Nowcasting and Forecasting
Description
Situational awareness involves using nowcasting to analyze current conditions and forecasting to anticipate changes, thus enabling the implementation of appropriate actions. In the context of public health, nowcasting provides a means to interpret current data, which is often delayed and incomplete, while forecasting provides the early warnings necessary to anticipate and plan for surges in disease burden. Our work focuses on two lines of research, based on a range of methodologies, including mechanistic modeling, statistical modeling, machine learning, and semi-mechanistic hybrid approaches:
- Respiratory pathogens: The team develops modeling tools to monitor pathogens such as influenza viruses, RSV, and SARS-CoV-2. This involves nowcasting current epidemiological metrics (e.g., cases, hospitalizations) and providing short-term forecasts to inform clinical preparedness.
- Mosquito vector species: The team develops modeling tools for the nowcasting and short-term forecasting of the relative abundance of epidemiologically-relevant mosquito species to inform the deployment of mosquito control interventions. The goal of this project is to enhance public health situational awareness to support decision-making.
Goals and Learning Outcomes
- Apply core concepts of infectious disease epidemiology and mosquito ecology to real-world public health surveillance
- Contribute to the design, development, and analysis of nowcasting and forecasting modeling tools • Develop proficiency in programming (e.g., R, Python, C++) and learn best coding practices
- Translate scientific research into actionable public health policy
- Collaborate effectively within a multidisciplinary research team
- Communicate and disseminate research findings through journal articles, preprints, poster presentations, and technical reports
Majors and Schools Sought
- Mathematics
- Biostatistics
- Public Health
- Epidemiology
- Physics
- Data Science
- Computer Science
- Biology
- Other quantitative fields
Desired Skills and Interests
We are looking for students who are organized, motivated to learn, and work well on a team. Prior experience in coding is welcome.