Institution:
IDExtremes – R package
The tool is based on a Bayesian statistical modelling framework and provides functionalities for performing exploratory analysis, fitting spatiotemporal models, testing and validating the predictive ability of the models, and forecasting the probability of a disease outbreak.
Research Group:
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Researcher/s:
Giovenale Moirano, Chloe Fletcher, Martin Lotto, Daniela Lührsen, Raúl Capellán, Rachel Lowe
Website:
https://www.bsc.es/research-and-development/projects/id-extremes-digital-technology-development-award-climate-sensitive
Description:
The tool is based on a Bayesian statistical modelling framework and provides functionalities for performing exploratory analysis, fitting spatiotemporal models, testing and validating the predictive ability of the models, and forecasting the probability of a disease outbreak. Additionally, the tool includes functions to visualise outputs for all modelling steps and final predictions. Its flexible design allows users to input both observed (long-lag) and forecast (short-lag) hydrometeorological indicators, such as drought and flood indicators, and output the probability of an outbreak of a given climate-sensitive disease (e.g., dengue, malaria, or leptospirosis) several months in advance.
Problem:
N/A
Solution:
N/A
Aplication areas:
N/A
Novelty:
N/A
Protection:
GPL License (Version 2.0)
Target market:
N/A
Keywords:
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TRL: N/A
CRL: N/A
BRL: N/A
IPRL: N/A
TmRL: N/A
FRL: N/A
More information
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