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𝗔𝗰𝗮𝗱𝗲𝗺𝗶𝗰 𝗣𝗿𝗶𝗱𝗲! 

We are delighted to announce that Consolas HODONOU, an alumna of the Master’s Programme in 𝗕𝗶𝗼𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 , at the 𝗟𝗮𝗯𝗼𝗿𝗮𝘁𝗼𝗿𝘆 𝗼𝗳 𝗕𝗶𝗼𝗺𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝘀𝘁 𝗘𝘀𝘁𝗶𝗺𝗮𝘁𝗶𝗼𝗻𝘀 (𝗟𝗔𝗕𝗘𝗙), 𝗵𝗮𝘀 𝗯𝗲𝗲𝗻 𝗮𝗽𝗽𝗼𝗶𝗻𝘁𝗲𝗱 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗔𝗱𝘃𝗶𝘀𝗼𝗿 𝗳𝗼𝗿 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗮𝘁 𝘁𝗵𝗲 𝗠𝗶𝗻𝗶𝘀𝘁𝗿𝘆 𝗼𝗳 𝗔𝗴𝗿𝗶𝗰𝘂𝗹𝘁𝘂𝗿𝗲, 𝗟𝗶𝘃𝗲𝘀𝘁𝗼𝗰𝗸 𝗮𝗻𝗱 𝗙𝗶𝘀𝗵𝗲𝗿𝗶𝗲𝘀 (𝗠𝗔𝗘𝗣).

This prestigious appointment highlights not only the excellence of our academic programmes, but also the quality of the academic and scientific support provided by Laboratoire de Biomathématiques et d'Estimations Forestières. It also demonstrates our female students' ability to apply their expertise to technological innovation, governance, and agricultural development in Benin.

👏𝗖𝗼𝗻𝗴𝗿𝗮𝘁𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗵𝗲𝗿 𝗼𝗻 𝘁𝗵𝗶𝘀 𝗶𝗻𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗮𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁, 𝘄𝗵𝗶𝗰𝗵 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 𝘁𝗵𝗲 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗿𝗼𝗹𝗲 𝗼𝗳 𝘄𝗼𝗺𝗲𝗻 𝗶𝗻 𝘀𝗰𝗶𝗲𝗻𝗰𝗲, 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴!

𝗙𝗶𝗲𝗿𝘁𝗲́ 𝗮𝗰𝗮𝗱𝗲́𝗺𝗶𝗾𝘂𝗲 !

Nous sommes heureux d’annoncer que Consolas HODONOU, une ancienne ancienne étudiante du Master en 𝗕𝗶𝗼𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗾𝘂𝗲, au Laboratoire de Biomathématiques et d'Estimations Forestières (LABEF), a été promue 𝗖𝗼𝗻𝘀𝗲𝗶𝗹𝗹𝗲̀𝗿𝗲 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲 𝗮̀ 𝗹’𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗮𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗲𝗹𝗹𝗲 𝗮𝘂 𝗠𝗶𝗻𝗶𝘀𝘁𝗲̀𝗿𝗲 𝗱𝗲 𝗹’𝗔𝗴𝗿𝗶𝗰𝘂𝗹𝘁𝘂𝗿𝗲, 𝗱𝗲 𝗹’𝗘́𝗹𝗲𝘃𝗮𝗴𝗲 𝗲𝘁 𝗱𝗲 𝗹𝗮 𝗣𝗲̂𝗰𝗵𝗲 (𝗠𝗔𝗘𝗣).

Cette nomination prestigieuse illustre non seulement l’excellence de nos formations, mais également la qualité de l’accompagnement académique et scientifique offert par le LABEF. Elle témoigne aussi de la capacité de nos étudiantes à mettre leurs compétences au service de l’innovation technologique, de la gouvernance et du développement agricole du Bénin.

👏 𝗙𝗲́𝗹𝗶𝗰𝗶𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝗮̀ 𝗲𝗹𝗹𝗲 𝗽𝗼𝘂𝗿 𝗰𝗲 𝗽𝗮𝗿𝗰𝗼𝘂𝗿𝘀 𝗶𝗻𝘀𝗽𝗶𝗿𝗮𝗻𝘁, 𝗾𝘂𝗶 𝗺𝗲𝘁 𝗲𝗻 𝗹𝘂𝗺𝗶𝗲̀𝗿𝗲 𝗹𝗲 𝗿𝗼̂𝗹𝗲 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗲𝗹 𝗱𝗲𝘀 𝗳𝗲𝗺𝗺𝗲𝘀 𝗱𝗮𝗻𝘀 𝗹𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲, 𝗹’𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗲𝘁 𝗹𝗮 𝗽𝗿𝗶𝘀𝗲 𝗱𝗲 𝗱𝗲́𝗰𝗶𝘀𝗶𝗼𝗻 !


#LABEF #Biostatistics #ArtificialIntelligence #WomenInScience 
#ScientificResearch #Innovation #UAC #Benin

Activez pour voir l’image en plus grand.

🔬𝗡𝗘𝗪 𝗦𝗖𝗜𝗘𝗡𝗧𝗜𝗙𝗜𝗖 𝗣𝗨𝗕𝗟𝗜𝗖𝗔𝗧𝗜𝗢𝗡 𝗛𝗼𝘄 𝗰𝗮𝗻 𝗰𝗮𝘂𝘀𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝘀 𝗮𝗻𝗱 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹 𝗲𝗾𝘂𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗲𝗻𝗵𝗮𝗻𝗰𝗲 𝗼𝘂𝗿 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗼𝗳 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗵𝗲𝗮𝗹𝘁𝗵 𝗽𝗵𝗲𝗻𝗼𝗺𝗲𝗻𝗮?

How can causal models and structural equation modeling enhance our understanding of complex health phenomena?

A systematic and critical review of 458 articles published between 1987 and 2025 examines the application of Structural Equation Modeling (SEM) and Causal Modeling (CM) in epidemiology.

📊 Key findings:

• Extensive use in research on neuropsychiatric and chronic diseases;
• Limited application to infectious diseases;
• A growing need for methodological rigour, transparency, and reproducibility of research findings.

This study opens up new perspectives for strengthening epidemiological analyses and improving our understanding of the complex mechanisms influencing population health, particularly in resource-limited settings.

🎓 This research was conducted as part of a doctoral thesis supported by the SSACAB II project (Sub-Saharan Africa Consortium for Advanced Biostatistics II).

📖 Scientific reference:

Essetcheou, S.M.M. et al. (2026). Applications of causal and structural equation modeling in epidemiology: a systematic and critical review. BMC Medical Research Methodology, 26, 213.

🔗 https://lnkd.in/eSsb6MUT

#Epidemiology #CausalModeling #StructuralEquationModeling #PublicHealth #HealthResearch #Biostatistics #LABEF #UAC #SSACAB #Science

🎓🌍 𝗠𝗔𝗦𝗧𝗘𝗥’𝗦 𝗦𝗖𝗛𝗢𝗟𝗔𝗥𝗦𝗛𝗜𝗣 𝗢𝗣𝗣𝗢𝗥𝗧𝗨𝗡𝗜𝗧𝗬 — FORTRESS-SSA 𝗣𝗥𝗢𝗚𝗥𝗔𝗠𝗠𝗘

𝗔𝗿𝗲 𝘆𝗼𝘂 𝗮 𝗽𝘂𝗯𝗹𝗶𝗰 𝗵𝗲𝗮𝗹𝘁𝗵 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝘁𝗼 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝗲𝗻 𝘆𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗶𝗻 𝗯𝗶𝗼𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀, 𝗲𝗽𝗶𝗱𝗲𝗺𝗶𝗼𝗹𝗼𝗴𝘆, 𝗱𝗶𝘀𝗲𝗮𝘀𝗲 𝘀𝘂𝗿𝘃𝗲𝗶𝗹𝗹𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗼𝘂𝘁𝗯𝗿𝗲𝗮𝗸 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲?

The FORTRESS-SSA Programme is launching a new Master’s scholarship opportunity, primarily aimed at early- to mid-career public health professionals committed to strengthening public health systems across sub-Saharan Africa ( SSACAB Programme).

This opportunity provides a valuable platform to develop advanced skills and contribute more effectively to research, epidemiological surveillance, evidence-based decision-making, and public health interventions.

🌍𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝘁𝗼𝗱𝗮𝘆’𝘀 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀 𝗺𝗲𝗮𝗻𝘀 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝗲𝗻𝗶𝗻𝗴 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄’𝘀 𝗽𝘂𝗯𝗹𝗶𝗰 𝗵𝗲𝗮𝗹𝘁𝗵 𝘀𝘆𝘀𝘁𝗲𝗺𝘀.
For details on eligibility requirements, application procedures, and deadlines, please see the call for applications below.

#FORTRESS-SSA #SSACAB #LABEF #UAC #UniversityOfAbomeyCalavi #Scholarship #MastersScholarship #Biostatistics #Epidemiology #PublicHealth #GlobalHealth #Research #Benin #Africa

𝗖𝗔𝗡 𝗪𝗘 𝗣𝗥𝗘𝗗𝗜𝗖𝗧 𝗧𝗛𝗘 𝗡𝗘𝗫𝗧 𝗘𝗣𝗜𝗗𝗘𝗠𝗜𝗖? 

𝗪𝗵𝗮𝘁 𝗶𝗳 𝗺𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗮𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗰𝗼𝘂𝗹𝗱 𝗵𝗲𝗹𝗽 𝘂𝘀 𝗮𝗻𝘁𝗶𝗰𝗶𝗽𝗮𝘁𝗲 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗼𝘂𝘁𝗯𝗿𝗲𝗮𝗸?

In an interview with # newswise, 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗼𝗿 Romain Glèlè Kakaï, Professor of Biostatistics at the 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝗲́ 𝗱’𝗔𝗯𝗼𝗺𝗲𝘆-𝗖𝗮𝗹𝗮𝘃𝗶 and Director of the Laboratoire de Biomathématiques et d'Estimations Forestières (Labef), sheds light on how mathematical models help researchers understand and forecast the spread of diseases such as cholera and malaria.

From evaluating vaccination strategies to supporting public-health planning, mathematical modelling is transforming complex epidemiological data into valuable insights for decision-making.

𝗕𝘂𝘁 𝘁𝗵𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗴𝗼𝗲𝘀 𝗲𝘃𝗲𝗻 𝗳𝘂𝗿𝘁𝗵𝗲𝗿: 𝗖𝗮𝗻 𝗮𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗺𝗮𝗸𝗲 𝗱𝗶𝘀𝗲𝗮𝘀𝗲 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗳𝗮𝘀𝘁𝗲𝗿, 𝗺𝗼𝗿𝗲 𝗮𝗰𝗰𝘂𝗿𝗮𝘁𝗲 𝗮𝗻𝗱 𝗺𝗼𝗿𝗲 𝗮𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲?

Watch the interview to see how mathematics, data, and AI are opening new perspectives on epidemic preparedness through this link: https://lnkd.in/eTt8hzjb

𝗖𝘂𝗿𝗶𝗼𝘂𝘀 𝗯𝘆 𝗻𝗮𝘁𝘂𝗿𝗲. 𝗗𝗿𝗶𝘃𝗲𝗻 𝗯𝘆 𝘀𝗰𝗶𝗲𝗻𝗰𝗲. 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗮𝗵𝗲𝗮𝗱.

𝗣𝗘𝗨𝗧-𝗢𝗡 𝗣𝗥𝗘́𝗗𝗜𝗥𝗘 𝗟𝗔 𝗣𝗥𝗢𝗖𝗛𝗔𝗜𝗡𝗘 𝗘́𝗣𝗜𝗗𝗘́𝗠𝗜𝗘 ? 

𝗘𝘁 𝘀𝗶 𝗹𝗲𝘀 𝗺𝗮𝘁𝗵𝗲́𝗺𝗮𝘁𝗶𝗾𝘂𝗲𝘀 𝗲𝘁 𝗹’𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗮𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗲𝗹𝗹𝗲 𝗽𝗼𝘂𝘃𝗮𝗶𝗲𝗻𝘁 𝗻𝗼𝘂𝘀 𝗮𝗶𝗱𝗲𝗿 𝗮̀ 𝗮𝗻𝘁𝗶𝗰𝗶𝗽𝗲𝗿 𝗹𝗮 𝗽𝗿𝗼𝗰𝗵𝗮𝗶𝗻𝗲 𝗳𝗹𝗮𝗺𝗯𝗲́𝗲 𝗲́𝗽𝗶𝗱𝗲́𝗺𝗶𝗾𝘂𝗲 ?

Dans une interview accordée à Newswise, le professeur Romain Glèlè Kakaï, professeur de biostatistique à l’Université d’Abomey-Calavi et directeur du Laboratoire de Biomathématiques et d'Estimations Forestières, met en lumière la manière dont les modèles mathématiques permettent aux chercheurs de mieux comprendre et de prévoir la propagation de maladies telles que le choléra et le paludisme.

De l’évaluation des stratégies de vaccination à l’appui à la planification en santé publique, la modélisation mathématique transforme des données épidémiologiques complexes en informations précieuses pour éclairer la prise de décision.

𝗠𝗮𝗶𝘀 𝗹𝗮 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝘃𝗮 𝗲𝗻𝗰𝗼𝗿𝗲 𝗽𝗹𝘂𝘀 𝗹𝗼𝗶𝗻 : 𝗹’𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗮𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗲𝗹𝗹𝗲 𝗽𝗲𝘂𝘁-𝗲𝗹𝗹𝗲 𝗿𝗲𝗻𝗱𝗿𝗲 𝗹𝗮 𝗽𝗿𝗲́𝘃𝗶𝘀𝗶𝗼𝗻 𝗱𝗲𝘀 𝗺𝗮𝗹𝗮𝗱𝗶𝗲𝘀 𝗽𝗹𝘂𝘀 𝗿𝗮𝗽𝗶𝗱𝗲, 𝗽𝗹𝘂𝘀 𝗽𝗿𝗲́𝗰𝗶𝘀𝗲 𝗲𝘁 𝗽𝗹𝘂𝘀 𝗼𝗽𝗲́𝗿𝗮𝘁𝗶𝗼𝗻𝗻𝗲𝗹𝗹𝗲 ?

Lien : https://lnkd.in/eTt8hzjb

#LABEF #Biostatistics #Epidemiology #MathematicalModelling #ArtificialIntelligence #DiseaseForecasting #PublicHealth #HealthData #Research #sciencecommunication