Jane M. Heffernan

5.5k total citations · 2 hit papers
104 papers, 3.2k citations indexed

About

Jane M. Heffernan is a scholar working on Modeling and Simulation, Infectious Diseases and Epidemiology. According to data from OpenAlex, Jane M. Heffernan has authored 104 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Modeling and Simulation, 37 papers in Infectious Diseases and 37 papers in Epidemiology. Recurrent topics in Jane M. Heffernan's work include COVID-19 epidemiological studies (50 papers), Mathematical and Theoretical Epidemiology and Ecology Models (29 papers) and SARS-CoV-2 and COVID-19 Research (22 papers). Jane M. Heffernan is often cited by papers focused on COVID-19 epidemiological studies (50 papers), Mathematical and Theoretical Epidemiology and Ecology Models (29 papers) and SARS-CoV-2 and COVID-19 Research (22 papers). Jane M. Heffernan collaborates with scholars based in Canada, United States and China. Jane M. Heffernan's co-authors include Lindi M. Wahl, Robert J. Smith, Jian Wu, S.Karen Collinson, Shelly Bolotin, Natasha S. Crowcroft, Ye Li, Yicang Zhou, Fiona Guerra and Gillian Lim and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Jane M. Heffernan

102 papers receiving 3.1k 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.

Perspectives on the basic reproductive ratio

2005 839 citations Jane M. Heffernan et al. profile →
The basic reproduction number (R0) of measles: a systematic review

2017 385 citations Jane M. Heffernan et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jane M. Heffernan Canada	27	1.5k	1.3k	901	882	652	104	3.2k
Christian L. Althaus Switzerland	30	1.3k 0.8×	532 0.4×	621 0.7×	1.4k 1.6×	223 0.3×	80	3.5k
Nicholas C. Grassly United Kingdom	42	857 0.6×	625 0.5×	1.3k 1.4×	2.8k 3.2×	436 0.7×	124	5.6k
Stephen S. Morse United States	30	750 0.5×	1.9k 1.5×	1.1k 1.2×	2.1k 2.4×	412 0.6×	104	5.1k
Shweta Bansal United States	30	1.5k 1.0×	942 0.7×	763 0.8×	733 0.8×	453 0.7×	118	3.9k
Eduardo Massad Brazil	41	1.3k 0.9×	3.4k 2.6×	1.4k 1.5×	2.1k 2.4×	386 0.6×	302	6.3k
Katia Koelle United States	34	861 0.6×	861 0.7×	1.2k 1.4×	1.3k 1.5×	768 1.2×	78	3.7k
Sebastian Funk United Kingdom	35	2.9k 1.9×	1.6k 1.2×	1.2k 1.4×	1.6k 1.8×	439 0.7×	117	5.3k
Libin Rong United States	34	1.1k 0.8×	1.7k 1.3×	961 1.1×	1.1k 1.3×	731 1.1×	117	4.1k
Ken Eames United Kingdom	28	2.2k 1.4×	997 0.8×	1.4k 1.5×	824 0.9×	394 0.6×	44	4.6k
Yanni Xiao China	39	3.7k 2.5×	3.2k 2.5×	689 0.8×	1.7k 1.9×	1.5k 2.2×	180	6.4k

Countries citing papers authored by Jane M. Heffernan

Since Specialization

Citations

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

Fields of papers citing papers by Jane M. Heffernan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jane M. Heffernan. 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 Jane M. Heffernan. The network helps show where Jane M. Heffernan may publish in the future.

Co-authorship network of co-authors of Jane M. Heffernan

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

All Works

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20 of 20 papers shown

O’Brien, Sheila F., Carmen Charlton, Steven J. Drews, et al.. (2025). The impact of statistical adjustment for assay performance on inferences from SARS-CoV-2 serological surveillance studies. American Journal of Epidemiology. 194(11). 3373–3381. 1 indexed citations

Yang, Yang, Avneet Kaur, Zahid A Butt, et al.. (2023). Monkeypox: a review of epidemiological modelling studies and how modelling has led to mechanistic insight. Epidemiology and Infection. 151. e121–e121. 25 indexed citations

Heffernan, Jane M., et al.. (2023). A pair formation model with recovery: Application to mpox. Epidemics. 44. 100693–100693. 7 indexed citations

Yuan, Pei, Yi Tan, Liu Yang, et al.. (2022). Assessing transmission risks and control strategy for monkeypox as an emerging zoonosis in a metropolitan area. Journal of Medical Virology. 95(1). e28137–e28137. 32 indexed citations

Ghaemi, Mohammad Sajjad, et al.. (2022). A machine learning approach to differentiate between COVID-19 and influenza infection using synthetic infection and immune response data. Mathematical Biosciences & Engineering. 19(6). 5813–5831. 5 indexed citations

Yuan, Pei, Yi Tan, Liu Yang, et al.. (2022). Modeling vaccination and control strategies for outbreaks of monkeypox at gatherings. Frontiers in Public Health. 10. 1026489–1026489. 16 indexed citations

Thiébaut, Rodolphe, et al.. (2021). Barrier Gesture Relaxation during Vaccination Campaign in France: Modelling Impact of Waning Immunity. COVID. 1(2). 472–488. 4 indexed citations

Bragazzi, Nicola Luigi, et al.. (2021). Could a New COVID-19 Mutant Strain Undermine Vaccination Efforts? A Mathematical Modelling Approach for Estimating the Spread of B.1.1.7 Using Ontario, Canada, as a Case Study. Vaccines. 9(6). 592–592. 10 indexed citations

Vegvari, Carolin, Sam Abbott, Frank Ball, et al.. (2021). Commentary on the use of the reproduction number R during the COVID-19 pandemic. Statistical Methods in Medical Research. 31(9). 1675–1685. 25 indexed citations

10.

Tang, Biao, Francesca Scarabel, Nicola Luigi Bragazzi, et al.. (2020). De-escalation by Reversing the Escalation with a Stronger Synergistic Package of Contact Tracing, Quarantine, Isolation and Personal Protection: Feasibility of Preventing a COVID-19 Rebound in Ontario, Canada, as a Case Study. SSRN Electronic Journal. 1 indexed citations

11.

Xiao, Yanyu, Francesca Scarabel, Biao Tang, et al.. (2020). Quantifying the shift in social contact patterns in response to non-pharmaceutical interventions. SHILAP Revista de lepidopterología. 10(1). 28–28. 15 indexed citations

12.

Tang, Biao, Francesca Scarabel, Nicola Luigi Bragazzi, et al.. (2020). De-Escalation by Reversing the Escalation with a Stronger Synergistic Package of Contact Tracing, Quarantine, Isolation and Personal Protection: Feasibility of Preventing a COVID-19 Rebound in Ontario, Canada, as a Case Study. Biology. 9(5). 100–100. 39 indexed citations

13.

Nah, Kyeongah, et al.. (2020). Impact of influenza vaccine-modified infectivity on attack rate, case fatality ratio and mortality. Journal of Theoretical Biology. 492. 110190–110190. 5 indexed citations

14.

Rahman, Quazi Abidur, Tahir Janmohamed, Hance Clarke, et al.. (2019). Interpretability and Class Imbalance in Prediction Models for Pain Volatility in Manage My Pain App Users: Analysis Using Feature Selection and Majority Voting Methods. JMIR Medical Informatics. 7(4). e15601–e15601. 14 indexed citations

15.

Teslya, Alexandra, et al.. (2019). A threshold delay model of HIV infection of newborn infants through breastfeeding. Infectious Disease Modelling. 4. 188–214. 1 indexed citations

16.

Rahman, Quazi Abidur, Tahir Janmohamed, Meysam Pirbaglou, et al.. (2018). Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods. Journal of Medical Internet Research. 20(11). e12001–e12001. 30 indexed citations

17.

Rahman, Quazi Abidur, Tahir Janmohamed, Meysam Pirbaglou, et al.. (2017). Patterns of User Engagement With the Mobile App, Manage My Pain: Results of a Data Mining Investigation. JMIR mhealth and uhealth. 5(7). e96–e96. 39 indexed citations

18.

Laurie, Karen, Teagan Guarnaccia, Louise Carolan, et al.. (2016). The time-interval between infections and viral hierarchies are determinants of viral interference following influenza virus infection in a ferret model. European Journal of Immunology. 46. 3 indexed citations

19.

Brauer, Fred, et al.. (2014). A delay-dependent model with HIV drug resistance during therapy. Journal of Mathematical Analysis and Applications. 414(2). 514–531. 22 indexed citations

20.

Duvvuri, Venkata R., Seyed M. Moghadas, Hongbin Guo, et al.. (2010). Original Article: Highly conserved cross‐reactive CD4+ T‐cell HA‐epitopes of seasonal and the 2009 pandemic influenza viruses. Influenza and Other Respiratory Viruses. 4(5). 249–258. 34 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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