A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka
Abstract Background Dengue is one of the major health problems in Sri Lanka causing an enormous social and economic burden to the country. An accurate early warning system can enhance the efficiency of preventive measures. The aim of the study was to develop and validate a simple accurate forecastin...
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doaj-ad9eefc51f23475594d0dab9a45c83af2020-11-25T00:35:10ZengBMCParasites & Vectors1756-33052018-04-0111111010.1186/s13071-018-2828-2A forecasting model for dengue incidence in the District of Gampaha, Sri LankaGayan P. Withanage0Sameera D. Viswakula1Y. I. Nilmini Silva Gunawardena2Menaka D. Hapugoda3Molecular Medicine Unit, Faculty of Medicine, University of KelaniyaDepartment of Statistics, Faculty of Science, University of ColomboMolecular Medicine Unit, Faculty of Medicine, University of KelaniyaMolecular Medicine Unit, Faculty of Medicine, University of KelaniyaAbstract Background Dengue is one of the major health problems in Sri Lanka causing an enormous social and economic burden to the country. An accurate early warning system can enhance the efficiency of preventive measures. The aim of the study was to develop and validate a simple accurate forecasting model for the District of Gampaha, Sri Lanka. Three time-series regression models were developed using monthly rainfall, rainy days, temperature, humidity, wind speed and retrospective dengue incidences over the period January 2012 to November 2015 for the District of Gampaha, Sri Lanka. Various lag times were analyzed to identify optimum forecasting periods including interactions of multiple lags. The models were validated using epidemiological data from December 2015 to November 2017. Prepared models were compared based on Akaike’s information criterion, Bayesian information criterion and residual analysis. Results The selected model forecasted correctly with mean absolute errors of 0.07 and 0.22, and root mean squared errors of 0.09 and 0.28, for training and validation periods, respectively. There were no dengue epidemics observed in the district during the training period and nine outbreaks occurred during the forecasting period. The proposed model captured five outbreaks and correctly rejected 14 within the testing period of 24 months. The Pierce skill score of the model was 0.49, with a receiver operating characteristic of 86% and 92% sensitivity. Conclusions The developed weather based forecasting model allows warnings of impending dengue outbreaks and epidemics in advance of one month with high accuracy. Depending upon climatic factors, the previous month’s dengue cases had a significant effect on the dengue incidences of the current month. The simple, precise and understandable forecasting model developed could be used to manage limited public health resources effectively for patient management, vector surveillance and intervention programmes in the district.http://link.springer.com/article/10.1186/s13071-018-2828-2DengueDistrict of GampahaPrediction modelTime series regression |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Gayan P. Withanage Sameera D. Viswakula Y. I. Nilmini Silva Gunawardena Menaka D. Hapugoda |
spellingShingle |
Gayan P. Withanage Sameera D. Viswakula Y. I. Nilmini Silva Gunawardena Menaka D. Hapugoda A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka Parasites & Vectors Dengue District of Gampaha Prediction model Time series regression |
author_facet |
Gayan P. Withanage Sameera D. Viswakula Y. I. Nilmini Silva Gunawardena Menaka D. Hapugoda |
author_sort |
Gayan P. Withanage |
title |
A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka |
title_short |
A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka |
title_full |
A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka |
title_fullStr |
A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka |
title_full_unstemmed |
A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka |
title_sort |
forecasting model for dengue incidence in the district of gampaha, sri lanka |
publisher |
BMC |
series |
Parasites & Vectors |
issn |
1756-3305 |
publishDate |
2018-04-01 |
description |
Abstract Background Dengue is one of the major health problems in Sri Lanka causing an enormous social and economic burden to the country. An accurate early warning system can enhance the efficiency of preventive measures. The aim of the study was to develop and validate a simple accurate forecasting model for the District of Gampaha, Sri Lanka. Three time-series regression models were developed using monthly rainfall, rainy days, temperature, humidity, wind speed and retrospective dengue incidences over the period January 2012 to November 2015 for the District of Gampaha, Sri Lanka. Various lag times were analyzed to identify optimum forecasting periods including interactions of multiple lags. The models were validated using epidemiological data from December 2015 to November 2017. Prepared models were compared based on Akaike’s information criterion, Bayesian information criterion and residual analysis. Results The selected model forecasted correctly with mean absolute errors of 0.07 and 0.22, and root mean squared errors of 0.09 and 0.28, for training and validation periods, respectively. There were no dengue epidemics observed in the district during the training period and nine outbreaks occurred during the forecasting period. The proposed model captured five outbreaks and correctly rejected 14 within the testing period of 24 months. The Pierce skill score of the model was 0.49, with a receiver operating characteristic of 86% and 92% sensitivity. Conclusions The developed weather based forecasting model allows warnings of impending dengue outbreaks and epidemics in advance of one month with high accuracy. Depending upon climatic factors, the previous month’s dengue cases had a significant effect on the dengue incidences of the current month. The simple, precise and understandable forecasting model developed could be used to manage limited public health resources effectively for patient management, vector surveillance and intervention programmes in the district. |
topic |
Dengue District of Gampaha Prediction model Time series regression |
url |
http://link.springer.com/article/10.1186/s13071-018-2828-2 |
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