Infectious Disease

The National Surveillance Team in the Enteric Diseases Epidemiology Branch of the Centers for Disease Control and Prevention (CDC) collects electronic data from all state and regional public health laboratories on human infections caused by Campylobacter, Salmonella, Shiga toxin-producing E. coli, and Shigella in LEDS. These data inform annual estimates of the burden of illness, assessments of patterns in bacterial subtypes, and can be used to describe trends in incidence. Robust digital infrastructure is required to process, validate, and summarize data on approximately 60,000 infections annually while optimizing use of financial and personnel resources.

Objective:

The œledsmanageR, a data management platform built in R, aims to improve the timeliness and accuracy of national foodborne surveillance data submitted to the Laboratory-based Enteric Disease Surveillance (LEDS) system by automating the data processing, validating, and reporting workflow.

Sexually transmitted disease treatment guidelines have incrementally added repeat testing recommendations for Chlamydia trachomatis infections over time, including test-of-cure 3 to 4 weeks following completion of treatment for pregnant women and test-of-reinfection for all patients approximately 3 months after infection. However, few studies have investigated adherence to these recommendations and whether the evolution of guidelines have led to changes in repeat testing patterns over time.

Objective:

To evaluate current rates and temporal trends in adherence with national guidelines recommending chlamydia test-of-cure for pregnant females and test-of-reinfection for all patients.

In May 2015, the MERS-CoV outbreaks in South Korea was sparkled from a hospital of Gyeonggi-do province. In response to this outbreak, the provincial government and infectious disease control center (GIDCC) initiated an emergency department (ED) based Gyeonggi-do provincial acute febrile illness (AFI) surveillance network (GAFINet) to monitor for a subsequent outbreak of emerging or imported infectious diseases since September 2016. Gyeonggi-do province is located in the North-West of South Korea, surrounds the capital city Seoul, and borders North Korea (Figure 1). Considering the geographical coverage, GAFINet Initiative involves ten hospitals, consisted of four university-affiliated hospitals and six provincial medical centers in Gyeonggi-do province. These hospitals participated in this network voluntarily, and most staffs including five infectious diseases specialists had direct or indirect experiences in dealing with MERS-CoV patients.

Objective:

The objectives are to introduce a provincial level surveillance system, which has been initiated in response to the MERS-CoV outbreak of South Korea, and describe findings from systematic investigation of individual admissions attributed to acute febrile illness for the first year.`

The Foot and mouth disease (FMD) virus is a highly contagious and economically devastating trans boundery disease of cloven-hooved domestic and wild animals.

Objective:

To determine the sero-prevalnce of FMD and indicate patterns of animal movement in Borena zone, Ethiopia.

On 3/29/2017, the Maricopa County Department of Public Health (MCDPH) received three reports of confirmed HAV infection from an onsite clinic at Campus A that assists individuals experiencing homelessness, a population at risk for HAV transmission. To identify the scope of the problem, the department initiated rapid HAV infection case detection using NSSP ESSENCE.

Objective:

To demonstrate the utility of the National Syndromic Surveillance Program’s (NSSP) version of the Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE) for case detection during a 2017 outbreak of hepatitis A virus (HAV) infection among persons experiencing homelessness in Maricopa County, Arizona.

An estimated one in six Americans experience illness from the consumption of contaminated food (foodborne illness) annually; most are neither diagnosed nor reported to health departments1. Eating food prepared outside of the home is an established risk factor for foodborne illness2. New York City (NYC) has approximately 24,000 restaurants and >8.5 million residents, of whom 78% report eating food prepared outside of the home at least once per week3. Residents and visitors can report incidents of restaurant-associated foodborne illness to a citywide non-emergency information service, 311. In 2012, the NYC Department of Health and Mental Hygiene (DOHMH) began collaborating with Columbia University to improve the detection of restaurant-associated foodborne illness complaints using a machine learning algorithm and a daily feed of Yelp reviews to identify reports of foodborne illness4. Annually, DOHMH manages over 4,000 restaurant-associated foodborne illness reports received via 311 and identified on Yelp which lead to the detection of about 30 outbreaks associated with a restaurant in NYC. Given the small number of foodborne illness outbreaks identified, it is probable that many restaurant-associated foodborne illness incidents remain unreported. DOHMH sought to incorporate and evaluate an additional data source, Twitter, to enhance foodborne illness complaint and outbreak detection efforts in NYC.

Objective:

To incorporate data from Twitter into the New York City Department of Health and Mental Hygiene foodborne illness surveillance system and evaluate its utility and impact on foodborne illness complaint and outbreak detection.

Timely identification of arboviral disease is key to prevent transmission in the community, but traditional surveillance may take up to 14 days between specimen collection and health department notification. Arizona state and county health agencies began monitoring National Syndromic Surveillance Program BioSense 2.0 data for patients infected with West Nile virus (WNV), St. Louis encephalitis virus (SLEV), chikungunya, or dengue virus in August 2015. Zika virus was added in April 2016. Our novel methods were presented at the International Society for Disease Surveillance 2015 Annual Conference. Twice per week, we queried patient records from 15 Maricopa County BioSense-enrolled emergency department and inpatient hospitals for chief complaint keywords and discharge diagnosis codes. Our Case Investigation Decision Tree helped us determine whether records had a high or low degree of evidence for arboviral disease and necessitated further investigation. This study evaluated how Arizona’s protocol for conducting syndromic surveillance compared to traditional arboviral surveillance in terms of accuracy and timeliness in Maricopa County from August 2015 through December 2016.

Objective:

To evaluate Arizona’s arboviral syndromic surveillance protocol in Maricopa County.