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Acute respiratory infections have epidemic and pandemic potential and thusare being studied worldwide, albeit in many different contexts and studyformats. Predicting infection from symptom data is critical, though usingsymptom data from varied studies in aggregate is challenging because the datais collected in different ways. Accordingly, different symptom profiles couldbe more predictive in certain studies, or even symptoms of the same name couldhave different meanings in different contexts. We assess state-of-the-arttransfer learning methods for improving prediction of infection from symptomdata in multiple types of health care data ranging from clinical, to home-visitas well as crowdsourced studies. We show interesting characteristics regardingsix different study types and their feature domains. Further, we demonstratethat it is possible to use data collected from one study to predict infectionin another, at close to or better than using a single dataset for prediction onitself. We also investigate in which conditions specific transfer learning anddomain adaptation methods may perform better on symptom data. This work has thepotential for broad applicability as we show how it is possible to transferlearning from one public health study design to another, and data collectedfrom one study may be used for prediction of labels for another, even collectedthrough different study designs, populations and contexts.

Domain Adaptation for Infection Prediction from Symptoms Based on Data from Different Study Designs and Contexts