Host contact and shedding patterns clarify variation in pathogen exposure and transmission in threatened tortoiseGopherus agassizii: implications for disease modelling and management

Abstract

Most directly transmitted infections require some form of close contact between infectious and susceptible hosts to spread. Often disease models assume contacts are equal and use mean field estimates of transmission probability for all interactions with infectious hosts. Such methods may inaccurately describe transmission when interactions differ substantially in their ability to cause infection. Understanding this variation in transmission risk may be critical to properly model and manage some infectious diseases. In this study, we investigate how varying exposure and transmission may be key to understanding disease dynamics in the threatened desert tortoise Gopherus agassizii. We created heterogeneity in Mycoplasma agassizii exposure (the putative bacterial agent of a respiratory disease) by varying the duration of interactions between naturally infected and uninfected captive desert tortoises. Using qPCR, we identified new infections and compared models of transmission probability as a function of contact duration and pathogen load. We then examined the contact patterns of a wild tortoise population using proximity loggers to identify heterogeneity in contact duration. The top-ranked model predicting M. agassizii transmission included a dose term defined as the product of the number of days in proximity to an infected host and the infection level of that host. Models predicted low transmission probability for short interactions, unless the infectious host had a high load of M. agassizii: such hosts were predicted to transmit infection at higher rates with any amount of contact. We observed predominantly short-lived interactions in a free-ranging tortoise population and thus, expect transmission patterns in this population to vary considerably with the frequency and duration of high infection levels. Mean field models may misrepresent natural transmission patterns in this and other populations depending on the distribution of high-risk contact and shedding events. Rapid outbreaks in generally solitary species may result from changes to their naturally low-risk contact patterns or due to increases in the frequency of severe infections or super-shedding events - population characteristics that should be further investigated to develop effective management strategies.

Disease implications of animal social network structure: A synthesis across social systems

“…First, because the impact of edge weights on disease transmission can be context dependent, depending on the type of interaction, transmission mode of pathogen, and the relative time‐scale of network collection and pathogen spread, we have chosen to not include edge weights while performing our computational disease experiments. Future meta‐analytic studies can leverage a growing number of transmission studies to explicitly incorporate the role of contact intensity on disease transmission (Aiello et al., ; Manlove, Cassirer, Plowright, Cross, & Hudson, ). Second, we assume that social contacts do not covary with pathogen characteristics and remain unaltered after an infection is introduced into a population.…”

Breaking beta: deconstructing the parasite transmission function

“…Some host–parasite systems can, however, be used in this context: in a recent study Aiello et al . [51] undertook experiments with desert tortoises ( Gopherus agassizii ) and showed transmission likelihood was a function of time an infected and susceptible host spent together (usually in a burrow) and were able to estimate transmission patterns from data collected from proximity loggers. Notably the teleost-gyrodactylid systems where the metazoan parasites behave as if they were microparasites, can also be used in this context, where stage 5, the dose acquired, can be observed in vivo under a dissecting microscope using anaesthesia to immobilize the host [57,58].…”

Transmission risk predicts avoidance of infected conspecifics in Trinidadian guppies

“…Social interactions between individuals influence infectious disease dynamics at the population level (Aiello et al., ; Clay, Lehmer, Previtali, St. Jeor, & Dearing, ; Grear, Perkins, & Hudson, ), so understanding factors affecting these interactions and how they change in the presence of disease will facilitate more accurate predictions of how diseases spread (Aiello et al., ; Hawley, Etienne, Ezenwa, & Jolles, ; Lloyd‐Smith, Schreiber, Kopp, & Getz, ; Paull et al., ; VanderWaal & Ezenwa, ). Social animals associating with infected conspecifics likely increase their risk of infection, particularly with directly transmitted disease‐causing organisms, and there is evidence from multiple taxa that they avoid doing so (Behringer, Butler, & Shields, ; Croft et al., ; Goodall, ; Kavaliers, Fudge, Colwell, & Choleris, ; Kiesecker, Skelly, Beard, & Preisser, ; Poirotte et al., ; Schaller, ).…”

“…Historically, models have assumed homogeneous population mixing and transmission risk, that is mean field estimates of β c and β p , but this typically leads to overestimated transmission rates (Keeling & Grenfell, ). More recent work has demonstrated that incorporating empirical estimates of heterogeneity in both β c and β p improves model fit to natural disease dynamics (see Aiello et al., and references therein), but that β c and β p may themselves covary has been largely ignored. However, this covariation has potentially powerful implications for disease dynamics.…”