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

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.

Multiplicative Mixed-Effects Modelling of Dengue Incidence: An Analysis of the 2019 Outbreak in the Dominican Republic

“…Dengue outbreaks are a consequence of complex interactions among multiple factors. In particular, dengue disease depends on the development of mosquitoes through a four-stage life cycle that is heavily influenced by environmental conditions [29]. This implies that the current number of cases can be influenced by past conditions that impact the mosquito life cycle.…”

The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China

“…LSTM model has been widely used to predict infectious disease incidence, such as HIV [ 15 ], hepatitis E [ 52 ], Dengue fever [ 53 ], hand, foot and mouth disease (HFMD) [ 54 ], COVID-19 [ 39 ], and so on. It is focused on resolving the issues of vanishing gradient [ 52 ].…”

Using compartmental models and Particle Swarm Optimization to assess Dengue basic reproduction number R0 for the Republic of Panama in the 1999-2022 period

“…Dengue cases were grouped by epidemiological week and were represented as a time series of 24 years (1999-2022). It is importance to note that this period has been well studied, with publications focusing on molecular aspects of the serotypes, studying the demographic groups it affects via statistical analysis and finally the relationship with incidence and climate variables [15] , [24] . The data set used in this study is similar to that used in these publications before mentioned, which is taking into account cases of Dengue confirmed by laboratory and with a known epidemiological link.…”

“…In particular, the temperature shifts associated with El Niño-Southern Oscillation and La Niña can have a negative impact on the Central and South American regions, making it an important consideration in the study of vector-borne diseases and the development of effective control strategies [23] . These fluctuations in precipitation could result in a heightened number of Dengue cases during years of increased rainfall, and could also lead to Dengue outbreaks of unprecedented aggression [22] , [24] . Parameterizing an epidemiological model is a crucial step in understanding the spread of diseases, such as Dengue, within populations and the interactions that contribute to its transmission.…”

“…The evaluation of the dynamics of Dengue outbreaks in the Republic of Panama is a crucial step forward in the use of compartmental models for disease control, as it provides insights into the intricacies of the disease transmission process, which will aid in the formulation of evidence-based strategies to mitigate the impact of Dengue outbreaks. By accurately parameterizing an epidemiological model, decision-makers will be better equipped to respond to Dengue outbreaks in an informed and effective manner, ultimately reducing their negative impact on public health and society [23] , [24] . The objective of this study was to gain a thorough understanding of the dynamics of Dengue transmission in the Republic of Panama by utilizing compartmental models over the period of 1999 to 2022.…”