Mathematical Modelling of Infectious Disease Dynamics: A PLOS Cross-Journal Call for Papers

Image credit: Spencer J. Fox

– The submission deadline for this Call for Papers has been extended to October 3, 2019 –


– Guest Editors –

Konstantin Blyuss

Sara del Valle

Jennifer Flegg

Louise Matthews

Jane Heffernan

– Call for Papers for a PLOS Cross-Journal Collection –  

Mathematical modelling of biological processes has contributed to improving our understanding of real-world phenomena and predicting dynamics about how life operates. Mathematical approaches have significantly shaped research on disease and evolving epidemics across the globe by providing real-time decision support. Modelling can help describe and predict how diseases develop and spread, both on local and global scales. In addition, mathematical modelling has played a critical role in understanding and measuring the impact of intervention strategies such as vaccination, isolation, and treatment.

PLOS ONE, PLOS Biology and PLOS Computational Biology are delighted to announce a Call for Papers in Mathematical Modelling of Infectious Disease Dynamics, where submissions will be included in a Collection bringing together different disciplines such as mathematics, biology, medicine and physics in order to shed light on this crucial topic, and to present this research to the broad readership of the three journals. We particularly welcome submissions that further the field by improving on existing methods and algorithms to better model real-world phenomena, and work tackling common problems related to disease forecasting, outbreak detection and containment, disease spread, vaccination and other management techniques.

We encourage all contributors to share their code and algorithms alongside the manuscript, in order to enhance reproducibility and reuse. Submissions for which new algorithms or software are central to the work should make code openly available, either by making it public on a code repository or providing it with the manuscript.

Topics include:

  • Disease modelling and forecasting in both local and global contexts
  • Modelling of strategies for various preventive approaches, such as vaccination programmes and intervention strategies
  • New methods and models for disease mapping, for instance geospatial and spatiotemporal approaches
  • The role of human behaviour and decision-making in outbreak control
  • The role of population movement on various scales in altering the spread of disease
  • The modelling of vectors in vector-borne diseases
  • Modelling of recent outbreaks and public health challenges
  • Modelling of historical outbreaks and outcomes

Sharing the data underlying the studies’ findings will be a requirement of publication, per the PLOS data policy. Under this call for papers, authors are expected to provide, upon submission, the source code needed to replicate their findings, ideally in a repository (such as Zenodo, which can also import from GitHub) or a suitable cloud computing service (such as Code Ocean). Authors should explain in the manuscript’s Data Availability Statement how readers can access the shared code.

Authors are encouraged to provide executable documents, where applicable, such as a Jupyter Notebook or an RMarkdown. The Software Sustainability Institute provides guidance on choosing a repository and sharing code, as do these PLOS articles on Best Practices and Good Enough Practices in scientific computing.

Articles should be submitted by the 5th of September. Accepted articles that fall into the scope described above will be included in a cross-journal Collection that will be published in early 2020. When submitting, please specify in your cover letter that you are submitting to the “Mathematical Modelling of Infectious Disease Dynamics” Call for Papers.  

Please note that the deadline for this Call for Papers has been extended to the 3rd of October, 2019.

Meet the Editors

Konstantin Blyuss

Guest Editor, PLOS ONEPLOS Biology, and PLOS Computational Biology

Konstantin Blyuss is a Reader in the Department of Mathematics at the University of Sussex, UK. He obtained his PhD in applied mathematics at the University of Surrey, which was followed by PostDocs at Universities of Exeter and Oxford. Before coming to Sussex in 2010, he was a Lecturer in Complexity at the University of Bristol. His main research interests are in the area of dynamical systems applied to biology, with particular interest in modelling various aspects of epidemiology, dynamics of immune responses and autoimmunity, as well as understanding mechanisms of interactions between plants and their pathogens.

Sara del Valle

Guest Editor, PLOS ONEPLOS Biology, and PLOS Computational Biology

Dr. Sara Del Valle is a scientist and deputy group leader in the Information Systems and Modeling Group at Los Alamos National Laboratory. She earned her Ph.D. in Applied Mathematics and Computational Science in 2005 from the University of Iowa. She works on developing, integrating, and analyzing mathematical, computational, and statistical models for the spread of infectious diseases such as smallpox, anthrax, HIV, influenza, malaria, Zika, Chikungunya, dengue, and Ebola. Most recently, she has been investigating the role of heterogeneous data streams such as satellite imagery, Internet data, and climate on detecting, monitoring, and forecasting diseases around the globe. Her research has generated new insights on the impact of behavioral changes on diseases spread as well as the role of non-traditional data streams on disease forecasting.

Jennifer Flegg

Guest Editor, PLOS ONEPLOS Biology, and PLOS Computational Biology

Jennifer Flegg is a Senior Lecturer and DECRA fellow in the School of Mathematics and Statistics at the University of Melbourne. Her research focuses on mathematical biology in areas such as wound healing, tumour growth and epidemiology. She was awarded a PhD in 2009 from Queensland University of Technology on mathematical modelling of tissue repair. From 2010 – 2013, she was at the University of Oxford developing statistical models for the spread of resistance to antimalarial drugs. From 2014 – April 2017 she was a Lecturer in the School of Mathematical Sciences at Monash University. In May 2017 she joined the School of Mathematics and Statistics at the University of Melbourne as a Senior Lecturer in Applied Mathematics.

Louise Matthews

Guest Editor, PLOS ONEPLOS Biology, and PLOS Computational Biology

Louise Matthews is Professor of Mathematical Biology and Infectious Disease Ecology at the Institute of Biodiversity, Animal Health and Comparative Medicine (BAHCM) at the University of Glasgow. She holds a degree and PhD in mathematics and has over 20 years research experience as an epidemiologist, with a particular focus on diseases of veterinary and zoonotic importance. Her current interests include a focus on drug resistance; antibiotic resistance in livestock; the community and the healthcare setting; anthelminthic resistance in livestock; and drug resistance in African Animal Trypanosomiasis. She is also interested in the integration of economic and epidemiological approaches such as game theory to understand farmer behaviour and micro-costing approaches to promote adoption of measures to reduce antibiotic resistance.

Jane Heffernan

Guest Editor, PLOS ONEPLOS Biology, and PLOS Computational Biology

Jane Heffernan is a Professor in the Department of Mathematics and Statistics at York University, and York Research Chair (Tier II). She is also the Director of the Centre for Disease Modelling (CDM), and serves on the Board of Directors of the Canadian Applied and Industrial Mathematics Society (CAIMS). She is also very active in the Society for Mathematical Biology (SMB). Dr. Heffernan’s research program centers on understanding the spread and persistence of infectious diseases. Her Modelling Infection and Immunity Lab focuses on the development of new biologically motivated models of infectious diseases (deterministic and stochastic) that describe pathogen dynamics in-host (mathematical immunology) and in a population of hosts (mathematical epidemiology), as well as models in immuno-epidemiology, which integrate the in-host dynamics with population level models. More recently, Heffernan is focusing on applying mathematics and modelling to studying pollinator health and disease biology.

PLOS ONE Handling Editors

Laith Abu-Raddad Bedr'Eddine Ainseba Karin Bammann Roberto Barrio
Lidia Braunstein Nakul Chitnis David Dingli Peter Dodd
Alberto d'Onofrio Eric Forgoston David Gerberry Emanuele Giorgi
Sebastien Gourbiere Oliver Gruebner Esteban Hernandez-Vargas Ying-Hen Hsieh
Eunok Jung Nikos Kavallaris Roberto Kraenkel Beatrice Laroche
David Larsen Doron Levy Viviane Lima Cesar Munayco
Lucy Okell Tjeerd olde Scheper Donald Olson William Ott
Joseph Páez Chávez Chiara Poletto Laurent Pujo-Menjouet Alessandro Rizzo
Gergely Röst Abdallah Samy Fabio Sanchez Dena Schanzer
Sungrim Seirin-Lee Adélia Sequeira Constantinos Siettos Max Souza
Michele Tizzoni Suzanne Touzeau Susana Vinga Max von Kleist
Zhongnan Zhang      


PLOS Papers Illustrating the Collection Scope

Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza A virus frequency in swine in Ontario, Canada

Authors: Tatiana Petukhova, Davor Ojkic, Beverly McEwen, Rob Deardon, Zvonimir Poljak

PLOS ONEJune 1, 2018

Timescales of influenza A/H3N2 antibody dynamics

Authors: Adam J. Kucharski, Justin Lessler, Derek A. T. Cummings, Steven Riley

PLOS Biology: August 20, 2018

Risk prediction system for dengue transmission based on high resolution weather data

Authors: Chathurika Hettiarachchige, Stefan von Cavallar, Timothy Lynar, Roslyn I. Hickson, Manoj Gambhir

PLOS ONE: December 6, 2018

Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models

Authors: Oswaldo Santos Baquero, Lidia Maria Reis Santana, Francisco Chiaravalloti-Neto

PLOS ONE: April 2, 2018

Assessment of optimal strategies in a two-patch dengue transmission model with seasonality

Authors: Jung Eun Kim, Hyojung Lee, Chang Hyeong Lee, Sunmi Lee

PLOS ONE: March 17, 2017

Real-time decision-making during emergency disease outbreaks

Authors: William J. M. Probert, Chris P. Jewell, Marleen Werkman, Christopher J. Fonnesbeck, Yoshitaka Goto, Michael C. Runge, Satoshi Sekiguchi, Katriona Shea, Matt J. Keeling, Matthew J. Ferrari, Michael J. Tildesley

PLOS Computational Biology: July 24, 2018

Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions

Authors: Logan C. Brooks, David C. Farrow, Sangwon Hyun, Ryan J. Tibshirani, Roni Rosenfeld

PLOS Computational Biology: June 15, 2018

Prediction of infectious disease epidemics via weighted density ensembles

Authors: Evan L. Ray, Nicholas G. Reich

PLOS Computational Biology: February 20, 2018

The US President's Malaria Initiative, Plasmodium falciparum transmission and mortality: A modelling study

Authors: Peter Winskill, Hannah C. Slater, Jamie T. Griffin, Azra C. Ghani, Patrick G. T. Walker

PLOS Medicine: November 21, 2017

Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States

Authors: Teresa K. Yamana, Sasikiran Kandula, Jeffrey Shaman

PLOS Computational Biology: November 6, 2017


Publishing Process

We aim to be as transparent as possible about our publishing and peer review processes. Papers submitted to PLOS ONE and under consideration for the Mathematical Modelling of Infectious Disease Dynamics Collection will be specially handled by hand-selected active researchers from our Editorial Board working in this area, in partnership with staff editor Hanna Landenmark.

PLOS Biology is a highly selective Open Access journal that features Research Articles, Short Reports and Methods & Resources articles of exceptional significance, originality, and quality in all areas of biological science, from molecules to ecosystems, including works at the interface of other disciplines, such as chemistry, medicine, and mathematics. In addition to outstanding Research Articles, we also publish provocative Short Reports, which can be based on a more limited number of experiments, and exceptional Methods and Resources. The journal has a unique editorial model, which combines expert Academic Editors and professional editors on every peer reviewed paper, resulting in expertise, fairness and efficiency.

PLOS Computational Biology is an Open access journal featuring Research Articles, Methods, Software, and Benchmarking papers across computational biology. The journal publishes works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Run by a volunteer editorial board, papers submitted to the journal will be handled by experienced editors who are all working scientists.

Authors are advised to select the appropriate journal based on each journal’s scope criteria. Submissions that do not meet the criteria or scope for PLOS Biology or PLOS Computational Biology may be offered the opportunity to transfer to PLOS ONE.

For more information please see the Editorial and Peer Review Information page for PLOS ONEPLOS Biology, or PLOS Computational Biology, as relevant. 


Submission Instructions

Are you ready to submit or want to learn more about how the submission process works? To make it as easy as possible for our communities, PLOS ONE has all of our submission instructions posted online. If there are any general queries or if you have any questions for PLOS ONE, please email us at

Submission instructions for PLOS Biology are posted here and questions can be directed to

Submission instructions for PLOS Computational Biology are posted here and questions can be directed to

Authors should specify the Call for Papers, “Mathematical Modelling of Infectious Disease Dynamics,” in their cover letter and, additionally for PLOS ONE, in the ‘Collections and Calls for Papers’ field under ‘Additional Information’ on the submission form.


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