Kimberlyn Roosa

1.8k total citations
18 papers, 924 citations indexed

About

Kimberlyn Roosa is a scholar working on Modeling and Simulation, Infectious Diseases and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Kimberlyn Roosa has authored 18 papers receiving a total of 924 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Modeling and Simulation, 9 papers in Infectious Diseases and 9 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Kimberlyn Roosa's work include COVID-19 epidemiological studies (17 papers), Viral Infections and Outbreaks Research (7 papers) and COVID-19 Pandemic Impacts (5 papers). Kimberlyn Roosa is often cited by papers focused on COVID-19 epidemiological studies (17 papers), Viral Infections and Outbreaks Research (7 papers) and COVID-19 Pandemic Impacts (5 papers). Kimberlyn Roosa collaborates with scholars based in United States, Canada and Japan. Kimberlyn Roosa's co-authors include Gerardo Chowell, Ruiyan Luo, Ping Yan, James M. Hyman, Richard Rothenberg, Alexander Kirpich, Amna Tariq, Yiseul Lee, Cécile Viboud and Stefan Ma and has published in prestigious journals such as Scientific Reports, Emerging infectious diseases and PLoS Computational Biology.

In The Last Decade

Kimberlyn Roosa

17 papers receiving 897 citations

Peers

Kimberlyn Roosa

RNMI

EPIDE

Alexander Kirpich United States

Srinivasan Venkatramanan United States

Amna Tariq United States

Yichi Yang Japan

Cleo Anastassopoulou Greece

Yiseul Lee United States

Katsuma Hayashi Japan

Qianying Lin Hong Kong

Chong You China

Edouard Mathieu United Kingdom

Alexander Kirpich United States

Kimberlyn Roosa

431 ×0.6

209 ×0.7

154 ×0.6

172 ×1.1

RNMI

104 ×0.8

EPIDE

Citations per year, relative to Kimberlyn Roosa Kimberlyn Roosa (= 1×) peers Alexander Kirpich

Countries citing papers authored by Kimberlyn Roosa

Since Specialization

Citations

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

Fields of papers citing papers by Kimberlyn Roosa

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kimberlyn Roosa

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

All Works

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18 of 18 papers shown

Chowell, Gerardo, Sushma Dahal, Amna Tariq, et al.. (2024). GrowthPredict: A toolbox and tutorial-based primer for fitting and forecasting growth trajectories using phenomenological growth models. Scientific Reports. 14(1). 1630–1630. 7 indexed citations

Chowell, Gerardo, Sushma Dahal, Amna Tariq, et al.. (2022). An ensemble n-sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA. PLoS Computational Biology. 18(10). e1010602–e1010602. 13 indexed citations

Roosa, Kimberlyn & Nina H. Fefferman. (2022). A general modeling framework for exploring the impact of individual concern and personal protection on vector-borne disease dynamics. Parasites & Vectors. 15(1). 361–361. 2 indexed citations

Roosa, Kimberlyn. (2022). Comparative Assessment of Epidemiological Models for Analyzing and Forecasting Infectious Disease Outbreaks. Digital Archive @ GSU.

Roosa, Kimberlyn, Ruiyan Luo, Alexander Kirpich, et al.. (2020). Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infectious Disease Modelling. 5. 256–263. 408 indexed citations

Tariq, Amna, Yiseul Lee, Kimberlyn Roosa, et al.. (2020). Real-time monitoring the transmission potential of COVID-19 in Singapore, March 2020. BMC Medicine. 18(1). 166–166. 82 indexed citations

Roosa, Kimberlyn, Yiseul Lee, Ruiyan Luo, et al.. (2020). Short-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13–23, 2020. Journal of Clinical Medicine. 9(2). 596–596. 126 indexed citations

Lee, Yiseul, Kimberlyn Roosa, & Gerardo Chowell. (2020). Systematic comparison of epidemic growth patterns using two different estimation approaches. Infectious Disease Modelling. 6. 5–14. 1 indexed citations

Hackney, David N., et al.. (2020). Forecasting the impact of coronavirus disease during delivery hospitalization: an aid for resource utilization. American Journal of Obstetrics & Gynecology MFM. 2(3). 100127–100127. 16 indexed citations

10.

Tariq, Amna, Kimberlyn Roosa, & Gerardo Chowell. (2020). Using Simple Dynamic Analytic Framework To Characterize And Forecast Epidemics. 15. 30–44. 1 indexed citations

11.

Roosa, Kimberlyn, Amna Tariq, Ping Yan, James M. Hyman, & Gerardo Chowell. (2020). Multi-model forecasts of the ongoing Ebola epidemic in the Democratic Republic of Congo, March–October 2019. Journal of The Royal Society Interface. 17(169). 20200447–20200447. 15 indexed citations

12.

Chowell, Gerardo, Yiseul Lee, Sylvia K. Ofori, et al.. (2020). Doubling Time of the COVID-19 Epidemic by Province, China. Emerging infectious diseases. 26(8). 1912–1914. 64 indexed citations

13.

Roosa, Kimberlyn, Ruiyan Luo, & Gerardo Chowell. (2019). Comparative assessment of parameter estimation methods in the presence of overdispersion: a simulation study. Mathematical Biosciences & Engineering. 16(5). 4299–4313. 15 indexed citations

14.

Roosa, Kimberlyn & Gerardo Chowell. (2019). Assessing parameter identifiability in compartmental dynamic models using a computational approach: application to infectious disease transmission models. Theoretical Biology and Medical Modelling. 16(1). 1–1. 81 indexed citations

15.

Tariq, Amna, Kimberlyn Roosa, Kenji Mizumoto, & Gerardo Chowell. (2019). Assessing reporting delays and the effective reproduction number: The Ebola epidemic in DRC, May 2018–January 2019. Epidemics. 26. 128–133. 29 indexed citations

16.

Chowell, Gerardo, Ruiyan Luo, Kaiyuan Sun, et al.. (2019). Real-time forecasting of epidemic trajectories using computational dynamic ensembles. Epidemics. 30. 100379–100379. 47 indexed citations

17.

Mizumoto, Kenji, Amna Tariq, Kimberlyn Roosa, et al.. (2019). Spatial variability in the reproduction number of Ebola virus disease, Democratic Republic of the Congo, January–September 2019. Eurosurveillance. 24(42). 7 indexed citations

18.

Ganyani, Tapiwa, Kimberlyn Roosa, Christel Faes, Niel Hens, & Gerardo Chowell. (2018). Assessing the relationship between epidemic growth scaling and epidemic size: The 2014–16 Ebola epidemic in West Africa. Epidemiology and Infection. 147. e27–e27. 10 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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