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Mathematical Models for Dengue Fever

Laufzeit: 01.01.2015 - 31.12.2016

Partner: Dr. Nico Stollenwerk, Centro de Mathematica e Aplicacoes Fundamentais, Universidade de Lisboa

Förderung durch: gefördert vom DAAD aus Mitteln des Bundesministeriums für Bildung und Forschung (BMBF), Projekt ID 57128360

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Kurzfassung



Dengue fever is a viral mosquito-borne infection which in recent years has become a major international public health concern. In the recent years, mathematical modeling has become an interesting tool for the understanding of infectious diseases epidemiology and dynamics. A series of deterministic and nowadays also stochastic models have been proposed to describe the host population. Multi-strain dynamics, such as dengue epidemiology are generally modelled with extended SIR-type models. Since...

Dengue fever is a viral mosquito-borne infection which in recent years has become a major international public health concern. In the recent years, mathematical modeling has become an interesting tool for the understanding of infectious diseases epidemiology and dynamics. A series of deterministic and nowadays also stochastic models have been proposed to describe the host population. Multi-strain dynamics, such as dengue epidemiology are generally modelled with extended SIR-type models. Since no medication is available to cure Dengue, one has to control the disease transmission by controlling the mosquito population as the disease vector. Two different strategies are usually applied in tropical countries like Indonesia: disseminating chemicals to kill eggs and larvae or fumigation of the adult mosquitoes. Mathematical tools from optimal control can help to simulate, predict and improve the effectiveness of such counter mosquito campaigns. The German project partner has gained in the past years experience in optimal control applied to extended SIR-type models especially for vector based diseases. The Portuguese group focusses on both deterministic and stochastic modeling for multi-strain epidemics, vector dynamics as well as data analysis. In a nutshell, the projects aims to combine the complementary expertises of both groups to be able to answer optimization questions for involved models for Dengue epidemics. Both groups have access to field data from different countries. Combining these data sets can be particularly helpful for the validating the models and estimating so far unknown or inexact parameters. In the proposed project we plan a series of workshops and mutual exchange visits to combine the research efforts of both groups in Koblenz and Lisbon. The obtained results will be documented by publications and will be presented in conferences.
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