Conan Zhao

503 total citations
10 papers, 253 citations indexed

About

Conan Zhao is a scholar working on Infectious Diseases, Molecular Biology and Modeling and Simulation. According to data from OpenAlex, Conan Zhao has authored 10 papers receiving a total of 253 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Infectious Diseases, 4 papers in Molecular Biology and 4 papers in Modeling and Simulation. Recurrent topics in Conan Zhao's work include SARS-CoV-2 and COVID-19 Research (4 papers), COVID-19 epidemiological studies (4 papers) and Cystic Fibrosis Research Advances (3 papers). Conan Zhao is often cited by papers focused on SARS-CoV-2 and COVID-19 Research (4 papers), COVID-19 epidemiological studies (4 papers) and Cystic Fibrosis Research Advances (3 papers). Conan Zhao collaborates with scholars based in United States and Canada. Conan Zhao's co-authors include Joshua S. Weitz, Ashley R. Coenen, Jonathan Dushoff, Chung Yin Leung, Shashwat Shivam, Guanlin Li, Stephen J. Beckett, Sang Woo Park, Sam P. Brown and Rogelio A. Rodriguez-Gonzalez and has published in prestigious journals such as Nature Medicine, Nature Communications and The Journal of Infectious Diseases.

In The Last Decade

Conan Zhao

9 papers receiving 247 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Conan Zhao United States	7	119	105	46	32	28	10	253
Wing Yin Venus Lau Canada	5	137 1.2×	128 1.2×	115 2.5×	27 0.8×	36 1.3×	5	329
Sebastián Contreras Chile	11	154 1.3×	94 0.9×	34 0.7×	38 1.2×	34 1.2×	27	289
Jessica E. Stockdale Canada	7	231 1.9×	186 1.8×	29 0.6×	44 1.4×	66 2.4×	15	364
Hanan Hasan Jordan	6	39 0.3×	113 1.1×	61 1.3×	21 0.7×	86 3.1×	22	311
Zhifei Zhan China	8	195 1.6×	194 1.8×	32 0.7×	25 0.8×	96 3.4×	17	363
Chao-Chih Lai Taiwan	11	74 0.6×	174 1.7×	18 0.4×	20 0.6×	59 2.1×	24	277
Camilla Holten Møller Denmark	9	110 0.9×	364 3.5×	73 1.6×	6 0.2×	66 2.4×	15	433
Haowei Wang United Kingdom	10	172 1.4×	219 2.1×	25 0.5×	27 0.8×	68 2.4×	21	336
Shoya Iwanami Japan	10	132 1.1×	258 2.5×	39 0.8×	10 0.3×	47 1.7×	23	351
Joe Hilton United Kingdom	5	89 0.7×	51 0.5×	39 0.8×	30 0.9×	45 1.6×	6	162

Countries citing papers authored by Conan Zhao

Since Specialization

Citations

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

Fields of papers citing papers by Conan Zhao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Conan Zhao

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

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Zhao, Conan, et al.. (2024). Racial inequities and rare CFTR variants: Impact on cystic fibrosis diagnosis and treatment. Journal of Clinical & Translational Endocrinology. 36. 100344–100344. 6 indexed citations

2.

Zhao, Conan, et al.. (2023). Mathematical models of cystic fibrosis as a systemic disease. PubMed. 15(6). e1625–e1625.

3.

O’Connor, Kathleen, et al.. (2022). Frequency of quorum-sensing mutations in Pseudomonas aeruginosa strains isolated from different environments. Microbiology. 168(12). 22 indexed citations

4.

Varga, John, et al.. (2022). Antibiotics Drive Expansion of Rare Pathogens in a Chronic Infection Microbiome Model. mSphere. 7(5). e0031822–e0031822. 14 indexed citations

5.

Zhao, Conan, et al.. (2021). Causes and Consequences of COVID-19-Associated Bacterial Infections. Frontiers in Microbiology. 12. 682571–682571. 37 indexed citations

6.

Kraay, Alicia N.M., et al.. (2021). Modeling serological testing to inform relaxation of social distancing for COVID-19 control. Nature Communications. 12(1). 7063–7063. 10 indexed citations

7.

Zhao, Conan, Yifei Wang, John Varga, et al.. (2020). Microbiome Data Enhances Predictive Models of Lung Function in People With Cystic Fibrosis. The Journal of Infectious Diseases. 223(Supplement_3). S246–S256. 8 indexed citations

8.

Weitz, Joshua S., Stephen J. Beckett, Ashley R. Coenen, et al.. (2020). Modeling shield immunity to reduce COVID-19 epidemic spread. Nature Medicine. 26(6). 849–854. 143 indexed citations

9.

Weitz, Joshua S., Stephen J. Beckett, Ashley R. Coenen, et al.. (2020). Code for: Intervention Serology and Interaction Substitution: Modeling the Role of 'Shield Immunity' in Reducing COVID-19 Epidemic Spread. PubMed. 10 indexed citations

10.

Giuste, Felipe, Mythreye Venkatesan, Conan Zhao, et al.. (2020). Automated Classification of Acute Rejection from Endomyocardial Biopsies. 1–9. 3 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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