Kris V. Parag

11.5k total citations · 1 hit paper
38 papers, 726 citations indexed

About

Kris V. Parag is a scholar working on Modeling and Simulation, Epidemiology and Genetics. According to data from OpenAlex, Kris V. Parag has authored 38 papers receiving a total of 726 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Modeling and Simulation, 19 papers in Epidemiology and 9 papers in Genetics. Recurrent topics in Kris V. Parag's work include COVID-19 epidemiological studies (25 papers), Data-Driven Disease Surveillance (12 papers) and Influenza Virus Research Studies (10 papers). Kris V. Parag is often cited by papers focused on COVID-19 epidemiological studies (25 papers), Data-Driven Disease Surveillance (12 papers) and Influenza Virus Research Studies (10 papers). Kris V. Parag collaborates with scholars based in United Kingdom, Hong Kong and Brazil. Kris V. Parag's co-authors include Christl A. Donnelly, Oliver G. Pybus, Robin N. Thompson, Benjamin J. Cowling, Louis du Plessis, Glenn Vinnicombe, Alexander E. Zarebski, Caroline E. Walters, Kylie E. C. Ainslie and H. Juliette T. Unwin and has published in prestigious journals such as Nature Communications, Bioinformatics and American Journal of Epidemiology.

In The Last Decade

Kris V. Parag

35 papers receiving 707 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe

2020 307 citations Kris V. Parag, Christl A. Donnelly et al. profile →

Peers

Kris V. Parag

MS

ID

EPIDE

EE

PHEOH

Xiaodan Sun China

King-Pan Chan Hong Kong

Ganna Rozhnova Netherlands

Áine B. Collins Ireland

Sam Abbott United Kingdom

Max S. Y. Lau United States

Timothy Russell United Kingdom

Dongxuan Chen Hong Kong

Dillon C. Adam Australia

Jon Parker United States

Xiaodan Sun China

Kris V. Parag

319 ×0.7

MS

196 ×0.8

ID

115 ×0.6

EPIDE

91 ×0.7

EE

169 ×1.5

PHEOH

Citations per year, relative to Kris V. Parag Kris V. Parag (= 1×) peers Xiaodan Sun

Countries citing papers authored by Kris V. Parag

Since Specialization

Citations

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

Fields of papers citing papers by Kris V. Parag

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kris V. Parag. 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 Kris V. Parag. The network helps show where Kris V. Parag may publish in the future.

Co-authorship network of co-authors of Kris V. Parag

This figure shows the co-authorship network connecting the top 25 collaborators of Kris V. Parag. A scholar is included among the top collaborators of Kris V. Parag 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 Kris V. Parag. Kris V. Parag 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:

20 of 20 papers shown

1.

Steyn, Nicholas & Kris V. Parag. (2025). Robust uncertainty quantification in popular estimators of the instantaneous reproduction number. American Journal of Epidemiology. 194(11). 3355–3363. 1 indexed citations

2.

Steyn, Nicholas, Kris V. Parag, Robin N. Thompson, & Christl A. Donnelly. (2025). A Primer on Inference and Prediction With Epidemic Renewal Models and Sequential Monte Carlo. Statistics in Medicine. 44(18-19). e70204–e70204.

3.

Stolerman, Lucas M., et al.. (2023). Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States. Science Advances. 9(3). eabq0199–eabq0199. 20 indexed citations

4.

Parag, Kris V., Benjamin J. Cowling, & Ben Lambert. (2023). Angular reproduction numbers improve estimates of transmissibility when disease generation times are misspecified or time-varying. Proceedings of the Royal Society B Biological Sciences. 290(2007). 20231664–20231664. 6 indexed citations

5.

Dye, Christopher, Nuno R. Faria, Fábio E. Leal, et al.. (2023). Scale-free dynamics of COVID-19 in a Brazilian city. Applied Mathematical Modelling. 121. 166–184. 6 indexed citations

6.

Parag, Kris V. & Uri Obolski. (2023). Risk averse reproduction numbers improve resurgence detection. PLoS Computational Biology. 19(7). e1011332–e1011332. 2 indexed citations

7.

Robinson, Martin, et al.. (2022). A Bayesian nonparametric method for detecting rapid changes in disease transmission. Journal of Theoretical Biology. 558. 111351–111351. 8 indexed citations

8.

Parag, Kris V., et al.. (2022). Using multiple sampling strategies to estimate SARS-CoV-2 epidemiological parameters from genomic sequencing data. Nature Communications. 13(1). 5587–5587. 6 indexed citations

9.

Parag, Kris V., Christl A. Donnelly, & Alexander E. Zarebski. (2022). Quantifying the information in noisy epidemic curves. Nature Computational Science. 2(9). 584–594. 14 indexed citations

10.

Dasgupta, Abhishek, et al.. (2022). Impact of spatiotemporal heterogeneity in COVID-19 disease surveillance on epidemiological parameters and case growth rates. Epidemics. 41. 100627–100627. 1 indexed citations

11.

Raghwani, Jayna, Louis du Plessis, John T. McCrone, et al.. (2022). Genomic Epidemiology of Early SARS-CoV-2 Transmission Dynamics, Gujarat, India. Emerging infectious diseases. 28(4). 751–758. 4 indexed citations

12.

Parag, Kris V., Robin N. Thompson, & Christl A. Donnelly. (2022). Are Epidemic Growth Rates More Informative than Reproduction Numbers?. Journal of the Royal Statistical Society Series A (Statistics in Society). 185(Supplement_1). S5–S15. 32 indexed citations

13.

Parag, Kris V. & Christl A. Donnelly. (2022). Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers. PLoS Computational Biology. 18(4). e1010004–e1010004. 7 indexed citations

14.

Parag, Kris V., Oliver G. Pybus, & Chieh‐Hsi Wu. (2021). Are Skyline Plot-Based Demographic Estimates Overly Dependent on Smoothing Prior Assumptions?. Systematic Biology. 71(1). 121–138. 7 indexed citations

15.

Parag, Kris V., Louis du Plessis, & Oliver G. Pybus. (2020). Jointly Inferring the Dynamics of Population Size and Sampling Intensity from Molecular Sequences. Molecular Biology and Evolution. 37(8). 2414–2429. 21 indexed citations

16.

Parag, Kris V. & Christl A. Donnelly. (2020). Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models. Systematic Biology. 69(6). 1163–1179. 15 indexed citations

17.

Parag, Kris V., et al.. (2020). An exact method for quantifying the reliability of end-of-epidemic declarations in real time. PLoS Computational Biology. 16(11). e1008478–e1008478. 23 indexed citations

18.

Parag, Kris V. & Oliver G. Pybus. (2019). Robust Design for Coalescent Model Inference. Systematic Biology. 68(5). 730–743. 16 indexed citations

19.

Parag, Kris V.. (2019). On signalling and estimation limits for molecular birth-processes. Journal of Theoretical Biology. 480. 262–273. 4 indexed citations

20.

Parag, Kris V. & Oliver G. Pybus. (2017). Optimal point process filtering and estimation of the coalescent process. Journal of Theoretical Biology. 421. 153–167. 8 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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