State and parameter estimation for a class of schistosomiasis models - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Mathematical Biosciences Year : 2019

State and parameter estimation for a class of schistosomiasis models


We develop a general framework to estimate the proportion of infected snails and snail-human transmission parameter of a class of models that describes the evolution of schistosomiasis. To do so, we consider simultaneously the dynamics of schistosomiasis, captured by the homogeneous version of the classical MacDonald's model, and the measurable output: the number of female schistosomes per single host. The proposed method consists of designing an auxiliary dynamical system, called observer, whose solutions converge exponentially to those of the system capturing the schistosomiasis model. Moreover, we derive an estimation of the snail-human transmission rate, an unknown but key parameter in the dynamics of schistosomiasis. These estimations are central in two of the strategies of controlling schistosomiasis, namely the use of molluscicides and mass drug administration. To further investigate control strategies on a larger scale, we consider a heterogeneous model which consists of an arbitrary number of human groups or patches and an arbitrary number of freshwater sources, natural habitats of snails. Provided that the data of infected humans' worm burden in each patch or group is available, we provide a method of estimating the proportion of infected snails in each snail natural habitat, thereby providing a map on where to implement control strategy to mitigate or eliminate Schistosomiasis.
Fichier principal
Vignette du fichier
MBS-2018-440R3NoMarks.pdf (536.2 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-02189643 , version 1 (19-07-2019)



Derdei Bichara, Aboudramane Guiro, Abderrahman Iggidr, Diène Ngom. State and parameter estimation for a class of schistosomiasis models. Mathematical Biosciences, 2019, 315, ⟨10.1016/j.mbs.2019.108226⟩. ⟨hal-02189643⟩
189 View
211 Download



Gmail Facebook X LinkedIn More