Disease Ecology

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Disease Ecology

Hamish I McCallum, Griffith University, Brisbane, Queensland, Australia

Published online: December 2018

DOI: 10.1002/9780470015902.a0021215

Full Article on Wiley Online Library

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Abstract

The role of infectious disease in human history is well known, but its importance in ecology has been neglected until relatively recently. Disease ecology involves using ecological principles and concepts to understand and manage infectious diseases, in humans, other animals and plants. It is strongly based on mathematical theory and modelling approaches. The most important concept is R₀, the number of secondary cases per primary case when disease is rare. If it is greater than one, infection will increase in the population, but if it can be driven below one, disease can be eliminated. Most infectious agents affect more than one host species, and most host species harbour several species of parasite. Understanding the community context of infectious disease is, therefore, essential. Human‐induced changes, such as habitat destruction and fragmentation and climate change, can be expected to have major effects on the impact of infectious disease in natural ecosystems.

Key Concepts

Parasites and pathogens are important factors in understanding the evolution and ecology of organisms, but their importance is often not recognised.
The basic reproductive number R₀ (the number of secondary cases per primary case when disease is rare) is the single most important concept in understanding the ecology of disease.
If R₀ > 1, the disease can increase in the host population, but if R₀ < 1, disease cannot establish.
Microparasites, such as viruses, bacteria, and protozoans, can often be modelled by dividing the host population into susceptible, infected and resistant classes. For macroparasites such as worms, however, it is essential to consider the level of infection and recognise that a few hosts usually harbour most of the total parasite population.
Most parasite species infect more than one host species, and most hosts harbour several parasite species. It is essential to consider interactions between parasites within hosts and the effect of differing susceptibility of multiple host taxa.
Environmental changes produced by humans, such as climate change and habitat destruction, have major impacts on parasites and their effect on host populations.

Keywords: basic reproductive number; parasites; pathogens; conservation biology; climate change

Article Title:	Disease Ecology
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	Figure 1. Compartmental SIR models for infectious diseases. This is a description of a simple model in which transmission is density‐dependent, the pathogen affects only survival (and not fecundity), the pathogen is the only constraint on host population growth and there is no exposed but not yet infectious stage. The model can be modified to relax these assumptions. Hosts are divided into susceptible (S) infected (I) and resistant (R) compartments. Hosts in all compartments have a per capita birth rate a, and a death rate independent of disease b. In infected hosts, the disease increases the death rate by α. Infection occurs in a density‐dependent manner, with a per capita infection rate of susceptible hosts of βI. Infected hosts recover at a rate γ, and resistant hosts lose resistance at a rate ν. (a) The model represented as a flow diagram. (b) An example of an epidemic generated from these equations (a = 0.06, b = 0.04, β = 0.1, ν = 0.02, α = 0.5, γ = 0.5). S and R are scaled on the left axis, and I is scaled on the right axis. The time units are arbitrary.
	Figure 2. Counts of frog captures at El Copé, Panama before and after arrival of the amphibian chytrid fungus Batrachochytrium dendrobatidis. Frogs were sampled along four permanent transects, with counts shown as the number of captures per kilometre of stream per person. Counts of diurnal frogs from a number of species were recorded. The fungus was first detected at the site in late September 2004, and reached an overall prevalence of more than 50% by October 2004, with 47 species infected. The line shown is a segmented regression, with the point at which the slope changed estimated from the data as occurring in early September 2004. Source: Data from Lips et al. .
	Figure 3. Effect of temperature on malaria transmission. (a) Thermal performance curves for the components of vector biology contributing to R₀ (Anopheles spp. mosquitos and Plasmodium falciparum malaria parasites). (b) Overall effect of temperature on R₀. The blue line shows the estimate calculated taking into account the nonlinearities shown in (a), in contrast to the previous estimate in red, which was derived using linear relationships between traits and temperatures. The points represent observed data. It is important to note that the calculated R₀ represents an upper boundary to the potential malaria transmission: actual transmission rates may be constrained by factors other than temperature, and therefore fall below the theoretical line. Reproduced with permission from Mordecai et al. . © John Wiley and Sons.

Disease Ecology

Abstract

Key Concepts

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