimating the baseline behavior to prevent seasonal effects.

The article provides an overview of the current activities in the syndromic biosurveillance in Texas. It states that the Texas Association of Local Health Officials (TALHO) has built a network of biosurveillance systems to serve public health agencies in obtaining alerts and reports of health threats or outbreaks. It adds that the TALHO system copies patients' medical records from hospital management systems to their databases to analyze any health anomaly.

The purposeful acts of bioterrorism and the emergence of new pathogens call for developing efficient biosurveillance systems that are capable of detecting health security threats and localizing their origins. For this sake, several biosurveillance systems have been developed and deployed in different regions around the world. The objectives of these systems are to efficiently detect threats and effectively manage responses. Although modern biosurveillance systems start to leverage recent advances in internet-of-things and communication technologies to detect natural biological outbreaks, it is not evident whether these systems can be generalized to detect threats related to purposeful biological attacks. As opposed to natural disease outbreaks, biological attacks can be triggered concurrently at different locations and in vital areas that are traversed by a large number of humans. In this paper, we propose an efficient framework to detect multiple concurrent threats and localize their origins based on real-time information and mobile-edge computing support. The proposed framework is organized into a hierarchical architecture. At the micro-level, monitored humans are equipped with wearable sensors that periodically capture the monitored human vital signs. To process the captured information efficiently, the monitored environment is partitioned into cells. Each cell is assigned a mobile-edge computing (MEC) component that is responsible for processing the monitoring information within its cell. The MEC components then send the processed information to a super component for final processing and to detect possible threats. The experimental results show that the proposed framework accurately detects multiple concurrent health security threats and localize their origins. [ABSTRACT FROM AUTHOR]