Jointly conducted by UGB and IRD [Île-de-France], this research proposes an advanced modeling of malaria integrating climatic factors.
Justin-Hervé Noubissi, researcher at UMMISCO (Université Gaston Berger de Saint-Louis Sénégal).
Thesis defended in 2019 at the doctoral school École doctorale Informatique, télécommunications et électronique de Paris (1992-....).
This research is the result of an international co-supervision between several partner institutions.
Referenced in the ABES/STAR network, this thesis meets the rigorous criteria of French higher education.
The eradication of malaria represents a complex and multifaceted challenge that requires a truly multidisciplinary approach. Justin-Hervé Noubissi's thesis, conducted in co-supervision between Gaston Berger University of Saint-Louis and the Research Institute for Development (IRD), highlights significant gaps in collaboration among scientists from various fields. Indeed, the lack of effective synergy between computer scientists, epidemiologists, and mathematicians has led to models that are often incomplete, thus limiting the impact of research in the field. This situation is all the more concerning in the context of West African countries, where malaria remains one of the leading causes of morbidity and mortality.
Environmental and climatic factors, often overlooked in traditional models, play a crucial role in the dynamics of malaria transmission. For example, variations in temperature and humidity directly influence the reproduction and survival of vector mosquitoes, as well as the life cycle of the Plasmodium parasite. Tropical countries, particularly affected by this disease, suffer from a lack of reliable data on these environmental parameters, making it difficult to develop effective strategies. Noubissi proposes a modeling approach that integrates these migratory and climatic elements, thus offering a more realistic view of malaria transmission. This approach could allow for better anticipation of periods of high transmission, particularly during the rainy seasons, when conditions are conducive to mosquito proliferation.
Three meta-population models have been developed and compared as part of this research. The most advanced model takes into account climatic factors throughout the mosquito's life, making it particularly relevant for understanding transmission dynamics. By integrating data on population movements and climatic variations, this model offers a dynamic and evolving view of malaria spread. This approach could revolutionize malaria control strategies by allowing for better anticipation of epidemics and more efficient allocation of resources. For example, prevention and treatment campaigns could be targeted at geographic areas and periods identified as high-risk, thereby maximizing their effectiveness.
It is imperative that decision-makers take these new data into account to develop appropriate public health policies. Collaboration among different health stakeholders must be strengthened to maximize the impact of research. This involves not only better communication among researchers but also active involvement of local health authorities and communities. By integrating more comprehensive models, it becomes possible to target interventions where they are most needed, thereby increasing the chances of malaria eradication. Furthermore, this collaborative approach could also foster the emergence of innovative solutions tailored to local realities, such as the use of mobile technologies for case monitoring and community awareness.
In summary, Noubissi's research underscores the importance of an integrated and collaborative approach in the fight against malaria. The results obtained pave the way for more effective public policies that are adapted to the realities of countries affected by this disease. By taking into account environmental and social dimensions in the modeling of malaria transmission, it is possible to design interventions that not only meet immediate needs but also fit within a sustainability perspective. Thus, the eradication of malaria could become a tangible reality, not only for Benin but for all of West Africa, where this disease continues to pose a major public health problem.
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Justin-Hervé Noubissi. Modélisation et simulation spatio-temporelles de systèmes dynamiques complexes avec application en épidémiologie : cas du paludisme. Modélisation et simulation. Sorbonne Université; Saint Monica University (Buéa, Cameroun), 2019. Français. ⟨NNT : 2019SORUS281⟩. ⟨tel-03140342⟩