Remote Sensing-Derived Environmental Variables to Estimate Transmission Risk and Predict Malaria Cases in Argentina: A Pre-Certification Study (1986–2005)
Early warning systems rely on statistical prediction models, with environmental risks and remote sensing data serving as essential sources of information for their development. The present work is focused on the use of remote sensing for the estimation of transmission risk and the prediction of mala...
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2025-05-01
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| author | Ana C. Cuéllar Roberto D. Coello-Peralta Davis Calle-Atariguana Martha Palacios-Macias Paul L. Duque Liliana M. Galindo Mario O. Zaidenberg María J. Dantur-Juri |
| author_facet | Ana C. Cuéllar Roberto D. Coello-Peralta Davis Calle-Atariguana Martha Palacios-Macias Paul L. Duque Liliana M. Galindo Mario O. Zaidenberg María J. Dantur-Juri |
| author_sort | Ana C. Cuéllar |
| collection | DOAJ |
| description | Early warning systems rely on statistical prediction models, with environmental risks and remote sensing data serving as essential sources of information for their development. The present work is focused on the use of remote sensing for the estimation of transmission risk and the prediction of malaria cases in northwest Argentina. This study was conducted in the city of San Ramón de la Nueva Orán, where cases of the disease have been reported from 1986 to 2005. The relationship between reported malaria cases and climatic/environmental variables—including the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and land surface temperature (LST)—obtained from Landsat 5 and 7 satellite images was analyzed using multilevel Poisson regression analyses. An increased abundance of reported malaria cases was observed in summer. An ARIMA (autoregressive integrated moving average) temporal series model incorporating environmental variables was developed to forecast malaria cases in the year 2000. The analysis of the relationship between malaria cases and environmental and climatic factors showed that malaria cases were associated with increases in LST and mean temperature and a decrease in the NDVI. Early warning systems that provide information about spatial and temporal predictions of epidemics could help to control and prevent malaria outbreaks. Based on these findings, this study is expected to support the development of future prevention and control measures by health officials. |
| format | Article |
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| institution | Kabale University |
| issn | 2076-0817 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Pathogens |
| spelling | doaj-art-1f0eef77431d4dc8ad459627f0d9b83f2025-08-20T03:48:02ZengMDPI AGPathogens2076-08172025-05-0114544810.3390/pathogens14050448Remote Sensing-Derived Environmental Variables to Estimate Transmission Risk and Predict Malaria Cases in Argentina: A Pre-Certification Study (1986–2005)Ana C. Cuéllar0Roberto D. Coello-Peralta1Davis Calle-Atariguana2Martha Palacios-Macias3Paul L. Duque4Liliana M. Galindo5Mario O. Zaidenberg6María J. Dantur-Juri7National Veterinary Institute, Technical University of Denmark, Bülowsvej, 2750 Frederiksberg, DenmarkDepartment of Microbiology, Faculty of Veterinary Medicine and Zootechnics, Universidad de Guayaquil, Guayaquil 090511, EcuadorDepartment of Microbiology, Faculty of Veterinary Medicine and Zootechnics, Universidad de Guayaquil, Guayaquil 090511, EcuadorDepartment of Microbiology, Faculty of Veterinary Medicine and Zootechnics, Universidad de Guayaquil, Guayaquil 090511, EcuadorUnidad Ejecutora Lillo (CONICET-Fundación Miguel Lillo), San Miguel de Tucumán 4000, ArgentinaFacultad de Medicina, Universidad Nacional de Tucumán, San Miguel de Tucumán 4000, ArgentinaCoordinación Nacional de Control de Vectores, Ministerio de Salud de la Nación, Salta 4400, ArgentinaUnidad Ejecutora Lillo (CONICET-Fundación Miguel Lillo), San Miguel de Tucumán 4000, ArgentinaEarly warning systems rely on statistical prediction models, with environmental risks and remote sensing data serving as essential sources of information for their development. The present work is focused on the use of remote sensing for the estimation of transmission risk and the prediction of malaria cases in northwest Argentina. This study was conducted in the city of San Ramón de la Nueva Orán, where cases of the disease have been reported from 1986 to 2005. The relationship between reported malaria cases and climatic/environmental variables—including the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and land surface temperature (LST)—obtained from Landsat 5 and 7 satellite images was analyzed using multilevel Poisson regression analyses. An increased abundance of reported malaria cases was observed in summer. An ARIMA (autoregressive integrated moving average) temporal series model incorporating environmental variables was developed to forecast malaria cases in the year 2000. The analysis of the relationship between malaria cases and environmental and climatic factors showed that malaria cases were associated with increases in LST and mean temperature and a decrease in the NDVI. Early warning systems that provide information about spatial and temporal predictions of epidemics could help to control and prevent malaria outbreaks. Based on these findings, this study is expected to support the development of future prevention and control measures by health officials.https://www.mdpi.com/2076-0817/14/5/448malariapredictive modelssatellite imagesARIMA temporal seriesdisease prevention |
| spellingShingle | Ana C. Cuéllar Roberto D. Coello-Peralta Davis Calle-Atariguana Martha Palacios-Macias Paul L. Duque Liliana M. Galindo Mario O. Zaidenberg María J. Dantur-Juri Remote Sensing-Derived Environmental Variables to Estimate Transmission Risk and Predict Malaria Cases in Argentina: A Pre-Certification Study (1986–2005) Pathogens malaria predictive models satellite images ARIMA temporal series disease prevention |
| title | Remote Sensing-Derived Environmental Variables to Estimate Transmission Risk and Predict Malaria Cases in Argentina: A Pre-Certification Study (1986–2005) |
| title_full | Remote Sensing-Derived Environmental Variables to Estimate Transmission Risk and Predict Malaria Cases in Argentina: A Pre-Certification Study (1986–2005) |
| title_fullStr | Remote Sensing-Derived Environmental Variables to Estimate Transmission Risk and Predict Malaria Cases in Argentina: A Pre-Certification Study (1986–2005) |
| title_full_unstemmed | Remote Sensing-Derived Environmental Variables to Estimate Transmission Risk and Predict Malaria Cases in Argentina: A Pre-Certification Study (1986–2005) |
| title_short | Remote Sensing-Derived Environmental Variables to Estimate Transmission Risk and Predict Malaria Cases in Argentina: A Pre-Certification Study (1986–2005) |
| title_sort | remote sensing derived environmental variables to estimate transmission risk and predict malaria cases in argentina a pre certification study 1986 2005 |
| topic | malaria predictive models satellite images ARIMA temporal series disease prevention |
| url | https://www.mdpi.com/2076-0817/14/5/448 |
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