Towards a spatio-temporal deep learning approach to predict malaria outbreaks using earth observation measurements in South Asia
Environmental indicators can play a crucial role in forecasting infectious disease outbreaks, holding promise for community-level interventions. Yet, significant gaps exist in the literature regarding the influence of changes in environmental conditions on disease spread over time and across differe...
| Main Authors: | , , , , |
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| Format: | Conference item |
| Language: | English |
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Climate Change AI
2023
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| _version_ | 1797112542336647168 |
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| author | Nazir, U Ejaz, A Quddoos, MT Uppal, M Khalid, S |
| author_facet | Nazir, U Ejaz, A Quddoos, MT Uppal, M Khalid, S |
| author_sort | Nazir, U |
| collection | OXFORD |
| description | Environmental indicators can play a crucial role in forecasting infectious disease outbreaks, holding promise for community-level interventions. Yet, significant gaps exist in the literature regarding the influence of changes in environmental conditions on disease spread over time and across different regions and climates making it challenging to obtain reliable forecasts. This paper aims to propose an approach to predict malaria incidence over time and space by employing a multi-dimensional long short-term memory model (M-LSTM) to simultaneously analyse environmental indicators such as vegetation, temperature, night-time lights, urban/rural settings, and precipitation. We developed and validated a spatio-temporal data fusion approach to predict district-level malaria incidence rates for the year 2017 using spatio-temporal data from 2000 to 2016 across three South Asian countries: Pakistan, India, and Bangladesh. In terms of predictive performance the proposed M-LSTM model results in lower country-specific error rates compared to existing spatio-temporal deep learning models. The data and code have been made publicly available at the study GitHub repository. |
| first_indexed | 2024-03-07T08:27:05Z |
| format | Conference item |
| id | oxford-uuid:090181c0-3124-4ffc-b4f4-7c24c85b87e6 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T08:27:05Z |
| publishDate | 2023 |
| publisher | Climate Change AI |
| record_format | dspace |
| spelling | oxford-uuid:090181c0-3124-4ffc-b4f4-7c24c85b87e62024-02-22T09:00:39ZTowards a spatio-temporal deep learning approach to predict malaria outbreaks using earth observation measurements in South AsiaConference itemhttp://purl.org/coar/resource_type/c_5794uuid:090181c0-3124-4ffc-b4f4-7c24c85b87e6EnglishSymplectic ElementsClimate Change AI2023Nazir, UEjaz, AQuddoos, MTUppal, MKhalid, SEnvironmental indicators can play a crucial role in forecasting infectious disease outbreaks, holding promise for community-level interventions. Yet, significant gaps exist in the literature regarding the influence of changes in environmental conditions on disease spread over time and across different regions and climates making it challenging to obtain reliable forecasts. This paper aims to propose an approach to predict malaria incidence over time and space by employing a multi-dimensional long short-term memory model (M-LSTM) to simultaneously analyse environmental indicators such as vegetation, temperature, night-time lights, urban/rural settings, and precipitation. We developed and validated a spatio-temporal data fusion approach to predict district-level malaria incidence rates for the year 2017 using spatio-temporal data from 2000 to 2016 across three South Asian countries: Pakistan, India, and Bangladesh. In terms of predictive performance the proposed M-LSTM model results in lower country-specific error rates compared to existing spatio-temporal deep learning models. The data and code have been made publicly available at the study GitHub repository. |
| spellingShingle | Nazir, U Ejaz, A Quddoos, MT Uppal, M Khalid, S Towards a spatio-temporal deep learning approach to predict malaria outbreaks using earth observation measurements in South Asia |
| title | Towards a spatio-temporal deep learning approach to predict malaria outbreaks using earth observation measurements in South Asia |
| title_full | Towards a spatio-temporal deep learning approach to predict malaria outbreaks using earth observation measurements in South Asia |
| title_fullStr | Towards a spatio-temporal deep learning approach to predict malaria outbreaks using earth observation measurements in South Asia |
| title_full_unstemmed | Towards a spatio-temporal deep learning approach to predict malaria outbreaks using earth observation measurements in South Asia |
| title_short | Towards a spatio-temporal deep learning approach to predict malaria outbreaks using earth observation measurements in South Asia |
| title_sort | towards a spatio temporal deep learning approach to predict malaria outbreaks using earth observation measurements in south asia |
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