|epigrowthfit Resource1 #685196|
epigrowthfit is an R package for fitting nonlinear mixed effects models of epidemic growth to collections of one or more disease incidence time series. It can be applied to birth processes other than epidemics, as the statistical machinery is agnostic to the precise interpretation of supplied count data. epigrowthfit is built on Template Model Builder. 
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|Link Estimating initial epidemic growth rates|
Author: J. Ma, J. Dushoff, B. M. Bolker, D. J. D. Earn
Publication date: 2014
Publication info: Bulletin of Mathematical Biology, 76(1):245-260.
Cited by: David Price 1:12 PM 15 September 2022 GMT
|Excerpt / Summary|
The initial exponential growth rate of an epidemic is an important measure of disease spread, and is commonly used to infer the basic reproduction number [Formula: see text]. While modern techniques (e.g., MCMC and particle filtering) for parameter estimation of mechanistic models have gained popularity, maximum likelihood fitting of phenomenological models remains important due to its simplicity, to the difficulty of using modern methods in the context of limited data, and to the fact that there is not always enough information available to choose an appropriate mechanistic model. However, it is often not clear which phenomenological model is appropriate for a given dataset. We compare the performance of four commonly used phenomenological models (exponential, Richards, logistic, and delayed logistic) in estimating initial epidemic growth rates by maximum likelihood, by fitting them to simulated epidemics with known parameters. For incidence data, both the logistic model and the Richards model yield accurate point estimates for fitting windows up to the epidemic peak. When observation errors are small, the Richards model yields confidence intervals with better coverage. For mortality data, the Richards model and the delayed logistic model yield the best growth rate estimates. We also investigate the width and coverage of the confidence intervals corresponding to these fits.
|Link Acceleration of plague outbreaks in the second pandemic|
Author: David J. D. Earn, Junling Ma, Hendrik Poinar, Jonathan Dushoff, Benjamin M. Bolker
Publication date: 19 October 2020
Publication info: PNAS – Proceedings of the National Academy of Sciences of the U.S.A., 117(44):27703–27711
Cited by: David Price 1:15 PM 15 September 2022 GMT
|Excerpt / Summary|
Epidemics of plague devastated Europe's population throughout the Medieval and Renaissance periods. Genetic studies of modest numbers of skeletal remains indicate that the causative agent of all these epidemics was the bacterium Yersinia pestis, but such analyses cannot identify overall patterns of transmission dynamics. Analysis of thousands of archival records from London, United Kingdom, reveals that plague epidemics spread much faster in the 17th century than in the 14th century.Historical records reveal the temporal patterns of a sequence of plague epidemics in London, United Kingdom, from the 14th to 17th centuries. Analysis of these records shows that later epidemics spread significantly faster (``accelerated''). Between the Black Death of 1348 and the later epidemics that culminated with the Great Plague of 1665, we estimate that the epidemic growth rate increased fourfold. Currently available data do not provide enough information to infer the mode of plague transmission in any given epidemic; nevertheless, order-of-magnitude estimates of epidemic parameters suggest that the observed slow growth rates in the 14th century are inconsistent with direct (pneumonic) transmission. We discuss the potential roles of demographic and ecological factors, such as climate change or human or rat population density, in driving the observed acceleration.