Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City

Vicente Navarro Valencia, Yamilka Díaz, Juan Miguel Pascale, Maciej F. Boni and Javier E. Sanchez-Galan
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Vicente Navarro Valencia: Facultad de Ciencias y Tecnología, Universidad Tecnológica de Panamá (UTP), El Dorado 0819-07289, Panama
Yamilka Díaz: Department of Research in Virology and Biotechnology, Gorgas Memorial Institute of Health Studies, Justo Arosemena Avenue and 35st Street, Panama 0816-02593, Panama
Juan Miguel Pascale: Unit of Diagnosis, Clinical Research and Tropical Medicine, Gorgas Memorial Institute of Health Studies, Justo Arosemena Avenue and 35st Street, Panama 0816-02593, Panama
Maciej F. Boni: Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA 16802, USA
Javier E. Sanchez-Galan: Facultad de Ciencias y Tecnología, Universidad Tecnológica de Panamá (UTP), El Dorado 0819-07289, Panama

IJERPH, 2021, vol. 18, issue 22, 1-18

Abstract: The present analysis uses the data of confirmed incidence of dengue cases in the metropolitan region of Panama from 1999 to 2017 and climatic variables (air temperature, precipitation, and relative humidity) during the same period to determine if there exists a correlation between these variables. In addition, we compare the predictive performance of two regression models (SARIMA, SARIMAX) and a recurrent neural network model (RNN-LSTM) on the dengue incidence series. For this data from 1999–2014 was used for training and the three subsequent years of incidence 2015–2017 were used for prediction. The results show a correlation coefficient between the climatic variables and the incidence of dengue were low but statistical significant. The RMSE and MAPE obtained for the SARIMAX and RNN-LSTM models were 25.76, 108.44 and 26.16, 59.68, which suggest that any of these models can be used to predict new outbreaks. Although, it can be said that there is a limited role of climatic variables in the outputs the models. The value of this work is that it helps understand the behaviour of cases in a tropical setting as is the Metropolitan Region of Panama City, and provides the basis needed for a much needed early alert system for the region.

Keywords: dengue; time series; climatic variables; correlation; prediction; SARIMA; SARIMAX; RNN; LSTM (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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