Beyond Infection: Integrating Competence into Reservoir Host Prediction

Abstract

Most efforts to predict novel reservoirs of zoonotic pathogens use information about host exposure and infection rather than competence, defined as the ability to transmit pathogens. Better obtaining and integrating competence data into statistical models as covariates, as the response variable, and through postmodel validation should improve predictive research.

Keywords: SARS-CoV-2; machine learning; vector-borne disease; within-host; zoonoses.

Figure 1

Integrating Competence into Reservoir Host Predictive Models. We take a simplified statistical model (in matrix notation, where β represents regression coefficients and ε represents errors) and illustrate how data can be used as the response (arrows towards y), covariates (arrows towards X), and/or validation (arrows from y). Presence of pathogen antibody or antigen is commonly used as the response but conflates competence with exposure. Predictions from these models can be validated by field measures of competence, such as isolating live virus or diagnostics of certain arthropod vectors, both of which can also be more informative response variables. Both in silico and in vitro analyses can characterize receptor binding (i.e., informing susceptibility) and reveal host factors required for viral replication, which can be used as covariates or validation for models using PCR or serology responses. Lastly, in vivo experimental challenge studies can confirm pathogen replication and transmission to susceptible hosts or vectors, and results can serve as either the response or model validation.