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      Time series analysis of leishmaniasis incidence in Sri Lanka: evidence for humidity-associated fluctuations

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          Abstract

          Leishmaniasis is a vector-borne disease of which the transmission is highly influenced by climatic factors, whereas the nature and magnitude differ between geographical regions. The effects of climatic variables on leishmaniasis in Sri Lanka are poorly investigated. The present study focused on time-series analysis of leishmaniasis cases reported from Sri Lanka with selected climatic variables. Variance stabilized time series of leishmaniasis patients of entire Sri Lanka and major districts from 2014 to 2018 was fitted to autoregressive integrated moving average (ARIMA) models. All the possible models were generated by assigning different values for autoregression and moving average terms using a function written in R statistical program. The top ten models with the lowest Akaike information criterion (AIC) values were selected by writing another function. These models were further evaluated using RMSE and MAPE error parameters to select the optimal model for each area. Cross-autocorrelation analyses were performed to assess the associations between climate and the leishmaniasis incidence. Most associated lags of each variable were integrated into the optimal models to determine the true effects imposed. The optimal models varied depending on the area. SARIMA (0,1,1) (1,0,0) 12 was optimal for the country level. All the forecasts were within the 95% confidence intervals. Humidity was the most notable factor associated with leishmaniasis, which could be attributed to the positive impacts on sand fly activity. Rainfall showed a negative impact probably as a result of flooding of sand fly larval habitats. The ARIMA-based models performed well for the prediction of leishmaniasis in the short term.

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          Control of neglected tropical diseases.

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            Introduction to Time Series and Forecasting

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              • Record: found
              • Abstract: not found
              • Article: not found

              Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing

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                Author and article information

                Contributors
                tharaka.wijerathna90@gmail.com
                n.gunathilaka@kln.ac.lk
                Journal
                Int J Biometeorol
                Int J Biometeorol
                International Journal of Biometeorology
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0020-7128
                1432-1254
                15 November 2022
                : 1-10
                Affiliations
                GRID grid.45202.31, ISNI 0000 0000 8631 5388, Department of Parasitology, Faculty of Medicine, , University of Kelaniya, ; Ragama, Sri Lanka
                Author information
                http://orcid.org/0000-0003-4110-9718
                http://orcid.org/0000-0002-2690-8565
                Article
                2404
                10.1007/s00484-022-02404-0
                9666979
                36378349
                629eaea4-a3e2-4bb8-a143-a23fff2c8dbc
                © The Author(s) under exclusive licence to International Society of Biometeorology 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 21 September 2021
                : 25 June 2022
                : 4 November 2022
                Categories
                Original Paper

                Atmospheric science & Climatology
                leishmaniasis,modelling,time series,arima,climate,temperature,humidity,sri lanka

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