Surveillance Systems

In December 2009, Taiwan’s CDC stopped its sentinel physician surveillance system. Currently, infectious disease surveillance systems in Taiwan rely on not only the national notifiable disease surveillance system but also real-time outbreak and disease surveillance (RODS) from emergency rooms, and the outpatient and hospitalization surveillance system from National Health Insurance data. However, the timeliness of data exchange and the number of monitored syndromic groups are limited. The spatial resolution of monitoring units is also too coarse, at the city level. Those systems can capture the epidemic situation at the nationwide level, but have difficulty reflecting the real epidemic situation in communities in a timely manner. Based on past epidemic experience, daily and small area surveillance can detect early aberrations. In addition, emerging infectious diseases do not have typical symptoms at the early stage of an epidemic. Traditional disease-based reporting systems cannot capture this kind of signal. Therefore, we have set up a clinic-based surveillance system to monitor 23 kinds of syndromic groups. Through longitudinal surveillance and sensitive statistical models, the system can automatically remind medical practitioners of the epidemic situation of different syndromic groups, and will help them remain vigilant to susceptible patients. Local health departments can take action based on aberrations to prevent an epidemic from getting worse and to reduce the severity of the infected cases.

Objective: Sentinel physician surveillance in the communities has played an important role in detecting early aberrations in epidemics. The traditional approach is to ask primary care physicians to actively report some diseases such as influenza-like illness (ILI), and hand, foot, and mouth disease (HFMD) to health authorities on a weekly basis. However, this is labor-intensive and time-consuming work. In this study, we try to set up an automatic sentinel surveillance system to detect 23 syndromic groups in the communites.

Kerala is a small state in India, having a population of only 34 million (2011 census) but with excellent health indices, human development index and a worthy model of decentralised governance. Integrated Disease Surveillance Program, a centrally supported surveillance program, in place since 2006 and have carved its own niche among the best performing states, in India. Laboratory confirmation of health related events/disease outbreaks is the key to successful and timely containment of such events, which need support from a wide range of Laboratories-from Primary care centers to advanced research laboratories, including private sector. In a resource constraint setting, an effective model of Partnership have helped this state in achieving great heights. Networking with laboratories of Medical Education Department, and Premier Private sector laboratories, Financing equipment and reagents through decentralised governance program, resource sharing with other National programs, Laboratories of Food Safety, Fisheries and Water authorities have resulted in laboratory confirmation of public health events to the extend of 75-80% in the past 5 years in the state. Etiological confirmation accelerated response measures, often multidisciplinary, involving Human health sector, Animal Health, Agriculture, wild life and even environmental sectors, all relevant in One Health context.

Objective: To prove the role of partnerships in Disease Surveillance and Response to emerging public health threats in Kerala state, India.

During an influenza pandemic, when hospitals and doctors'™ offices are or are perceived to be overwhelmed, many ill people may not seek medical care. People may also avoid medical facilities due to fear of contracting influenza or transmitting it to others. Therefore, syndromic methods for monitoring illness outside of health care settings are important adjuncts to traditional disease reporting. Monitoring absenteeism trends in schools and workplaces provide the archetypal examples for such approaches. NIOSH's early experience with workplace absenteeism surveillance during the 2009 - 2010 H1N1 pandemic established that workplace absenteeism correlates well with the occurrence of influenza-like illness (ILI) and significant increases in absenteeism can signal concomitant peaks in disease activity. It also demonstrated that, while population-based absenteeism surveillance using nationally representative survey data is not as timely, it is more valid and reliable than surveillance based on data from sentinel worksites.1 In 2017, NIOSH implemented population-based, monthly surveillance of health-related workplace absenteeism among full-time workers.

Objective: To describe the methodology of the National Institute for Occupational Safety and Health (NIOSH) system for national surveillance of health-related workplace absenteeism among full-time workers in the United States and to present initial findings from October through July of the 2017 - 2018 influenza season.

Anthrax is an acute infectious disease of historical importance caused by Bacillus anthracis (B. anthracis), a spore-forming, soil-borne bacterium with a remarkable ability to persist in the environment. Anthrax is endemic in many countries, including Georgia. Laboratory of the Ministry of Agriculture (LMA) has been actively working on the disease science 1907 and constantly improving diagnostics. In 2009-2017 the laboratory participated in cooperative biological studies. One of the main objectives of these studies was to improve Anthrax laboratory diagnostics in order to properly monitor the prevalence and distribution of the disease in Georgia.

Objective: One of the main objectives of these studies was to improve Anthrax laboratory diagnostics in order to properly monitor the prevalence and distribution of the disease in Georgia. For this geographic information system (GIS) was implemented and used as the additional tool to the laboratory tests for better visualization, summary results and risk assessment.

The Acute Care Enhanced Surveillance (ACES) system provides syndromic surveillance for Ontario's acute care hospitals. ACES receives over 99% of acute care records for emergency department (ED) visits; mean daily volume is 17,500 visits. ACES uses a maximum entropy classifier and generates more than 80 standard syndromes, fifteen of which are actively monitored for aberrational activity and are considered of higher public health relevance, including RESP (respiratory infection, non-croup), ILI (influenza-like illness), TOX (toxicological, chemical/drug exposure), AST (asthma), OPI (opioid exposure), CELL (cellulitis), GASTRO (gastroenteritis), ENVIRO (environmental, heat/cold exposure), MH (mental health), EOH (alcohol intoxication), DERM (rash), and SEP (bacteremia, sepsis). Syndromic surveillance provides a salient source of public health surveillance during extreme heat events; monitoring real-time ED visits can inform local public health authorities of health impacts, provide situation awareness to initiate and/or inform public health response, and help decision-makers allocate resources according to geographic (or demographic) vulnerability. While the use of syndromic surveillance has been well-characterized to monitor infectious disease outbreaks, its use to monitor the heat- health impacts is relatively novel for ACES users, specifically local public health authorities. This report describes the data collected during an extended extreme heat event in Ontario, Canada, to highlight the value of syndromic surveillance during extreme heat events and make recommendations regarding incorporating ACES data into routine workflows.

Objective: To describe the lessons learned for public health decision-makers from an analysis of Acute Care Enhanced Surveillance (ACES) data for the heatwaves experienced in Ontario, Canada in the summer of 2018.

India has an Integrated Disease Surveillance project that reports key communicable and infectious diseases at the district and sub-district level. However, recent reviews suggest structural and functional deficiencies resulting in poor data quality [1]. Hence evidence-based actions are often delayed. Piramal Swasthya in collaboration with Government of Andhra Pradesh launched a mobile medical unit (MMU) programme in 2016. This Mobile medical service delivers primary care services to rural population besides reporting and alerting unusual health events to district and state health authorities for timely and appropriate action. The MMU service in the Indian state of Andhra Pradesh is one of the oldest and largest public-private initiatives in India. Two hundred and ninety-two MMUs provide fixed-day services to nearly 20,000 patients a day across 14,000 villages in rural Andhra Pradesh. Every day an MMU equipped with medical (a doctor) and non-medical (1 nurse, 1 registration officer, 1 driver, 1 pharmacist, 1 lab technician, 1 driver) staff visit 2 service points (villages) as per prefixed route map. Each MMU also has its own mobile tablet operated by registration officer for capturing patient details. The core services delivered through MMUs are the diagnosis, treatment, counseling, and free drug distribution to the beneficiaries suffering from common ailments ranging from seasonal diseases to acute communicable and common chronic non-communicable diseases. The routinely collected patient data is daily synchronized on a centrally managed data servers. 

Objective: We report the findings of Andhra Pradesh state's mobile medical service programme and how It is currently used to strengthen the disease surveillance mechanisms at the village level.

Syndromic data is shifting the way surveillance has been done traditionally. Most recently, surveillance has gone beyond city limits and county boundary lines. In southeast Texas, a regional consortium of public health agencies and stakeholders in the 13-County area governs the local ESSENCE system. The Houston Health Department, (HHD) is responsible for deploying ESSENCE to the entire region. To effectively monitor the health of the region's population, a need arose to establish clear guidelines for disease investigation and data sharing triggered by syndromic surveillance across the area. Since Houston's instance of ESSENCE serves all 13 counties, the consortium instituted a cross- jurisdictional etiquette group. The purpose of the group is to determine the standard protocol for responding to ESSENCE alerts and best practices for data sharing and use among consortium members.

Objective: To demonstrate the importance of a cross-jurisdictional etiquette workgroup in the Texas Southeast region that leverages on the Syndromic Surveillance Consortium. To promote data sharing and communicate the findings of disease to assist rapid investigation and data sharing.

Reducing HIV incidence requires a precision public health approach encompassing prevention campaigns, targeted interventions, and next-generation surveillance through multimodal instruments, including sequencing. Molecular epidemiology methods (phylogenetics and phylodynamics) have recently gained traction for use in identifying and tracking epidemic transmission clusters, as well as reconstructing the demographic history of viral pathogen populations. However, such methods are not equipped to identify both transmission clusters and their corresponding dynamics in real time, and transmission clusters are assumed to be unrealistically static over the course of the epidemic. We will focus on the ongoing HIV epidemic in Florida, which has one of the highest HIV incidence rates in the United States. Although key HIV transmission risk groups have been identified in Florida through classical epidemiology surveillance methods, there remains a critical need for detection and tracking of expanding transmission clusters in near-real time.

Objective: We aim to 1) develop and implement a novel theoretical and technical framework able to dynamically model HIV transmission clusters in near-real time; 2) validate the model with real data; and 3) host focus groups with governmental stakeholders to identify optimal strategies for precision public health interventions.

In response to the February 2016 Zika virus (ZIKV) outbreak, an inter-agency agreement between the U.S. Centers for Disease Control and Prevention (CDC) and U.S. Agency for International Development (USAID) commissioned further research on the epidemiology, transmission, diagnosis, and birth defects associated with ZIKV. The surveillance and research activities conducted included ecology studies focusing on the transmission dynamics; pregnancy and infant cohort studies to look for birth defects, developmental outcomes and risk factors associated with ZIKV infection; and laboratory studies evaluating the usefulness of multiple Zika diagnostic platforms. These studies were established by either setting up new systems, or leveraging on existing surveillance systems to include ZIKV research specific data elements. Conducted using country-specific protocols, these research systems included key data elements for cross-site analysis. Challenges faced included collection of non-standardized data, differing functional requirements, varying security and confidentiality protocols and limitations of informatics infrastructure. These challenges highlight an opportunity to evaluate and present the informatics-based components necessary to rapidly deploy surveillance and research study activities during a global health emergency situation. We highlight the key challenges and presents strategies for setting up rapid surveillance and research study activities. Additional areas of focus also include system architecture, global partnerships, and workforce development.

Objective: To assess challenges of establishing surveillance and research study systems and present strategies for rapid deployment in global health for the outbreak response.

Influenza surveillance has been a major focus of Data Science efforts to use novel data sources in population and public health. This interest reflects the public health utility of timely identification of flu outbreaks and characterization of their severity and dynamics. Such information can inform mitigation efforts including the targeting of interventions and public health messaging. The key requirement for influenza surveillance systems based on novel data streams is establishing their relationship with underlying influenza patterns. We assess the potential utility of wearable mHealth devices by establishing the aggregate responses to ILI along three dimensions: steps, sleep, and heart rate. Surveillance based on mHealth devices may have several desirable characteristics including 1) high resolution individual-level responses that can be prospectively analyzed in near real-time, 2) indications of physiological responses to flu that should be resistant to feedback loops, changes in health seeking behavior, and changes in technology use, 3) a growing user-base often organized into networks by providers or payers with increasing data quality and completeness, 4) the ability to query individual users underlying aggregate signals, and 5) demographic and geographic information enabling detailed characterization. These features suggest the potential of mHealth data to deliver faster, more locally relevant surveillance systems.

Objective: To describe population-level response to influenza-like illness (ILI) as measured by wearable mobile health (mHealth) devices across multiple dimensions including steps, heart rate, and sleep duration and to assess the potential for using large networks of mHealth devices for influenza surveillance.