Earth Observation (EO) data can be leveraged to estimate environmental variables that influence the transmission cycle of the pathogens that lead to mosquito-borne diseases (MBDs). The aim of this scoping review is to examine the state-of-the-art and identify knowledge gaps on the latest methods that used satellite EO data in their epidemiological models focusing on malaria, dengue and West Nile Virus (WNV). In total, 43 scientific papers met the inclusion criteria and were considered in this review. Researchers have examined a wide variety of methodologies ranging from statistical to machine learning algorithms. A number of studies used models and EO data that seemed promising and claimed to be easily replicated in different geographic contexts, enabling the realization of systems on regional and national scales. The need has emerged to leverage furthermore new powerful modeling approaches, like artificial intelligence and ensemble modeling and explore new and enhanced EO sensors towards the analysis of big satellite data, in order to develop accurate epidemiological models and contribute to the reduction of the burden of MBDs.
PARSELIA Elisavet;
KONTOES Charalambos;
TSOUNI Alexia;
HADJICHRISTODOULOU Christos;
KIOUTSIOUKIS Ioannis;
MAGIORKINIS Gkikas;
STILIANAKIS Nikolaos;
2020-01-17
MDPI
JRC119557
2072-4292 (online),
https://www.mdpi.com/2072-4292/11/16/1862,
https://publications.jrc.ec.europa.eu/repository/handle/JRC119557,
10.3390/rs11161862 (online),
| Name | Country | City | Type |
|---|
This document is only visible at the Commission level.
You are not authorized to publish or distribute it outside the European Commission.
This is a public document. You can share this publication.
Datasets
| ID | Title | Public URL |
|---|
Dataset collections
| ID | Acronym | Title | Public URL |
|---|
Scripts / source codes
| Description | Public URL |
|---|
Additional supporting files
| File name | Description | File type |
|---|