Back

Active Surveillance for Heartland virus in North Carolina: Clinical and Genomic Epidemiology

Zychowski, D. L.; Ursery, L.; Sukkestad, S.; Ahmed, A.; Giandomenico, D.; Zhou, S.; Miller, M.; Juliano, J. J.; Piantadosi, A.; Boyce, R. M.

2026-03-04 infectious diseases
10.64898/2026.02.27.26347100 medRxiv
Show abstract

BackgroundHeartland virus (HRTV) is an emerging tick-borne virus capable of causing severe illness and death. The burden of disease is likely underestimated due to limited seroprevalence studies, lack of commercially available diagnostic tests, and an overlapping clinical syndrome with more commonly diagnosed bacterial diseases such as spotted fever group rickettsiosis or ehrlichiosis. MethodsActive surveillance for Heartland virus disease was conducted at a large academic center from March to September 2024. Enrolled subjects included those who had testing sent for Ehrlichia polymerase chain reaction (PCR) along with fever and 2 of the 3 criteria: leukopenia, thrombocytopenia, and/or elevated liver function tests. Specimens with detectable RNA underwent whole genome sequencing and analysis. FindingsOver 800 specimens were received with 53 individuals meeting enrollment criteria. Among these 53, two (3.8%) had detectable HRTV RNA in whole blood during the time of Ehrlichia PCR testing. The two cases had disparate clinical manifestations: one with mild disease which was identified in an outpatient setting, while a second case required intensive care unit-level support. Heartland virus genome sequences from the two cases were more similar to viruses from other states than they were to one another. InterpretationDespite only two prior reported cases of Heartland virus disease in North Carolina, we identified two individuals with acute HRTV viremia. Further surveillance for HRTV disease is necessary to understand the burden of disease and to facilitate further studies of virus pathogenesis and host responses. FundingFunding for the study was provided by a Creativity Hub Award to Dr. Boyce from the UNC Office of the Vice Chancellor for Research. Dr. Zychowskis effort was supported by the T32 NIAD grant AI070114.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.