Malaria Epidemics under Climate Change Scenarios in Thailand
The objective of this study was to estimate avoidable burden on disease of malaria in Thailand under climate conditions in the future. The study was based on climate projection under 2 different situations which included the regionally economic development (A2) and the local environmental sustainab...
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| Format: | Article |
| Language: | English |
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Environmental Research Institute, Chulalongkorn University
2013-11-01
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| Series: | Applied Environmental Research |
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| Online Access: | https://ph01.tci-thaijo.org/index.php/aer/article/view/13377 |
| _version_ | 1817998364282716160 |
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| author | Chayut Pinichka Kampanad Bhaktikul Saranya Sucharitakul Kanitta Bundhamcharoen |
| author_facet | Chayut Pinichka Kampanad Bhaktikul Saranya Sucharitakul Kanitta Bundhamcharoen |
| author_sort | Chayut Pinichka |
| collection | DOAJ |
| description |
The objective of this study was to estimate avoidable burden on disease of malaria in Thailand under climate conditions in the future. The study was based on climate projection under 2 different situations which included the regionally economic development (A2) and the local environmental sustainability (B2). 1991-2011 climate data collection was used to create nonlinear mixed regression model. The variables in monthly time step, which included maximum temperature, minimum temperature, precipitation, humidity, average wind speed. The results were found the best fitting model, model 2, which adjusted R-Square = 0.818 and RMSE = 763.27. The average disease incidence in the year of 2003-2011 on B2 = 26,869 persons a-1, baseline = 28,521 persons a-1, and A2 = 30,734 persons a-1. These burdens converted to DAL Ys for international comparison which were, baseline = 1,391 DALYs a-1, A2 = 1,500 DALYs a-1, and B2 = 1,301 DAL Ys a-1. The compared model with actual climate data to predict the incidence of malaria in 2012-2020 found malaria incidence has increased the incidence with trend line equation Y = 312.55X + 2480.1, R2 = 0.74 average incidences 79,703 persons a-1 or 4,042.9 DALYs a-1. The scenario B2 has been decreased incidence of malaria with trend line equation Y = 20.223X3 – 363X2 + 1801.4X– 19.483, R2 = 0.57, Average incidence 40,407 persons a-1, or 2,042.8 DALYs a-1. Scenarios B2 could have been avoided by A2 = 1,119.5 DALYs a-1 or 49.3 %.
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| first_indexed | 2024-04-14T02:52:08Z |
| format | Article |
| id | doaj.art-26fefbb62d4f4f9980c58212782777fd |
| institution | Directory Open Access Journal |
| issn | 2287-0741 2287-075X |
| language | English |
| last_indexed | 2024-04-14T02:52:08Z |
| publishDate | 2013-11-01 |
| publisher | Environmental Research Institute, Chulalongkorn University |
| record_format | Article |
| series | Applied Environmental Research |
| spelling | doaj.art-26fefbb62d4f4f9980c58212782777fd2022-12-22T02:16:15ZengEnvironmental Research Institute, Chulalongkorn UniversityApplied Environmental Research2287-07412287-075X2013-11-01352Malaria Epidemics under Climate Change Scenarios in ThailandChayut Pinichka0Kampanad Bhaktikul1Saranya Sucharitakul2Kanitta Bundhamcharoen3Faculty of Environment and Resource Studies, Mahidol University, Nakhon Pathom, ThailandFaculty of Environment and Resource Studies, Mahidol University, Nakhon Pathom, ThailandFaculty of Environment and Resource Studies, Mahidol University, Nakhon Pathom, ThailandInternational Health Policy Program, Ministry of Public Health, Nonthaburi, Thailand The objective of this study was to estimate avoidable burden on disease of malaria in Thailand under climate conditions in the future. The study was based on climate projection under 2 different situations which included the regionally economic development (A2) and the local environmental sustainability (B2). 1991-2011 climate data collection was used to create nonlinear mixed regression model. The variables in monthly time step, which included maximum temperature, minimum temperature, precipitation, humidity, average wind speed. The results were found the best fitting model, model 2, which adjusted R-Square = 0.818 and RMSE = 763.27. The average disease incidence in the year of 2003-2011 on B2 = 26,869 persons a-1, baseline = 28,521 persons a-1, and A2 = 30,734 persons a-1. These burdens converted to DAL Ys for international comparison which were, baseline = 1,391 DALYs a-1, A2 = 1,500 DALYs a-1, and B2 = 1,301 DAL Ys a-1. The compared model with actual climate data to predict the incidence of malaria in 2012-2020 found malaria incidence has increased the incidence with trend line equation Y = 312.55X + 2480.1, R2 = 0.74 average incidences 79,703 persons a-1 or 4,042.9 DALYs a-1. The scenario B2 has been decreased incidence of malaria with trend line equation Y = 20.223X3 – 363X2 + 1801.4X– 19.483, R2 = 0.57, Average incidence 40,407 persons a-1, or 2,042.8 DALYs a-1. Scenarios B2 could have been avoided by A2 = 1,119.5 DALYs a-1 or 49.3 %. https://ph01.tci-thaijo.org/index.php/aer/article/view/13377MalariaNonlinear mixed regressionClimate change projection dataDALYs |
| spellingShingle | Chayut Pinichka Kampanad Bhaktikul Saranya Sucharitakul Kanitta Bundhamcharoen Malaria Epidemics under Climate Change Scenarios in Thailand Applied Environmental Research Malaria Nonlinear mixed regression Climate change projection data DALYs |
| title | Malaria Epidemics under Climate Change Scenarios in Thailand |
| title_full | Malaria Epidemics under Climate Change Scenarios in Thailand |
| title_fullStr | Malaria Epidemics under Climate Change Scenarios in Thailand |
| title_full_unstemmed | Malaria Epidemics under Climate Change Scenarios in Thailand |
| title_short | Malaria Epidemics under Climate Change Scenarios in Thailand |
| title_sort | malaria epidemics under climate change scenarios in thailand |
| topic | Malaria Nonlinear mixed regression Climate change projection data DALYs |
| url | https://ph01.tci-thaijo.org/index.php/aer/article/view/13377 |
| work_keys_str_mv | AT chayutpinichka malariaepidemicsunderclimatechangescenariosinthailand AT kampanadbhaktikul malariaepidemicsunderclimatechangescenariosinthailand AT saranyasucharitakul malariaepidemicsunderclimatechangescenariosinthailand AT kanittabundhamcharoen malariaepidemicsunderclimatechangescenariosinthailand |