Skip to menu Skip to content Skip to footer
Dr Helen Mayfield
Dr

Helen Mayfield

Email: 
Phone: 
+61 7 336 55393

Overview

Background

Dr Helen Mayfield is an interdisciplinary researcher whose work lies at the intersection of epidemiology, infectious diseases and environmental conservation. With a decade of experience studying zoonotic and vector-borne diseases, she employs advanced data modelling techniques like Bayesian networks and spatial models to explore the environmental drivers of disease. Helen holds a PhD in machine learning for environmental management. Her research focus is on refining and testing new disease surveillance methods and strategies, such as molecular xenomonitoring of mosquitoes, and targeted sampling to combat lymphatic filariasis in the Pacific islands. In addition, her current project collaborating with the NSW Saving our Species programme aims to facilitate adaptive management for threatened species using structured expert knowledge to improve decision outcomes for biodiversity.

Helen teaches in courses for conservation planning and practice, and conservation policy. She is currently president of the Bayesian Network Modelling Association and a member of the International Union for the Conservation of Nature (IUCN) Decision Science Working Group.

Availability

Dr Helen Mayfield is:
Available for supervision

Qualifications

  • Doctor of Philosophy, The University of Queensland
  • Member, Australasian Bayesian Network Society, Australasian Bayesian Network Society
  • Member, Centre for Biodiversity and Conservation Science, Centre for Biodiversity and Conservation Science
  • Collaboration / Affiliation, Griffith University Systems Modelling Group, Griffith University Systems Modelling Group
  • Associate Fellow, QUT Centre for Data Science, QUT Centre for Data Science

Research interests

  • Improving indicator selection and design

    In both conservation science and infectious disease epidemiology, what we measure and how, will affect how accurately we detect changes in the system. This could be as part of a threatened species management plan or for informing programmatic decisions for disease elimination. By exploring alternative indicators and sampling methods, we can improve our understanding of the system to informed management decisions.

  • Linking local environments to infectious disease risk

    The environment where we live and work can affect our exposure to vector-borne or zoonotic diseases. This potentially creates win-win scenarios where we can optimise interventions so that they benefit both people and nature.

  • Decision support and analysis tools

    Tools and models that help to structure and analyse data from a range of sources including academic studies, monitoring and expert elicitation can facilitate informed decision making. This is equally true in data-rich and data-poor scenarios. Using the right modelling technique to suit the problem at hand, and making the tool user-friendly for the intended audience are crucial for ensuring the product is fit-for-purpose

Works

Search Professor Helen Mayfield’s works on UQ eSpace

72 works between 2002 and 2025

41 - 60 of 72 works

2021

Other Outputs

Designing an evidence-based Bayesian network for estimating the risk versus benefits of AstraZeneca COVID-19 vaccine

Mayfield, Helen J., Lau, Colleen L., Sinclair, Jane E., Brown, Samuel J., Baird, Andrew, Litt, John, Vuorinen, Aapeli, Short, Kirsty R., Waller, Michael and Mengersen, Kerrie (2021). Designing an evidence-based Bayesian network for estimating the risk versus benefits of AstraZeneca COVID-19 vaccine. doi: 10.1101/2021.10.28.21265588

Designing an evidence-based Bayesian network for estimating the risk versus benefits of AstraZeneca COVID-19 vaccine

2021

Other Outputs

Risk-benefit analysis of the AstraZeneca COVID-19 vaccine in Australia using a Bayesian network modelling framework

Lau, Colleen L, Mayfield, Helen J, Sinclair, Jane E, Brown, Samuel J, Waller, Michael, Enjeti, Anoop K, Baird, Andrew, Short, Kirsty, Mengersen, Kerrie and Litt, John (2021). Risk-benefit analysis of the AstraZeneca COVID-19 vaccine in Australia using a Bayesian network modelling framework. doi: 10.1101/2021.09.30.21264337

Risk-benefit analysis of the AstraZeneca COVID-19 vaccine in Australia using a Bayesian network modelling framework

2021

Journal Article

Implications of a travel connectivity-based approach for infectious disease transmission risks in Oceania

Cadavid Restrepo, Angela, Furuya-Kanamori, Luis, Mayfield, Helen, Nilles, Eric and Lau, Colleen L. (2021). Implications of a travel connectivity-based approach for infectious disease transmission risks in Oceania. BMJ Open, 11 (8) e046206, e046206. doi: 10.1136/bmjopen-2020-046206

Implications of a travel connectivity-based approach for infectious disease transmission risks in Oceania

2021

Journal Article

WWT London Wetland Centre - the first 20 years

Bullock, Rich, Arbon, John, Arnold, Laurence, Briggs, Philip, Haines, Bill, Honey, Martin, Hutchins, Emma, Klavins, Jake, Mayfield, Helen, Salmon, Adam, Smallshire, Penny and Widdowson, David (2021). WWT London Wetland Centre - the first 20 years. British Wildlife, 32 (8), 584-591.

WWT London Wetland Centre - the first 20 years

2021

Journal Article

Novel statistical approaches to identify risk factors for soil-transmitted helminth infection in Timor-Leste

Yi Han Aw, Jessica, Clarke, Naomi E., Mayfield, Helen, Lau, Colleen L., Richardson, Alice and Nery, Susana V. (2021). Novel statistical approaches to identify risk factors for soil-transmitted helminth infection in Timor-Leste. International Journal for Parasitology, 51 (9), 729-739. doi: 10.1016/j.ijpara.2021.01.005

Novel statistical approaches to identify risk factors for soil-transmitted helminth infection in Timor-Leste

2021

Book Chapter

Control and elimination of lymphatic filariasis in Oceania: prevalence, geographical distribution, mass drug administration, and surveillance in Samoa, 1998–2017

Graves, Patricia, Joseph, Hayley, Coutts, Shaun P., Mayfield, Helen J., Maiava, Fuatai, Leong-Lui, Tile Ann Ah, Toelupe, Palanitina Tupuimatagi, Iosia, Vailolo Toeaso, Loau, Siatua, Pemita, Paulo, Naseri, Take, Thomsen, Robert, Berg Soto, Alvaro Berg, Burkot, Thomas R., Wood, Peter, Melrose, Wayne, Aratchige, Padmasiri, Capuano, Corinne, Kim, Sung Hye, Ozaki, Masayo, Yajima, Aya, Lammie, Patrick J., Ottesen, Eric, Hansell, Lepaitai, Baghirov, Rasul, Lau, Colleen L. and Ichimori, Kazuyo (2021). Control and elimination of lymphatic filariasis in Oceania: prevalence, geographical distribution, mass drug administration, and surveillance in Samoa, 1998–2017. Advances in parasitology. (pp. 27-73) edited by David Rollinson and Russell Stothard. San Diego, CA, United States: Academic Press. doi: 10.1016/bs.apar.2021.03.002

Control and elimination of lymphatic filariasis in Oceania: prevalence, geographical distribution, mass drug administration, and surveillance in Samoa, 1998–2017

2021

Other Outputs

RestEcol Supplementary Data

Gibson, Michelle, Walsh, Jessica, Mayfield, Helen, Friedman, Rachel and Powell, Owen (2021). RestEcol Supplementary Data. The University of Queensland. (Dataset) doi: 10.48610/340b96d

RestEcol Supplementary Data

2020

Journal Article

Supporting elimination of lymphatic filariasis in Samoa by predicting locations of residual infection using machine learning and geostatistics

Mayfield, Helen J., Sturrock, Hugh, Arnold, Benjamin F., Andrade-Pacheco, Ricardo, Kearns, Therese, Graves, Patricia, Naseri, Take, Thomsen, Robert, Gass, Katherine and Lau, Colleen L. (2020). Supporting elimination of lymphatic filariasis in Samoa by predicting locations of residual infection using machine learning and geostatistics. Scientific Reports, 10 (1) 20570, 1-11. doi: 10.1038/s41598-020-77519-8

Supporting elimination of lymphatic filariasis in Samoa by predicting locations of residual infection using machine learning and geostatistics

2020

Journal Article

Lymphatic filariasis epidemiology in Samoa in 2018: geographic clustering and higher antigen prevalence in older age groups

Lau, Colleen L., Meder, Kelley, Mayfield, Helen J., Kearns, Therese, McPherson, Brady, Naseri, Take, Thomsen, Robert, Hedtke, Shannon M., Sheridan, Sarah, Gass, Katherine and Graves, Patricia M. (2020). Lymphatic filariasis epidemiology in Samoa in 2018: geographic clustering and higher antigen prevalence in older age groups. PLoS Neglected Tropical Diseases, 14 (12) e0008927, 1-22. doi: 10.1371/journal.pntd.0008927

Lymphatic filariasis epidemiology in Samoa in 2018: geographic clustering and higher antigen prevalence in older age groups

2020

Conference Publication

Evidence-based conservation for Australian woodland birds: a systematic review

Walsh, J., Gibson, M., Bracy, C., Freidman, R., Mayfield, H., Melton, C., Reside, A., Simmonds, J. and Maron, M. (2020). Evidence-based conservation for Australian woodland birds: a systematic review . Ecological Society of Australia, 60th Annual Conference, Virtual, 30 November-4 December 2020. Ecological Society of Australia.

Evidence-based conservation for Australian woodland birds: a systematic review

2020

Journal Article

A community survey of coverage and adverse events following country-wide triple-drug mass drug administration for lymphatic filariasis elimination, Samoa 2018

Willis, Gabriela A., Mayfield, Helen J., Kearns, Therese, Naseri, Take, Thomsen, Robert, Gass, Katherine, Sheridan, Sarah, Graves, Patricia M. and Lau, Colleen L. (2020). A community survey of coverage and adverse events following country-wide triple-drug mass drug administration for lymphatic filariasis elimination, Samoa 2018. PLoS Neglected Tropical Diseases, 14 (11) e0008854, 1-18. doi: 10.1371/journal.pntd.0008854

A community survey of coverage and adverse events following country-wide triple-drug mass drug administration for lymphatic filariasis elimination, Samoa 2018

2020

Conference Publication

How the Saving our Species program ensures threatened species monitoring leads to conservation outcomes

Hansen, N. and Mayfield, H. (2020). How the Saving our Species program ensures threatened species monitoring leads to conservation outcomes. 60th Annual Conference of the Ecological Society of Australia, Virtual, 30 November – 4 December 2020.

How the Saving our Species program ensures threatened species monitoring leads to conservation outcomes

2020

Journal Article

Estimating species response to management using an integrated process: a case study from New South Wales, Australia

Mayfield, Helen J., Brazill‐Boast, James, Gorrod, Emma, Evans, Megan C., Auld, Tony, Rhodes, Jonathan R. and Maron, Martine (2020). Estimating species response to management using an integrated process: a case study from New South Wales, Australia. Conservation Science and Practice, 2 (11) e269. doi: 10.1111/csp2.269

Estimating species response to management using an integrated process: a case study from New South Wales, Australia

2020

Other Outputs

Saving our species: guidelines for estimating and evaluating species' response to management

Mayfield, Helen, Rhodes, Jonathan, Evans, Megan and Maron, Martine (2020). Saving our species: guidelines for estimating and evaluating species' response to management. Sydney, NSW, Australia: NSW Department of Planning, Industry and Environment.

Saving our species: guidelines for estimating and evaluating species' response to management

2020

Journal Article

Impact of 2019–2020 mega-fires on Australian fauna habitat

Ward, Michelle, Tulloch, Ayesha I. T., Radford, James Q., Williams, Brooke A., Reside, April E., Macdonald, Stewart L., Mayfield, Helen J., Maron, Martine, Possingham, Hugh P., Vine, Samantha J., O’Connor, James L., Massingham, Emily J., Greenville, Aaron C., Woinarski, John C. Z., Garnett, Stephen T., Lintermans, Mark, Scheele, Ben C., Carwardine, Josie, Nimmo, Dale G., Lindenmayer, David B., Kooyman, Robert M., Simmonds, Jeremy S., Sonter, Laura J. and Watson, James E. M. (2020). Impact of 2019–2020 mega-fires on Australian fauna habitat. Nature Ecology and Evolution, 4 (10), 1321-1326. doi: 10.1038/s41559-020-1251-1

Impact of 2019–2020 mega-fires on Australian fauna habitat

2020

Journal Article

Considerations for selecting a machine learning technique for predicting deforestation

Mayfield, Helen J. , Smith, Carl , Gallagher, Marcus and Hockings, Marc (2020). Considerations for selecting a machine learning technique for predicting deforestation. Environmental Modelling and Software, 131 104741, 1-10. doi: 10.1016/j.envsoft.2020.104741

Considerations for selecting a machine learning technique for predicting deforestation

2020

Other Outputs

Six million hectares of threatened species habitat up in smoke

Ward, Michelle, Greenville, Aaron, Reside, April, Tulloch, Ayesha, Williams, Brooke, Massingham, Emily, Mayfield, Helen, Possingham, Hugh, Watson, James, Radford, Jim and Sonter, Laura (2020, 01 19). Six million hectares of threatened species habitat up in smoke The Conversation

Six million hectares of threatened species habitat up in smoke

2020

Journal Article

Travel connectivity and infectious disease transmission risks in Oceania

Cadavid Restrepo, Angela Maria, Furuya-Kanamori, Luis, Mayfield, Helen, Nilles, Eric J. and Lau, Colleen L. (2020). Travel connectivity and infectious disease transmission risks in Oceania. SSRN Electronic Journal. doi: 10.2139/ssrn.3709854

Travel connectivity and infectious disease transmission risks in Oceania

2019

Journal Article

Collaboration across boundaries in the Amazon

Prist, Paula Ribeiro, Levin, Noam, Metzger, Jean Paul, de Mello, Kaline, de Paula Costa, Micheli Duarte, Castagnino, Romi, Cortes-Ramirez, Javier, Lin, Da-Li, Butt, Nathalie, Lloyd, Thomas J., López-Cubillos, Sofía, Mayfield, Helen J., Negret, Pablo José, Oliveira-Bevan, Isabella, Reside, April E., Rhodes, Jonathan R., Simmons, B. Alexander, Suárez-Castro, A. Felipe and Kark, Salit (2019). Collaboration across boundaries in the Amazon. Science, 366 (6466), 699.1-700. doi: 10.1126/science.aaz7489

Collaboration across boundaries in the Amazon

2019

Journal Article

Use of a structure aware discretisation algorithm for Bayesian networks applied to water quality predictions

Mayfield, Helen J., Bertone, Edoardo, Smith, Carl and Sahin, Oz (2019). Use of a structure aware discretisation algorithm for Bayesian networks applied to water quality predictions. Mathematics and Computers in Simulation, 175, 192-201. doi: 10.1016/j.matcom.2019.07.005

Use of a structure aware discretisation algorithm for Bayesian networks applied to water quality predictions

Funding

Current funding

  • 2024 - 2027
    New metrics to track fauna community condition in Australia
    ARC Linkage Projects
    Open grant

Past funding

  • 2023 - 2024
    The effectiveness and evaluation of a species response to management approach
    New South Wales Department of Planning and Environment
    Open grant
  • 2023 - 2025
    Targeted surveillance studies linked to Surveillance and Monitoring to Eliminate Lymphatic Filariasis and Scabies from Samoa (SaMELFS)
    Task Force for Global Health
    Open grant
  • 2019 - 2020
    Developing indices and process models for estimating offset benefits to species
    New South Wales Department of Planning, Industry and Environment
    Open grant

Supervision

Availability

Dr Helen Mayfield is:
Available for supervision

Before you email them, read our advice on how to contact a supervisor.

Available projects

  • Analysing antibodies as indicators for lymphatic filariasis surveillance in Samoa using Bayesian networks.

    Location: UQ CCR, Herston

    Description: Lymphatic filariasis (LF) is a mosquito-transmitted disease which causes a significant disease burden, particularly in low-income countries. Surveillance plays a crucial role in global elimination efforts, with human antigen being the most widely used indicator. There is evidence however that antibodies may be more sensitive than antigen for detecting changes in infection prevalence. This project will use an established methodology (https://shorturl.at/fqzAP) to design and implement a data-driven Bayesian network model to evaluate the relative utility of different infection markers for detecting signals of transmission in Samoa.

    Expected outcomes and deliverables: The student will develop skills in Bayesian network modelling and an understanding of how data modelling can be applied to operational research in infectious diseases. Deliverables will be a fully parameterised Bayesian network, and a report including a sensitivity analysis and discussion. Upon completion of the project, there will be an opportunity to write up and publish the results as a scientific paper.

    Suitable for: Students with basic epidemiology, good analytical skills and in interest in data science. No previous knowledge of Bayesian networks is assumed.

Supervision history

Current supervision

Media

Enquiries

For media enquiries about Dr Helen Mayfield's areas of expertise, story ideas and help finding experts, contact our Media team:

communications@uq.edu.au