Naffeti Bechir

@pasteur.tn

Carthage University
Laboratory of Bio-Informatics, Bio-Mathematics and Bio-Statistics, Institute Pasteur of Tunis, Tunis, Tunisia



           

https://researchid.co/bechir.naffeti

Dr. Bechir Naffeti is a bio-mathematicians in laboratory of Bio-Informatics, Bio-Mathematics and Bio-Statistics of Pasteur Institute of Tunis. Detail-oriented, accurate and analytical mathematician with a solid understanding of the modeling of infectious diseases, mono and multi-objective optimization algorithms. He is currently a postdoctoral researcher at the Pasteur Institute of Tunis. Passionate about coding with excellent knowledge of Python, MATLAB, SPSS, R coding as well as all office software. Able to propose a model or a simulation of the situation on which the whole team is working by bringing my own perspective to a problem which is approached from various angles by specialized people.

EDUCATION

National Diploma of Doctorate in Mathematics

RESEARCH INTERESTS

Modeling of infectious diseases.
mono and multi-objective optimization algorithms.
Modeling of covid-19

9

Scopus Publications

Scopus Publications

  • Comparative reconstruction of SARS-CoV-2 transmission in three African countries using a mathematical model integrating immunity data
    Bechir Naffeti, Walid BenAribi, Amira Kebir, Maryam Diarra, Matthieu Schoenhals, Inès Vigan-Womas, Koussay Dellagi, and Slimane BenMiled
    Elsevier BV


  • Complex Network Approaches for Epidemic Modeling: A Case Study of COVID-19
    Akhil Kumar Srivastav, Vizda Anam, Rubén Blasco-Aguado, Carlo Delfin S. Estadilla, Bruno V. Guerrero, Amira Kebir, Luís Mateus, Bechir Naffeti, Fernando Saldaña, Vanessa Steindorf,et al.
    Springer Nature Switzerland


  • Spatio-temporal evolution of the COVID-19 across African countries
    Bechir Naffeti, Sebastien Bourdin, Walid Ben Aribi, Amira Kebir, and Slimane Ben Miled
    Frontiers Media SA
    The aim of this study is to make a comparative study on the reproduction number R0 computed at the beginning of each wave for African countries and to understand the reasons for the disparities between them. The study covers the two first years of the COVID-19 pandemic and for 30 African countries. It links pandemic variables, reproduction number R0, demographic variable, median age of the population, economic variables, GDP and CHE per capita, and climatic variables, mean temperature at the beginning of each waves. The results show that the diffusion of COVID-19 in Africa was heterogeneous even between geographical proximal countries. The difference of the basic reproduction number R0 values is very large between countries and is significantly correlated with economic and climatic variables GDP and temperature and to a less extent with the mean age of the population.

  • Global Stability and Numerical Analysis of a Compartmental Model of the Transmission of the Hepatitis A Virus (HAV): A Case Study in Tunisia
    Walid Ben Aribi, Bechir Naffeti, Kaouther Ayouni, Hamadi Ammar, Henda Triki, Slimane Ben Miled, and Amira Kebir
    Springer Science and Business Media LLC


  • Hepatitis a virus infection in Central-West Tunisia: An age structured model of transmission and vaccination impact
    Kaouther Ayouni, Bechir Naffeti, Walid Ben Aribi, Jihène Bettaieb, Walid Hammami, Afif Ben Salah, Hamadi Ammar, Slimane Ben Miled, and Henda Triki
    Springer Science and Business Media LLC
    Abstract Background The epidemiological pattern of hepatitis A infection has shown dynamic changes in many parts of the world due to improved socio-economic conditions and the accumulation of seronegative subjects, which leads to possible outbreaks and increased morbidity rate. In Tunisia, the epidemiological status of hepatits A virus is currently unknown. However, over the past years higher numbers of symptomatic hepatitis A virus infection in school attendants and several outbreaks were reported to the Ministry of Health, especially from regions with the lowest socio-economic levels in the country. The aim of this study was to investigate the current seroprevalence of hepatitis A virus antibodies in central-west Tunisia and assess the impact of hepatitis A virus vaccination on hepatitis A epidemiology. Methods Serum samples from 1379 individuals, aged 5–75 years, were screened for hepatitis A virus antibodies. Adjusted seroprevalence, incidence and force of infection parameters were estimated by a linear age structured SEIR (Susceptible-Exposed-Infectious-Recovered) compartmental model. A vaccine model was then constructed to assess the impact on hepatitis A virus epidemiology of 3 scenarios of vaccination strategies: one dose at 12-months of age, one dose at 6-years and one dose at 12-months and another at 6-years of age during 6 years. Results A rapid increase in anti-hepatitis A virus seroprevalence was noted during infancy and adolescence: 47% of subjects under 10-years-old are infected; the prevalence increases to 77% at 15-years and reaches 97% in subjects aged 30-years. The force of infection is highest between 10 and 30-years of age and the incidence declines with increasing age. The vaccine model showed that the 3-scenarios lead to a significant reduction of the fraction of susceptibles. The two doses scenario gives the best results. Single-dose vaccination at 6-years of age provides more rapid decrease of disease burden in school-aged children, as compared to single-dose vaccination at 12-months, but keeps with a non-negligible fraction of susceptibles among children < 6-years. Conclusions Our study confirms the epidemiological switch from high to intermediate endemicity of hepatitis A virus in Tunisia and provides models that may help undertake best decisions in terms of vaccinations strategies.

  • Filled Function Method for Nonlinear Model Predictive Control
    Hajer Degachi, Bechir Naffeti, Wassila Chagra, and Moufida Ksouri
    Hindawi Limited
    A new method is used to solve the nonconvex optimization problem of the nonlinear model predictive control (NMPC) for Hammerstein model. Using nonlinear models in MPC leads to a nonlinear and nonconvex optimization problem. Since control performances depend essentially on the results of the optimization method, in this work, we propose to use the filled function as a global optimization method to solve the nonconvex optimization problem. Using this method, the control law can be obtained through two steps. The first step consists of determining a local minimum of the objective function. In the second step, a new function is constructed using the local minimum of the objective function found in the first step. The new function is called the filled function; the new constructed function allows us to obtain an initialization near the global minimum. Once this initialization is determined, we can use a local optimization method to determine the global control sequence. The efficiency of the proposed method is proved firstly through benchmark functions and then through the ball and beam system described by Hammerstein model. The results obtained by the presented method are compared with those of the genetic algorithm (GA) and the particle swarm optimization (PSO).

Publications

Spatio-temporal evolution of the COVID-19 across African countries.
A branch and bound algorithm for Holder bi-objective optimization. Implementation to multidimensional optimization.
Global Stability and Numerical Analysis of a Compartmental Model of the Transmission of the Hepatitis A Virus (HAV): A Case Study in Tunisia.
Hepatitis a virus infection in Central-West Tunisia: an age structured model of transmission and vaccination impact
A new trisection method for solving Lipschitz bi-objective optimization problems.
Filled function method for nonlinear model predictive control.
Non-pharmaceutical interventions and COVID-19 vaccination strategies in Senegal: a modelling study.