Abstract
Estimating eco-epidemiological parameters in free-ranging populations can be challenging. As known individuals may be undetected during a field session, or their health status uncertain, the collected data are typically “imperfect”. Multi-event capture-mark-recapture (MECMR) models constitute a substantial methodological advance by accounting for such imperfect data. In these models, animals can be “undetected” or “detected” at each time step. Detected animals can be assigned an infection state, such as “susceptible” (S), “infected” (I), or “recovered” (R), or an “unknown” (U) state, when for instance no biological sample could be collected. There may be heterogeneity in the assignment of infection states, depending on the manifestation of the disease in the host or the diagnostic method. For example, if obtaining the samples needed to prove viral infection in a detected animal is difficult, this can result in a low chance of assigning the I state. Currently, it is unknown how much uncertainty MECMR models can tolerate to provide reliable estimates of eco-epidemiological parameters and whether these parameters are sensitive to heterogeneity in the assignment of infection states. We used simulations to assess how estimates of the survival probability of individuals in different infection states and the probabilities of infection and recovery responded to (1) increasing infection state uncertainty (i.e., the proportion of U) from 20 to 90%, and (2) heterogeneity in the probability of assigning infection states. We simulated data, mimicking a highly virulent disease, and used SIR-MECMR models to quantify bias and precision. For most parameter estimates, bias increased and precision decreased gradually with state uncertainty. The probabilities of survival of I and R individuals and of detection of R individuals were very robust to increasing state uncertainty. In contrast, the probabilities of survival and detection of S individuals, and the infection and recovery probabilities showed high biases and low precisions when state uncertainty was >50%, particularly when the assignment of the S state was reduced. Considering this specific disease scenario, SIR-MECMR models are globally robust to state uncertainty and heterogeneity in state assignment, but the previously mentioned parameter estimates should be carefully interpreted if the proportion of U is high.
Highlights
Describing the dynamics of infectious diseases and accurately quantifying their effects on hosts is of critical relevance for human public health and the associated economic costs
When state uncertainty increased from 20% to 90%, bias tended to increase for most parameter estimates, irrespective of whether the increase was homogeneous across infection states (Table 2, Figure 3) or heterogeneous (Table 2, Figure 5)
We show (Figure 3) how bias varied with increasing state uncertainty when we homogeneously decreased the assignment probability of infection states
Summary
Describing the dynamics of infectious diseases and accurately quantifying their effects on hosts is of critical relevance for human public health and the associated economic costs. Infectious diseases can seriously reduce the population size of endangered wildlife species [e.g., fungal infection chytridiomycosis in frogs and salamanders: [6]; white-nose syndrome in bats: [7]; facial tumor disease in the Tasmanian devil: [8]]. Individually known animals may be [1] undetected during a field session, or detected but their health status may be [2] unknown e.g., when no biological sample could be collected, or [3] uncertain e.g., when clinical signs are subtle and similar for different pathogens, or when diagnostic tests include false negative or false positive results [16]. These situations regularly occur when the model species is elusive, or when biological samples can only be collected opportunistically or by noninvasive techniques [15]
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