[ABE-L] Statistical and Mathematical Modeling for a Better Understanding of Dengue Dynamics -- Bernard Cazelles (ENS-Paris)

[Basilio de Bragança Pereira]

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From: Nelson Maculan <nelson.maculan em gmail.com>
Date: 2016-01-12 20:20 GMT-02:00
Subject: Fwd: *18-1-2016* : Statistical and Mathematical Modeling for a
Better Understanding of Dengue Dynamics -- Bernard Cazelles (ENS-Paris)
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From: S Boatto <boattostefanella em gmail.com>
Date: 2016-01-12 16:13 GMT-05:00
Subject: *18-1-2016* : Statistical and Mathematical Modeling for a Better
Understanding of Dengue Dynamics -- Bernard Cazelles (ENS-Paris)
To:


Prezados
   estao todos convidados a palestra do Prof Bernard Cazelles (ENS-Paris)
sobre a modelagem da dengue.

Sala  C-116, Bloco C, CT, 15:00-16:00, segunda feira 18/1

Statistical and Mathematical Modeling for a Better Understanding of Dengue
Dynamics

Dengue is the most important arboviral disease worldwide and a major public
health problem in the tropics and subtropics. The dengue vector and virus
are extremely sensitive to environmental conditions such as temperature,
humidity and precipitation that influence mosquito biology, abundance,
habitat and viral replication rate. Thus, such climatic factors must have
significant influence on dengue propagation in the population.
The first analyses presented concern the quantification of the role of
climate on dengue epidemics in Thailand and Cambodia provinces using
wavelet decomposition to account for the non-stationary relationships.
The second analyses presented are related to mathematical modeling at
different scales: provinces, districts or rural villages, using classical
1-strain or 2-strain dengue stochastic models with Bayesian inference.
Exact inference was conducted using recently developed algorithms such as
particle MCMC, coupled with an initial exploration of the likelihood
surface with the extended Kalman filter. This allows model selection by a
quantification of the importance of different models and of their
underlying hypothesis through likelihood computation and statistical
information criteria. First results show that vector dynamics or strain
coexistence appears crucial to provide a coherent epidemic trajectory. This
approach also permits reconstruction of the dynamics with time-varying
transmission parameters showing that these time-varying parameters can be
statistically related to local or global climatic forcing. Therefore, one
can expect that forecast climate information has utility in a dengue
decision support system using mechanistic models.





-- 
Nelson Maculan
Professor Emérito
Universidade Federal do Rio de Janeiro
PESC-COPPE e Instituto de Matemática
Caixa Postal 68511
21941-972 Rio de Janeiro, RJ, Brasil
Tel.: + 55 21 39388708
E-mail: maculan em cos.ufrj.br




-- 
Basilio de Bragança Pereira, DIC and PhD(Imperial College), DL(COPPE)
UFRJ-Federal University of Rio de Janeiro
*Titular Professor of  Bioestatistics and of Applied Statistics
*FM-School of Medicine and COPPE-Posgraduate School of Engineering and
HUCFF-University Hospital Clementino Fraga Filho.
*Tel: 55 21 3938-7045/7047/2618
www.po.ufrj.br/basilio/

*Mail Address:
Programa de Produção - COPPE/UFRJ
Centro de Tecnologia, Bloco F, Sala105 -  Ilha do Fundão
Caixa Postal 68507
CEP 21941-972 Rio de Janeiro,RJ
Brazil
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