JMIR Publications

ABSTRACT

Background:

Rapid reporting of human infections with novel influenza A viruses accelerates detection of viruses with pandemic potential and implementation of effective public health responses. After detection of human infections with influenza A(H3N2) variant viruses (“H3N2v”) associated with agricultural fairs during August of 2016, the Michigan Department of Health and Human Services worked with Centers for Disease Control and Prevention (CDC) to identify infections with variant influenza viruses using a text-based illness monitoring system.

Objective:

To enhance detections of influenza infections using text-based monitoring and evaluate the feasibility and acceptability of the system for use in future outbreaks of novel influenza viruses.

Methods:

During an outbreak of H3N2v virus infections among agricultural fair attendees, we deployed text-illness monitoring (TIM) to conduct active illness surveillance among households of youth who exhibited swine at fairs. We selected fairs with suspected H3N2v virus infections and fairs without suspect infections that met predefined criteria. Eligible respondents were identified and recruited through email outreach and/or on-site meetings at fairs. During and for 10 days after selected fairs, enrolled households received daily, automated text-messages inquiring about illness; reports of illness were investigated by local health departments. To understand the feasibility and acceptability of the system, we monitored enrollment and trends in participation and distributed a web-based survey to households of exhibitors from 5 fairs.

Results:

Among an estimated 500 households with a member who exhibited swine at one of 9 selected fairs, representatives of 87 (17%) households were enrolled, representing 392 household members. For fairs that were ongoing when TIM was deployed, the number of respondents peaked at 56 on the third day of the fair and then steadily declined throughout the rest of the monitoring period; 26 (30%) of 87 household representatives responded through the end of the 10-day monitoring period. We detected 2 H3N2v virus infections using TIM, which represents 17% (2/12) of all H3N2v virus infections detected during this outbreak in Michigan. Of the 70 survey respondents, 16 (23%) had participated in TIM. Of those, 73% (11/15) participated because it was recommended by fair coordinators and 80% (11/15) said they would participate again.

Conclusions:

Using a text-message system, we were able to monitor a large number of individuals and households for illness and detected H3N2v virus infections through active surveillance. Text-based illness monitoring systems are useful to detect novel influenza virus infections when active monitoring is deemed necessary. Participant retention and testing of persons reporting illness are critical elements for system improvement.