Estimating Incidence Curves of Several Infections Using Symptom Surveillance Data
Edward Goldstein, Benjamin Cowling, Allison Aiello, Saki Takahashi, Gary King, Ying Lu, Marc Lipsitch. 2011.
"Estimating Incidence Curves of Several Infections Using Symptom Surveillance Data".
PLoS ONE, 6, 8, Pp. e23380.
Abstract
We introduce a method for estimating incidence curves of several co-circulating infectious pathogens, where each infection has its own probabilities of particular symptom profiles. Our deconvolution method utilizes weekly surveillance data on symptoms from a defined population as well as additional data on symptoms from a sample of virologically confirmed infectious episodes. We illustrate this method by numerical simulations and by using data from a survey conducted on the University of Michigan campus. Last, we describe the data needs to make such estimates accurate.
See Also
- [Paper] Automated Cognitive Debriefing (2024)
- [Paper] Death by Survey: Estimating Adult Mortality Without Selection Bias from Sibling Survival Data (2006)
- [Paper] Designing Verbal Autopsy Studies (2010)
- [Paper] Differentially Private Survey Research (2024)
- [Paper] Evaluating COVID-19 Public Health Messaging in Italy: Self-Reported Compliance and Growing Mental Health Concerns (2020)
- [Paper] How to Measure Legislative District Compactness If You Only Know It When You See It (2021)
- [Paper] Measuring Total Health Inequality: Adding Individual Variation to Group-Level Differences (2002)
- [Paper] Pre-Election Survey Methodology: Details From Nine Polling Organizations, 1988 and 1992 (1995)