Forecasting cross-border malaria case number: towards an early warning system to support malaria elimination plans - RIIP - Réseau International des Instituts Pasteur Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Forecasting cross-border malaria case number: towards an early warning system to support malaria elimination plans

Résumé

Malaria elimination, one of the Sustainable Development Goals of the United Nation, is challenged by cross-border context specificities. At the French Guiana-Brazil border, a system was developed to harmonized epidemiological data providing by the two countries. This study evaluates the feasibility of using such harmonized data to build a cross-border early warning system. To this end, the study compared ARI-MAX and LSTM approaches. Time-lagged meteorological data were introduced to improve the forecasts. LSTM outperformed ARIMAX, with a 10 to 39% decrease of Mean Absolute and Root Means Square Errors, and better predicted low case numbers. Meteorological data improved significantly model predictions, by considering time-lags from 3 to 7 weeks compatible with the knowledge found in the literature. This study demonstrated the feasibility of building a cross-border malaria early warning system, that would significantly contribute to malaria control and elimination.
Fichier principal
Vignette du fichier
Schincariol_et_al_v2.pdf (1.13 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03381934 , version 1 (18-10-2021)

Identifiants

  • HAL Id : hal-03381934 , version 1

Citer

Thomas Schincariol, Emmanuel Roux, Sylvaine Jégo, Thibault Catry, Florian Girond, et al.. Forecasting cross-border malaria case number: towards an early warning system to support malaria elimination plans. 7th International conference on Time Series and Forecasting (ITISE 2021), Jul 2021, Gran Canaria, Spain. ⟨hal-03381934⟩
150 Consultations
155 Téléchargements

Partager

Gmail Facebook X LinkedIn More