EM2005F "Looking into the COVID-19 Crystal Ball – Why, How, and What Forecasting Tells us about Disease Trends" (IM GR-050820)
COVID-19 has spread rapidly disrupting many lives and health systems. Modeling this trajectory is important for government policy decision making, health system capacity planning, and population perception. This form of modeling requires significant amounts of high quality, real-time data. In this era of mobile internet, prevalence of advanced mobile devices and global positioning system, there is an enormous amount of data available and through the data for good initiative much of it is available. Modeling pandemics divides into three major types: SIR models, agent-based models, and curve fitting models. These models leverage well established math, and each has advantages and disadvantages. Establish comprehension of fundamental epidemiological concepts used for monitoring the spread of COVID-19 in a given population. Specifically, Rt measures the contagiousness of the disease over time and helps health policy makers determine the effectiveness of interventions, including non-pharmaceutical interventions (NPI), such as social distancing. Building models to forecast the spread of COVID-19 can help community officials anticipate the timing and severity of future infections surges, including the dependence on activating or inactivating various NPI measures over time. As the pandemic has progressed, there is a shift away from forecasting to real-time monitoring to assess how changes in non-pharmaceutical interventions impact case volume, reproductive number and hospitalizations.
UT Southwestern faculty, fellows, residents and medical students, community physicians, nurse clinicians, physician assistants and nurses.
At the conclusion of this activity, the participant should be able to:
- Understand and identify the strengths and weaknesses of the 3 main strategies of infectious diseases modeling including the SIR/SEIR, agent-based and curve-fitting/extrapolation models.
- Understand basic concepts of modeling including R(0) and R(t) and apply these to commonly seen graphs/figures
- Understand the impact of data quality and timeliness on model development.
- Highlight the impact of modeling on policy decisions (internally and externally).
- 1.00 AMA