Matthew Biggerstaff

12.0k total citations · 4 hit papers
81 papers, 4.0k citations indexed

About

Matthew Biggerstaff is a scholar working on Epidemiology, Modeling and Simulation and Infectious Diseases. According to data from OpenAlex, Matthew Biggerstaff has authored 81 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Epidemiology, 51 papers in Modeling and Simulation and 20 papers in Infectious Diseases. Recurrent topics in Matthew Biggerstaff's work include Influenza Virus Research Studies (60 papers), COVID-19 epidemiological studies (51 papers) and Respiratory viral infections research (22 papers). Matthew Biggerstaff is often cited by papers focused on Influenza Virus Research Studies (60 papers), COVID-19 epidemiological studies (51 papers) and Respiratory viral infections research (22 papers). Matthew Biggerstaff collaborates with scholars based in United States, United Kingdom and Kenya. Matthew Biggerstaff's co-authors include Carrie Reed, Lyn Finelli, Michael A. Johansson, Rachel B. Slayton, John T. Brooks, Manoj Gambhir, Simon Cauchemez, Jay C. Butler, Talía M. Quandelacy and Pragati Prasad and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and PLoS ONE.

In The Last Decade

Matthew Biggerstaff

76 papers receiving 3.8k 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.

SARS-CoV-2 Transmission From People Without COVID-19 Symptoms

2021 604 citations Michael A. Johansson, Talía M. Quandelacy et al. profile →
Emergence of SARS-CoV-2 B.1.1.7 Lineage — United States, December 29, 2020–January 12, 2021

2021 409 citations Michael A. Johansson, Rachel B. Slayton et al. profile →
Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: a systematic review of the literature

2014 397 citations Matthew Biggerstaff, Carrie Reed et al. profile →
Community Mitigation Guidelines to Prevent Pandemic Influenza — United States, 2017

2017 312 citations Matthew Biggerstaff, Carrie Reed et al. profile →

Peers

Matthew Biggerstaff

EPIDE

PHEOH

HEALT

Joël Mossong Luxembourg

Ashleigh R. Tuite Canada

Christel Faes Belgium

Jonathan M. Read United Kingdom

Stefania Salmaso Italy

Marco Massari Italy

Małgorzata Sadkowska-Todys Poland

Emma S. McBryde Australia

Shengjie Lai China

Martin Bootsma Netherlands

Joël Mossong Luxembourg

Matthew Biggerstaff

2.1k ×1.3

EPIDE

1.9k ×1.2

1.7k ×1.2

468 ×1.3

PHEOH

561 ×1.6

HEALT

Citations per year, relative to Matthew Biggerstaff Matthew Biggerstaff (= 1×) peers Joël Mossong

Countries citing papers authored by Matthew Biggerstaff

Since Specialization

Citations

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

Fields of papers citing papers by Matthew Biggerstaff

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew Biggerstaff

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Biggerstaff. A scholar is included among the top collaborators of Matthew Biggerstaff 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 Matthew Biggerstaff. Matthew Biggerstaff 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

Morris, Sinead E., Melissa S. Stockwell, Natalie M. Bowman, et al.. (2025). Estimating the generation time for influenza transmission using household data in the United States. Epidemics. 50. 100815–100815. 1 indexed citations

Morris, Sinead E., Jessie R. Chung, Brendan Flannery, et al.. (2025). Estimating historical impacts of vaccination against influenza B/Yamagata in the United States to inform possible risks of re-emergence in the absence of vaccination. Vaccine X. 26. 100640–100640. 1 indexed citations

Nguyen, Huong Q., H. Keipp Talbot, Melissa A. Rolfes, et al.. (2024). Asymptomatic and Mildly Symptomatic Influenza Virus Infections by Season: Case-Ascertained Household Transmission Studies, United States, 2017–2023. The Journal of Infectious Diseases. 232(4). e637–e641. 1 indexed citations

Morris, Sinead E., Huong Q. Nguyen, Carlos G. Grijalva, et al.. (2024). Influenza virus shedding and symptoms: Dynamics and implications from a multiseason household transmission study. PNAS Nexus. 3(9). 3 indexed citations

Biggerstaff, Matthew, et al.. (2024). Influenza Vaccination Timing. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information).

Borchering, Rebecca K., Matthew Biggerstaff, Lynnette Brammer, et al.. (2024). Responding to the Return of Influenza in the United States by Applying Centers for Disease Control and Prevention Surveillance, Analysis, and Modeling to Inform Understanding of Seasonal Influenza. JMIR Public Health and Surveillance. 10. e54340–e54340. 2 indexed citations

Morris, Sinead E., Lynnette Brammer, Alicia Budd, et al.. (2024). Detection of Novel Influenza Viruses Through Community and Healthcare Testing: Implications for Surveillance Efforts in the United States. Influenza and Other Respiratory Viruses. 18(5). e13315–e13315. 1 indexed citations

García‐Carreras, Bernardo, Matt D. T. Hitchings, Michael A. Johansson, et al.. (2023). Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S.. Nature Communications. 14(1). 2235–2235. 3 indexed citations

Chrétien, Jean-Paul, Michael A. Johansson, Jeffrey J. Morgan, et al.. (2019). A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern. PLoS neglected tropical diseases. 13(10). e0007451–e0007451. 26 indexed citations

10.

Reich, Nicholas G, Craig McGowan, Teresa K. Yamana, et al.. (2019). Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S.. PLoS Computational Biology. 15(11). e1007486–e1007486. 95 indexed citations

11.

Johansson, Michael A., Jeffrey J. Morgan, Harshini Mukundan, et al.. (2019). A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern. bioRxiv (Cold Spring Harbor Laboratory). 5 indexed citations

12.

Biggerstaff, Matthew, Cheryl Cohen, Carrie Reed, et al.. (2019). A cost-effectiveness analysis of antenatal influenza vaccination among HIV-infected and HIV-uninfected pregnant women in South Africa. Vaccine. 37(46). 6874–6884. 9 indexed citations

13.

Lu, Fred, et al.. (2019). Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches. Nature Communications. 10(1). 147–147. 65 indexed citations

14.

Stewart, Rebekah J., John Rossow, Seth Eckel, et al.. (2018). Text-Based Illness Monitoring for Detection of Novel Influenza A Virus Infections During an Influenza A (H3N2)v Virus Outbreak in Michigan, 2016: Surveillance and Survey. JMIR Public Health and Surveillance. 5(2). e10842–e10842. 4 indexed citations

15.

Stewart, Rebekah J., John Rossow, Seth Eckel, et al.. (2017). Do animal exhibitors support and follow recommendations to prevent transmission of variant influenza at agricultural fairs? A survey of animal exhibitor households after a variant influenza virus outbreak in Michigan. Zoonoses and Public Health. 65(1). 195–201. 6 indexed citations

16.

Gambhir, Manoj, Catherine H. Bozio, Justin J. O’Hagan, et al.. (2015). Infectious Disease Modeling Methods as Tools for Informing Response to Novel Influenza Viruses of Unknown Pandemic Potential. Clinical Infectious Diseases. 60(suppl_1). S11–S19. 8 indexed citations

17.

Carias, Cristina, Carrie Reed, Ivo Foppa, et al.. (2015). Net Costs Due to Seasonal Influenza Vaccination — United States, 2005–2009. PLoS ONE. 10(7). e0132922–e0132922. 14 indexed citations

18.

Viray, Melissa, Joseph Francis Wamala, Ryan Fagan, et al.. (2014). Outbreak of type A foodborne botulism at a boarding school, Uganda, 2008. Epidemiology and Infection. 142(11). 2297–2301. 11 indexed citations

19.

Regan, Joanna J., Ashley Fowlkes, Matthew Biggerstaff, et al.. (2012). Epidemiology of influenza A (H1N1)pdm09‐associated deaths in the United States, September–October 2009. Influenza and Other Respiratory Viruses. 6(6). e169–77. 3 indexed citations

20.

Pezzoli, Lorenzo, Richard Elson, C.L. Little, et al.. (2008). Packed with Salmonella —Investigation of an International Outbreak of Salmonella Senftenberg Infection Linked to Contamination of Prepacked Basil in 2007. Foodborne Pathogens and Disease. 5(5). 661–668. 77 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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