Long-term predictors of dengue outbreaks in Bangladesh: A data mining approach
The effects of weather variables on the transmission of vector-borne diseases are complex. Relationships can be non-linear, specific to particular geographic locations, and involve long lag times between predictors and outbreaks of disease. This study expands the geographical and temporal range of p...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2018-01-01
|
| Series: | Infectious Disease Modelling |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468042717300817 |
| _version_ | 1797203216830562304 |
|---|---|
| author | Olav Titus Muurlink Peter Stephenson Mohammad Zahirul Islam Andrew W. Taylor-Robinson |
| author_facet | Olav Titus Muurlink Peter Stephenson Mohammad Zahirul Islam Andrew W. Taylor-Robinson |
| author_sort | Olav Titus Muurlink |
| collection | DOAJ |
| description | The effects of weather variables on the transmission of vector-borne diseases are complex. Relationships can be non-linear, specific to particular geographic locations, and involve long lag times between predictors and outbreaks of disease. This study expands the geographical and temporal range of previous studies in Bangladesh of the mosquito-transmitted viral infection dengue, a major threat to human public health in tropical and subtropical regions worldwide. The analysis incorporates new compound variables such as anomalous events, running averages, consecutive days of particular weather characteristics, seasonal variables based on the traditional Bangla six-season annual calendar, and lag times of up to one year in predicting either the existence or the magnitude of each dengue epidemic. The study takes a novel, comprehensive data mining approach to show that different variables optimally predict the occurrence and extent of an outbreak. The best predictors of an outbreak are the number of rainy days in the preceding two months and the average daily minimum temperature one month prior to the outbreak, while the best predictor of the number of clinical cases is the average humidity six months prior to the month of outbreak. The magnitude of relationships between humidity 6, 7 and 8 months prior to the outbreak suggests the relationship is multifactorial, not due solely to the cyclical nature of prevailing weather conditions but likely due also to the immunocompetence of human hosts. Keywords: Vector-borne disease, Climate, Data mining, Bangladesh, Dengue, Long-term predictors |
| first_indexed | 2024-04-24T08:15:49Z |
| format | Article |
| id | doaj.art-3044f49113cb46a5ad34dfb9dc5e9b70 |
| institution | Directory Open Access Journal |
| issn | 2468-0427 |
| language | English |
| last_indexed | 2024-04-24T08:15:49Z |
| publishDate | 2018-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Infectious Disease Modelling |
| spelling | doaj.art-3044f49113cb46a5ad34dfb9dc5e9b702024-04-17T03:38:42ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272018-01-013322330Long-term predictors of dengue outbreaks in Bangladesh: A data mining approachOlav Titus Muurlink0Peter Stephenson1Mohammad Zahirul Islam2Andrew W. Taylor-Robinson3Central Queensland University, Brisbane, Australia; Griffith Institute of Educational Research, Australia; Corresponding author. Central Queensland University, Brisbane, Australia.Central Queensland University, Brisbane, Australia; International Centre for Diarrhoeal Disease Research, Bangladesh; Griffith Institute of Educational Research, AustraliaInternational Centre for Diarrhoeal Disease Research, BangladeshCentral Queensland University, Brisbane, AustraliaThe effects of weather variables on the transmission of vector-borne diseases are complex. Relationships can be non-linear, specific to particular geographic locations, and involve long lag times between predictors and outbreaks of disease. This study expands the geographical and temporal range of previous studies in Bangladesh of the mosquito-transmitted viral infection dengue, a major threat to human public health in tropical and subtropical regions worldwide. The analysis incorporates new compound variables such as anomalous events, running averages, consecutive days of particular weather characteristics, seasonal variables based on the traditional Bangla six-season annual calendar, and lag times of up to one year in predicting either the existence or the magnitude of each dengue epidemic. The study takes a novel, comprehensive data mining approach to show that different variables optimally predict the occurrence and extent of an outbreak. The best predictors of an outbreak are the number of rainy days in the preceding two months and the average daily minimum temperature one month prior to the outbreak, while the best predictor of the number of clinical cases is the average humidity six months prior to the month of outbreak. The magnitude of relationships between humidity 6, 7 and 8 months prior to the outbreak suggests the relationship is multifactorial, not due solely to the cyclical nature of prevailing weather conditions but likely due also to the immunocompetence of human hosts. Keywords: Vector-borne disease, Climate, Data mining, Bangladesh, Dengue, Long-term predictorshttp://www.sciencedirect.com/science/article/pii/S2468042717300817 |
| spellingShingle | Olav Titus Muurlink Peter Stephenson Mohammad Zahirul Islam Andrew W. Taylor-Robinson Long-term predictors of dengue outbreaks in Bangladesh: A data mining approach Infectious Disease Modelling |
| title | Long-term predictors of dengue outbreaks in Bangladesh: A data mining approach |
| title_full | Long-term predictors of dengue outbreaks in Bangladesh: A data mining approach |
| title_fullStr | Long-term predictors of dengue outbreaks in Bangladesh: A data mining approach |
| title_full_unstemmed | Long-term predictors of dengue outbreaks in Bangladesh: A data mining approach |
| title_short | Long-term predictors of dengue outbreaks in Bangladesh: A data mining approach |
| title_sort | long term predictors of dengue outbreaks in bangladesh a data mining approach |
| url | http://www.sciencedirect.com/science/article/pii/S2468042717300817 |
| work_keys_str_mv | AT olavtitusmuurlink longtermpredictorsofdengueoutbreaksinbangladeshadataminingapproach AT peterstephenson longtermpredictorsofdengueoutbreaksinbangladeshadataminingapproach AT mohammadzahirulislam longtermpredictorsofdengueoutbreaksinbangladeshadataminingapproach AT andrewwtaylorrobinson longtermpredictorsofdengueoutbreaksinbangladeshadataminingapproach |