Faïçal Ndaïrou

920 citations
10 papers · 602 indexed · 1 hit paper · h-index 5
Topics
COVID-19 epidemiological studies (8 papers)Mathematical and Theoretical Epidemiology and Ecology Models (8 papers)Fractional Differential Equations Solutions (6 papers)
Partner nations
PortugalSpainFinland

In The Last Decade

Faïçal Ndaïrou

8 papers receiving 584 citations

Hit Papers

Mathematical modeling of COVID-19 transmission dynamics w...20202026202220242020100200300400

Peers

Faïçal Ndaïrou
Comparison fields: 5 of 68
  • Modeling and Simulation 544
  • Public Health, Environmental and Occupational Health 279
  • Infectious Diseases 188
  • Economics and Econometrics 108
  • Applied Mathematics 47
Replace Sarbaz H. A. Khoshnaw with:
Sarbaz H. A. Khoshnaw Iraq
Ousmane Seydi France
Jayanta Mondal India
Zhihua Liu France
Manotosh Mandal India
Amar Nath Chatterjee India
C. W. Chukwu South Africa
Swapan Kumar Nandi India
Guihong Fan China
Omar Balatif Morocco
Faïçal Ndaïrou relative to Sarbaz H. A. Khoshnaw Iraq Sarbaz H. A. Khoshnaw's profile →
Citations per field
00.5×
Sarbaz H. A. Khoshnaw · 1×
Citations per year

Countries citing papers authored by Faïçal Ndaïrou

Since Specialization
Citations

This map shows the geographic impact of Faïçal Ndaïrou's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Faïçal Ndaïrou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Faïçal Ndaïrou more than expected).

Fields of papers citing papers by Faïçal Ndaïrou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Faïçal Ndaïrou. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Faïçal Ndaïrou. The network helps show where Faïçal Ndaïrou may publish in the future.

Co-authorship network of co-authors of Faïçal Ndaïrou

This figure shows the co-authorship network connecting the top 25 collaborators of Faïçal Ndaïrou. A scholar is included among the top collaborators of Faïçal Ndaïrou based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Faïçal Ndaïrou. Faïçal Ndaïrou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

10 of 10 papers shown
#WorkIndexed citations
1 0
2 1
3 2
4 8
5 1
6 24
7 69
8
Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhanbreakdown →
469
9 4
10 24

About Faïçal Ndaïrou

Faïçal Ndaïrou is a scholar working on Modeling and Simulation, Public Health, Environmental and Occupational Health and Infectious Diseases, having authored 10 papers that have together received 602 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (8 papers), Mathematical and Theoretical Epidemiology and Ecology Models (8 papers) and Fractional Differential Equations Solutions (6 papers). The work is most often cited by research in Modeling and Simulation (544 citations), Infectious Diseases (188 citations) and Public Health, Environmental and Occupational Health (279 citations). Faïçal Ndaïrou has collaborated with scholars based in Portugal, Spain and Finland. Frequent co-authors include Delfim F. M. Torres, Iván Area, Juan J. Nieto, Cristiana J. Silva, Jorge Losada and Leo Lahti. Their work appears in journals such as Chaos Solitons & Fractals, Mathematical Biosciences and Mathematical Methods in the Applied Sciences.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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