Know more

Our use of cookies

Cookies are a set of data stored on a user’s device when the user browses a web site. The data is in a file containing an ID number, the name of the server which deposited it and, in some cases, an expiry date. We use cookies to record information about your visit, language of preference, and other parameters on the site in order to optimise your next visit and make the site even more useful to you.

To improve your experience, we use cookies to store certain browsing information and provide secure navigation, and to collect statistics with a view to improve the site’s features. For a complete list of the cookies we use, download “Ghostery”, a free plug-in for browsers which can detect, and, in some cases, block cookies.

Ghostery is available here for free:

You can also visit the CNIL web site for instructions on how to configure your browser to manage cookie storage on your device.

In the case of third-party advertising cookies, you can also visit the following site:, offered by digital advertising professionals within the European Digital Advertising Alliance (EDAA). From the site, you can deny or accept the cookies used by advertising professionals who are members.

It is also possible to block certain third-party cookies directly via publishers:

Cookie type

Means of blocking

Analytical and performance cookies

Google Analytics

Targeted advertising cookies


The following types of cookies may be used on our websites:

Mandatory cookies

Functional cookies

Social media and advertising cookies

These cookies are needed to ensure the proper functioning of the site and cannot be disabled. They help ensure a secure connection and the basic availability of our website.

These cookies allow us to analyse site use in order to measure and optimise performance. They allow us to store your sign-in information and display the different components of our website in a more coherent way.

These cookies are used by advertising agencies such as Google and by social media sites such as LinkedIn and Facebook. Among other things, they allow pages to be shared on social media, the posting of comments, and the publication (on our site or elsewhere) of ads that reflect your centres of interest.

Our EZPublish content management system (CMS) uses CAS and PHP session cookies and the New Relic cookie for monitoring purposes (IP, response times).

These cookies are deleted at the end of the browsing session (when you log off or close your browser window)

Our EZPublish content management system (CMS) uses the XiTi cookie to measure traffic. Our service provider is AT Internet. This company stores data (IPs, date and time of access, length of the visit and pages viewed) for six months.

Our EZPublish content management system (CMS) does not use this type of cookie.

For more information about the cookies we use, contact INRA’s Data Protection Officer by email at or by post at:

24, chemin de Borde Rouge –Auzeville – CS52627
31326 Castanet Tolosan CEDEX - France

Dernière mise à jour : Mai 2018

Menu Logo Principal Oniris

Home page

Defense of Hélène Cécilia's thesis

Defense Cécilia
Hélène Cécilia will defend her thesis on October 28, 2021 at 1:30 pm in room Amphi Godfrain - La Chantrerie - Nantes at Oniris on: Modeling Rift Valley fever transmission dynamics : insight from micro- to macro-scale studies

Members of the jury :

  • Reviewers before defense :
    • Cécile VIBOUD, Staff scientist, International Center, National Institutes of Health, États-Unis
    • Samuel ALIZON, Research Director, CNRS, Montpellier, France
  • Examiners :
    • Catherine LINARD, Professor, Université de Namur, Belgique
    • Sébastien LEQUIME, Associate Professor, Université de Groningen, Pays-Bas
    • Frédérick ARNAUD, Research Director, INRAE, Lyon, France
  • PhD supervisor :
    • Pauline EZANNO, Research Director, INRAE, Nantes, France
  • PhD co-supervisor :
    • Raphaëlle MÉTRAS, Researcher, INSERM, Paris, France
    • Renaud LANCELOT, veterinary epidemiologist, CIRAD, La Réunion

Abstract :

Rift Valley fever (RVF-V) is a viral, vector-borne, zoonotic disease. It affects multiple hosts, mainly livestock such as cattle, sheep, and goats. We aimed at enhancing knowledge on the relative contribution of host species to transmission, focusing on Senegal, a region with recurring RVFV circulation. With a systematic review of existing mechanistic models of RVFV transmission dynamics, we identified critical knowledge gaps and characterised the diversity of modelling approaches. We mapped RVF epidemic potential in northern Senegal for three consecutive rainy seasons (2014-2016), using the basic reproduction number. September was identified as a period of high risk of amplification following RVFV introduction. To quantify differences in infectiousness between host species, we developed the first within-host model of RVFV production by host cells, and estimated the relationship between host infectious viral loads and the probability to infect mosquitoes. The results showed that sheep were the most infectious host species, and that Aedes vectors were more likely to get infected than Culex when fed with similarly infectious bloodmeals. We incorporated this heterogeneity into a metapopulation model representing seasonal herd movements in northern Senegal. We quantified the delay between RVFV introduction through nomadic herds and infection of sedentary populations. We highlighted a systematic earlier infection in cattle than goats and sheep. This PhD used modeling to disentangle the complex transmission dynamics of a multi-host multivector pathogen, highlighting possible avenues for the development of efficient and targeted control strategies, crucial to ultimately prevent human cases.

Keywords :

Rift Valley fever virus, mathematical modeling, vector-borne disease, multi-host, Senegal