Spatio-temporal prediction of dengue fever in Costa Rica, 2005-2018
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Keywords
Dengue, prediction model, lineal regression, geospatial, Costa Rica
Abstract
Objective: To apply a program for spatiotemporal probabilistic prediction of dengue in Costa Rica. Methodology: Annual numerical data on total and severe dengue in Costa Rica obtained by the Ministry between 1993 and 2018 are used. Data from the 12 years prior to the estimation year are used and a linear regression model is run to calculate estimates of dengue cases for the period 2005-2018. Results: On average, 8 years out of 14 falls within the estimation range. The results that exceed the maximum estimates correspond to epidemics. The Chorotega region is presented in first place with more cases and the Central Pacific region in third place throughout the study period both and in the estimates. The average reported incidence for each socioeconomic region almost completely matches the average mean estimated incidences. Conclusions: The use of predictive models for dengue could contribute to decision making by generating public health impact. Efforts should be continued to improve the understanding of dengue behavior at the local level by considering as many variables as possible and to develop more elaborate predictive models.
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