Manotosh Mandal

657 citations
14 papers · 481 indexed · h-index 7
Topics
Mathematical and Theoretical Epidemiology and Ecology Models (13 papers)COVID-19 epidemiological studies (9 papers)Evolution and Genetic Dynamics (8 papers)
Partner nations
India

In The Last Decade

Manotosh Mandal

14 papers receiving 465 citations

Peers

Manotosh Mandal
Comparison fields: 5 of 70
  • Modeling and Simulation 396
  • Public Health, Environmental and Occupational Health 277
  • Infectious Diseases 107
  • Economics and Econometrics 82
  • Genetics 79
Replace Anupam Khatua with:
Anupam Khatua India
Swapan Kumar Nandi India
Kankan Sarkar India
Jayanta Mondal India
Attila Dénes Hungary
Sayani Adak India
Syafruddin Side Indonesia
Faïçal Ndaïrou Portugal
Chayu Yang United States
Suwardi Annas Indonesia
Manotosh Mandal relative to Anupam Khatua India Anupam Khatua's profile →
Citations per field
00.5×1.5×
Anupam Khatua · 1×
Citations per year

Countries citing papers authored by Manotosh Mandal

Since Specialization
Citations

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

Fields of papers citing papers by Manotosh Mandal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Manotosh Mandal

This figure shows the co-authorship network connecting the top 25 collaborators of Manotosh Mandal. A scholar is included among the top collaborators of Manotosh Mandal 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 Manotosh Mandal. Manotosh Mandal is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
#WorkIndexed citations
1 33
2 1
3 4
4 4
5 1
6 35
7 289
8 11
9 14
10 1
11 15
12 4
13 63
14 6

About Manotosh Mandal

Manotosh Mandal is a scholar working on Modeling and Simulation, Public Health, Environmental and Occupational Health and Genetics, having authored 14 papers that have together received 481 indexed citations. Recurring topics across this work include Mathematical and Theoretical Epidemiology and Ecology Models (13 papers), COVID-19 epidemiological studies (9 papers) and Evolution and Genetic Dynamics (8 papers). The work is most often cited by research in Modeling and Simulation (396 citations), Public Health, Environmental and Occupational Health (277 citations) and Infectious Diseases (107 citations). Manotosh Mandal has collaborated with scholars based in India. Frequent co-authors include Soovoojeet Jana, T. K. Kar, Swapan Kumar Nandi, Sayani Adak and Anupam Khatua. Their work appears in journals such as Chaos Solitons & Fractals, Chaos An Interdisciplinary Journal of Nonlinear Science and Modeling Earth Systems and Environment.

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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