The CEPH Lab develops mathematical, computational, and statistical models to advance infectious disease epidemiology and public health through five primary research lines. By quantifying how human behavior, ecology, and pathogen dynamics drive epidemic spread, we provide situational awareness and scenario modeling needed to inform public health planning.

Human Social Behavior

Social behavior drives pathogen transmission, leading to heterogeneous exposure risks across different socio-demographic groups. Our objective is to understand the social behavior of individuals and its determinants to better characterize transmission pathways.

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Mosquito Ecology and Behavior

The relative abundance and behavior of mosquito vector species are main determinants of human risks for mosquito-borne diseases. Our objective is to investigate the drivers of seasonal and diel trends in mosquito population dynamics and behavior across space and time to identify the underlying mechanisms. Through a mechanistic understanding of these processes, we carry out model-based evaluations to inform mosquito-control and public health authorities.

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Infectious Disease Dynamics

The transmission of infectious pathogens is a complex process driven by the interplay of biological, behavioral, and socio-economic factors. Our objective is to characterize the fundamental mechanisms of pathogen spread, ranging from the clinical progression of individual infections to the broad patterns observed at the population level. We also aim to understand how cross-scale (within-host, between-host) interactions and socio-economic drivers shape the heterogeneous landscape of infectious disease epidemiology.

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Situational Awareness

Situational awareness is key for public health policy making as it allows anticipating surges in disease burden (e.g., hospitalizations) and triggers early warnings. Our objective is to enhance situational awareness through nowcasting and forecasting of epidemic trajectories using a variety of approaches, from mechanistic to semi-mechanistic and statistical/machine learning approaches. We also aim to improve situational awareness of mosquito control authorities by forecasting mosquito population dynamics.

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Public Health Planning

Mathematical and computational models have increasingly been used to inform epidemic preparedness and response. Our objective is to support public health decision-making through the scenario analysis and model-based evaluations of pharmaceutical and non-pharmaceutical interventions. We provide actionable insights to our national and international public health partners and conduct fundamental research in this area.

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