Santé publique France is France’s national public health agency. A public institution under the authority of the Minister of Health, created through the merger of several public institutions by Order No. 2016-246 of April 15, 2016, the agency works to promote public health. As a scientific, expert, and public health safety agency, its missions include:

1. Epidemiological observation and monitoring of the health status of the population; 
2. Monitoring health risks threatening the population;
3. Promoting health and reducing health risks;
4. Developing prevention and health education;
5. Preparation for and response to health threats, alerts, and crises;
6. Issuing health alerts.

The agency is organized into 12 scientific, cross-functional, or support divisions.

The agency’s strategic priorities and work program, established by its Board
of Directors, are organized into three areas: Strengthening the capacity for anticipation and rapid response to address health threats; Measuring and assessing the extent of diseases and risk factors to guide their prevention and control; Strengthening the health impact of all public policies and the prevention and promotion of health.

Division of Infectious Diseases

Responsibilities

The successful candidate will be responsible for developing, adapting, and validating mathematical and statistical models
for the analysis of epidemiological data collected by
Santé publique France or its partners as part of infectious disease
surveillance, particularly for decision-making purposes. In this context, they will have access
to the databases of the National Health Data System (SNDS) and to data from the
agency’s surveillance systems.

Responsibilities

Reporting to the Deputy Director, in collaboration with epidemiologists from the DMI or other divisions
of the agency, the successful candidate will be responsible for the following
activities in the field of mathematical modeling of vector-borne
infectious diseases:

• Develop and adapt mathematical and statistical models to better
  understand and describe the dynamics of vector-borne diseases, particularly
  arboviruses. Various approaches may be employed; these
  may notably include statistical models and machine learning
  methods to identify factors associated with transmission, as well as
  mechanistic models explicitly describing transmission processes between
  hosts and vectors; spatio-temporal models may also be developed
  to account for environmental, climatic, and
  demographic heterogeneity.
• Develop and adapt epidemiological models incorporating genomic sequencing
  data (particularly produced by the CNR) to analyze transmission dynamics
  at the macro (inter-regional flows) and micro (local outbreaks
  , chains of transmission) levels.
• Produce short- and medium-term forecasts (nowcasting and forecasting) using the
  developed models, particularly in cases of high import pressure or during
  epidemics within the country (including overseas territories), to anticipate the risk of epidemic spread
  . These forecasts will rely on optimized use of available epidemiological data
  to improve accuracy. The objective is to utilize existing time series
  to produce robust projections, adapting methods
  to the specificities of local contexts, whose dynamics may differ
  significantly between mainland France and overseas territories.
• Initiate or contribute to epidemiological modeling efforts, incorporating a
  One Health approach for certain vector-borne systems (e.g., West Nile virus, Usutu virus, etc.)
  in collaboration with the modeling teams of Santé
  publique France’s main partners. The objective will be to enhance understanding of risks through an
  integrated surveillance approach and to inform strategies for the surveillance and control of
  these diseases.
• Test scenarios for the evolution of epidemic dynamics in the medium to long term, and
  the impact of strategies for the prevention and control of arbovirus epidemics.
• 
  Model and evaluate the impact of preventive strategies, particularly vaccination, by
  incorporating herd immunity dynamics and the specific characteristics of target populations,
  including the analysis of intervention scenarios, the estimation of their effectiveness, and the
  simulation of their effects on pathogen transmission and the disease burden.
• Estimate hidden parameters in the epidemiological surveillance of infectious diseases
  (incidence, coverage rates, risk of contamination in blood donations, etc.).