Changes in capture availability due to infection can lead to detectable biases in population-level infectious disease parameters.

Correctly identifying the strength of selection that parasites impose on hosts is key to predicting epidemiological and evolutionary outcomes of host-parasite interactions. However, behavioral changes due to infection can alter the capture probability of infected hosts and thereby make selection difficult to estimate by standard sampling techniques. Mark-recapture approaches, which allow researchers to determine if some groups in a population are less likely to be captured than others, can be used to identify infection-driven capture biases. If a metric of interest directly compares infected and uninfected populations, calculated detection probabilities for both groups may be useful in identifying bias. Here, we use an individual-based simulation to test whether changes in capture rate due to infection can alter estimates of three key metrics: 1) reduction in the reproductive success of infected parents relative to uninfected parents, 2) the relative risk of infection for susceptible genotypes compared to resistant genotypes, and 3) changes in allele frequencies between generations. We explore the direction and underlying causes of the biases that emerge from these simulations. Finally, we argue that short series of mark-recapture sampling bouts, potentially implemented in under a week, can yield key data on detection bias due to infection while not adding a significantly higher burden to disease ecology studies.