{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T19:16:13Z","timestamp":1768590973586,"version":"3.49.0"},"reference-count":56,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T00:00:00Z","timestamp":1637193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007061","name":"Secretar\u00eda Nacional de Ciencia, Tecnolog\u00eda e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["Sistema Nacional de Investigaci\u00f3n (SNI)"],"award-info":[{"award-number":["Sistema Nacional de Investigaci\u00f3n (SNI)"]}],"id":[{"id":"10.13039\/501100007061","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>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\u20132014 was used for training and the three subsequent years of incidence 2015\u20132017 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.<\/jats:p>","DOI":"10.3390\/ijerph182212108","type":"journal-article","created":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T08:53:56Z","timestamp":1637225636000},"page":"12108","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4901-2806","authenticated-orcid":false,"given":"Vicente","family":"Navarro Valencia","sequence":"first","affiliation":[{"name":"Facultad de Ciencias y Tecnolog\u00eda, Universidad Tecnol\u00f3gica de Panam\u00e1 (UTP), El Dorado 0819-07289, Panama"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1651-9670","authenticated-orcid":false,"given":"Yamilka","family":"D\u00edaz","sequence":"additional","affiliation":[{"name":"Department of Research in Virology and Biotechnology, Gorgas Memorial Institute of Health Studies, Justo Arosemena Avenue and 35st Street, Panama 0816-02593, Panama"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan Miguel","family":"Pascale","sequence":"additional","affiliation":[{"name":"Unit of Diagnosis, Clinical Research and Tropical Medicine, Gorgas Memorial Institute of Health Studies, Justo Arosemena Avenue and 35st Street, Panama 0816-02593, Panama"},{"name":"Sistema Nacional de Investigaci\u00f3n (SNI) SENACYT, Panama 0816-02852, Panama"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0830-9630","authenticated-orcid":false,"given":"Maciej F.","family":"Boni","sequence":"additional","affiliation":[{"name":"Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA 16802, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8806-7901","authenticated-orcid":false,"given":"Javier E.","family":"Sanchez-Galan","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias y Tecnolog\u00eda, Universidad Tecnol\u00f3gica de Panam\u00e1 (UTP), El Dorado 0819-07289, Panama"},{"name":"Sistema Nacional de Investigaci\u00f3n (SNI) SENACYT, Panama 0816-02852, Panama"},{"name":"Grupo de Investigaciones en Biotecnolog\u00eda, Bioinform\u00e1tica y Biolog\u00eda de Sistemas (GIBBS), Facultad de Ingenieria de Sistemas Computacionales, Universidad Tecnol\u00f3gica de Panam\u00e1 (UTP), El Dorado 0819-07289, Panama"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1038\/nature12060","article-title":"The global distribution and burden of dengue","volume":"496","author":"Bhatt","year":"2013","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1508","DOI":"10.1038\/s41564-019-0476-8","article-title":"The current and future global distribution and population at risk of dengue","volume":"4","author":"Messina","year":"2019","journal-title":"Nat. Microbiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1016\/S1473-3099(16)00026-8","article-title":"The global burden of dengue: An analysis from the Global Burden of Disease Study 2013","volume":"16","author":"Stanaway","year":"2016","journal-title":"Lancet Infect. Dis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1016\/S1473-3099(16)00146-8","article-title":"The global economic burden of dengue: A systematic analysis","volume":"16","author":"Shepard","year":"2016","journal-title":"Lancet Infect. Dis."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1016\/j.pt.2018.07.007","article-title":"The Estimates of the Health and Economic Burden of Dengue in Vietnam","volume":"34","author":"Hung","year":"2018","journal-title":"Trends Parasitol."},{"key":"ref_6","first-page":"86","article-title":"Dengue en Panam\u00e1, 1993","volume":"49","author":"Quiroz","year":"1997","journal-title":"Rev. Cuba. Med. Trop."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"D\u00edaz, Y., Chen-Germ\u00e1n, M., Quiroz, E., Carrera, J.P., Cisneros, J., Moreno, B., Cerezo, L., Martinez-Torres, A.O., Moreno, L., and Barahona de Mosca, I. (2019). Molecular epidemiology of dengue in Panama: 25 years of circulation. Viruses, 11.","DOI":"10.3390\/v11080764"},{"key":"ref_8","unstructured":"Gorgas, W.C. (1915). Sanitation in Panama, D. Appleton."},{"key":"ref_9","first-page":"383","article-title":"Emergence of epidemic dengue\/dengue hemorrhagic fever as a public health problem in the Americas","volume":"2","author":"Gubler","year":"1993","journal-title":"Infect. Agents Dis."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Whiteman, A., Desjardins, M.R., Eskildsen, G.A., and Loaiza, J.R. (2019). Detecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance data. PLoS Negl. Trop. Dis., 13.","DOI":"10.1101\/561902"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1933","DOI":"10.1007\/s10530-021-02482-y","article-title":"The role of heterogenous environmental conditions in shaping the spatiotemporal distribution of competing Aedes mosquitoes in Panama: Implications for the landscape of arboviral disease transmission","volume":"23","author":"Bennett","year":"2021","journal-title":"Biol. Invasions"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13071-019-3522-8","article-title":"High infestation of invasive Aedes mosquitoes in used tires along the local transport network of Panama","volume":"12","author":"Bennett","year":"2019","journal-title":"Parasites Vectors"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1007\/s10393-019-01417-3","article-title":"Aedes mosquito infestation in socioeconomically contrasting neighborhoods of Panama city","volume":"16","author":"Whiteman","year":"2019","journal-title":"EcoHealth"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1289\/ehp.1306556","article-title":"Climate and dengue transmission: Evidence and implications","volume":"121","author":"Morin","year":"2013","journal-title":"Environ. Health Perspect."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Alves, L.D., Lana, R.M., and Coelho, F.C. (2021). A Framework for Weather-Driven Dengue Virus Transmission Dynamics in Different Brazilian Regions. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18189493"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dhamodharavadhani, S., and Rathipriya, R. (2020). Forecasting dengue incidence rate in Tamil Nadu using ARIMA time series model. Machine Learning for Healthcare, Chapman and Hall\/CRC.","DOI":"10.1201\/9780429330131-13"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-84176-y","article-title":"Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models","volume":"11","author":"Thiruchelvam","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xavier, L.L., Hon\u00f3rio, N.A., Pessanha, J.F.M., and Peiter, P.C. (2021). Analysis of climate factors and dengue incidence in the metropolitan region of Rio de Janeiro, Brazil. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0251403"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2458-9-395","article-title":"Time series analysis of dengue fever and weather in Guangzhou, China","volume":"9","author":"Lu","year":"2009","journal-title":"BMC Public Health"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.actatropica.2018.03.006","article-title":"Time series analysis of dengue surveillance data in two Brazilian cities","volume":"182","author":"Cortes","year":"2018","journal-title":"Acta Trop."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2334-11-166","article-title":"Time series analysis of dengue incidence in Guadeloupe, French West Indies: Forecasting models using climate variables as predictors","volume":"11","author":"Gharbi","year":"2011","journal-title":"BMC Infect. Dis."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"281","DOI":"10.4103\/0019-557X.106415","article-title":"Forecasting incidence of dengue in Rajasthan, using time series analyses","volume":"56","author":"Bhatnagar","year":"2012","journal-title":"Indian J. Public Health"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102157","DOI":"10.1016\/j.artmed.2021.102157","article-title":"Dengue Models Based on Machine-Learning Techniques: A Systematic Literature Review","volume":"119","author":"Hoyos","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xu, J., Xu, K., Li, Z., Meng, F., Tu, T., Xu, L., and Liu, Q. (2020). Forecast of dengue cases in 20 Chinese cities based on the deep learning method. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17020453"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"012054","DOI":"10.1088\/1742-6596\/1566\/1\/012054","article-title":"A comparison of SARIMA and LSTM in forecasting dengue hemorrhagic fever incidence in Jambi, Indonesia","volume":"1566","author":"Khaira","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Col\u00f3n-Gonz\u00e1lez, F.J., Soares Bastos, L., Hofmann, B., Hopkin, A., Harpham, Q., Crocker, T., Amato, R., Ferrario, I., Moschini, F., and James, S. (2021). Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles. PLoS Med., 18.","DOI":"10.1371\/journal.pmed.1003542"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100372","DOI":"10.1016\/j.sste.2020.100372","article-title":"Large-scale multivariate forecasting models for Dengue-LSTM versus random forest regression","volume":"35","author":"Mussumeci","year":"2020","journal-title":"Spat. Spatio-Temporal Epidemiol."},{"key":"ref_28","unstructured":"Ministerio de Salud de Panama (MINSA) (2014). Decreto Ejecutivo N 1617. Gaceta OFICIAL, MINSA."},{"key":"ref_29","unstructured":"Instituto Nacional de Estad\u00edstica y Censo (2020). Panama en Cifras, Contraloria General de la Republica."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3570","DOI":"10.1016\/j.cub.2020.06.092","article-title":"Climate and urbanization drive mosquito preference for humans","volume":"30","author":"Rose","year":"2020","journal-title":"Curr. Biol."},{"key":"ref_31","unstructured":"Paton, S. (2021). Parque Metropolitano Crane, 25 m Relative Humidity. Smithsonian Tropical Research Institute. Dataset. Figshare, Available online: https:\/\/smithsonian.figshare.com\/articles\/dataset\/Parque_Metropolitano_Crane_25m_Relative_Humidity\/10042661."},{"key":"ref_32","unstructured":"Paton, S. (2021). Parque Metropolitano Crane Precipitation. Smithsonian Tropical Research Institute. Dataset. Figshare, Available online: https:\/\/smithsonian.figshare.com\/articles\/dataset\/Parque_Metropolitano_Crane_Precipitation\/10042676."},{"key":"ref_33","unstructured":"Paton, S. (2021). Parque Metropolitano Crane, 25 m Air Temperature. Smithsonian Tropical Research Institute. Dataset. Figshare, Available online: https:\/\/smithsonian.figshare.com\/articles\/dataset\/Parque_Metropolitano_Crane_25m_Air_Temperature\/10042652."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bellido-Jim\u00e9nez, J.A., Gualda, J.E., and Garc\u00eda-Mar\u00edn, A.P. (2021). Assessing Machine Learning Models for Gap Filling Daily Rainfall Series in a Semiarid Region of Spain. Atmosphere, 12.","DOI":"10.3390\/atmos12091158"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1098\/rspl.1895.0041","article-title":"VII. Note on regression and inheritance in the case of two parents","volume":"58","author":"Pearson","year":"1895","journal-title":"Proc. R. Soc. Lond."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"441","DOI":"10.2307\/1422689","article-title":"The proof and measurement of association between two things","volume":"100","author":"Spearman","year":"1987","journal-title":"Am. J. Psychol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1080\/09603123.2011.572279","article-title":"The influence of climate variables on dengue in Singapore","volume":"21","author":"Pinto","year":"2011","journal-title":"Int. J. Environ. Health Res."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Carrington, L.B., Armijos, M.V., Lambrechts, L., and Scott, T.W. (2013). Fluctuations at a low mean temperature accelerate dengue virus transmission by Aedes aegypti. PLoS Negl. Trop. Dis., 7.","DOI":"10.1371\/journal.pntd.0002190"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Carrera, J.P., D\u00edaz, Y., Denis, B., Barahona de Mosca, I., Rodriguez, D., Cede\u00f1o, I., Arauz, D., Gonz\u00e1lez, P., Cerezo, L., and Moreno, L. (2017). Unusual pattern of chikungunya virus epidemic in the Americas, the Panamanian experience. PLoS Negl. Trop. Dis., 11.","DOI":"10.1371\/journal.pntd.0005338"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.cmi.2017.05.029","article-title":"Preparing clinicians for (re-) emerging arbovirus infectious diseases in Europe","volume":"24","author":"Sigfrid","year":"2018","journal-title":"Clin. Microbiol. Infect."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"658","DOI":"10.4067\/S0716-10182018000600658","article-title":"Co-infecci\u00f3n por los virus del dengue y chikungunya. Revisi\u00f3n narrativa","volume":"35","author":"Vargas","year":"2018","journal-title":"Rev. Chil. Infectol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.actatropica.2017.09.031","article-title":"The reintroduction of DENV-2 in 2011 in Panama and subsequent outbreak characteristic","volume":"177","author":"Cisneros","year":"2018","journal-title":"Acta Trop."},{"key":"ref_45","unstructured":"Wu, N., Green, B., Ben, X., and O\u2019Banion, S. (2020). Deep transformer models for time series forecasting: The influenza prevalence case. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kondo, K., Ishikawa, A., and Kimura, M. (2019, January 17\u201319). Sequence to sequence with attention for influenza prevalence prediction using google trends. Proceedings of the 2019 3rd International Conference on Computational Biology and Bioinformatics, Nagoya, Japan.","DOI":"10.1145\/3365966.3365967"},{"key":"ref_47","first-page":"e2322v1","article-title":"The correlation of climate factors on dengue transmission in urban area: Bangkok and Singapore cases","volume":"4","author":"Polwiang","year":"2016","journal-title":"PeerJ Prepr."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1080\/00139157.2020.1708170","article-title":"The challenges of managing the urban waters of the Americas","volume":"62","author":"Vaux","year":"2020","journal-title":"Environ. Sci. Policy Sustain. Dev."},{"key":"ref_49","first-page":"1","article-title":"A spatio-temporal analysis of dengue spread in a Brazilian dry climate region","volume":"11","author":"Murari","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kong, L., Wang, J., Li, Z., Lai, S., Liu, Q., Wu, H., and Yang, W. (2018). Modeling the heterogeneity of dengue transmission in a city. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15061128"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Metelmann, S., Liu, X., Lu, L., Caminade, C., Liu, K., Cao, L., Medlock, J.M., Baylis, M., Morse, A.P., and Liu, Q. (2021). Assessing the suitability for Aedes albopictus and dengue transmission risk in China with a delay differential equation model. PLoS Negl. Trop. Dis., 15.","DOI":"10.1371\/journal.pntd.0009153"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5019","DOI":"10.1007\/s00382-019-04842-w","article-title":"Future precipitation changes over Panama projected with the atmospheric global model MRI-AGCM3. 2","volume":"53","author":"Kusunoki","year":"2019","journal-title":"Clim. Dyn."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez, M.M., Nakaegawa, T., Pinz\u00f3n, R., Kusunoki, S., Gord\u00f3n, R., and Sanchez-Galan, J.E. (2020). Using a Statistical Crop Model to Predict Maize Yield by the End-Of-Century for the Azuero Region in Panama. Atmosphere, 11.","DOI":"10.3390\/atmos11101097"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"106","DOI":"10.3178\/hrl.11.106","article-title":"Virtually experiencing future climate changes in Central America with MRI-AGCM: Climate analogues study","volume":"11","author":"Hibino","year":"2017","journal-title":"Hydrol. Res. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2745","DOI":"10.1175\/JCLI-D-14-00243.1","article-title":"Investigation of climatological onset and withdrawal of the rainy season in Panama based on a daily gridded precipitation dataset with a high horizontal resolution","volume":"28","author":"Nakaegawa","year":"2015","journal-title":"J. Clim."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Nakaegawa, T., Pinzon, R., Fabrega, J., Cuevas, J.A., De Lima, H.A., Cordoba, E., Nakayama, K., Batista Lao, J.I., Lau Melo, A., and Gonzalez, D.A. (2019). Seasonal changes of the diurnal variation of precipitation in the upper R\u00edo Chagres basin, Panam\u00e1. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0224662"}],"container-title":["International Journal of Environmental Research and Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1660-4601\/18\/22\/12108\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:32:12Z","timestamp":1760167932000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1660-4601\/18\/22\/12108"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,18]]},"references-count":56,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["ijerph182212108"],"URL":"https:\/\/doi.org\/10.3390\/ijerph182212108","relation":{},"ISSN":["1660-4601"],"issn-type":[{"value":"1660-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,18]]}}}