Publication: A marginal moment matching approach for fitting endemic‐epidemic models to underreported disease surveillance counts
A marginal moment matching approach for fitting endemic‐epidemic models to underreported disease surveillance counts
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Bracher, J., & Held, L. (2021). A marginal moment matching approach for fitting endemic‐epidemic models to underreported disease surveillance counts. Biometrics, 77(4), 1202–1214. https://doi.org/10.1111/biom.13371
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Count data are often subject to underreporting, especially in infectious disease surveillance. We propose an approximate maximum likelihood method to fit count time series models from the endemic‐epidemic class to underreported data. The approach is based on marginal moment matching where underreported processes are approximated through completely observed processes from the same class. Moreover, the form of the bias when underreporting is ignored or taken into account via multiplication factors is analyzed. Notably, we show that this
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- Bracher, Johannes
- Held, Leonhard
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Bracher, J., & Held, L. (2021). A marginal moment matching approach for fitting endemic‐epidemic models to underreported disease surveillance counts. Biometrics, 77(4), 1202–1214. https://doi.org/10.1111/biom.13371
