Modeling the Role of Weather and Pilgrimage Variables on Dengue Fever Incidence in Saudi Arabia
The first case of dengue fever (DF) in Saudi Arabia appeared in 1993 but by 2022, DF incidence was 11 per 100,000 people. Climatologic and population factors, such as the annual Hajj, likely contribute to DF’s epidemiology in Saudi Arabia. In this study, we assess the impact of these variables on th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Pathogens |
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| Online Access: | https://www.mdpi.com/2076-0817/13/3/214 |
| _version_ | 1797239707596226560 |
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| author | Kholood K. Altassan Cory W. Morin Jeremy J. Hess |
| author_facet | Kholood K. Altassan Cory W. Morin Jeremy J. Hess |
| author_sort | Kholood K. Altassan |
| collection | DOAJ |
| description | The first case of dengue fever (DF) in Saudi Arabia appeared in 1993 but by 2022, DF incidence was 11 per 100,000 people. Climatologic and population factors, such as the annual Hajj, likely contribute to DF’s epidemiology in Saudi Arabia. In this study, we assess the impact of these variables on the DF burden of disease in Saudi Arabia and we attempt to create robust DF predictive models. Using 10 years of DF, weather, and pilgrimage data, we conducted a bivariate analysis investigating the role of weather and pilgrimage variables on DF incidence. We also compared the abilities of three different predictive models. Amongst weather variables, temperature and humidity had the strongest associations with DF incidence, while rainfall showed little to no significant relationship. Pilgrimage variables did not have strong associations with DF incidence. The random forest model had the highest predictive ability (R<sup>2</sup> = 0.62) when previous DF data were withheld, and the ARIMA model was the best (R<sup>2</sup> = 0.78) when previous DF data were incorporated. We found that a nonlinear machine-learning model incorporating temperature and humidity variables had the best prediction accuracy for DF, regardless of the availability of previous DF data. This finding can inform DF early warning systems and preparedness in Saudi Arabia. |
| first_indexed | 2024-04-24T17:55:49Z |
| format | Article |
| id | doaj.art-1ee04aaf69ee4f1eaba3b4c9f107a932 |
| institution | Directory Open Access Journal |
| issn | 2076-0817 |
| language | English |
| last_indexed | 2024-04-24T17:55:49Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pathogens |
| spelling | doaj.art-1ee04aaf69ee4f1eaba3b4c9f107a9322024-03-27T13:58:51ZengMDPI AGPathogens2076-08172024-02-0113321410.3390/pathogens13030214Modeling the Role of Weather and Pilgrimage Variables on Dengue Fever Incidence in Saudi ArabiaKholood K. Altassan0Cory W. Morin1Jeremy J. Hess2Department of Family and Community Medicine, King Saud University, Riyadh 11421, Saudi ArabiaDepartment of Environmental and Occupational Health, University of Washington, Seattle, WA 98195, USADepartment of Emergency Medicine, University of Washington, Seattle, WA 98195, USAThe first case of dengue fever (DF) in Saudi Arabia appeared in 1993 but by 2022, DF incidence was 11 per 100,000 people. Climatologic and population factors, such as the annual Hajj, likely contribute to DF’s epidemiology in Saudi Arabia. In this study, we assess the impact of these variables on the DF burden of disease in Saudi Arabia and we attempt to create robust DF predictive models. Using 10 years of DF, weather, and pilgrimage data, we conducted a bivariate analysis investigating the role of weather and pilgrimage variables on DF incidence. We also compared the abilities of three different predictive models. Amongst weather variables, temperature and humidity had the strongest associations with DF incidence, while rainfall showed little to no significant relationship. Pilgrimage variables did not have strong associations with DF incidence. The random forest model had the highest predictive ability (R<sup>2</sup> = 0.62) when previous DF data were withheld, and the ARIMA model was the best (R<sup>2</sup> = 0.78) when previous DF data were incorporated. We found that a nonlinear machine-learning model incorporating temperature and humidity variables had the best prediction accuracy for DF, regardless of the availability of previous DF data. This finding can inform DF early warning systems and preparedness in Saudi Arabia.https://www.mdpi.com/2076-0817/13/3/214dengue feverSaudi Arabiavector-borne diseasepredictive modelsmachine learning |
| spellingShingle | Kholood K. Altassan Cory W. Morin Jeremy J. Hess Modeling the Role of Weather and Pilgrimage Variables on Dengue Fever Incidence in Saudi Arabia Pathogens dengue fever Saudi Arabia vector-borne disease predictive models machine learning |
| title | Modeling the Role of Weather and Pilgrimage Variables on Dengue Fever Incidence in Saudi Arabia |
| title_full | Modeling the Role of Weather and Pilgrimage Variables on Dengue Fever Incidence in Saudi Arabia |
| title_fullStr | Modeling the Role of Weather and Pilgrimage Variables on Dengue Fever Incidence in Saudi Arabia |
| title_full_unstemmed | Modeling the Role of Weather and Pilgrimage Variables on Dengue Fever Incidence in Saudi Arabia |
| title_short | Modeling the Role of Weather and Pilgrimage Variables on Dengue Fever Incidence in Saudi Arabia |
| title_sort | modeling the role of weather and pilgrimage variables on dengue fever incidence in saudi arabia |
| topic | dengue fever Saudi Arabia vector-borne disease predictive models machine learning |
| url | https://www.mdpi.com/2076-0817/13/3/214 |
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