\makeatletter
\@ifundefined{HCode}
{\documentclass[CRBIOL,Unicode,screen,biblatex,published]{cedram}
\addbibresource{crbiol20260062.bib}
\usepackage[T1]{fontenc}
\newenvironment{noXML}{}{}
\let\citep\parencite
\let\citet\textcite
\def\xcitealp#1#2{\citeauthor{#1}, \citelink{#1}{#2}}
\def\citealt#1#2{\citeauthor{#1}, \citelink{#1}{#2}}
\newcommand*{\citelink}[2]{\hyperlink{cite.\therefsection @#1}{#2}}
\def\defcitealias#1#2{}
\let\citepalias\parencite
\let\citetalias\textcite
\newenvironment{Table}{\begin{table}}{\end{table}}
\def\thead{\noalign{\relax}\hline}
\def\endthead{\noalign{\relax}\hline}
\def\tabnote#1{\vskip4pt\parbox{.9\linewidth}{#1}}
\def\tsup#1{\textsuperscript{#1}}
\def\tsub#1{\textsubscript{#1}}
\RequirePackage{etoolbox}
\usepackage[most]{tcolorbox}
\def\jobid{crbiol20260062}
%\graphicspath{{/tmp/\jobid_figs/web/}}
\graphicspath{{./figures/}}
\newcounter{runlevel}
\def\Custref#1#2{\hyperlink{#2}{#1}}
\def\enlabel#1{\hypertarget{#1}{}}
\setlength{\fboxsep}{1em}
\newlength\boxedlength
\boxedlength=\textwidth
\advance\boxedlength-2em
\advance\boxedlength-2\fboxrule
\newsavebox{\mybox}
\newenvironment{frtextbox}[1]
{\begin{tcolorbox}[top=10pt,bottom=10pt,leftupper=10pt,rightupper=10pt,toptitle=11mm,breakable, size=fbox, 
boxrule=.6pt, pad at break*=1mm, arc=-1pt,titlerule=-1pt,boxsep=8pt, colback=white,colbacktitle=white,coltitle=black,  
title={\large #1}%,code=\vskip -2\baselineskip
]}
{\end{tcolorbox}}
\let\MakeYrStrItalic\relax
\def\refinput#1{}
\def\back#1{}
\def\hyphen{\text{-}}
\def\0{\phantom{0}}
\def\xsection#1{}
\def\botline{\\\hline}
\DOI{10.5802/crbiol.204}
\datereceived{2026-01-26}
\daterevised{2026-07-09}
\dateaccepted{2026-07-10}
\ItHasTeXPublished
\makeatletter
\g@addto@macro{\UrlBreaks}{\UrlOrds}
\gappto{\UrlBreaks}{\UrlOrds}
\usepackage{hyperref}
\makeatother
}
{\documentclass[crbiol]{article}
\def\CDRdoi{10.5802/crbiol.204}
\let\newline\break
\def\selectlanguage#1{}
\def\enlabel#1{\label{#1}}
\makeatletter
\usepackage[T1]{fontenc}
\let\citep\parencite
\let\citet\textcite
\def\citelink#1#2{\citeyear{#1}}
\def\xcitealp#1#2{\citealp{#1}}
\PassOptionsToPackage{authoryear}{natbib} 
\def\href#1#2{\url[#1]{#2}}
\def\xsection#1{}
\def\no{n$^{\mathrm{o}}$}
\newcommand\@coi{}
\newcommand\COI[1]{\gdef\@coi{#1}}
\newcommand\printCOI{\ifx\@coi\@empty\else%
\section*{Declaration of interests}
\@coi\fi
}
\let\newline\break
}
\makeatother

\usepackage{upgreek}

\COI{The author does not work for, advise, own shares in, or receive
funds from any organization that could benefit from this article, and
has declared no affiliations other than their research organization.}

\dateposted{2026-08-17}
\begin{document}

\begin{noXML}

\CDRsetmeta{articletype}{opinion}

\title{The value of ``One Health'' modelling in zoonotic diseases
epidemiology and public health}

\alttitle{L'int\'{e}r\^{e}t de la mod\'{e}lisation \og Une Seule
Sant\'{e} \fg en sant\'{e} publique et \'{e}pid\'{e}miologie des
maladies zoonotiques}

\author{\firstname{Rapha\"{e}lle} \lastname{M\'{e}tras}\CDRorcid{0000-0002-2646-196X}}
\address{Sorbonne Universit\'{e}, INSERM, Sorbonne Public Health
Institute (SPH), F75012 Paris, France}
\curraddr{Sorbonne Universit\'{e} -- Facult\'{e} de
M\'{e}decine, Site Saint-Antoine, UMRS-1136 -- BC 2908, 27 rue
Chaligny, 75012 Paris, France}
\email{raphaelle.metras@inserm.fr}

\keywords{\kwd{Zoonoses}\kwd{Public health}\kwd{One Health
modelling}\kwd{Epidemiology of infectious diseases}}

\altkeywords{\kwd{Zoonoses}\kwd{Sant\'{e}
publique}\kwd{Mod\'{e}lisation \og Une Seule Sant\'{e}
\fg}\kwd{\'{E}pid\'{e}miologie des maladies infectieuses}}

\begin{abstract} 
Assessing the impact of control and prevention strategies on zoonotic
diseases in human populations requires the use of formal modelling
frameworks, integrating animal, environmental and human data. However,
such quantitative ``One Health'' approaches to guide public health
interventions are, to date, overlooked. Their implementation could be
facilitated by improved access to epidemiological, demographic, and
contact data on different hosts, by improved training of
epidemiologists and modellers through relevant multidisciplinary
research career paths, and finally by improving institutional support
for better visibility.
\vspace*{-3pt}
\end{abstract}

\begin{altabstract}
\'{E}valuer l'impact des mesures de contr\^{o}le et de pr\'{e}vention
des zoonoses en population humaine n\'{e}cessite l'utilisation
d'approches de mod\'{e}lisation quantitatives, int\'{e}grant des
donn\'{e}es sur les animaux, l'environnement et l'humain. Cependant,
ces approches quantitatives \og Une Seule Sant\'{e} \fg visant
\`{a} guider les interventions en sant\'{e} publique sont \`{a} ce jour
peu d\'{e}velopp\'{e}es. Leur mise en \oe{}uvre serait facilit\'{e}e
par un meilleur acc\`{e}s aux donn\'{e}es \'{e}pid\'{e}miologiques,
d\'{e}mographiques, et de contact entre les diff\'{e}rents h\^{o}tes
impliqu\'{e}s, par une meilleure orientation des jeunes chercheurs en
\'{e}pid\'{e}miologie et mod\'{e}lisation vers une trajectoire de
recherche multidisciplinaire adapt\'{e}e, et enfin par un soutien
institutionnel am\'{e}liorant la visibilit\'{e} de ces approches.
\end{altabstract}

\editornote{This work has been submitted at the invitation of the
editorial committee following the award of a Subvention Scientifique de
la Fondation Simone et Cino Del Duca - Institut de France in 2024.}

\thanks{Fondation Simone et Cino Del Duca - Institut de France, MoZArt
project (ANR-22-CE35-0003), ARCHE project (ANR-23-PEPZ-0003)}

%\input{CR-pagedemetas}

\maketitle

\twocolumngrid

\end{noXML}

\xsection{}
Zoonotic diseases are infectious diseases that can be transmitted from
animals to humans, and {vice versa}. They represent about 60\%
of infectious diseases and 70\% of emerging ones \citep{Jonesetal2008,
Tayloretal2001, Woolhouse2002}. Zoonotic pathogens are maintained
within animal populations (or at the interface between animals and the
environment) and can be transmitted to humans upon circumstances
favouring contact between animals and humans, such as handling
infectious animals or during outdoors activities.

Current global challenges, such as climate change, urbanisation, human
population growth and mobility, agricultural practices or deforestation
\citep{IPBES2019}, contribute to increasing opportunities of  contacts
between animals and humans, and, therefore, of zoonotic pathogen
transmission. In this context, approaches that integrate animals,
humans and the environment are needed to better prevent, limit and
control the impact of future (re)emergences. This is one of the visions
that is being promoted by ``One Health'' initiatives, which have been
under the spotlight and redefined in recent years by the quadripartite
One Health High-Level Expert Panel (OHHLEP), including the Food and
Agriculture Organization of the United Nations (FAO), the United
Nations Environment Programme (UNEP), the World Health Organization
(WHO) and the World Organisation for Animal Health (WOAH)
\citep{WHO2026} and highlighted further at the international ``One
Health'' summit in Lyon in April 2026
\citep{OneHealthsummitExpertgroup2026}.\looseness=-1

To combat zoonoses in human populations, it is important to distinguish
two types of zoonotic pathogens based on their between-human
transmission potential (Figure~\ref{fig1}, Text Box~\Custref{1}{box1}):  (i)
zoonotic pathogens for which between-human transmission is the
predominant transmission route, such as Ebola, Dengue, SARS-COV-2
(defined as ``case~1'' in Figure~\ref{fig1}A and Text Box~\Custref{1}{box1}),
and (ii) zoonotic pathogens with none or limited between-human
transmission, such as West Nile fever, \textit{Borrelia burgdorferi},
rabies (defined as ``case~2'' in Figure~\ref{fig1}C and Text
Box~\Custref{1}{box1}) \citep{Baum2008}. In ``case~1'', most human infections
result from  between-human transmission events, whilst in ``case~2''
the large majority of human infections will result from several
zoonotic transmission events.  For details on definitions, see Text
Box~\Custref{1}{box1}.

\onecolumngrid

\vspace*{-12pt}

\mbox{}\unskip\noindent
\begin{frtextbox}{\textbf{Text Box 1. Two types of zoonotic
pathogens based on their between-human transmission potential and
implications in public health, and diversity of zoonotic transmission
pathways}}\enlabel{box1}

\vspace*{-.7pc}

{\advance\baselineskip by -.075pt

\textbf{Case~1. Zoonotic pathogens with predominant between-human
transmission (Figure~\ref{fig1}A,B)}. For such pathogens, the majority
of human infections result from between-human transmission events (via
a vector or not). Exemplar pathogens are Ebola, Dengue, SARS-COV-2,
etc. Factors such as human mobility, contact patterns, and
entomological factors, when relevant, are major components to study
pathogen transmission \citep{Chengetal2025, Harishetal2024,
Taubeetal2025}.  Combating such epidemics focuses mainly on
implementing control strategies for the human host, and the value of
modelling in this context has now been established, especially
following the SARS-CoV-2 pandemic \citep{Colosietal2022,
deMeijereetal2023, Kucharskietal2020, vanKleefetal2025}. For ``case~1''
zoonotic pathogens, the ``One Health'' vision may be used to trace back
the animal origin of the pathogen, for example by combining genomics
approaches and outbreak investigations \citep{Catalanoetal2024,
Kawasakietal2025, Pekaretal2022}.  It may then be useful to stress that
reducing human encroachment in wild habitats and the trade of wild
animal products may limit risks of novel emergence in the long term
\citep{Morandetal2014}.

\textbf{Case~2. Zoonotic pathogens with none or limited between-human
transmission (Figure~\ref{fig1}C,D).} For such pathogens, an infected
human is unable or has limited ability to multiply and transmit the
zoonotic pathogen to another human, and most human cases will result
from a multitude of animal-to-human (via a vector or not) spillover
transmission events. They represent the large majority of zoonotic
pathogens \citep{WoolhouseGowtageSequeria2005}.  Exemplar ones are
Crimean-Congo Haemorragic fever virus (CCHFV),  \textit{Borrelia
burgdorferi} s.l., West Nile virus (WNV), Rift Valley fever virus
(RVFV), High pathogenicity Avian Influenza virus (H5-HPAI),  rabies
virus, \textit{Leptospirosis} bacterium, Lassa fever virus,  Nipah
virus, etc. These zoonotic infections translate into fewer human  cases
or outbreaks of smaller size than those illustrated in ``case~1''. 
Yet, they are of considerable concern for public health. Beyond severe
human disease or fatalities they may cause \citep{Belhadietal2022}, 
they represent a threat because of their potential for spread into
ecosystems (in hosts or in the environment), and their potential for
increased incidence, especially in the context of climate change. Also,
their possible genomic evolution may lead to increased virulence in
some species or widened host range \citep{GandonLion2022,
Mirandaetal2021, WoolhouseGowtageSequeria2005, Woolhouseetal2005}.

\textbf{Zoonotic transmission pathways.} For ``case~1'', but mostly for
``case~2'' pathogens, estimating zoonotic transmission routes is
paramount to estimate the burden of zoonotic disease in human
populations. Pathogen transmission from animals can be indirect (e.g.\ 
through arthropod vectors, oral through the environment, or fomites),
or direct (e.g.\ contact with infectious tissues or materials, by
inhalation), or a combination of these \citep{Cossaboometal2026}. The
variety of zoonotic transmission routes  are listed below
\citep{CenterforFoodSecurityandPublicHealth2026, WHO2024}:
\begin{itemize}
\item Inhalation: infectious respiratory particles from an infected
animal infect humans through breathing

\item Direct contact: pathogens in the environment or in infected
animals, infect humans through wound, mucous membrane or skin

\item Vector-borne: pathogens in the vector, infect humans through
infectious arthropod bite

\item Oral: pathogens in water or food infect humans following
ingestion

\item Fomite: pathogens on inanimate object carried from animals to
humans.
\end{itemize}

}

\end{frtextbox}

\begin{figure*}
\vspace*{-2pt}
\includegraphics{fig01}
\vspace*{-2pt}
\caption{\label{fig1}Zoonotic pathogens transmission pathways and
implications in disease control and prevention options for (A,B)~pathogens
with predominant between-human transmission; (C,D) pathogens with none
or limited between-human transmission. Examples of prevention and
control strategies in the human, animal/environment  compartment, and
at the interface between the two (red crosses); solid green arrows
represent zoonotic transmission, solid black lines represent
between-human transmission, examples of options for disease control and
prevention are illustrated in red.}
\vspace*{-10pt}
\end{figure*}

\twocolumngrid

This difference in between-human transmission has major public health
implications. Indeed, combating epidemics of ``case~1'' zoonotic
pathogens (case~1, Figure~\ref{fig1}A) will focus mainly on
interventions targeting the human host (Figure~\ref{fig1}B, e.g.\ 
vaccination, or non-pharmaceuticaI interventions such as reducing
between-human contacts). In such a setting, the use of modelling to
assess the impact of interventions is now widely recognised
\citep{Sofoneaetal2022}. However, reducing the public health burden 
of zoonotic pathogens with none or limited between-human transmission
(case~2, Figure~\ref{fig1}C) can be addressed by implementing control
and prevention measures at different levels: within animal populations,
within human populations, and/or at the interface between the two 
(Figure~\ref{fig1}D). In this context, ``One Health modelling'', which
is  the use of data-driven model frameworks developed at the
animal--environment--human interface, appears highly valuable to
estimate various zoonotic transmission routes 
(Text Box~\Custref{1}{box1},
zoonotic transmission routes) and to formally assess control options
and guide public health decisions. For example, modelling the Rift
Valley fever (RVF) epidemic in Mayotte at the interface
{between} cattle, humans, and the environment in combination with surveillance data was
useful (i) to estimate that emergences in animals and in humans were
synchronised; (ii) to estimate that zoonotic transmission by mosquito
bites and by direct contact resulted in an almost similar proportion of
infections during the 2018--2019 epidemics, therefore stressing the
importance of targeting prevention campaigns to both the general
population and amongst farmers; and (iii) to compare vaccination
strategies in an epidemic context \citep{Metrasetal2020}. Similarly,
``One Health'' Crimean-Congo Haemorrhagic  fever (CCHF) modelling was
also useful to assess that in high-endemic areas, vaccinating at-risk
human populations was more effective than vaccinating livestock in the
long run \citep{Vesgaetal2022}. Regarding Lyme borreliosis, a ``One
Health''  quantitative framework was valuable to identify that tick
bites were a major risk factor in the spatial distribution of the
disease, therefore stressing the importance of limiting human exposure
\citep{Fuetal2023}.\looseness=-1

The importance and relevance of modelling to study zoonotic diseases
and address ``One Health'' questions has been highlighted over the past
decade \citep{Heerdenetal2023, Scoonesetal2017}. Recent reviews
conducted on modelling zoonotic diseases have underlined efforts made
towards modelling transmission between hosts, but highlighted that
their use and implementation in public health remain, to date, limited
\citep{deWitetal2024, Doohanetal2024, Laidlowetal2025, Layanetal2021,
Reesetal2021}.  In this context, and from our experience, we identify
three key challenges to the implementation of ``One Health'' modelling
to guide public health decisions and suggest directions for
improvement. \textbf{Data needs.} The scarcity of available
epidemiological and demographic data on the different incrimimated
hosts in the same ecosystems under study, and the lack of quantitative
between-species contact data, specifically at the animal--human
interface, limits the possibilities for model calibration or validation
\citep{deWitetal2024, EuropeanFoodSafetyAuthorityEFSAetal2025,
Reesetal2021}. We stress here the importance of implementing
multi-hosts  synchronised data collection, which should be designed
jointly by veterinary epidemiologists, human epidemiologists and animal
population ecologists \citep{Bordieretal2020, Hallidayetal2012,
OneHealthHighLevelExpertPanelOHHLEPetal2023}. We advocate to
support,  build upon or learn from existing multi-host long-term data
collection, as implemented for West Nile surveillance in Italy
\citep{Bellinietal2014, Gobboetal2025} or for Rift Valley fever in
Mayotte, where an entomological component may be useful to add 
\citep{Kimetal2021, Lernoutetal2013, Metrasetal2020, Metrasetal2016}; 
and when implementing new protocols, we advocate for building them 
with a long-term vision. Finally, we encourage quantifying
animal--human  contacts to improve model parametrisation, a subject
which is tackled in the research funded by the Fondation Simone and
Cino Del Duca award we  received in 2024 (``Estimating contacts at the
animal--human interface''). \textbf{Modelling needs.} The expertise to
carry out such ``One Health'' modelling approaches needs
multidisciplinary leadership that integrates public health, animal
health, ecology, epidemiology, and modelling expertise, but also peer
recognition in those fields, which is long and complex to acquire;
putting in place multidisciplinary university research degrees is not
enough for this. We underline here the importance and the need to
promote the next generation of ``One Health'' modellers, by guiding
research degree students and young researchers early in their career
path. In practice, this can be done by mentoring young researchers
through multidisciplinary career pathways once they have established
their initial training in a modelling discipline, or, conversely, by
mentoring young researchers in quantitative disciplines once they are
trained in the field of application they wish to target.
\textbf{Institutional support}. Whilst ``One Health'' questions have
undoubtedly gained institutional visibility nationally and
internationally \citep{AcademieVeterinairedeFrance2026,
OneHealthsummitExpertgroup2026}, ``One Health'' modelling to guide
public  health decisions is still overlooked, and lacks visibility and
institutional support. To gain visibility, we advocate borrowing
strength from established scientific communities or research groups in
infectious disease dynamics from a public health perspective. Several
exemplar national and international initiatives can be cited, such as
the French Research Action on Modelling Epidemics (FRAME) network
\citep{ANRSMIE2026}, or the Global Society for Infectious Disease
Dynamics \citep{GSIDD2025}. Finally, being hosted within human health
institutions is  essential to allow the acculturation of the research
to public health  questions, whilst acculturating human health
institutions to ``One Health'' visions. This path has proved
successful, as illustrated by Sorbonne University Modeling Outbreaks
Center (SUMOC), within Sorbonne Public Health Institute, which has made
``One Health'' modelling one of their structural pillars.

\section*{Acknowledgements}

We thank Sorbonne University Modeling Outbreaks Center (SUMOC),
supported by INSERM, Sorbonne Universit\'{e}, and ANRS-MIE, for
integrating ``One Health'' modelling into their forthcoming
{priorities.} This perspective is nourished from almost 20 years of
research in the fields of zoonotic epidemiology, modelling and public
health, mainly funded by a Sir Henry Wellcome postdoctoral fellowship
(101581/Z/13/Z, 2014--2017), an ANR JCJC (MoZArt project,
ANR-22-CE35-0003, 2023--2027), a young researcher grant Fondation Simone
et Cino Del Duca - Institut de France (2024--2027), and ARCHE project
(PEPR PREZODE, ANR-23-PEPZ-0003, 2024--2029). We wish to thank Thierry
Boulinier for complementary discussions on the importance of ``One
health'' in ecology and biodiversity and for comments on the paper; and
Vittoria Colizza, SUMOC Director, for support in leveraging ``One
Health'' modelling at the institutional level, and for valuable remarks
in shaping the final version of the manuscript.\looseness=1

\CDRGrant[ANR]{ANR-22-CE35-0003}
\CDRGrant[ANR]{ANR-23-PEPZ-0003}

\printCOI

\back{}

\printbibliography
\refinput{crbiol20260062-reference.tex}

\end{document}
