State-Specific Detection Probabilities and Disease Prevalence

Abstract

Investigations of disease dynamics in wild animal populations often use estimated prevalence or incidence as a measure of true disease frequency. Such indices, almost always based solely on raw counts of infected and uninfected individuals, are often used as the basis for analysis of temporal and spatial dynamics of diseases. Generally, such studies do not account for potential differences in observer detection probabilities of host individuals stratified by biotic and/or abiotic factors. We demonstrate the potential effects of heterogeneity in state-specific detection probabilities on estimated disease prevalence using mark-recapture data from previous work in a House Finch (Carpodacus mexicanus) and Mycoplasma gallisepticum system. In this system, detection probabilities of uninfected finches were generally higher than infected individuals. We show that the magnitude and seasonal pattern of variation in estimated prevalence, corrected for differences in detection probabilities, differed markedly from uncorrected (apparent) prevalence. When the detection probability of uninfected individuals is higher than infected individuals (as in our study), apparent prevalence is negatively biased, and vice versa. In situations where state-specific detection probabilities strongly interact over time, we show that the magnitude and pattern of apparent prevalence can change dramatically; in such cases, observed variations in prevalence may be completely spurious artifacts of variation in detection probability, rather than changes in underlying disease dynamics. Accounting for differential detection probabilities in estimates of disease frequency removes a potentially confounding factor in studies seeking to identify biotic and/or abiotic drivers of disease dynamics. Given that detection probabilities of different groups of individuals are likely to change temporally and spatially in most field studies, our results underscore the importance of estimating and incorporating detection probabilities in estimated disease prevalence (specifically), and more generally, any ecological index used to estimate some parameter of interest. While a mark-recapture approach makes it possible to estimate detection probabilities, it is not always practical, especially at large scales. We discuss several alternative approaches and categorize the assumptions under which analysis of uncorrected prevalence may be acceptable.

Impact of Puumala Virus Infection on Maturation and Survival in Bank Voles: A Capture-Mark-Recapture Analysis

“…Our model shows a strong spatial and temporal variation in detection probabilities of bank voles related to their reproductive or PUUV infection status. This aspect is of major importance, as it implies that the use of PUUV infection indices (e.g., prevalence), not corrected for detection probability (i.e., site-or time-dependent detection probabilities), may result in a biased interpretation of patterns in bank vole PUUV epidemiology (see Jennelle et al, 2007, for a broader context). In contrast, the selected model based on AIC c did not include spatial or temporal variation in survival probabilities.…”

“…Our statistical analyses allowed comparison of survival between infected and noninfected individuals in the presence of temporally and spatially variable trappability (Williams et al, 2002;Jennelle et al, 2007). These methods add a probability of detection to the analysis that accounts for potential variation in the capture probability of subpopulations, such as infected vs. noninfected individuals (Jennelle et al, 2007).…”

Prevalence of West Nile Virus in Migratory Birds during Spring and Fall Migration

“…Additionally, it is not known whether an individual birds virus status would influence its capture probability thereby effecting the virus prevalence we detected. 25,26 Other studies of free living, wild species have yielded few detections of live virus at the time of capture. In the first year of widespread WNV activity in California, investigators were unable to detect viral activity in spring and fall migratory birds.…”

Avian malaria in Hawaiian forest birds: infection and population impacts across species and elevations

“…The importance of these epizootiological parameters for understanding hostpathogen dynamics, population effects, and on host-pathogen evolution have long been recognized, but are infrequently addressed (Scott 1988, Oli et al 2006, Murray et al 2009, Lachish et al 2011a. Quantifying epizootiological parameters in wildlife populations is difficult because methods used in human epidemiology seldom apply (McCallum et al 2001, Caley and Hone 2004, Lachish et al 2011a; however, recent applications of multi-state mark-recapture models have provided an important tool to assess infection dynamics and population impacts (Faustino et al 2004, Senar and Conroy 2004, Conn and Cooch 2009, Atkinson and Samuel 2010 while accounting for differential capture heterogeneity (Jennelle et al 2007). …”