Natasha Markuzon

742 total citations
21 papers, 441 citations indexed

About

Natasha Markuzon is a scholar working on Infectious Diseases, Statistical and Nonlinear Physics and Modeling and Simulation. According to data from OpenAlex, Natasha Markuzon has authored 21 papers receiving a total of 441 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Infectious Diseases, 5 papers in Statistical and Nonlinear Physics and 5 papers in Modeling and Simulation. Recurrent topics in Natasha Markuzon's work include COVID-19 epidemiological studies (5 papers), Complex Network Analysis Techniques (5 papers) and Opinion Dynamics and Social Influence (5 papers). Natasha Markuzon is often cited by papers focused on COVID-19 epidemiological studies (5 papers), Complex Network Analysis Techniques (5 papers) and Opinion Dynamics and Social Influence (5 papers). Natasha Markuzon collaborates with scholars based in United States, Switzerland and Russia. Natasha Markuzon's co-authors include Corey M. Benedum, Louis Y. Kim, Elfatih A. B. Eltahir, Osama M. E. Seidahmed, Marta C. González, Sumiko R. Mekaru, John S. Brownstein, James M. Wilson, Timothy A. Postlethwaite and Amar Gupta and has published in prestigious journals such as Journal of the American College of Cardiology, PLoS ONE and Human Molecular Genetics.

In The Last Decade

Natasha Markuzon

20 papers receiving 428 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Natasha Markuzon United States 12 134 127 109 96 77 21 441
Romana Haneef France 14 214 1.6× 88 0.7× 166 1.5× 65 0.7× 95 1.2× 32 765
Dan Yamin Israel 17 55 0.4× 361 2.8× 353 3.2× 88 0.9× 264 3.4× 53 835
Lhakpa Tsamlag China 9 57 0.4× 189 1.5× 289 2.7× 103 1.1× 79 1.0× 12 596
Aili Wang China 10 260 1.9× 70 0.6× 234 2.1× 71 0.7× 52 0.7× 34 530
Elena Petelos Greece 9 82 0.6× 61 0.5× 62 0.6× 34 0.4× 44 0.6× 30 409
Jeremy Ratcliff United Kingdom 8 26 0.2× 134 1.1× 55 0.5× 34 0.4× 64 0.8× 15 468
Eunyoung Shim South Korea 13 54 0.4× 19 0.1× 45 0.4× 88 0.9× 144 1.9× 16 453
Taimur Qureshi Norway 6 180 1.3× 106 0.8× 214 2.0× 141 1.5× 136 1.8× 9 600
André T. Nguyen United States 9 54 0.4× 35 0.3× 221 2.0× 87 0.9× 395 5.1× 15 616
Andrew Schroeder United States 8 39 0.3× 89 0.7× 247 2.3× 61 0.6× 163 2.1× 13 521

Countries citing papers authored by Natasha Markuzon

Since Specialization
Citations

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

Fields of papers citing papers by Natasha Markuzon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Natasha Markuzon

This figure shows the co-authorship network connecting the top 25 collaborators of Natasha Markuzon. A scholar is included among the top collaborators of Natasha Markuzon 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 Natasha Markuzon. Natasha Markuzon 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.
Pickett, Sarah J., David Stein, Natasha Markuzon, et al.. (2022). Dynamics of the most common pathogenic mtDNA variant m.3243A > G demonstrate frequency-dependency in blood and positive selection in the germline. Human Molecular Genetics. 31(23). 4075–4086. 7 indexed citations
2.
Ward, Timothy J., et al.. (2020). UTILIZATION OF COMPLETE VS. LIMITED TRANSTHORACIC ECHOCARDIOGRAPHY: AN OPPORTUNITY FOR COST SAVINGS IN THE ERA OF VALUE BASED CARE. Journal of the American College of Cardiology. 75(11). 1781–1781. 1 indexed citations
3.
Benedum, Corey M., Kimberly M. Shea, Helen E. Jenkins, Louis Y. Kim, & Natasha Markuzon. (2020). Weekly dengue forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore. PLoS neglected tropical diseases. 14(10). e0008710–e0008710. 26 indexed citations
4.
Kebed, Kalie, Deyu Sun, Karima Addetia, et al.. (2020). Progression of aortic stenosis and echocardiographic criteria for its severity. European Heart Journal - Cardiovascular Imaging. 21(7). 737–743. 13 indexed citations
5.
Liu, Jin, et al.. (2020). Application of Bayesian networks to generate synthetic health data. Journal of the American Medical Informatics Association. 28(4). 801–811. 51 indexed citations
6.
Welsch, Roy E., et al.. (2019). The Univariate Flagging Algorithm (UFA): An interpretable approach for predictive modeling. PLoS ONE. 14(10). e0223161–e0223161. 4 indexed citations
7.
Benedum, Corey M., et al.. (2019). The association between autoimmune disease and 30-day mortality among sepsis ICU patients: a cohort study. Critical Care. 23(1). 93–93. 27 indexed citations
8.
Kebed, Kalie, Deyu Sun, Karima Addetia, et al.. (2019). Measurement errors in serial echocardiographic assessments of aortic valve stenosis severity. International journal of cardiac imaging. 36(3). 471–479. 15 indexed citations
9.
González, Marta C., et al.. (2019). Role of persistent cascades in diffusion. Physical review. E. 99(1). 12323–12323. 2 indexed citations
10.
Kim, Louis Y., et al.. (2019). Incorporating media data into a model of infectious disease transmission. PLoS ONE. 14(2). e0197646–e0197646. 71 indexed citations
11.
Benedum, Corey M., Osama M. E. Seidahmed, Elfatih A. B. Eltahir, & Natasha Markuzon. (2018). Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore. PLoS neglected tropical diseases. 12(12). e0006935–e0006935. 102 indexed citations
12.
Kim, Louis Y., et al.. (2017). Predicting social response to infectious disease outbreaks from internet-based news streams. Annals of Operations Research. 263(1-2). 551–564. 18 indexed citations
13.
Markuzon, Natasha, et al.. (2017). Bias reduction of peer influence effects with latent coordinates and community membership. 58. 3098–3103. 1 indexed citations
14.
González, Marta C., et al.. (2016). Persistent cascades: Measuring fundamental communication structure in social networks. 1. 969–975. 3 indexed citations
15.
González, Marta C., et al.. (2015). Cost-Effective Control of Infectious Disease Outbreaks Accounting for Societal Reaction. PLoS ONE. 10(8). e0136059–e0136059. 13 indexed citations
16.
Mekaru, Sumiko R., et al.. (2015). The Role of Social Mobilization in Controlling Ebola Virus in Lofa County, Liberia. PLoS Currents. 7. 29 indexed citations
17.
González, Marta C., et al.. (2015). Modelling the propagation of social response during a disease outbreak. Journal of The Royal Society Interface. 12(104). 20141105–20141105. 35 indexed citations
18.
Mekaru, Sumiko R., et al.. (2015). The Role of Social Mobilization in Controlling Ebola Virus in Lofa County, Liberia. PLoS Currents. 13 indexed citations
19.
20.
Markuzon, Natasha, et al.. (2009). Data driven approach to estimating fire danger from satellite images and weather information. 1–7. 7 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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