Epidemiological Models

Infectious diseases of fish and wildlife are an increasing concern for resource managers. To manage fish and wildlife stocks in the presence of disease burdens, managers need information about the persistence of disease. A common measure of the extent of disease in a population is “apparent prevalence”—the proportion of organisms that test positively for or show symptoms of infection with the focal disease.

Apparent prevalence is relatively straightforward to measure; however, the measure depends highly on age structure. The true extent of a disease’s effect on a population is likely to be misestimated if the estimate does not incorporate information about the age structure of the affected population. For example, if animals in a population are born disease-free, and the probability that they will be killed by the disease increases with age, then using apparent prevalence will underestimate the true extent of the disease’s effect on the population.

Even though apparent prevalence is not an ideal way to measure disease in a population, the measure can be used in an age-structured, cross-sectional sample to estimate the force-of-infection.

Force-of-infection is the rate at which disease-free animals become infected. It can be used to test more sharply focused hypotheses about the epidemiology of diseases of fish and wildlife because it accounts for age structure. In addition, force-of-infection is an important parameter for many epidemiological models, and therefore is a critical factor for understanding the spread and persistence of disease.

In this illustration, the hazard functions (δ(a), λ(a), and δ(a)+µ(a) are age-specific, and they control the rates at which animals make transitions from one state to the other (alive or dead; disease-free or diseased), allowing us to consider the force-of-infection (or infection hazard) to be age-specific.

Currently, the NOAA Chesapeake Bay Office is working with the Maryland Department of Natural Resources to develop a model of the force-of-infection of the disease mycobacteria among striped bass.

Bioeconomic Allocation Models

Development of a multidisciplinary, scientific, integrated model that operates under a clear set of goals and objectives is a key step toward achieving transparency and integration in ecosystem-based fisheries management. Humans are a part of the ecosystem—a fact that is rarely included in ecosystem models—and their behavior as described in computer models must accurately reflect their goals and objectives. Bioeconomic allocation models include human behavior. For example, resource managers might want to establish an area as off-limits to all fishing, but recreational and commercial fishermen might disagree; enforcement problems with such a reserve could result. Bioeconomic allocation models could help find a balance more amenable to both sides.

Staff members at the NOAA Chesapeake Bay Office are working with partners at the Maryland Department of Natural Resources and other offices in NOAA Fisheries to develop a bioeconomic modeling framework that can help to provide open and transparent methods for making decisions about allocating fisheries resources.