Event-based surveillance during EXPO Milan 2015. Rationale, tools, procedures, and initial results

More than 21 million participants attended EXPO Milan from May to October 2015, making it one of the largest protracted mass gathering events in Europe. Given the expected national and international population movement and health security issues associated with this event, Italy fully implemented, for the first time, an event-based surveillance (EBS) system focusing on naturally occurring infectious diseases and the monitoring of biological agents with potential for intentional release. The system started its pilot phase in March 2015 and was fully operational between April and November 2015. In order to set the specific objectives of the EBS system, and its complementary role to indicator-based surveillance, we defined a list of priority diseases and conditions. This list was designed on the basis of the probability and possible public health impact of infectious disease transmission, existing statutory surveillance systems in place, and any surveillance enhancements during the mass gathering event. This article reports the methodology used to design the EBS system for EXPO Milan and the results of 8 months of surveillance

Public Health and Epidemiology Informatics: Recent Research and Trends in the United States

Objectives To survey advances in public health and epidemiology informatics over the past three years. Methods We conducted a review of English-language research works conducted in the domain of public health informatics (PHI), and published in MEDLINE between January 2012 and December 2014, where information and communication technology (ICT) was a primary subject, or a main component of the study methodology. Selected articles were synthesized using a thematic analysis using the Essential Services of Public Health as a typology. Results Based on themes that emerged, we organized the advances into a model where applications that support the Essential Services are, in turn, supported by a socio-technical infrastructure that relies on government policies and ethical principles. That infrastructure, in turn, depends upon education and training of the public health workforce, development that creates novel or adapts existing infrastructure, and research that evaluates the success of the infrastructure. Finally, the persistence and growth of infrastructure depends on financial sustainability. Conclusions Public health informatics is a field that is growing in breadth, depth, and complexity. Several Essential Services have benefited from informatics, notably, “Monitor Health,” “Diagnose & Investigate,” and “Evaluate.” Yet many Essential Services still have not yet benefited from advances such as maturing electronic health record systems, interoperability amongst health information systems, analytics for population health management, use of social media among consumers, and educational certification in clinical informatics. There is much work to be done to further advance the science of PHI as well as its impact on public health practice

Research Note: Surveillance in contemporary health and social care: friend or foe?

Peer reviewedPublisher PD

What's unusual in online disease outbreak news?

Background: Accurate and timely detection of public health events of international concern is necessary to help support risk assessment and response and save lives. Novel event-based methods that use the World Wide Web as a signal source offer potential to extend health surveillance into areas where traditional indicator networks are lacking. In this paper we address the issue of systematically evaluating online health news to support automatic alerting using daily disease-country counts text mined from real world data using BioCaster. For 18 data sets produced by BioCaster, we compare 5 aberration detection algorithms (EARS C2, C3, W2, F-statistic and EWMA) for performance against expert moderated ProMED-mail postings. Results: We report sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), mean alerts/100 days and F1, at 95% confidence interval (CI) for 287 ProMED-mail postings on 18 outbreaks across 14 countries over a 366 day period. Results indicate that W2 had the best F1 with a slight benefit for day of week effect over C2. In drill down analysis we indicate issues arising from the granular choice of country-level modeling, sudden drops in reporting due to day of week effects and reporting bias. Automatic alerting has been implemented in BioCaster available from http://born.nii.ac.jp. Conclusions: Online health news alerts have the potential to enhance manual analytical methods by increasing throughput, timeliness and detection rates. Systematic evaluation of health news aberrations is necessary to push forward our understanding of the complex relationship between news report volumes and case numbers and to select the best performing features and algorithms

Analysis of Heterogeneous Data Sources for Veterinary Syndromic Surveillance to Improve Public Health Response and Aid Decision Making

The standard technique of implementing veterinary syndromic surveillance (VSyS) is the detection of temporal or spatial anomalies in the occurrence of health incidents above a set threshold in an observed population using the Frequentist modelling approach. Most implementation of this technique also requires the removal of historical outbreaks from the datasets to construct baselines. Unfortunately, some challenges exist, such as data scarcity, delayed reporting of health incidents, and variable data availability from sources, which make the VSyS implementation and alarm interpretation difficult, particularly when quantifying surveillance risk with associated uncertainties. This problem indicates that alternate or improved techniques are required to interpret alarms when incorporating uncertainties and previous knowledge of health incidents into the model to inform decision-making. Such methods must be capable of retaining historical outbreaks to assess surveillance risk. In this research work, the Stochastic Quantitative Risk Assessment (SQRA) model was proposed and developed for detecting and quantifying the risk of disease outbreaks with associated uncertainties using the Bayesian probabilistic approach in PyMC3. A systematic and comparative evaluation of the available techniques was used to select the most appropriate method and software packages based on flexibility, efficiency, usability, ability to retain historical outbreaks, and the ease of developing a model in Python. The social media datasets (Twitter) were first applied to infer a possible disease outbreak incident with associated uncertainties. Then, the inferences were subsequently updated using datasets from the clinical and other healthcare sources to reduce uncertainties in the model and validate the outbreak. Therefore, the proposed SQRA model demonstrates an approach that uses the successive refinement of analysis of different data streams to define a changepoint signalling a disease outbreak. The SQRA model was tested and validated to show the method's effectiveness and reliability for differentiating and identifying risk regions with corresponding changepoints to interpret an ongoing disease outbreak incident. This demonstrates that a technique such as the SQRA method obtained through this research may aid in overcoming some of the difficulties identified in VSyS, such as data scarcity, delayed reporting, and variable availability of data from sources, ultimately contributing to science and practice

Technical handbook for dengue surveillance, dengue outbreak prediction/detection and outbreak response (Model contingency plan)

This handbook was produced by TDR together with WHO’s Neglected Tropical Diseases (NTD) Department and WHO regional offices in the context of a European Union-financed research programme, the International Research Consortium on Dengue Risk Assessment, Management and Surveillance (IDAMS), to develop an evidence-based handbook for the early outbreak detec-tion and management of dengue fever outbreaks. The handbook targets public health providers, in particular those at national level. It is not an implementation guideline, but a framework for developing a national contingency plan with local adaptations that acknowledge micro-level pro-gramme components. Response planning requires contextual details encompassing the structure of the health and vector control services, the availability of infrastructure and budget, and human resources, and the willingness of staff to cooperate, among others. The aim of this “model contingency plan” is to assist programme managers and planners in devel-oping a national, context-specific, dengue outbreak response plan in order to: (a) detect a dengue outbreak at an early stage through clearly defined and validated alarm signals; (b) precisely define when a dengue outbreak has started; and (c) organize an early response to the alarm signals or an “emergency response” once an outbreak has started. A summary of this document, "Dengue Contingency Planning: From Research to Policy and Practice" (PNTD-D-16-00407R1) has also been published in PLOS Neglected Tropical Diseases

International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance: Building the Future of Public Health Surveillance

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-04204pubpub1117

Syndromic Surveillance for Bioterrorism-related Inhalation Anthrax in an Emergency Department Population

Objective: To utilize clinical data from emergency department admissions and published clinical case reports from the 2001 bioterrorism-related inhalation anthrax (IA) outbreak to develop a detection algorithm for syndromic surveillance. Methods: A comprehensive review of case reports and medical charts was undertaken to identify clinical characteristics of IA. Eleven historical cases were compared to 160 patients meeting a syndromic case definition based on acute respiratory failure and the presence of mediastinal widening or lymphadenopathy on a chest radiograph. Results: The majority of syndromic group patients admitted were due to motor vehicle accident (52%), followed by fall (10%), or other causes (4%). Positive culture for a gram positive rod was the most predictive feature for anthrax cases. Among signs and symptoms, myalgias, fatigue, sweats, nausea, headache, cough, confusion, fever, and chest pain were found to best discriminate between IA and syndromic patients. When radiological findings were examined, consolidation and pleural effusions were both significantly higher among IA patients. A four step algorithm was devised based on combinations of the most accurate clinical features and the availability of data during the course of typical patient care. The sensitivity (91%) and specificity (96%) of the algorithm were found to be high. Conclusions: Surveillance based on late stage findings of IA can be used by clinicians to identify high risk patients in the Emergency Department using a simple decision tree. Implications for public health: Monitoring pre-diagnostic indicators of IA can provide enough credible evidence to initiate an epidemiological investigation leading to earlier outbreak detection and more effective public health response

Ready or Not? Protecting the Public's Health From Diseases, Disasters, and Bioterrorism, 2009

Based on ten indicators, assesses progress in the readiness of states, federal government, and hospitals to respond to public health emergencies, with a focus on the H1N1 flu. Outlines improvements and concerns in funding, accountability, and other areas