Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018
Abstract Accurate malaria predictions are essential for implementing timely interventions, particularly in Mozambique, where climate factors strongly influence transmission. This study aims to develop and evaluate a spatial–temporal prediction model for malaria incidence in Mozambique for potential...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-97072-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850183852435177472 |
|---|---|
| author | Chaibo Jose Armando Joacim Rocklöv Mohsin Sidat Yesim Tozan Alberto Francisco Mavume Maquins Odhiambo Sewe |
| author_facet | Chaibo Jose Armando Joacim Rocklöv Mohsin Sidat Yesim Tozan Alberto Francisco Mavume Maquins Odhiambo Sewe |
| author_sort | Chaibo Jose Armando |
| collection | DOAJ |
| description | Abstract Accurate malaria predictions are essential for implementing timely interventions, particularly in Mozambique, where climate factors strongly influence transmission. This study aims to develop and evaluate a spatial–temporal prediction model for malaria incidence in Mozambique for potential use in a malaria early warning system (MEWS). We used monthly data on malaria cases from 2001 to 2018 in Mozambique, the model incorporated lagged climate variables selected through Deviance Information Criterion (DIC), including mean temperature and precipitation (1–2 months), relative humidity (5–6 months), and Normalized Different Vegetation Index (NDVI) (3–4 months). Predictive distributions from monthly cross-validations were employed to calculate threshold exceedance probabilities, with district-specific thresholds set at the 75th percentile of historical monthly malaria incidence. The model’s ability to predict high and low malaria seasons was evaluated using receiver operating characteristic (ROC) analysis. Results indicated that malaria incidence in Mozambique peaks from November to April, offering a predictive lead time of up to 4 months. The model demonstrated high predictive power with an area under the curve (AUC) of 0.897 (0.893–0.901), sensitivity of 0.835 (0.827–0.843), and specificity of 0.793 (0.787–0.798), underscoring its suitability for integration into a MEWS. Thus, incorporating climate information within a multisectoral approach is essential for enhancing malaria prevention interventions effectiveness. |
| format | Article |
| id | doaj-art-648bf3f5bc6740938071c8beb866d152 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-648bf3f5bc6740938071c8beb866d1522025-08-20T02:17:13ZengNature PortfolioScientific Reports2045-23222025-04-0115111210.1038/s41598-025-97072-6Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018Chaibo Jose Armando0Joacim Rocklöv1Mohsin Sidat2Yesim Tozan3Alberto Francisco Mavume4Maquins Odhiambo Sewe5Department of Public Health and Clinical Medicine, Sustainable Health Section Umeå UniversityDepartment of Public Health and Clinical Medicine, Sustainable Health Section Umeå UniversityFaculty of Medicine, Eduardo Mondlane UniversitySchool of Global Public Health, New York UniversityFaculty of Science, Eduardo Mondlane UniversityDepartment of Public Health and Clinical Medicine, Sustainable Health Section Umeå UniversityAbstract Accurate malaria predictions are essential for implementing timely interventions, particularly in Mozambique, where climate factors strongly influence transmission. This study aims to develop and evaluate a spatial–temporal prediction model for malaria incidence in Mozambique for potential use in a malaria early warning system (MEWS). We used monthly data on malaria cases from 2001 to 2018 in Mozambique, the model incorporated lagged climate variables selected through Deviance Information Criterion (DIC), including mean temperature and precipitation (1–2 months), relative humidity (5–6 months), and Normalized Different Vegetation Index (NDVI) (3–4 months). Predictive distributions from monthly cross-validations were employed to calculate threshold exceedance probabilities, with district-specific thresholds set at the 75th percentile of historical monthly malaria incidence. The model’s ability to predict high and low malaria seasons was evaluated using receiver operating characteristic (ROC) analysis. Results indicated that malaria incidence in Mozambique peaks from November to April, offering a predictive lead time of up to 4 months. The model demonstrated high predictive power with an area under the curve (AUC) of 0.897 (0.893–0.901), sensitivity of 0.835 (0.827–0.843), and specificity of 0.793 (0.787–0.798), underscoring its suitability for integration into a MEWS. Thus, incorporating climate information within a multisectoral approach is essential for enhancing malaria prevention interventions effectiveness.https://doi.org/10.1038/s41598-025-97072-6MalariaMozambiqueEarly warningClimatePrediction |
| spellingShingle | Chaibo Jose Armando Joacim Rocklöv Mohsin Sidat Yesim Tozan Alberto Francisco Mavume Maquins Odhiambo Sewe Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018 Scientific Reports Malaria Mozambique Early warning Climate Prediction |
| title | Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018 |
| title_full | Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018 |
| title_fullStr | Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018 |
| title_full_unstemmed | Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018 |
| title_short | Spatio-temporal modelling and prediction of malaria incidence in Mozambique using climatic indicators from 2001 to 2018 |
| title_sort | spatio temporal modelling and prediction of malaria incidence in mozambique using climatic indicators from 2001 to 2018 |
| topic | Malaria Mozambique Early warning Climate Prediction |
| url | https://doi.org/10.1038/s41598-025-97072-6 |
| work_keys_str_mv | AT chaibojosearmando spatiotemporalmodellingandpredictionofmalariaincidenceinmozambiqueusingclimaticindicatorsfrom2001to2018 AT joacimrocklov spatiotemporalmodellingandpredictionofmalariaincidenceinmozambiqueusingclimaticindicatorsfrom2001to2018 AT mohsinsidat spatiotemporalmodellingandpredictionofmalariaincidenceinmozambiqueusingclimaticindicatorsfrom2001to2018 AT yesimtozan spatiotemporalmodellingandpredictionofmalariaincidenceinmozambiqueusingclimaticindicatorsfrom2001to2018 AT albertofranciscomavume spatiotemporalmodellingandpredictionofmalariaincidenceinmozambiqueusingclimaticindicatorsfrom2001to2018 AT maquinsodhiambosewe spatiotemporalmodellingandpredictionofmalariaincidenceinmozambiqueusingclimaticindicatorsfrom2001to2018 |