| Title: |
Arbocarto: a mechanistic model based on the life cycle of Aedes mosquitoes |
| Authors: |
Marti, Renaud; Demarchi, Marie; Castets, Mathieu; Tran, Annelise |
| Contributors: |
Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS); Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE); Maison de la Télédétection; Annelise Tran; Eric Daudé Eric; Thibault Catry |
| Source: |
Remote sensing and spatial modelling. Applications to the surveillance and control of mosquito-borne diseases ; https://hal.science/hal-05316981 ; Annelise Tran; Eric Daudé Eric; Thibault Catry. Remote sensing and spatial modelling. Applications to the surveillance and control of mosquito-borne diseases, Ed. Quae, pp.109-118, 2025, Update sciences et technologies, 978-2-7592-4102-6 |
| Publisher Information: |
CCSD; Ed. Quae |
| Publication Year: |
2025 |
| Collection: |
Institut National de la Recherche Agronomique: ProdINRA |
| Subject Terms: |
[SDV.BA.ZI]Life Sciences [q-bio]/Animal biology/Invertebrate Zoology; [SDE.IE]Environmental Sciences/Environmental Engineering |
| Description: |
Source Agritrop Cirad (https://agritrop.cirad.fr/615039/) ; International audience ; Due to the nature of their design, process-based models, also termed “mechanistic models”, are explanatory models focused on the causality of relationships between inputs and outputs (Craver, 2006). This type of approach requires the explicit delineation of causal relationships (e.g., the effect of temperature on mosquito development) in a modelling framework (see Introduction to Part 2), usually in mathematical form (e.g., an equation expressing development rate as a function of temperature), based onpreviously established knowledge (from observational or experimental studies) about the system being modelled. Depending on the goals of the model, this approach also requires a mandatory and sometimes difficult step of simplification. This step, undertaken at the discretion of the modellers and based on their understanding of the significance of the processes to be considered, enables them to develop parcimonious models. In addition, an explanation of the processes through a mechanistic approach enables an in silico simulation of scenarios (e.g., impact of insecticide treatment using different protocols to control mosquito populations) and, through their analysis, identification of the control points for the system in question. Experimental studies would be comparatively difficult and costly to perform.Since the advent of big data, the interest of such approaches combined with the understanding, exploitation and validation of explanatory mechanisms is at times questionable, especially when considering the hegemony of data-driven approaches (see Chapter 2), and the predictive and sometimes spectacular power of machine learning methods (Baker et al., 2018). However, both of these approaches provide complementary information (Tran et al., 2020), and their integration can yield valuable insights (Baker et al., 2018).In this chapter, we present an example of a mechanistic model transformed into a software tool in the context of ... |
| Document Type: |
book part |
| Language: |
English |
| Relation: |
CIRAD: 615039 |
| Availability: |
https://hal.science/hal-05316981; https://hal.science/hal-05316981v1/document; https://hal.science/hal-05316981v1/file/615039.pdf |
| Rights: |
https://creativecommons.org/licenses/by-nc-nd/4.0/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.765CED0B |
| Database: |
BASE |