Factors in Modeling:
For measles specifically, both birth and vaccination rates heavily influence the resulting outbreak patterns associated with the disease. Through several case studies conducted by Earn, Rohani, and Bolker, it is revealed that higher birthrates typically indicate a cyclic pattern in measles epidemics, while lower birth rates combined with high vaccination rates result in irregular patterns (Earn, Rohani, & Bolker, 668). This phenomenon was especially apparent in the United States during the Great Depression when birth rates were low, and thus the epidemic of measles followed a very irregular pattern (668). Once World War II began, birth rates began to increase and the measles outbreaks transitioned into a more regular pattern …show more content…
The SEIR model provides impressive insight into the varying outbreak patterns associated with the measles disease due to factors such as mass vaccination, birth rates, and seasonal effects (Earn, Rohani, & Bolker, 669). The consideration of these factors is a unique aspect of the SEIR model. It is one of the only models to focus on more external factors than internal factors that may influence the disease outbreak conditions.
Phenomenological Modeling:
The second method used to model the measles disease is referred to as phenomenological modeling; these include the non-linear time series models, feed-forward neural networks (FNN), and the semi-nonparametric (SNP) models (Ellner et al., 427). In regards to phenomenological modeling, FNN is the favorable method to use for making inferences about measles outbreaks. However, the remaining two subtypes of phenomenological models have proved to be better in predicting other aspects and circumstances of the disease. Phenomenological models also incorporate measures for seasonality of the disease and combine knowledge from “current and past system states” in order to best forecast the future epidemics associated with a disease …show more content…
According to Ellner et al., this joined approach allows for “better forecast [accuracy], better characterizations of the dynamics, and a better understanding of the factors causing complex population dynamics” (425). One intention behind the creation of this method was to generate an improved understanding behind seasonal and irregular outbreak patterns among the measles disease, and other diseases as well
For measles specifically, both birth and vaccination rates heavily influence the resulting outbreak patterns associated with the disease. Through several case studies conducted by Earn, Rohani, and Bolker, it is revealed that higher birthrates typically indicate a cyclic pattern in measles epidemics, while lower birth rates combined with high vaccination rates result in irregular patterns (Earn, Rohani, & Bolker, 668). This phenomenon was especially apparent in the United States during the Great Depression when birth rates were low, and thus the epidemic of measles followed a very irregular pattern (668). Once World War II began, birth rates began to increase and the measles outbreaks transitioned into a more regular pattern …show more content…
The SEIR model provides impressive insight into the varying outbreak patterns associated with the measles disease due to factors such as mass vaccination, birth rates, and seasonal effects (Earn, Rohani, & Bolker, 669). The consideration of these factors is a unique aspect of the SEIR model. It is one of the only models to focus on more external factors than internal factors that may influence the disease outbreak conditions.
Phenomenological Modeling:
The second method used to model the measles disease is referred to as phenomenological modeling; these include the non-linear time series models, feed-forward neural networks (FNN), and the semi-nonparametric (SNP) models (Ellner et al., 427). In regards to phenomenological modeling, FNN is the favorable method to use for making inferences about measles outbreaks. However, the remaining two subtypes of phenomenological models have proved to be better in predicting other aspects and circumstances of the disease. Phenomenological models also incorporate measures for seasonality of the disease and combine knowledge from “current and past system states” in order to best forecast the future epidemics associated with a disease …show more content…
According to Ellner et al., this joined approach allows for “better forecast [accuracy], better characterizations of the dynamics, and a better understanding of the factors causing complex population dynamics” (425). One intention behind the creation of this method was to generate an improved understanding behind seasonal and irregular outbreak patterns among the measles disease, and other diseases as well