Mathias Mossoko

23 papers receiving 430 citations

Peers

Mathias Mossoko
Comparison fields: 5 of 68
  • Modeling and Simulation 152
  • Virology 66
  • Infectious Diseases 216
  • Endocrinology 55
  • Emergency Medical Services 68
Replace Muhammad Shakir Balogun with:
Muhammad Shakir Balogun Nigeria
Mahmood Dalhat Nigeria
Emery Manirambona Rwanda
Chioma Dan-Nwafor Nigeria
Francesco Grandesso France
Sandra Cohuet France
Charlotte Gryseels Belgium
Chinwe Lucia Ochu Nigeria
Watcharapong Piyaphanee Thailand
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Citations per field
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Citations per year

Countries citing papers authored by Mathias Mossoko

Since Specialization
Citations

This map shows the geographic impact of Mathias Mossoko'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 Mathias Mossoko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mathias Mossoko more than expected).

Fields of papers citing papers by Mathias Mossoko

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mathias Mossoko. 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 Mathias Mossoko. The network helps show where Mathias Mossoko may publish in the future.

Co-authors

The 25 scholars most cited alongside Mathias Mossoko, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mathias Mossoko Line = papers co-authored together Mathias Mossoko links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201574
2 201764
3 201955
4 202039
5 201836
6 201926
7 201922
8 202020
9 201918
10 202015
11 201913
12 201813
13 20219
14 20149
15 20157
16 20215
17 20175
18 20164
19 20224
20 20203

About Mathias Mossoko

Mathias Mossoko is a scholar working on Infectious Diseases, Modeling and Simulation, Emergency Medical Services, Epidemiology and Public Health, Environmental and Occupational Health, having authored 25 papers that have together received 445 indexed citations. Recurring topics across this work include Viral Infections and Outbreaks Research (21 papers), COVID-19 epidemiological studies (15 papers), Disaster Response and Management (8 papers), Viral Infections and Vectors (4 papers), Data-Driven Disease Surveillance (4 papers), Zoonotic diseases and public health (3 papers), COVID-19 Digital Contact Tracing (2 papers) and Global Maternal and Child Health (2 papers). The work is most often cited by research in Modeling and Simulation (152 citations), Virology (66 citations), Infectious Diseases (216 citations), Endocrinology (55 citations) and Emergency Medical Services (68 citations). Mathias Mossoko has collaborated with scholars based in Democratic Republic of the Congo, United States and Switzerland. Frequent co-authors include Steve Ahuka‐Mundeke, Anne W. Rimoin, Nicole A. Hoff, Emile Okitolonda-Wemakoy, Benoit Kebela, Brecht Ingelbeen, Benoît Kebela Ilunga, Albert Jan van Hoek, Brian Colwell and Sebastian Funk. Their work appears in journals such as BMJ Global Health, Emerging infectious diseases, Epidemics, PLoS neglected tropical diseases and Global Health Science and Practice.

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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