Hitchhiker bias distorts symptom-based surveillance of infectious diseases

Abstract Symptom-based infectious disease surveillance, which infers pathogen circulation by testing individuals who seek care for clinical symptoms, is widely used in public health but may introduce biases that distort our understanding of pathogen dynamics. One such bias, which we introduce and call “hitchhiker bias”, occurs when the observed number of hospitalizations of asymptomatic or mildly symptomatic pathogens is inflated because they coinfect individuals already being tested for more severe symptoms caused by other pathogens. Using a compartmental SEIR co-infection model, we demonstrate that co-circulation with a symptomatic pathogen significantly distorts the apparent severity and peak time of a hitchhiking virus. These distortions are amplified as the temporal overlap between pathogens increases. We develop and apply a modeling framework to adjust for hitchhiker bias, showing that it can recover the true pathogenicity of viruses that would otherwise be misinterpreted using standard symptom-based surveillance alone.