Title: Domain Adaptation for Infection Prediction from Symptoms Based on Data from Different Study Designs and Contexts,
Abstract: Acute respiratory infections have epidemic and pandemic potential and thus
are being studied worldwide, albeit in many different contexts and study
formats. Predicting infection from symptom data is critical, though using
symptom data from varied studies in aggregate is challenging because the data
is collected in different ways. Accordingly, different symptom profiles could
be more predictive in certain studies, or even symptoms of the same name could
have different meanings in different contexts. We assess state-of-the-art
transfer learning methods for improving prediction of infection from symptom
data in multiple types of health care data ranging from clinical, to home-visit
as well as crowdsourced studies. We show interesting characteristics regarding
six different study types and their feature domains. Further, we demonstrate
that it is possible to use data collected from one study to predict infection
in another, at close to or better than using a single dataset for prediction on
itself. We also investigate in which conditions specific transfer learning and
domain adaptation methods may perform better on symptom data. This work has the
potential for broad applicability as we show how it is possible to transfer
learning from one public health study design to another, and data collected
from one study may be used for prediction of labels for another, even collected
through different study designs, populations and contexts.