Surveillance Systems

The Lao PDR is aiming for measles elimination despite ongoing outbreaks of the disease. Outbreak detection in the country relies on recognising cases meeting a set fever and rash case definition incorporated into the syndromic surveillance system run by the National Center for Laboratory and Epidemiology (NCLE). Suspected cases are passively identified by presentations at health care facilities, with information forwarded to the NCLE's Early Alert and Response Network (EWARN) along with event-based reported data1. World Health Organization (WHO) measles surveillance guidelines require 80% of fever and rash cases be sampled for testing; currently only 20% sampling occurs in Laos2,3. Sampling using DBS has been proposed as an alternative to conventional venepuncture in facilitating suspected measles case detection. In this study, DBS was proposed to improve blood uptake of syndromic cases, by evaluating whether it increased ascertainment compared to conventional venepuncture. It also analysed reasons for poor diagnostic uptake among healthcare personnel involved in syndromic surveillance.

Objective: To evaluate whether dried blood spot (DBS) testing improves diagnostic uptake in Vientiane Capital City province, Lao People's Democratic Republic (PDR) compared to conventional diagnostic techniques (venous blood by venepuncture) during syndromic surveillance from 2016-17. To also explore reasons for low blood sampling uptake via quantitative results and qualitative responses from health care workers; in addition to the perceived acceptance of DBS compared to venepuncture.

The 2014-2016 Ebola outbreak in Guinea revealed systematic weaknesses in the existing disease surveillance system. The lack of public health workers adequately trained in Integrated Disease Surveillance and Response (IDSR) contributed to underreporting of cases and problems with data completeness, accuracy, and reliability. These data quality issues resulted in difficulty assessing the epidemic's scale and distribution and hindered the control effort (McNamara, 2016; Bell, 2016). In 2015, the Guinean Ministry of Health (MoH) recognized the importance of the IDSR framework as a tool for improving disease surveillance and emphasized IDSR strengthening as a priority activity in the post-Ebola transition (MoH, 2015). To support this strategic objective, we engaged with the MoH, CDC, and key surveillance partners to strengthen surveillance capacity through a national initiative to improve IDSR tools, including assistance with developing Guinea-specific IDSR technical guidelines, simplified and standardized case notification forms, and supportive job aids to facilitate appropriate IDSR implementation by health workers at all levels of the system.

Objective: The objective is to discuss capacity building for Integrated Disease Surveillance and Response in Guinea and synthesize lessons learned for implementing the Global Health Security Agenda in similar settings.

The West Africa Ebola outbreak of 2014-2016 demonstrated the importance of strong disease surveillance systems and the severe consequences of weak capacity to detect and respond to cases quickly. Challenges in the transmission and management of surveillance data were one factor that contributed to the delay in detecting and confirming the Ebola outbreak. To help address this challenge, we have collaborated with the U.S. Centers for Disease Control and Prevention (CDC), the Ministry of Health (MOH) in Guinea, the World Health Organization and various partners to strengthen the disease surveillance system through the implementation of an electronic reporting system using an open source software tool, the District Health Information Software Version 2 (DHIS 2). These efforts are part of the Global Health Security Agenda objective to strengthen real-time surveillance. This online system enables prefecture health offices to enter aggregate weekly disease reports from health facilities and for that information to be immediately accessible to designated staff at prefecture, regional and national levels. Incorporating DHIS 2 includes several advantages for the surveillance system. For one, the data is available in real time and can be analyzed quickly using built-in data analysis tools within DHIS 2 or exported to other analysis tools. In contrast, the existing system of reporting using Excel spreadsheets requires the MOH to manually compile spreadsheets from all the 38 prefectures to have case counts for the national level. For the individual case notification system, DHIS 2 enables a similar accessibility of information that does not exist with the current paper-based reporting system. Once a case notification form is completed in DHIS 2, the case-patient information is immediately accessible to the laboratories receiving specimens and conducting testing for case confirmation. The system is designed so that laboratories enter the date and time that a specimen is received, and any test results. The results are then immediately accessible to the reporting district and to the stakeholders involved including the National Health Security Agency and the Expanded Program on Vaccination. In addition, DHIS 2 can generate email and short message service (SMS) messages to notify concerned parties at critical junctures in the process, for example, when a laboratory result is available for a given case.

Objective: The objective is to share the progress and challenges in the implementation of the District Health Information Software Version 2 (DHIS 2) as an electronic disease surveillance system platform in Guinea, West Africa, to inform Global Health Security Agenda efforts to strengthen real-time surveillance in low-resource settings.

While influenza-like-illness (ILI) surveillance is well-organized at primary care level in Europe, little data is available on more severe cases. With retrospective data from ICU's we aim to fill this current knowledge gap and to explore its worth for prospective surveillance. Using multiple parameters proposed by the World Health Organization we estimated the burden of severe acute respiratory infections (SARI) to ICU and how this varies between influenza epidemics.

Objective: Intensive Care Unit (ICU) data are registered for quality monitoring in the Netherlands with near 100% coverage. They are a big data type source that may be useful for infectious disease surveillance. We explored their potential to enhance the surveillance of influenza which is currently based on the milder end of the disease spectrum. We ultimately aim to set up a real-surveillance system of severe acute respiratory infections.

Influenza peaks around June and December in Singapore every year. Facing an ageing population, hospitals in Singapore have been constantly reaching maximum bed occupancy. The ability to be able to make early decisions during peak periods is important. Tan Tock Seng Hospital is the second largest adult acute care general hospital in Singapore. Pneumonia-related emergency department (ED) admissions are a huge burden to the hospital's resources. The number of cases vary year on year as it depends on seasonal vaccine effectiveness and the population's immunity to the circulating strain. While many pneumonia cases are of unknown origin, they tend to mirror the influenza seasons very closely.

Objective: Using the information that we have available, our primary objective is to explore if there was any cross-correlation between pneumonia admissions and hospital influenza positivity. We then aim to develop a data driven approach to forecast pneumonia admissions using data from our hospital's weekly surveillance. We also attempted using external sources of information such as national infectious diseases notifications and climate data to see if they were useful for our model.

In Spring 2017, the Missouri Department of Health and Senior Services (MODHSS) launched the Missouri Public Health Information Management System (MOPHIMS) web-based health data platform. Missouri has supported a similar data system since the 1990s, allowing the public, local public health departments, and other stakeholders access to community level birth, death, and hospitalization data (among other datasets). The MOPHIMS system is composed of two separate pieces. Community Data Profiles are topic-, disease-, or demographic-specific reports that contain 15-10 indicators relevant to the report. Because these static reports are developed in-house a multilayered suppression rule is not required. The second piece of MOPHIMS, the Data MICAs, or Missouri Information for Community Assessent, can be used to create customized datasets that slice and dice up to a dozen demographic and system-specific variables to answer complex research questions. The MOPHIMS interface features, among other things, a new and innovative method for addressing confidentiality concerns through the suppression of health data. This pioneering approach integrates multi-level logic that uses inner and outer cell analytics, the use of exempt and conditionally exempt variables, and multiple levels of user access. Moving beyond a simple model of suppressing any values below a certain threshold, MOPHIMS takes a bold step in providing users exceptionally granular data while still protecting citizen privacy.

Objective: By the end of this session, users will be able to describe the innovative and multilayered suppression rules that are applied to Missouri's homegrown health data web query system. They will also be able to use the lessons learned and user feedback described in the session to facilitate discussions surrounding the application of suppression to their specific data systems.

Traditionally, public health agencies (PHAs) wait for hospital, laboratory or clinic staff to initiate case reports. However, this passive approach is burdensome for reporters and produces incomplete and delayed reports, which can hinder assessment of disease in the community and potentially delay recognition of patterns and outbreaks. Modern surveillance practice is shifting toward greater use of electronically transmitted disease information. The adoption of electronic health record (EHR) systems and health information exchange (HIE) among clinical organizations and systems, driven by policies such as the meaningful use™ program, is creating an information infrastructure that public health organizations can take advantage of to improve surveillance practice.

Objective: To enhance the process by which outpatient providers report surveillance case information to public health authorities following a laboratory-confirmed diagnosis of a reportable disease.

Disease surveillance is an integral part of public health system. It is an epidemiological method for monitoring disease patterns and trends. International Health Regulation (IHR) 2005 obligates WHO member countries to develop an effective disease surveillance system. Bangladesh is a signatory to IHR 2005. Institute of Epidemiology, Disease Control and Research (IEDCR ) is the mandated institute for surveillance and outbreak response on behalf of Government of the People's Republic of Bangladesh. The IEDCR has a good surveillance system including event-based surveillance system, which proved effective to manage public health emergencies. Routine disease profile is collected by Management Information System (MIS) of Directorate General of Health Services (DGHS). Expanded Program of Immunization (EPI) of DGHS collect surveillance data on EPI-related diseases. Disease Control unit, DGHS is responsible for implementing operational plan of disease surveillance system of IEDCR. The surveillance system maintain strategic collaboration with icddrr.

Objective: a) To observe trends and patterns of diseases of public health importance and response; b) To predict, prevent, detect, control and minimize the harm caused by public health emergencies; c) To develop evidence for managing any future outbreaks, epidemic and pandemic.

In recent years, mosquito-borne diseases such as Zika, chikungunya, and dengue have become particularly problematic due to global climate change. Rising temperatures and changes in precipitation are considered to be associated with habitat suitability of mosquito vectors and viruses. To address such cross-border infectious diseases, countries have come up with various strategies to control and manage mosquito-borne diseases. In line with this, international efforts have been made to minimize the burden of global infectious diseases. In 2014, Global Health Security Agenda (GHSA) has been launched in collaboration with the international organizations, member countries of GHSA, and non-governmental organizations in order to improve national and global capacities against global public health threat. In addition, various quarantine programs have been operated in and between countries borderlines and airports with cutting edge ICT technologies. These efforts could be made more effective when the authorities have reliable predicted future trends or events, utilize their capacities more efficiently and provide timely alerts to the public. However, very few studies have been conducted to deal with imported disease, while much attention has been paid to the endemic diseases. In this study, we aim to develop a prediction model for imported infectious disease by using the approach of ANN. We have chosen to model the imported cases of dengue in Korea, as the number of imported dengue cases is larger than other mosquito-borne diseases. Additionally, Japan, one of South Korea's neighboring countries, has recently experienced autochthonous dengue virus transmission, which has raised concerns about localization in Korea as well as in Japan.

Objective: We aim to develop a prediction model for the number of imported cases of infectious disease by using the recurrent neural network (RNN) with the Elman algorithm, a type of artificial neural network (ANN) algorithm. We have targeted to predict the number of imported dengue cases in South Korea as the number of dengue cases is greater than other mosquito-borne diseases.

After the 2009 H1N1 pandemic, the Assistant Secretary of Defense for Nuclear, Chemical and Biological Defense indicated œbiodefense would include emerging infectious disease. In response, DTRA launched an initiative for an innovative, rapidly emerging capability to enable real-time biosurveillance for early warning and course of action analysis. Through competitive prototyping, DTRA selected Digital Infuzion to develop the platform and next generation analytics. This work was extended to enhance collaboration capabilities and to harness data science and advanced analytics for multi-disciplinary surveillance including climate, crop, and animal as well as human data. New analysis tools ensure the BSVE supports a One Health paradigm to best inform public health action. Digital Infuzion and DTRA first introduced the BSVE to the ISDS community at the 2013 annual conference SWAP Meet. Digital Infuzion is pleased to present the mature platform to this community again as it is now a fully developed capability undergoing FedRAMP certification with the Department of Homeland Security's National Biosurveillance Integration Center and Is the basis for Digital Infuzion's HARBINGER ecosystem for biosurveillance.

Objective: While there is a growing torrent of data that disease surveillance could leverage, few effective tools exist to help public health professionals make sense of this data or that provide secure work-sharing and communication. Meanwhile, our ever more-connected world provides an increasingly receptive environment for diseases to emerge and spread rapidly making early warning and collaborative decision-making essential to saving lives and reducing the impact of outbreaks. Digital Infuzion's previous work on the Defense Threat Reduction Agency (DTRA)'s Biosurveillance Ecosystem (BSVE) built a cloud-based platform to ingest big data with analytics to provide users a robust surveillance environment. We next enhanced the BSVE data sources and analytics to support an integrated One Health paradigm. The resulting BSVE and Digital Infuzion's HARBINGER platform include: 1) identifying and ingesting data sources that span global human, animal and crop health; 2) inclusion of non-health data such as travel, weather, and infrastructure; 3) the data science tools, analytics and visualizations to make these data useful and 4) a fully-featured Collaboration Center for secure work-sharing and communication across agencies.