Text-Based Illness Monitoring for Detection of Novel Influenza A Virus Infections During an Influenza A (H3N2)v Virus Outbreak in Michigan, 2016: Surveillance and Survey

Sara Alert: An automated symptom monitoring tool for COVID-19 in 11 jurisdictions in the United States, June - August, 2021

OBJECTIVES

Health department personnel conduct daily active symptom monitoring for persons potentially exposed to SARS-CoV-2. This can be resource-intensive. Automation and digital tools can improve efficiency. We describe use of a digital tool, Sara Alert, for automated daily symptom monitoring across multiple public health jurisdictions.

METHODS

Eleven of the 20 U.S. public health jurisdictions using Sara Alert provided average daily activity data during June 29 to August 30, 2021. Data elements included demographics, communication preferences, timeliness of symptom monitoring initiation, responsiveness to daily messages, and reports of symptoms.

RESULTS

Participating jurisdictions served a U.S. population of over 22 million persons. Health department personnel used this digital tool to monitor more than 12,000 persons per day on average for COVID-19 symptoms. On average, monitoring began 3.9 days following last exposure and was conducted for an average of 5.7 days. Monitored persons were frequently < 18 years old (45%, 5,474/12,450) and preferred communication via text message (47%). Seventy-four percent of monitored persons responded to at least one daily automated symptom message.

CONCLUSIONS

In our geographically diverse sample, we found that use of an automated digital tool might improve public health capacity for daily symptom monitoring, allowing staff to focus their time on interventions for persons most at risk or in need of support. Future work should include identifying jurisdictional successes and challenges implementing digital tools; the effectiveness of digital tools in identifying symptomatic individuals, ensuring appropriate isolation, and testing to disrupt transmission; and impact on public health staff efficiency and program costs.

A generalizable one health framework for the control of zoonotic diseases

Effectively preventing and controlling zoonotic diseases requires a One Health approach that involves collaboration across sectors responsible for human health, animal health (both domestic and wildlife), and the environment, as well as other partners. Here we describe the Generalizable One Health Framework (GOHF), a five-step framework that provides structure for using a One Health approach in zoonotic disease programs being implemented at the local, sub-national, national, regional, or international level. Part of the framework is a toolkit that compiles existing resources and presents them following a stepwise schematic, allowing users to identify relevant resources as they are required. Coupled with recommendations for implementing a One Health approach for zoonotic disease prevention and control in technical domains including laboratory, surveillance, preparedness and response, this framework can mobilize One Health and thereby enhance and guide capacity building to combat zoonotic disease threats at the human-animal-environment interface.

An Evaluation of the Text Illness Monitoring (TIM) Platform for COVID-19: A Cross-Sectional Online Survey of Public Health Users

Background

The US public health response to the COVID-19 pandemic has required contact tracing and symptom monitoring at an unprecedented scale. The US Centers for Disease Control and Prevention and several partners created the Text Illness Monitoring (TIM) platform in 2015 to assist US public health jurisdictions with symptom monitoring for potential novel influenza virus outbreaks. Since May 2020, 142 federal, state, and local public health agencies have deployed TIM for COVID-19 symptom monitoring.

Objective

The aim of this study was to evaluate the utility, benefits, and challenges of TIM to help guide decision-making for improvements and expansion to support future public health emergency response efforts.

Methods

We conducted a brief online survey of previous and current TIM administrative users (admin users) from November 28 through December 21, 2020. Closed- and open-ended questions inquired about the onboarding process, decision to use TIM, groups monitored with TIM, comparison of TIM to other symptom monitoring systems, technical challenges and satisfaction with TIM, and user support. A total of 1479 admin users were invited to participate.

Results

A total of 97 admin users from 43 agencies responded to the survey. Most admin users represented the Indian Health Service (35/97, 36%), state health departments (26/97, 27%), and local or county health departments (18/97, 19%), and almost all were current users of TIM (85/94, 90%). Among the 43 agencies represented, 11 (26%) used TIM for monitoring staff exclusively, 13 (30%) monitored community members exclusively, and 19 (44%) monitored both staff and community members. Agencies most frequently used TIM to monitor symptom development in contacts of cases among community members (28/43, 65%), followed by symptom development among staff (27/43, 63%) and among staff contacts of cases (24/43, 56%). Agencies also reported using TIM to monitor patients with COVID-19 for the worsening of symptoms among staff (21/43, 49%) and community members (18/43, 42%). When asked to compare TIM to previous monitoring systems, 78% (40/51) of respondents rated TIM more favorably than their previous monitoring system, 20% (10/51) said there was no difference, and 2% (1/51) rated the previous monitoring system more favorably than TIM. Most respondents found TIM favorable in terms of time burden, staff burden, timeliness of the data, and the ability to monitor large population sizes. TIM compared negatively to other systems in terms of effort to enroll participants (ie, persons TIM monitors) and accuracy of the data. Most respondents (76/85, 89%) reported that they would highly or somewhat recommend TIM to others for symptom monitoring.

Conclusions

This evaluation of TIM showed that agencies used TIM for a variety of purposes and rated TIM favorably compared to previously used monitoring systems. We also identified opportunities to improve TIM; for example, enhancing the flexibility of alert deliveries would better meet admin users’ varying needs. We also suggest continuous program evaluation practices to assess and respond to implementation gaps.

Clinical validation of the FluChip-8G Influenza A+B Assay for influenza type and subtype identification

BACKGROUND

The FluChip-8G Influenza A+B Assay is a multiplexed influenza RT-PCR and microarray-based assay with same day turnaround time, developed to subtype seasonal A viruses (H1N1pdm2009 and H3N2), distinguish B viruses as Yamagata or Victoria lineage, and is the only FDA cleared assay capable of positive identification of a wide variety of A subtypes as "non-seasonal" A viruses from human nasal specimens.

OBJECTIVE

To evaluate clinical performance of the FluChip-8G Influenza A+B Assay for detection of seasonal influenza viruses in nasal and nasopharyngeal swab specimens, and to evaluate performance for detection of non-seasonal influenza viruses using contrived samples.

STUDY DESIGN

For seasonal viruses, a multisite study of the FluChip-8G Influenza A+B Assay using prospectively and retrospectively collected nasal and nasopharyngeal swabs was performed using the FDA-cleared CDC Human Flu Dx Panel as the comparator assay. For non-seasonal viruses, testing was performed at a single site using contrived samples from 100 unique non-seasonal strains representing 41 subtypes.

RESULTS

Sensitivity (95% CI) and specificity (95% CI) for each target group, respectively, from results of 1689 clinical specimens were: seasonal H1N1pdm2009: 96.4% (87.9-99.0), 99.3% (98.8-99.6), seasonal H3N2: 91.8% (87.7-94.7), 99.7% (99.2-99.9), Influenza B Victoria: 100% (94.0-100.0), 99.9% (99.6-100.0), and Influenza B Yamagata: 95.6% (89.2-98.3), 99.9% (99.6-100.0). The sensitivity and specificity from contrived influenza A non-seasonal viruses was determined to be 99.0% (94.6-99.8) and 100% (96.7-100.0).

CONCLUSION

The FluChip-8G Influenza A+B Assay has robust sensitivity and specificity for detecting and identifying all target virus groups, including non-seasonal influenza A, with same day results.