Prediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic data
Abstract Background The goal of this research is to create a system that can use the available relevant information about the factors responsible for the spread of dengue and; use it to predict the occurrence of dengue within a geographical region, so that public health experts can prepare for, mana...
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| Format: | Article |
| Language: | English |
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BMC
2019-03-01
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| Series: | BMC Infectious Diseases |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12879-019-3874-x |
| _version_ | 1818285398944645120 |
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| author | Raghvendra Jain Sra Sontisirikit Sopon Iamsirithaworn Helmut Prendinger |
| author_facet | Raghvendra Jain Sra Sontisirikit Sopon Iamsirithaworn Helmut Prendinger |
| author_sort | Raghvendra Jain |
| collection | DOAJ |
| description | Abstract Background The goal of this research is to create a system that can use the available relevant information about the factors responsible for the spread of dengue and; use it to predict the occurrence of dengue within a geographical region, so that public health experts can prepare for, manage and control the epidemic. Our study presents new geospatial insights into our understanding and management of health, disease and health-care systems. Methods We present a machine learning-based methodology capable of providing forecast estimates of dengue prediction in each of the fifty districts of Thailand by leveraging data from multiple data sources. Using a set of prediction variables, we show an increase in prediction accuracy of the model with an optimal combination of predictors which include: meteorological data, clinical data, lag variables of disease surveillance, socioeconomic data and the data encoding spatial dependence on dengue transmission. We use Generalized Additive Models (GAMs) to fit the relationships between the predictors (with a lag of one month) and the clinical data of Dengue hemorrhagic fever (DHF) using the data from 2008 to 2012. Using the data from 2013 to 2015 and a comparative set of prediction models, we evaluate the predictive ability of the fitted models according to RMSE and SRMSE as well as using adjusted R-squared value, deviance explained and change in AIC. Results The model allows for combining different predictors to make forecasts with a lead time of one month and also describe the statistical significance of the variables used to characterize the forecast. The discriminating ability of the final model was evaluated against Bangkok specific constant threshold and WHO moving threshold of the epidemic in terms of specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). Conclusions The out-of-sample validation showed poorer results than the in-sample validation, however it demonstrated ability in detecting outbreaks up-to one month ahead. We also determine that for the predicting dengue outbreaks within a district, the influence of dengue incidences and socioeconomic data from the surrounding districts is statistically significant. This validates the influence of movement patterns of people and spatial heterogeneity of human activities on the spread of the epidemic. |
| first_indexed | 2024-12-13T01:08:04Z |
| format | Article |
| id | doaj.art-e3f60dd837a04aa7b86fb4bdf9ada83a |
| institution | Directory Open Access Journal |
| issn | 1471-2334 |
| language | English |
| last_indexed | 2024-12-13T01:08:04Z |
| publishDate | 2019-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj.art-e3f60dd837a04aa7b86fb4bdf9ada83a2022-12-22T00:04:32ZengBMCBMC Infectious Diseases1471-23342019-03-0119111610.1186/s12879-019-3874-xPrediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic dataRaghvendra Jain0Sra Sontisirikit1Sopon Iamsirithaworn2Helmut Prendinger3National Institute of InformaticsAsian Institute of Technology, School of Engineering and TechnologyDepartment of Disease Control Thirteenth DivisionNational Institute of InformaticsAbstract Background The goal of this research is to create a system that can use the available relevant information about the factors responsible for the spread of dengue and; use it to predict the occurrence of dengue within a geographical region, so that public health experts can prepare for, manage and control the epidemic. Our study presents new geospatial insights into our understanding and management of health, disease and health-care systems. Methods We present a machine learning-based methodology capable of providing forecast estimates of dengue prediction in each of the fifty districts of Thailand by leveraging data from multiple data sources. Using a set of prediction variables, we show an increase in prediction accuracy of the model with an optimal combination of predictors which include: meteorological data, clinical data, lag variables of disease surveillance, socioeconomic data and the data encoding spatial dependence on dengue transmission. We use Generalized Additive Models (GAMs) to fit the relationships between the predictors (with a lag of one month) and the clinical data of Dengue hemorrhagic fever (DHF) using the data from 2008 to 2012. Using the data from 2013 to 2015 and a comparative set of prediction models, we evaluate the predictive ability of the fitted models according to RMSE and SRMSE as well as using adjusted R-squared value, deviance explained and change in AIC. Results The model allows for combining different predictors to make forecasts with a lead time of one month and also describe the statistical significance of the variables used to characterize the forecast. The discriminating ability of the final model was evaluated against Bangkok specific constant threshold and WHO moving threshold of the epidemic in terms of specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). Conclusions The out-of-sample validation showed poorer results than the in-sample validation, however it demonstrated ability in detecting outbreaks up-to one month ahead. We also determine that for the predicting dengue outbreaks within a district, the influence of dengue incidences and socioeconomic data from the surrounding districts is statistically significant. This validates the influence of movement patterns of people and spatial heterogeneity of human activities on the spread of the epidemic.http://link.springer.com/article/10.1186/s12879-019-3874-xDengue forecastingData-driven epidemiologyDisease surveillanceGeneralized additive models (GAMs) |
| spellingShingle | Raghvendra Jain Sra Sontisirikit Sopon Iamsirithaworn Helmut Prendinger Prediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic data BMC Infectious Diseases Dengue forecasting Data-driven epidemiology Disease surveillance Generalized additive models (GAMs) |
| title | Prediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic data |
| title_full | Prediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic data |
| title_fullStr | Prediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic data |
| title_full_unstemmed | Prediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic data |
| title_short | Prediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic data |
| title_sort | prediction of dengue outbreaks based on disease surveillance meteorological and socio economic data |
| topic | Dengue forecasting Data-driven epidemiology Disease surveillance Generalized additive models (GAMs) |
| url | http://link.springer.com/article/10.1186/s12879-019-3874-x |
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