Active syndromic surveillance of COVID-19 in Israel

Syndromic surveillance systems monitor disease indicators to detect emergence of diseases and track their progression. Here, we report on a rapidly deployed active syndromic surveillance system for tracking COVID-19 in Israel. The system was a novel combination of active and passive components: Ads were shown to people searching for COVID-19 symptoms on the Google search engine. Those who clicked on the ads were referred to a chat bot which helped them decide whether they needed urgent medical care. Through its conversion optimization mechanism, the ad system was guided to focus on those people who required such care. Over 6 months, the ads were shown approximately 214,000 times and clicked on 12,000 times, and 722 people were informed they needed urgent care. Click rates on ads and the fraction of people deemed to require urgent care were correlated with the hospitalization rate ($$R^2=0.54$$ R 2 = 0.54 and $$R^2=0.50$$ R 2 = 0.50 , respectively) with a lead time of 9 days. Males and younger people were more likely to use the system, and younger people were more likely to be determined to require urgent care (slope: $$- \,0.009$$ - 0.009 , $$P=0.01$$ P = 0.01 ). Thus, the system can assist in predicting case numbers and hospital load at a significant lead time and, simultaneously, help people determine if they need medical care.

Evaluation of the Kenya Livestock and Wildlife Syndromic Surveillance System for Rift Valley Fever, Narok County, Kenya, 2018-2019

Background Rift Valley Fever (RVF) outbreaks in livestock have had a detrimental impact on livestock trade, animal breeding, and productivity. Routine evaluation and data analysis of surveillance systems ensure that health events are efficiently and effectively monitored. This study evaluated Kenya Livestock and Wildlife Surveillance System (KLWSS) and characterized RVF cases reported for Narok County. Methods We evaluated KLWSS from January 2018 to December 2019 using CDC guidelines for evaluating surveillance systems. Attributes of simplicity, flexibility, data quality, acceptability, representativeness, timeliness, stability, sensitivity, and predictive value positive were examined. A retrospective review of RVF surveillance data for Narok County was performed. Demographic and clinical variables were assessed. Data were cleaned in Ms. Excel and descriptive analysis was done using Epi Info 7. Categorical variables were summarized using frequencies and proportions while continuous variables were summarized using measures of central tendency and dispersion. Study authorization was granted by the Directorate of Veterinary Services. Results System was simple in structure and operation, accommodated upgrading of its application, data quality performance was 69.8%, stakeholder’s participation rate was 80% with 842 reports coming from six sub-counties and 30 wards. The median time between event occurrence and event reporting was two days (range one to six days). The system had been operational since 2018 with no reports of any unscheduled outages and downtimes. Suspected cases of RVF reported were 11% (95/842) of the reported cases. The livestock species affected were cattle 56% (53/95) and Sheep 44% (42/95). About 96% (91/95) of the suspected cases were in mixed livestock production systems. The common syndrome was abortions 74% (95/129) with Loita ward recording 97% (92/95) suspected RVF cases. All suspected cases were reported in March 2018. Conclusions The KLWSS system was found to be stable but with below-par performance in data quality. Improvement in data quality is required to ensure that the surveillance system is efficient and effective.

Development and validation of an automated emergency department-based syndromic surveillance system to enhance public health surveillance in Yukon: a lower-resourced and remote setting

Background Automated Emergency Department syndromic surveillance systems (ED-SyS) are useful tools in routine surveillance activities and during mass gathering events to rapidly detect public health threats. To improve the existing surveillance infrastructure in a lower-resourced rural/remote setting and enhance monitoring during an upcoming mass gathering event, an automated low-cost and low-resources ED-SyS was developed and validated in Yukon, Canada. Methods Syndromes of interest were identified in consultation with the local public health authorities. For each syndrome, case definitions were developed using published resources and expert elicitation. Natural language processing algorithms were then written using Stata LP 15.1 (Texas, USA) to detect syndromic cases from three different fields (e.g., triage notes; chief complaint; discharge diagnosis), comprising of free-text and standardized codes. Validation was conducted using data from 19,082 visits between October 1, 2018 to April 30, 2019. The National Ambulatory Care Reporting System (NACRS) records were used as a reference for the inclusion of International Classification of Disease, 10th edition (ICD-10) diagnosis codes. The automatic identification of cases was then manually validated by two raters and results were used to calculate positive predicted values for each syndrome and identify improvements to the detection algorithms. Results A daily secure file transfer of Yukon’s Meditech ED-Tracker system data and an aberration detection plan was set up. A total of six syndromes were originally identified for the syndromic surveillance system (e.g., Gastrointestinal, Influenza-like-Illness, Mumps, Neurological Infections, Rash, Respiratory), with an additional syndrome added to assist in detecting potential cases of COVID-19. The positive predictive value for the automated detection of each syndrome ranged from 48.8–89.5% to 62.5–94.1% after implementing improvements identified during validation. As expected, no records were flagged for COVID-19 from our validation dataset. Conclusions The development and validation of automated ED-SyS in lower-resourced settings can be achieved without sophisticated platforms, intensive resources, time or costs. Validation is an important step for measuring the accuracy of syndromic surveillance, and ensuring it performs adequately in a local context. The use of three different fields and integration of both free-text and structured fields improved case detection.

Development and assessment of a hospital admissions-based syndromic surveillance system for COVID-19 in Ontario, Canada: ACES Pandemic Tracker

Background The COVID-19 pandemic has continued to pose a major global public health risk. The importance of public health surveillance systems to monitor the spread and impact of COVID-19 has been well demonstrated. The purpose of this study was to describe the development and effectiveness of a real-time public health syndromic surveillance system (ACES Pandemic Tracker) as an early warning system and to provide situational awareness in response to the COVID-19 pandemic in Ontario, Canada. Methods We used hospital admissions data from the Acute Care Enhanced Surveillance (ACES) system to collect data on pre-defined groupings of symptoms (syndromes of interest; SOI) that may be related to COVID-19 from 131 hospitals across Ontario. To evaluate which SOI for suspected COVID-19 admissions were best correlated with laboratory confirmed admissions, laboratory confirmed COVID-19 hospital admissions data were collected from the Ontario Ministry of Health. Correlations and time-series lag analysis between suspected and confirmed COVID-19 hospital admissions were calculated. Data used for analyses covered the period between March 1, 2020 and September 21, 2020. Results Between March 1, 2020 and September 21, 2020, ACES Pandemic Tracker identified 22,075 suspected COVID-19 hospital admissions (150 per 100,000 population) in Ontario. After correlation analysis, we found laboratory-confirmed hospital admissions for COVID-19 were strongly and significantly correlated with suspected COVID-19 hospital admissions when SOI were included (Spearman’s rho = 0.617) and suspected COVID-19 admissions when SOI were excluded (Spearman’s rho = 0.867). Weak to moderate significant correlations were found among individual SOI. Laboratory confirmed COVID-19 hospital admissions lagged in reporting by 3 days compared with suspected COVID-19 admissions when SOI were excluded. Conclusions Our results demonstrate the utility of a hospital admissions syndromic surveillance system to monitor and identify potential surges in severe COVID-19 infection within the community in a timely manner and provide situational awareness to inform preventive and preparatory health interventions.

Abstract 045: Declines In Acute Myocardial Infarction And Stroke Emergency Department Visits Observed During Covid-19 Restrictions In North Carolina

Introduction: Efforts to control the COVID-19 pandemic brought sweeping social change, with stay-at-home orders and physical distancing mandates in 43 of 50 states by April 2020. Early on, isolated studies around the world described reduced hospital admissions. Reports from some US hospitals also described declines in catheterization laboratory activations, and acute myocardial infarction (AMI) and stroke admissions. However, there have been few population-based analyses of emergency department (ED) visits to verify these initial reports and describe longer term impacts of the pandemic on care seeking behavior. Hypothesis: We hypothesized that AMI and stroke ED visits in North Carolina (NC) would decrease substantially after a statewide stay-at-home order was announced on March 27, 2020. Methods: We analyzed all ED visits from January 5 to August 28, 2020 using data collected by the NC Disease Event Tracking and Epidemiologic Collection Tool, a syndromic surveillance system that automatically gathers ED data in near-real time for all EDs in NC. Counts of AMI and stroke/transient ischemic attack (TIA) were ascertained using ICD-10-CM diagnosis codes. We compared weekly 2020 ED visit data before and after NC’s stay-at-home order, and to 2019 ED visit data. Results: Overall ED volume declined by 44% in the weeks before and after the stay-at-home order ( Figure ) while the prior year’s ED volume stayed steady at ~100,000 visits per week. From January 5 to March 28, there were 593 AMI and 791 stroke/TIA visits per week on average. By April 11, ED visits reached a nadir at 426 AMI and 543 stroke/TIA visits per week, representing a 28% and 31% decrease, respectively. Since June, AMI and stroke/TIA ED visits have rebounded slightly but have yet to reach pre-pandemic levels. Conclusions: We observed swift declines in AMI and stroke/TIA ED visits following NC’s stay-at-home order. These findings potentially reflect the avoidance of medical care due to fears of COVID-19 exposure and may eventually result in higher associated case fatality.

De-identification of Emergency Medical Records in French: Survey and Comparison of State-of-the-Art Automated Systems

In France, structured data from emergency room (ER) visits are aggregated at the national level to build a syndromic surveillance system for several health events. For visits motivated by a traumatic event, information on the causes are stored in free-text clinical notes. To exploit these data, an automated de-identification system guaranteeing protection of privacy is required.In this study we review available de-identification tools to de-identify free-text clinical documents in French. A key point is how to overcome the resource barrier that hampers NLP applications in languages other than English. We compare rule-based, named entity recognition, new Transformer-based deep learning and hybrid systems using, when required, a fine-tuning set of 30,000 unlabeled clinical notes. The evaluation is performed on a test set of 3,000 manually annotated notes.Hybrid systems, combining capabilities in complementary tasks, show the best performance. This work is a first step in the foundation of a national surveillance system based on the exhaustive collection of ER visits reports for automated trauma monitoring.