ArticleInternational journal of environmental research and public health2021
Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Dengue epidemiology and transmission intensity across Panama during 2000-2024: a modelling study.Lancet regional health. Americas · 2026Article
- Dengue fever prediction based on meteorological features and deep learning models.Infectious Disease Modelling · 2026Article
- Article
- Enhancing public health surveillance: SARIMAX model incorporating Baidu search index for HCV prediction in China.BMC medical research methodology · 2025Article
- Time series analysis of dengue incidence and its association with meteorological risk factors in Bangladesh.PloS one · 2025Article
- Spatiotemporal and meteorological relationships in dengue transmission in the Dominican Republic, 2015-2019.Tropical medicine and health · 2023Article
- Using compartmental models and Particle Swarm Optimization to assess Dengue basic reproduction numberHeliyon · 2023Article
- Article
- Driving effect of multiplex factors on human brucellosis in high incidence region, implication for brucellosis based on one health concept.One health (Amsterdam, Netherlands) · 2022Article
- Article
- Assessing temporal causal effects of climate on dengue incidence in Peninsular Malaysia.The journal of climate change and healthArticle
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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