Framework for heterogenous contact structures in bat-pathogen
interactions
Taken together, the information in this study emphasises that models of
bat disease dynamics that assume contact rate is density-dependent, but
assume transmission scales with total roost abundance, may not represent
actual contact structures. Such inadequate specification of transmission
may produce substantially biased estimates of the basic reproductive
number (R0) and propagate error to model predictions
like the probability of pathogen invasion and persistence, predicted
peak and timing of epidemics, and estimates of the force of infection
(Borremans et al. 2017; Hopkins et al. 2020).
Intermediate, non-linear or hybrid transmission functions are a possible
alternative to standard density-dependence (e.g. Antonovics, Iwasa &
Hassell 1995; Ryder et al. 2007; Cross et al. 2013;
Orlofske et al. 2017), but these may not reveal underlying
mechanisms for the relationship, and as a result, may be hard to selecta priori based on ecological information, and may not be
generalisable or predictive between bat roosts of the same species
(Smith et al. 2009; Ferrari et al. 2011). Instead of
modifying the transmission function, it may be better to investigate
alternative approaches to integrating contact structure within
host-pathogen models at ecologically relevant scales (De Jong 2002). We
therefore propose a framework (Fig. 3) to help guide the incorporation
of heterogenous contact structures into infectious disease models of
bats in ecologically relevant ways – for example by structuring groups
within roosts as metapopulations, with separate ecological processes
defining contacts within and between groups. Our framework prompts
ecological questions that may be relevant for specifying transmission
within wildlife disease models. They include whether hosts mix
homogenously throughout the roost or mix within smaller subgroups; how
population or group contacts are expected to change with increasing
abundance; and whether roost or group area fluctuates with abundance.
Given a roost has fluctuating abundance or density, the first step in
the framework is to consider the
nature of mixing in bat roosts. That is, whether bats mix
evenly/randomly throughout the roost, mix within smaller subgroups, or
have other structured contact networks. This will determine what scale
is ecologically relevant for transmission, and so, what scale(s) the
model should consider. If bats mix throughout the roost (i.e. all
individuals have equal likelihood of coming into contact) the mechanisms
driving contact rate will fall more simply to a choice between
density-dependent and frequency-dependent expected dynamics. If occupied
area changes with abundance, models will be best parameterised by
density at this roost scale, otherwise, by either abundance or density.
In cases where individuals interact within aggregate groups that include
only a proportion of the population, transmission mechanisms may need to
be more nuanced to include special structuring within the roost. This is
because the structure of the host population (and the strength of
coupling between local groups) may drive transmission between groups,
and be different to (and/or independent of) the nature of within-group
contacts (Jong, Diekmann & Heesterbeek 1995; Ferrari et al.2011). In other mammal systems, this paradox has led to cases where
dynamics appear to be density-dependent at the within-group scale, but
frequency dependent at the between-group scale (Ferrari et al.2011; Cross, Caillaud & Heisey 2013). In these cases, models that can
distinguish within- and between-group transmission pathways may be
useful (e.g. metapopulation models). If mixing is non-random and based
on individual contact networks, individual based models may provide a
good framework. Of course, the complexity of adopted models should be
driven by the objective of the investigation, and reflect a parsimonious
attempt to reproduce transmission patterns relevant to the system and
question. This need not necessarily capture every single mechanism in
the real system.
Consideration of these questions will provide a more ecologically
informed, mechanistic basis for specifying transmission, but will
require more data and more computational power. This may or may not be
achievable for many host species, for which basic ecological information
is lacking. Even if ecologically informed specification of transmission
is not possible, consideration of our framework will help to highlight
cases where traditional density-dependent transmission may fail to
reproduce data, and why. If integrated into research programmes, this
could create the opportunity for a model guided fieldwork approach
(Restif et al. 2012) and represent bat-disease systems in a more
holistic approach. This framework also assumes transmission between bats
is direct and occurs predominantly within the roost. This is consistent
with our knowledge of bat-virus systems of zoonotic importance
(Plowright et al. 2015). Nevertheless, understanding the nature
of density at transmission-relevant scales, and building this into
transmission dynamics, will be important to gain more realistic
predictions of pathogen invasion and persistence in bat populations.
This will be crucial for accurately forecasting disease risk from these
animals.
Conclusion
Transmission is the focal process
in host-pathogen interactions. The nature of infectious contacts, and
how transmission scales with animal density, is complex for host species
whose population structures are heterogenous and underpinned by
ecological processes across different scales. Using a high-profile
bat-virus system, we show that basic bat population measures from larger
scales were not strongly predictive of local scale measures where viral
transmission occurs. We also suggest that the
highly aggregative spatial
structuring of bats is likely to add substantial
heterogeneity to the contact
structure of roosting populations, further complicating models of
pathogen transmission. We urge researchers to carefully consider which
scale and modelling method is most relevant for transmission in
bat-virus models. More broadly, we propose a framework to guide the
structuring of transmission in more ecologically relevant contexts. This
approach can apply to many species that occupy communal breeding or
resting sites, and has an advantage over other statistically based
approaches by allowing selection of scale and transmission structurea priori based on ecological information. Outputs using this
ecologically informed approach will be more generalisable and predictive
of infection patterns, and can be used to gain mechanistic insight into
the drivers of transmission, local epidemics and pathogen spillover
risk.
Authors contributions
TJL conceived and designed the research, acquired funding and led
project administration; TJL and RB collected and curated the data; TJL,
AJP and HM analysed and visualised the data; AJP, HM, RKP and PE
provided supervision; TJL drafted the manuscript, and all authors
participated in review and editing of drafts.
Data Availability Statement
Acknowledgements
We would like to thank Beccy
Abbot, Kirk Silas, Devin Jones, Liam Chirio, Rachel Smethurst and Cara
Parsons for their assistance in the field.
We acknowledge the Danggan Balun,
Kabi Kabi, Turrbal, Widjabul Wia-bal, Yugambeh and Yuggera Ugarapul
people, who are the Traditional Custodians of the land upon which this
work was conducted. Fieldwork for this work was supported by the Paddy
Pallin Foundation, The Royal Zoological Society of NSW, The Foundation
for National Parks and Wildlife, the National Science Foundation (a
Dynamics of Coupled Natural and Human Systems grant DEB1716698) and a
DARPA PREEMPT program Cooperative Agreement (#D18AC00031). TJL was
supported by an Endeavour Postgraduate Leadership Award and a Research
Training Program scholarship sponsored by the Australian Government, AJP
was supported by an ARC DECRA fellowship (DE190100710) and a Queensland
Government Accelerate Postdoctoral Research Fellowship, RB was supported
by a Griffith University Honours Scholarship and an EFRI Thesis Write-Up
Scholarship, and RKP was supported by USDA National Institute of Food
and Agriculture (Hatch project 1015891). This research was conducted
under a Griffith University Animal Research Authority permit
(DEB-1716698), a Scientific Purposes Permit from the Queensland
Department of Environment and Heritage Protection (WISP17455716), a
permit to Take, Use, Keep or Interfere with Cultural or Natural
Resources (Scientific Purpose) from the Department of National Parks,
Sport and Racing (WITK18590417), a Scientific Licence from the New South
Wales Parks and Wildlife Service (SL101800) and general and products
liability protection permit (GRI 18 GPL), and with permission to
undertake research on council and private land. The content of the
information does not necessarily reflect the position or the policy of
the U.S. government, and no official endorsement should be inferred.
Tables and Figures
Table 1: Model comparison of
candidate model set. Best candidate models, as given by Akaike
information criterion (AIC), are bolded (ΔAIC<2).