Epidemic dynamics at the human-animal interface

Abstract

Few infectious diseases are entirely human-specific: Most human pathogens also circulate in animals or else originated in nonhuman hosts. Influenza, plague, and trypanosomiasis are classic examples of zoonotic infections that transmit from animals to humans. The multihost ecology of zoonoses leads to complex dynamics, and analytical tools, such as mathematical modeling, are vital to the development of effective control policies and research agendas. Much attention has focused on modeling pathogens with simpler life cycles and immediate global urgency, such as influenza and severe acute respiratory syndrome. Meanwhile, vector-transmitted, chronic, and protozoan infections have been neglected, as have crucial processes such as cross-species transmission. Progress in understanding and combating zoonoses requires a new generation of models that addresses a broader set of pathogen life histories and integrates across host species and scientific disciplines.

Fig. 1

(A) Schematic diagram of zoonotic transmission dynamics. Zoonoses can involve as many as four dynamical phases, including enzootic or epizootic circulation in the animal reservoir, spillover transmission from animals to humans, and sometimes self-limiting stuttering chains of human-to-human transmission or sustained transmission leading to outbreaks. Adapting Wolfe et al. (6), we classify zoonotic pathogens into three stages (II, III, and IV) according to their transmissibility among humans. (B) The spillover force of infection is determined by the product of three major components. The force of infection is defined as the per capita rate of infection of susceptible humans. Beneath each major component is a list of contributing factors drawn from many disciplines; these factors may pertain to all zoonoses or to particular transmission modes, as indicated.

Fig. 2

Dynamical models for epidemics. (A) Schematic diagram of the basic SIR model, showing progression of hosts from susceptible to infected to recovered states. The dotted arrows represent transmission of infection to new cases resulting from contacts between susceptible and infected individuals. (B) The simple epidemic curve (red) predicted by the SIR model for a closed population (i.e., without renewal by immigration or birth of new hosts) and R₀ > 1. The curves for the declining proportion of hosts that are susceptible to infection is black and for the rising proportion that are recovered and immune is blue. (C) Dynamical models provide unique insights and allow researchers to ask questions that cannot be addressed by other methods. For example, these models enable estimation of epidemiological parameters linked to key mechanisms, integration of data spanning multiple spatial scales, comparison of alternative control strategies, prediction of future trends, and explanation of observed patterns based on mechanistic hypotheses.

Fig. 3

Temporal profiles of total research effort (red) and modeling effort (blue) for recently emerged zoonoses. Figure panels have different y-axis scaling, but in each instance, the scaling for number of modeling studies (right axis) is 1/10th that for the total number of research papers (left axis).