Pulled from the Infectious Economics archives, these articles provide recommendations for the design and development of robust mathematical models of COVID-19 epidemiology and economics to reduce uncertainty in decision-making and inform evidence-based public policy.
Associations are easy; valid causal inference is hard
- Tutorial: how to estimate person-centered treatment effects so that public policy to control an infectious disease does not make things worse for the poor
- Instrumental variables are an econometric tool that can be used to strengthen causal inference in observational studies of COVID trying to estimate the effectiveness of policies
- Propensity scores can reduce bias in studies that compare the effectiveness of treatments for COVID during hospitalization
- Interrupted time series and difference-in-differences analyses are strong approaches for the design of quasi-experimental studies of the impact of national policy on the control of an infectious disease
- Approach to validating forecasts, enhancing forecasts with more recent supplemental data from Google Trends, and correlation between Google Trends and uptake of preventative drug infectious disease
- Visualization of progress toward infectious disease goals with vector representing trajectory over time
- Financial incentives to individuals for preventative behaviors can reduce the spread of infectious disease, a cost-effective way to maximize the spillover benefit to society
Enjoy the throwback posts. Random skills in forecasting epidemics, math modeling infectious diseases, causal inference in observational studies of policy effectiveness, and decision analysis are urgently needed to fight through this pandemic together and make smart decisions along the way.
By popular demand, I’ve added my UW HSERV 525 Advanced Methods Final Exam Cheat Sheet that was a well-used reference for me in the first few years analyzing EHR data.