We implemented five traditional time series models used for outbreak detection: a simple autoregressive model, a Serfling model, the trimmed seasonal model, a wavelet-based model, and a generalized linear model.
We also demonstrate the importance in assessing the sensitivity and specificity of these methods, of utilizing more realistic stochastic, rather than deterministic, models for outbreaks patterns.
Other approaches for designing surveillance have been developed in the past; for instance (Bajardi et al. 2012) developed a model for robust outbreak cluster detection based on network features.
The current study addresses this by developing a probabilistic simulation-based approach and then applying it to southern Ontario, using a model for the outbreak characteristics based on the tornado database for the regions neighboring southern Ontario and the tornado characteristics obtained from historical occurrences of tornadoes in southern Ontario.
To compare the incidence of outbreak and outbreak-associated cases during illegal state-years to that during legal state-years, we stratified the outbreaks by legal status of the state in which the outbreak occurred at the time of the outbreak and calculated incidence density ratios for reported outbreaks (Poisson model) and for outbreak-associated cases (zero-inflated negative binomial model).
We used univariate statistics to test if whether tree characteristics, other herbivores, stand properties, soil type, wind, and topography were associated with I. confusus outbreak, and logistic regression to create a predictive model for the outbreaks.
OO Climate Change Correlated to Zika, Ebhola Outbreaks - allowing scientists to develop a model for predicting outbreaks based on changes in climate.
This paper introduces a new model for disease outbreaks.
In a study by Halloran et al. (11 ), a stochastic model for smallpox outbreaks in small structured communities is described.
