Developing a forecasting model for cholera incidence in Dhaka megacity through time series climate data
| Autor: | Nuhu Amin, A. K. M. Saiful Islam, Ali S. Akanda, Peter Jensen, Abu Syed Golam Faruque, Salima Sultana Daisy |
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| Rok vydání: | 2020 |
| Předmět: |
Microbiology (medical)
medicine.medical_specialty Climate 030231 tropical medicine Daily maximum temperature 03 medical and health sciences 0302 clinical medicine Cholera Statistics medicine Humans 030212 general & internal medicine Cities Waste Management and Disposal Water Science and Technology Bangladesh Series (stratigraphy) Incidence Public health Incidence (epidemiology) Public Health Environmental and Occupational Health Climatic variables Models Theoretical medicine.disease Infectious Diseases El Niño Southern Oscillation Megacity Environmental science Seasons Forecasting |
| Zdroj: | Daisy, S S, Saiful Islam, A K M, Akanda, A S, Faruque, A S G, Amin, N & Jensen, P K M 2020, ' Developing a forecasting model for cholera incidence in Dhaka megacity through time series climate data ', Journal of Water and Health, vol. 18, no. 2, pp. 207-223 . https://doi.org/10.2166/wh.2020.133 |
| ISSN: | 1996-7829 1477-8920 |
| Popis: | Cholera, an acute diarrheal disease spread by lack of hygiene and contaminated water, is a major public health risk in many countries. As cholera is triggered by environmental conditions influenced by climatic variables, establishing a correlation between cholera incidence and climatic variables would provide an opportunity to develop a cholera forecasting model. Considering the auto-regressive nature and the seasonal behavioral patterns of cholera, a seasonal-auto-regressive-integrated-moving-average (SARIMA) model was used for time-series analysis during 2000–2013. As both rainfall (r = 0.43) and maximum temperature (r = 0.56) have the strongest influence on the occurrence of cholera incidence, single-variable (SVMs) and multi-variable SARIMA models (MVMs) were developed, compared and tested for evaluating their relationship with cholera incidence. A low relationship was found with relative humidity (r = 0.28), ENSO (r = 0.21) and SOI (r = −0.23). Using SVM for a 1 °C increase in maximum temperature at one-month lead time showed a 7% increase of cholera incidence (p < 0.001). However, MVM (AIC = 15, BIC = 36) showed better performance than SVM (AIC = 21, BIC = 39). An MVM using rainfall and monthly mean daily maximum temperature with a one-month lead time showed a better fit (RMSE = 14.7, MAE = 11) than the MVM with no lead time (RMSE = 16.2, MAE = 13.2) in forecasting. This result will assist in predicting cholera risks and better preparedness for public health management in the future. |
| Databáze: | OpenAIRE |
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