Faraimunashe Chirove

576 total citations
39 papers, 313 citations indexed

About

Faraimunashe Chirove is a scholar working on Infectious Diseases, Public Health, Environmental and Occupational Health and Modeling and Simulation. According to data from OpenAlex, Faraimunashe Chirove has authored 39 papers receiving a total of 313 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Infectious Diseases, 14 papers in Public Health, Environmental and Occupational Health and 13 papers in Modeling and Simulation. Recurrent topics in Faraimunashe Chirove's work include COVID-19 epidemiological studies (12 papers), Mathematical and Theoretical Epidemiology and Ecology Models (12 papers) and Viral Infections and Outbreaks Research (7 papers). Faraimunashe Chirove is often cited by papers focused on COVID-19 epidemiological studies (12 papers), Mathematical and Theoretical Epidemiology and Ecology Models (12 papers) and Viral Infections and Outbreaks Research (7 papers). Faraimunashe Chirove collaborates with scholars based in South Africa, United States and Kenya. Faraimunashe Chirove's co-authors include Farai Nyabadza, K. S. Govinder, C. W. Chukwu, Holly Gaff, Wilfred Ndifon, Winston Garira, Nuning Nuraini, Fatmawati Fatmawati, Edy Soewono and Suzanne Lenhart and has published in prestigious journals such as PLoS ONE, Scientific Reports and Frontiers in Immunology.

In The Last Decade

Faraimunashe Chirove

35 papers receiving 304 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Faraimunashe Chirove South Africa 11 150 136 125 38 36 39 313
Christine Kreuder Johnson United States 5 68 0.5× 291 2.1× 159 1.3× 45 1.2× 21 0.6× 5 416
Jonathan E. Pekar United States 6 165 1.1× 394 2.9× 99 0.8× 63 1.7× 58 1.6× 14 584
N’Faly Magassouba Guinea 11 129 0.9× 400 2.9× 140 1.1× 92 2.4× 21 0.6× 16 508
Jason Asher United States 11 93 0.6× 132 1.0× 58 0.5× 173 4.6× 24 0.7× 15 349
Cameron J. Browne United States 9 130 0.9× 53 0.4× 151 1.2× 22 0.6× 107 3.0× 20 249
Victoria C. Barclay United States 8 75 0.5× 39 0.3× 162 1.3× 61 1.6× 108 3.0× 9 326
Glenn Lahodny United States 6 145 1.0× 72 0.5× 164 1.3× 28 0.7× 87 2.4× 11 284
Senelani D. Hove‐Musekwa Zimbabwe 10 216 1.4× 73 0.5× 273 2.2× 40 1.1× 89 2.5× 32 395
Herieth Rwezaura Tanzania 12 408 2.7× 201 1.5× 368 2.9× 39 1.0× 98 2.7× 21 539
Roberto A. Saenz United States 10 117 0.8× 129 0.9× 105 0.8× 193 5.1× 70 1.9× 21 426

Countries citing papers authored by Faraimunashe Chirove

Since Specialization
Citations

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

Fields of papers citing papers by Faraimunashe Chirove

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Faraimunashe Chirove

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

All Works

20 of 20 papers shown
1.
Chirove, Faraimunashe, et al.. (2025). Mathematical Modelling of Malaria Integrating Temperature, Rainfall, and Vegetation Index. Acta Applicandae Mathematicae. 199(1).
2.
Chirove, Faraimunashe, et al.. (2024). A systematic review of mathematical models of Lassa fever. Mathematical Biosciences. 374. 109227–109227. 7 indexed citations
3.
Chirove, Faraimunashe, et al.. (2024). Mathematical modeling of hepatocellular carcinoma incorporating immunotherapy. 13(2). 2411256–2411256.
4.
Gaff, Holly, et al.. (2023). Multipatch stochastic epidemic model for the dynamics of a tick-borne disease. Frontiers in Applied Mathematics and Statistics. 9. 2 indexed citations
5.
Nyabadza, Farai, et al.. (2023). Modelling the impact of stigmatisation of Ebola survivors on the disease transmission dynamics. Scientific Reports. 13(1). 4859–4859. 7 indexed citations
6.
Mbega, Ernest R., et al.. (2023). Mathematical model to assess the impacts of aflatoxin contamination in crops, livestock and humans. Scientific African. 23. e01980–e01980. 8 indexed citations
7.
Chirove, Faraimunashe, et al.. (2023). Optimal control and cost effectiveness analysis of contamination associated with aflatoxins in maize kernels, livestock and humans. Results in Control and Optimization. 13. 100313–100313. 3 indexed citations
8.
Chukwu, C. W., et al.. (2022). Application of optimal control to the dynamics of COVID-19 disease in South Africa. Scientific African. 16. e01268–e01268. 18 indexed citations
9.
Chirove, Faraimunashe, et al.. (2022). Modelling the Potential Impact of Stigma on the Transmission Dynamics of COVID-19 in South Africa. Mathematics. 10(18). 3253–3253. 2 indexed citations
10.
Chirove, Faraimunashe, et al.. (2022). Understanding the transmission pathways of Lassa fever: A mathematical modeling approach. Infectious Disease Modelling. 8(1). 27–57. 12 indexed citations
11.
Nyabadza, Farai, et al.. (2021). An Ebola virus disease model with fear and environmental transmission dynamics. Infectious Disease Modelling. 6. 545–559. 12 indexed citations
12.
Lenhart, Suzanne, et al.. (2021). A Mathematical Model of Contact Tracing during the 2014–2016 West African Ebola Outbreak. Mathematics. 9(6). 608–608. 8 indexed citations
13.
Chirove, Faraimunashe, et al.. (2021). A systematic review of mathematical models of the Ebola virus disease. International Journal of Modelling and Simulation. 42(5). 814–830. 7 indexed citations
14.
Chirove, Faraimunashe, et al.. (2021). Modeling the Effect of HIV/AIDS Stigma on HIV Infection Dynamics in Kenya. Bulletin of Mathematical Biology. 83(5). 55–55. 8 indexed citations
15.
Chirove, Faraimunashe, et al.. (2020). Evidence-based modeling of combination control on Kenyan youth HIV/AIDS dynamics. PLoS ONE. 15(11). e0242491–e0242491. 3 indexed citations
16.
Nyabadza, Farai, et al.. (2020). Modelling the Potential Impact of Social Distancing on the COVID-19 Epidemic in South Africa. Computational and Mathematical Methods in Medicine. 2020. 1–12. 43 indexed citations
17.
Chirove, Faraimunashe, et al.. (2020). MODELING DISPROPORTIONAL EFFECTS OF EDUCATING INFECTED KENYAN YOUTH ON HIV/AIDS. Journal of Biological Systems. 28(2). 311–349. 2 indexed citations
18.
Garira, Winston & Faraimunashe Chirove. (2019). A general method for multiscale modelling of vector-borne disease systems. Interface Focus. 10(1). 20190047–20190047. 13 indexed citations
19.
Chirove, Faraimunashe, et al.. (2017). A model incorporating combined RTIs and PIs therapy during early HIV-1 infection. Mathematical Biosciences. 285. 102–111. 11 indexed citations
20.
Nuraini, Nuning, et al.. (2017). Modelling Multiple Dosing with Drug Holiday in Antiretroviral Treatment on HIV-1 Infection. Journal of Mathematical and Fundamental Sciences. 49(1). 1–17. 5 indexed citations

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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