Predicting seasonal influenza transmission using functional regression models with temporal dependence

Abstract

This paper proposes a novel approach that uses meteorological information to predict the incidence of influenza in Galicia (Spain). It extends the Generalized Least Squares (GLS) methods in the multivariate framework to functional regression models with dependent errors. These kinds of models are useful when the recent history of the incidence of influenza are readily unavailable (for instance, by delays on the communication with health informants) and the prediction must be constructed by correcting the temporal dependence of the residuals and using more accessible variables. A simulation study shows that the GLS estimators render better estimations of the parameters associated with the regression model than they do with the classical models. They obtain extremely good results from the predictive point of view and are competitive with the classical time series approach for the incidence of influenza. An iterative version of the GLS estimator (called iGLS) was also proposed that can help to model complicated dependence structures. For constructing the model, the distance correlation measure was employed to select relevant information to predict influenza rate mixing multivariate and functional variables. These kinds of models are extremely useful to health managers in allocating resources in advance to manage influenza epidemics.

Predicting plant disease epidemics from functionally represented weather series

“…Twelve models were built upon previously reported logistic regression (lr) models with variables summarizing weather in fixed-length windows near wheat anthesis (flowering) [14]. Six of these 12 lr models (1,2,3,5,15,17) were focused on pre-anthesis conditions, whereas the other six models (7,8,9,11,12,13) targeted post-anthesis conditions. Six penalized scalar-on-function regression (s-o-f ) models (4,6,10,14,16,18) were fit with inputs being weather times series from 120 days pre-anthesis to 20 days post-anthesis, a much longer time frame than the 30-day anthesis-centred period underlying the 12 lr models.…”

“…Four s-o-f models (10,14,16,18), all with three weather series, had the lowest misclassification rates (MR) overall (mean MR ¼ 0.237), compared with the entire set of lr models (mean MR ¼ 0.310). [25], of lupus flares from daily stress levels [26] and of influenza rates from weather in the previous weeks [8]. We illustrated, via application to a pernicious disease of wheat [27], the utility of scalar-on-function regression in predicting a binary plant disease outcome.…”

“…Scalar-on-function regression is applicable to any situation where interest lies in modelling a static outcome (binary or continuous) in relation to explanatory variables observed over time. Recent examples have included the modelling of myocardial infarction occurrences in relation to electrocardiographic traces [25], of lupus flares from daily stress levels [26] and of influenza rates from weather in the previous weeks [8]. We illustrated, via application to a pernicious disease of wheat [27], the utility of scalar-on-function regression in predicting a binary plant disease outcome.…”

“…Human disease risk (operationally defined in different ways) may be connected to weather-related variables at different times [3,4,8]. Identifying suitable weather-based predictors of disease is a significant challenge within any modelling paradigm, for humans, animals or plants.…”

Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

“…This technique has been used recently in many modeling applications, such as influenza incidence rate modeling with climate covariates [ 22 ], but has yet to be used in the context of malaria. In this study, we use such methods to identify the underlying factors that shape the patterns of malaria prevalence in Dangassa, a rural Malian village which experiences bimodal malaria transmission dynamics [ 3 , 23 ].…”