Deriving climate measures from remote sensing for predicting vector borne disease incidence | |
| Author | Nutcha Nontarak |
| Call Number | AIT Thesis no.RS-23-09 |
| Subject(s) | Communicable diseases--Prevention Communicable diseases--Remote sensing |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Remote Sensing and Geographic Information Systems |
| Publisher | Asian Institute of Technology |
| Abstract | The high mortality rate outbreaks from Vector-born disease (i.e. dengue and malaria) incidence are led to severe public health emergencies globally, particularly in tropical countries. Moreover, they are still the major public health problem at Tak Province because it still has a highest number of malaria incidences in the country. Weather variables are recognized as one of the influential factors that impact incidences and spread of vector-borne diseases. However, the utilization of weather data solely from meteorological stations to analysis vector-born disease has certain limitations such as spatial limitation. To overcome these limitations, alternative sources of weather data, such as remote sensing data. In the study, aim a model to predict disease cases by using remotely sensed data-derived climate measure by using corrected CHRIPS for rainfall, corrected ERA5 reanalysis for temperature, and gap filled MODIS for land surface temperature. To use satellite data as the main variables for building the predictive model, we examine correlation of weather data from observed station data and remote sensing data, we found r-square are high accuracy. Building prediction disease model lag times of weather variables were considered. The Spearman's rank correlation and cross correlation were used to find association between the lag times of weather variables and number of disease incidences. GAM and GLM, which are the models were used to predict number of disease incidences. In the results, we found GAM model predicts dengue incidences better than GLM model by using lag times of weather variables as dependent variables to prediction from spearman' rank correlation. While GLM model by using all lag times of weather variables is suitable to predict the malaria incidences. Understanding relationships betwween the weather variables and the vector- borne diseases and prediction disease incidences are crucial for effective disease prevention, control, and preparedness strategies. |
| Year | 2023 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Remote Sensing and Geographic Information Systems (RS) |
| Chairperson(s) | Virdis, Salvatore G.P.; |
| Examination Committee(s) | Tripathi, Nitin Kumar;Shrestha, Sangam; |
| Scholarship Donor(s) | Royal Thai Government Fellowship; |
| Degree | Thesis (M. Sc.) - Asian Institute of Technology, 2023 |