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  • Associate Professor
  • Clinical Research Assistant
  • Full Professor
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  • PhD Student
  • Physician
  • Post-doc
  • Project Manager
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  • Research Engineer
  • Retired scientist
  • Technician
  • Undergraduate Student
  • Veterinary
  • Visiting Scientist
  • Deputy Director of Center
  • Deputy Director of Department
  • Deputy Director of National Reference Center
  • Deputy Head of Facility
  • Director of Center
  • Director of Department
  • Director of Institute
  • Director of National Reference Center
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  • Head of Facility
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Starting Date
28
Feb 2019
Status
Ongoing
Members
6
Structures
1
Publications
10

About

Zoonotic viruses are viruses that have an animal reservoir but may also infect humans. The 2009 A(H1N1)pdm09 influenza pandemic, the SARS epidemic in 2003, the recent emergence of a novel coronavirus in the Middle East or the large epidemic of Ebola in West Africa are recent reminders of the global health threat posed by zoonotic viruses. Prior to widespread emergence in human populations, such pathogens can cause occasional infections in subpopulations that have been exposed to reservoir species (common reservoir species include for example bats, birds, swine or non-human primates). Whilst viruses causing such ‘‘spill-over’’ infections are usually poorly adapted for sustained human-to-human transmission, they can be under strong selective pressure to increase transmissibility once in humans. If such adaptation occurs, a widespread epidemic in humans is possible.

Each time the world is confronted by an emerging zoonotic virus, fast and sound risk assessment is essential to inform policy making. Determining the transmission scenario is a key priority. Are we in a scenario where most human cases are being infected by the animal reservoir or can transmission in humans be self-sustaining? It is also important to assess the severity, in particular the proportion of cases that die. However, characterizing the transmission and severity scenarios at the start of emerging zoonotic virus epidemics may prove challenging because surveillance data are often scarce (for example only a small proportion of cases are detected) and may provide a biased view of reality (for example if only severe cases are detected). The Mathematical Modelling of Infectious Diseases Unit is specialized in the development of methods to overcome these problems and perform robust risk assessment for emerging pathogens in a context where data are imperfect.

The zoonotic viruses we are or have been working on include among others Nipah, MERS-CoV, yellow fever, plague, ebola. Below are a few examples:

Nipah virus: Nipah virus (NiV) is a bat-borne paramyxovirus found throughout South and South East Asia. With a case fatality ratio of >70% and no available treatment or vaccines, NiV has been identified by the World Health Organization as an emerging infectious disease having a high risk of causing large epidemic if it adapts to humans. With our international partners, we are developing a One Health approach to better understand the threat posed by Nipah virus. See more here.

Plague: Plague, a disease caused by a gram-negative bacillus Yersinia pestis, has been linked to three major historic pandemics with devastating impact on human populations. Prompt treatment with appropriate antibiotics is usually effective; however, case fatality ratio of cases reported to the World Health Organization (WHO) has remained high, due to the difficulty to identify and treat cases quickly enough. A few countries continue to report plague cases annually and, despite surveillance and response efforts, Madagascar accounts for 75% of plague cases reported to WHO. In 2017, the country was affected by a major plague epidemic. Our Unit was heavily involved in the response, providing technical support to our colleagues from Institut Pasteur of Madagascar (IPM) where the Plague Reference Center is located. We collaborated with our colleagues from IPM to better understand the epidemiology and the transmission dynamics of plague, assess the performance of existing diagnostics and optimize the case classification.

Emerging arboviruses: Risk assessments about emerging pathogens are usually performed for viruses that can already generate large outbreaks in humans. There is hope that in the future, we might be able to identify potential viral threats, even before they have evolved to become transmissible in humans. In a collaboration with the team of Marco Vignuzzi at Institut Pasteur, we are going to monitor viral populations in mosquitoes to identify mutants that may gain a transmission advantage and develop mathematical models to assess implications for the risk of emergence.

MERS-CoV: The Middle East respiratory syndrome-related coronavirus (MERS-CoV) is a novel coronavirus that was first reported in 2012. In January 2019, >2,300 laboratory confirmed MERS-CoV cases had been reported to the World Health Organisation, resulting in >800 deaths, most of them from the Middle East. We have been strongly involved in early assessments of MERS-CoV transmission dynamics that made it possible to characterize how infections from the animal reservoir (camel), different levels of mixing and heterogeneities in transmission contributed to the build-up of MERS-CoV epidemics in Saudi Arabia, one of the most affected countries.

Yellow Fever: Yellow fever is an arbovirus transmitted by mosquitoes that can lead to severe symptoms and even death. It circulates in Brazil in a sylvatic transmission cycle where epidemics in the animal reservoir (non-human primates) can generate spillover events in human populations. The main concern for Public Health is that the virus manages to reach a domestic transmission cycle, where transmission becomes self-sustaining in human populations. In such scenario, the virus could potentially spread much more widely in Brazil and beyond and reach areas where vaccine coverage is limited. In 2016-2017, an important surge of Yellow Fever cases was observed in Brazil, and one of the key question was to determine whether this was the start of a new domestic transmission cycle. In a collaboration with Oxford University and Brazilian colleagues, we developed an integrative analytical framework that considered multiple data types (e.g Yellow Fever surveillance in humans and non-human primates, genomic viral sequences, age distribution of human cases) to address this complex research question.

References