Infectious Disease

To immediately monitor disease outbreaks, the application of laboratory-based surveillance is more popular in recent years. Taiwan Centers for Disease Control (TCDC) has developed LARS to collect the laboratory-confirmed cases caused by any of 20 pathogens daily via automated submitting of reports from hospital laboratory information system (LIS) to LARS since 2014 [1]. LOINC is used as standardized format for messaging inspection data [1, 2]. There are 37 hospitals have joined LARS, coverage rate about 59% of all hospitals in Taiwan. Recently, more than 10,000 of data are collected weekly and used in monitoring pathogen activity [3]. Therefore, it is important to ensure data quality that the data will lead to valuable information for public health surveillance.

Objective

To improve data quality and sustain a good quality data collected by Laboratory Automated Reporting System (LARS), we use a Threestage Data Quality Correction (3DQC) strategy to ensure data accuracy.

In France, the surveillance of GE is performed by several complementary systems including specific and syndromic surveillance systems.

The GP’s emergency associations “SOS Médecins” are part of the French syndromic surveillance system SurSaUD since 2006. SOS Médecins functions as a liberal medical regulation. In 9 years, the network has become almost exhaustive and contribute to the surveillance of seasonal and non-seasonal health events at different geographical scales, in the fields of infectious diseases and environmental health. GE is one of the 50 indicators daily followed by the by the French Institute for Public Health Surveillance (InVS) syndromic surveillance unit.

Objective

To illustrate the complementarity and added value of the GP’s emergency network “SOS Médecins” through an example of an epidemic of gastroenteritis (GE).

NYS (excluding NYC) has a very robust Communicable Disease Electronic Surveillance System (CDESS). This system provides disease specific modules, as well as a tracking system for contacts, and a perinatal infant tracking system. This system provides an easy way for users to quickly download a file with all of their data.

NYS (excluding NYC) tracks, on average, 300 infants of hepatitis B surface antigen (HBsAg) positive mothers annually. CDESS provides an infant tracking module for local health departments (LHDs) to enter and monitor vaccine information, add multiple infants per mother, and track patient movement and loss to follow-up. The tool allows LHDs to analyze infants’ data by birth year cohort, with all of their current vaccination and serology information available in one record.

In 2013 and 2014, more than 13,000 cases of gonorrhea were reported to CDESS in NYS (excluding NYC). From November 2013 through May 2014, only 61% of cases were adequately treated with a regimen recommended by the Centers for Disease Control and Prevention (CDC) STD Treatment Guidelines for Gonorrhea , and 29% were missing treatment information. The CDESS system allows the LHDs to track patients who have inadequate and/or missing treatment information.

Objective

Improved methods for user analysis of communicable disease surveillance data in New York State (NYS), excluding New York City (NYC).

Foodborne illness affects 1 in 4 Americans, annually. However, only a fraction of affected individuals seek medical attention. To supplement traditional approaches to foodborne disease surveillance, researchers and public health departments are considering reports of foodborne illness on social media sites. In this project, we work with local public health departments to develop a platform that uses digital data sources such as, Twitter and Yelp, to supplement foodborne disease surveillance efforts. In addition to monitoring reports of illness, this platform can also be used to respond to suspected foodborne illness reports and spur restaurant inspections to ensure food safety. To this end, we have developed a Dashboard that monitors social media chatter for reports of food poisoning in real-time. The Dashboard facilitates responding to illness reports and contacting consumers to provide additional information through a reporting form. The Dashboard is low cost, easy to use and designed to enable easy implementation for any region.

Objective

Develop a platform to enable local surveillance of foodborne illness reported on social media and restaurant review sites for supplementing traditional foodborne disease surveillance programs. In this presentation, we will discuss our collaboration with local public health departments to develop a foodborne disease surveillance Dashboard.

Poliomyelitis a disease targeted for eradication since 1988 still pose public health challenge. The Eastern Mediterranean and African Regions out of the six World Health Organization (WHO) Regions are yet to be certified polio free. The certification of the WHO Africa region is largely dependent on Nigeria, while the WHO Eastern Mediterranean is dependent on Pakistan and Afghanistan. Surveillance for acute flaccid paralysis (AFP) is one of the critical elements of the polio eradication initiative. It provides the needed information to alert health managers and clinician to timely initiate actions to interrupt transmission of the polio disease and evidence for the absence of the wild polio virus. One of the core assignments of the certification committee in all regions is to review documentation to verify the absence of wild poliovirus. Good and complete documentation is the proxy indication of the quality of the system while poor documentation translates to possibilities of missing wild poliovirus in the past. We evaluated the performance of the AFP surveillance system in Bauchi, which is among the 11 high risks states for wild polio virus in Nigeria to identify and address gaps in the surveillance system.

Objective

To identify and address gaps in acute flaccid surveillance for polio eradication in Buchi state

Newcastle disease (ND) is the most important infectious viral disease of poultry. The world-wide economic loss from it is 2-3 billion USD per year. ND is reportable to the World Organization for Animal Health (OIE). ND is caused by virulent strains of avian Paramyxoviruses belonging to type 1. Industrial poultry farming is rapidly developing in Ukraine. Ornithological fauna of Ukraine includes about four hundred species of birds, 207 of which nest within its borders. The territory of Ukraine transits 3 out of 14 transcontinental global migration flows. The wild birds are the main natural reservoir of ND agents. It is necessary to control the intensity of post-vaccination immunity in poultry and the timing of revaccinations. OIE recommends enzyme linked immunosorbent assays (ELISA) and HI test for these purposes. However, it should be noted that HI test, possessing high specificity and sensitivity, is much cheaper. Therefore, it is the excellent means for ND timely surveillance.

Objective

A test kit for the detection of antibodies to Newcastle disease virus (NDV) based on haemagglutination inhibition (HI) assay has been developed and introduced into practice for the first time in Ukraine.

Since the majority of emerging infectious diseases over the past several decades have been zoonotic, animal health surveillance is now recognized as a key element in predicting public health risks. Surveillance of animal populations can provide important early warnings of emerging threats to human populations from bioterrorism or naturally occurring infectious disease epidemics. This study investigated current animal data collection and surveillance systems, isolated major gaps in state and national surveillance capabilities, and provided recommendations to fill those gaps.

Objective

To identify gaps in current U.S. animal data collection and surveillance systems, describe how surveillance of animal populations can provide important early warnings of emerging threats to human populations from infectious disease epidemics, and explain the benefits of integrating human and animal surveillance data into a common linked system.

The Florida Department of Health in Hillsborough County (FDOH- Hillsborough) conducts enhanced syndromic surveillance on a daily basis. The Electronic Surveillance System for the Early Notification of Community-based Epidemics in Florida (ESSENCE-FL) is the syndromic surveillance system used by epidemiologists within the Florida Department of Health (FDOH). During the time of this study, ESSENCE-FL receives data from 210 of emergency departments (ED) and 33 urgent care centers (UCC) throughout the state of Florida, including 12 EDs and 3 UCCs in Hillsborough County. In 2014, the ESSENCE-FL system added a feature that delivers an automatic daily email to designated primary ESSENCE-FL users in each county containing all visits which have been detected by the state’s visits of interest (VOI) query. The email contains all visits which have been detected by the visits of interest (VOI) query for each ESSENCE-FL users designated county. The VOI query utilizes the combined chief complaint and discharge diagnosis (CCDD) field of a visit for keywords related to reportable diseases and exposures of public health interest. In addition to this VOI email, Hillsborough County analyzes time of arrival alerts, specialized emerging infectious disease queries, poison information center data, and volume levels of syndromes and subsyndromes predetermined by ESSENCE-FL. A daily summary report of the enhanced daily surveillance analysis is then provided to area public health officials within FDOH-Hillsborough and the surrounding counties. This study examines how visits requiring additional investigation are detected and the resources required to complete the investigation.

Objective

Enhanced daily surveillance is used to identify reportable diseases, outbreaks, and clusters and provides situational awareness. This project examines how health care visits requiring additional information are detected using enhanced syndromic surveillance and the resources required from detection through completion.

Syndromic surveillance is an alternative type of public health surveillance which utilizes pre-diagnostic data sources to detect outbreaks earlier than conventional (laboratory) surveillance and monitor the progression of illnesses in populations. These systems are often noted for their ability to detect a wider range of cases in under- reported illnesses, utilize existing data sources, and alert public health authorities of emerging crises. In addition, they are highly versatile and can be applied to a wide range of illnesses (communicable and non-communicable) and environmental conditions. As a result, their implementation in public health practice is expanding rapidly. This scoping review aimed to identify all existing literature detailing the necessary components in the defining, creating, implementing, and evaluating stages of human infectious disease syndromic surveillance systems. 

An increasing number of geo-coded information streams are available with possible use in disease surveillance applications. In this setting, multivariate modeling of health and non-health data allows assessment of concurrent patterns among data streams and conditioning on one another. Therefore it is appropriate to consider the analysis of their spatial distributions together. Specifically for vector-borne diseases, knowledge of spatial and temporal patterns of vector distribution could inform incidence in humans. Tularemia is an infectious disease endemic in North America and parts of Europe. In Finland tularemia is typically mosquito-transmitted with rodents serving as a host; however, a country-wide understanding of the relationship between rodents and the disease in humans is still lacking. We propose a methodology to help understand the association between human tularemia incidence and rodent population levels. 

Objective

We seek to integrate multiple streams of geo-coded information with the aim to improve public health surveillance accuracy and efficiency. Specifically for vector-borne diseases, knowledge of spatial and temporal patterns of vector distribution can help early prediction of human incidence. To this end, we develop joint modeling approaches to evaluate the contribution of vector or reservoir information on early prediction of human cases. A case study of spatiotemporal modeling of tularemia human incidence and rodent population data from Finnish health care districts during the period 1995-2013 is provided. Results suggest that spatial and temporal information of rodent abundance is useful in predicting human cases.