In recent months, the words “infection” and “outbreak” have not been far from anyone’s mind as we’ve faced the emergence of a new coronavirus, COVID-19. Across the globe, efforts are underway to control and limit
In recent months, the words “infection” and “outbreak” have not been far from anyone’s mind as we’ve faced the emergence of a new coronavirus, COVID-19. Across the globe, efforts are underway to control and limit the spread of the virus, and to find ways to treat those infected. As we watch these events unfurl, it is evident that there is still a lot that we, as a global community, do not yet understand about the dynamics of infectious diseases. The ways in which diseases spread are a concern that we all have a stake in?research that helps further our understanding of infectious diseases can influence each of our lives.
One distinct community of researchers working on understanding infectious disease dynamics is the mathematical modelling community, consisting of scientists from many different disciplines coming together to tackle a common problem through the use of mathematical models and computer simulations. Mathematics may sound like an unlikely hero to help us overcome a global epidemic; however, the insights we gain from studying the dynamics of infectious diseases by using equations describing fundamental variables are not to be underestimated. By approaching infectious diseases from a mathematical perspective, we can identify patterns and common systems in disease function, and it enables us to find some of the underlying structures that govern outbreaks and epidemics. Mathematical modellers make use of available data from current and previous outbreaks to predict who may get infected, where vaccination efforts will be most effective, and how to limit the spread of the disease.
Today at PLOS, we are launching a collection of new research papers submitted to a call for papers during the latter half of 2019 entitled “Mathematical Modelling of Infectious Disease Dynamics”, hosted by PLOS Biology, PLOS Computational Biology and PLOS ONE. The aim of this collection is to bring together different disciplines such as mathematics, biology, medicine and physics in order to shed light on the important topic of how mathematical models can help us understand infectious disease dynamics, and to present this research to the broad readership of these three journals and beyond. The accumulation of vital new research in a comprehensive collection will be a useful resource for understanding how infectious diseases operate, and how we can tackle them in real-time as well as in the future.
At PLOS we remain committed to our primary Open Access mission?ensuring that science is made as widely available as possible, and not locked behind paywalls. This is especially important in outbreak scenarios, such as the current COVID-19 epidemic, where it is critical that any new and relevant research be made easily accessible around the world, immediately at the time of publication.
Several of the papers in this collection present new methods that can be utilized in a range of scenarios. For instance, Patel and Sprouge developed a new estimator for predicting the basic reproduction number R0, which is the expected number of host cells infected by a single infected cell. This can be used for instance to understand the early stages of HIV infections, and for assessing the effectiveness of various therapies.
If two pathogen species, strains, or clones don’t interact, surely we can estimate the proportion of coinfected hosts as the simple product of the individual prevalences? A paper in PLOS Biology by Frédéric Hamelin, Nik Cunniffe and co-workers shows that this assumption is false; even if pathogens don’t interact, death of coinfected hosts causes net prevalences of individual pathogens to decrease simultaneously. The authors reinterpret data from previous studies accordingly.
Unusually large outbreaks of mumps across the United States in 2016 and 2017 raised questions about the extent of mumps circulation and the relationship between these and prior outbreaks. In this PLOS Biology paper, Shirlee Wohl, Pardis Sabeti and co-authors paired epidemiological data from public health investigations with analysis of mumps virus whole-genome sequences from 201 infected individuals. This allowed them to reconstruct mumps transmission links not evident from more traditional approaches and also revealed connections between apparently unrelated mumps outbreaks.
Endo and colleagues present a model of a phenomenon to which we can all relate, but which is still not well understood – the spread of infection within the household. They modelled the fine structures of family life to understand how disease typically enters and spreads through the household. Their findings support the idea that children are the most likely culprits of bringing disease into the household, and showed that there is a high level of transmission within generations, as well as between mother and child.
Rotavirus, the leading cause of diarrhea globally in children under 5, shows a biennial pattern of emergence in the US, while in many other high-income countries it exhibits an annual pattern. Ai and colleagues modelled the effect that higher vaccine coverage may have on this phenomenon, and found that increasing vaccine coverage from the current 70-75% to 85% would not only reduce the number of rotavirus cases, but also shift occurance to a more predictable annual epidemic pattern.
Two of the papers published in the collection are concerned with malaria. Kim and colleagues modelled the effectiveness of relapse control methods for Plasmodium vivax, finding that current vector control methods may have a negative effect on controlling disease prevalence, but that a shift towards control at a higher vector control level may be more efficient. Meanwhile, Wang and colleagues have constructed a stacking model for malaria prediction by combining two traditional time series models and two deep learning methods. Utilising malaria incidence data from Yunnan Province, China, they find that the ensemble architecture outperforms each of the sub-structure models in predicting malaria cases.
There are two papers in the collection that look at improving prediction of dengue infections. Leibig and colleagues present a network model of how international air travel can affect the spread of dengue across the world. By modelling the number of dengue-infected passengers arriving at various airports each month, the authors were able to study how dengue may be imported into different countries, and which routes would be the most likely for dengue-infected passengers to arrive by. Secondly, Liu and colleagues developed a model for predicting the spread of dengue infections that incorporates climate factors such as mean temperature, relative humidity and precipitation and applied this to data from dengue infections in Guangzhou, China, in order to help inform best practices in the early stages of a dengue outbreak.
The development of diseases can be influenced by personal factors such as age, which two of the papers in the collection address. Ku and Dodd developed a model for accounting for population aging when looking at tuberculosis incidence, as the impact of demographic change on disease forecasting is still not well understood. They applied the model to historical data of TB cases in Taiwan from 2005-2018, and used this to forecast what the incidence may look like until 2035. On the other end of the age spectrum, Rostgaard and colleagues used a Markov model to study the relationship between Epstein-Barr virus and infectious mononucleosis. Most people are typically infected with Epstein-Barr virus in early childhood, while infectious mononucleosis can sometimes follow in adolescence or later in life. The authors developed a statistical model to probe some of the uncertainties surrounding the origin and dynamics of infectious mononucleosis.
Some of the papers in the collection address new and emerging diseases. Dodero-Rojas and colleagues used the SEIR model to study the last three Chikungunya outbreaks in Rio de Janeiro, Brazil, and estimated their respective Basic Reproduction Numbers, R0. They also expanded their findings to include predictions for the Mayaro virus, which is an emerging disease in South America, and found that it has the possibility to become an epidemic disease in Rio de Janeiro.
The ability to accurately forecast disease patterns is crucial for ensuring that the right resources are in place to handle outbreaks. Morbey and colleagues looked at seasonal patterns in respiratory disease in England, and found that although syndromic indicators were affected by the timing of the peaks in seasonal disease, the demand for hospital beds was the highest on either 29th or 30th December, regardless of the timing of the syndromic peaks. Asadgol and colleagues also addressed seasonal patterns, this time in cholera in Iran, and predicted the effect of climate change on cholera incidence from 2020-2050 using an artificial neural network.
Given the interdisciplinary nature of the topic, we are grateful to countless authors, reviewers, Academic Editors and Guest Editors for making this collection a reality. We are especially grateful to our Guest Editor team, Konstantin Blyuss (University of Sussex), Sara Del Valle (Los Alamos National Laboratory), Jennifer Flegg (University of Melbourne), Louise Matthews (University of Glasgow) and Jane Heffernan (York University) for curating the collection. While 14 papers are included in this collection today, we’ll keep adding new papers as they are published, so please keep checking back for updates.
Guest Editor Konstantin Blyuss sums up the importance of this collection: “A recent and ongoing outbreak of coronavirus COVID-19 has highlighted the enormous significance of mathematical models for understanding the dynamics of infectious diseases and developing appropriate strategies for mitigating them. Mathematical models have helped identify the important factors affecting the spread of this infection both globally, and locally using country-specific information. They have also elucidated the effectiveness of different containment strategies and provided quantitative measures of disease severity”.
About the Guest Editors:
Guest Editor, PLOS ONE, PLOS 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
Guest Editor, PLOS ONE, PLOS 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.
Guest Editor, PLOS ONE, PLOS 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.
Guest Editor, PLOS ONE, PLOS 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.
Guest Editor, PLOS ONE, PLOS 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.
Featured Image : Spencer J. Fox, CC0
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Stories have the power to shape our identities and worldviews. They can be factual or fictional, text-based or visual and can take many forms—from novels and non-fiction to conspiracy theories, rumors and disinformation. We can characterize stories by their plot, their characters, their audience, their style, their themes or their purpose. Given the massive power of stories to alter the course of society, innovative methods to understand them empirically and quantitatively are necessary.
Today, we are pleased to introduce PLOS ONE’s Science of Stories Collection, which includes submissions invited through a Call for Papers last year. The Call for Papers welcomed primary research papers that propose solutions to real world, data-rich problems that use different empirical methods. The Guest Editors overseeing the scope and curating the Collection are Peter Dodds (University of Vermont), Mirta Galesic (Santa Fe Institute), Matthew Jockers (Washington State University), and Mohit Iyyer (University of Massachusetts Amherst).
At launch, the Collection includes over 15 papers illustrating data-driven approaches to understanding stories and their impact. Some articles explore the nature of narrative and narrative thinking in texts and other media, for instance, the role of similarity in narrative persuasion, the effects of choosing violence in narratives, the importance of characters in narratives communicating risk of natural disaster, the impact of storytelling in complex collaborative tasks such as food preparation, and the role of narrative in collaborative reasoning and intelligence analysis.
Other articles present new methods to extract stories from datasets and datasets from stories, including automated narrative analysis via machine learning, systematic modeling of narrative structure and dynamics, and large-scale analysis of gender stereotypes in movies and books.
A third group of papers analyze how narratives are transformed and how they can transform people, for example, looking at the co-evolution of contagion (e.g., disease, addiction, or rumor) and behavior, social media’s contribution to political misperceptions in US elections, how people’s intuitive theories of physics can partly account for how they think about imaginary worlds, how narrative can induce empathy for people engaging in negative health behaviors, and the impact of mental health recovery narratives on health outcomes.
A final group of papers explores the communication of data-rich narratives to the public, including the relative effectiveness of video abstracts and plain language summaries versus graphical abstracts and published abstracts, newly emerging platforms for writing and commenting on literary texts at unprecedented scale, and the role of narrative in perceived authenticity in science communication.
Papers will continue to be added to the Collection as they reach publication, so we invite you to revisit the Collection again for additional insights into the science of stories.
Peter Sheridan Dodds
Peter Dodds is Professor at the University of Vermont’s Department of Mathematics and Statistics. He is Director of the Vermont Complex Systems Center and co-runs the center’s Computational Story Lab. Having a general interest in stories and narratives, complexification, contagion, and robustness, Dodd’s research focuses on system-level, big data problems of all kinds, often networked, sociotechnical ones. His work has been supported by an NSF CAREER award to study sociotechnical phenomena, the McDonnell Foundation, the Office of Naval Research, NASA, the MITRE Corporation, Computer Associates, and Mass Mutual.
Mirta Galesic is Professor and Cowan Chair in Human Social Dynamics at the Santa Fe Institute, External Faculty at the Complexity Science Hub in Vienna, Austria, and Associate Researcher at the Harding Center for Risk Literacy at the Max Planck Institute for Human Development in Berlin, Germany. She studies how simple cognitive mechanisms interact with social and physical environments to produce seemingly complex social phenomena. She develops empirically grounded computational models of social judgments, social learning, collective problem solving, and opinion dynamics. She is also interested in how people understand and cope with uncertainty and complexity inherent in many everyday decisions.
Mohit Iyyer is an Assistant Professor in computer science at the University of Massachusetts, Amherst. Previously, he was a Young Investigator at the Allen Institute for Artificial Intelligence. Mohit obtained his PhD at the University of Maryland, College Park, advised by Jordan Boyd-Graber and Hal Daumé III. His research interests lie in natural language processing and machine learning. Much of his work uses deep learning to model language at the discourse level, tackling problems like generating long coherent units of text, answering questions about documents and understanding narratives in fictional text.
Matthew L. Jockers
Matthew L. Jockers is Dean of the College of Arts & Sciences and Professor of English and Data Analytics at Washington State University in Pullman, WA. Jockers has been leveraging computation to understand narrative and style since the early 1990s. His books on the subject include Macroanalysis: Digital Methods and Literary History, Text Analysis with R for Students of Literature, and The Bestseller Code. In addition to his academic work, Jockers helped launch two text mining startups and worked as Principal Research Scientist and Software Development Engineer in iBooks at Apple.
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PLOS ONE has an open Call for Papers on the Microbial Ecology of Changing Environments, with selected submissions to be featured in an upcoming Collection. We aim to highlight a range of interdisciplinary articles showcasing the diversity of systems, scales, interactions and applications in this dynamic field of research.
What makes microbes so interesting?
MC: Microorganisms are everywhere and are important members of all of the ecosystems they inhabit. There are microorganisms in soils, oceans, lakes, and even within our bodies. Within all of these habitats they are performing really important functions. In lakes, oceans, and soils, microorganisms are key to moving nutrients around. Within our bodies, they aid in things like digestion and disease prevention.
SK: Microorganisms are fascinating in how genetically diverse and numerous they are. Microorganisms can be found in almost every habitat on Earth and are often the first to respond to environmental disturbance and global change. Thus, microorganisms likely hold the key to solving most of Earth’s problems as we face global climate change.
How is microbial ecology relevant to major environmental and societal issues like climate change and food security?
MC: Given how ubiquitous microorganisms are across the world, understanding how they function is key if we want to understand and mitigate the consequences of climatic change and if we want to grow food more sustainably and in marginal lands. For instance, if we can get a better understanding of microbial carbon cycling, we can potentially use biological carbon capture as a mitigation strategy to help combat rising levels of atmospheric carbon dioxide. Additionally, researchers around the world are trying to understand how plants interact with microbial communities in an effort to harness these microbes to increase food production and the ability of plants to withstand changing abiotic conditions.
SK: Microorganisms are the key for innovating nature-based solutions to climate change. For example, specific fungal symbionts of plants can be tailored to increase agricultural plant drought tolerance. Other microorganisms may be deployed to remediate oil spills or other man-made pollutants. Finally, engineering plant-microbial associations may lead to a larger terrestrial carbon sink to offset atmospheric CO2 concentrations, creating a negative feedback to climate change itself.
Tell us a bit about your own research and how it ties in with some of these issues.
MC: A large portion of my research is focused on understanding how to use beneficial microbes to increase plant productivity and tolerance to drought, and also in understanding how these communities function in the soil environment with the ultimate goal of using them to enhance ecosystem stability. I am part of two large multi-disciplinary teams at Oak Ridge National Laboratory that are specifically focused on plant-microbe interactions in the potential biofuel feedstock, Populus. We are trying to characterize basic principles governing plant-microbe interactions in the hope of making Populus a better biofuel that can grow in marginal lands with limited input of fertilizer and water.
SK: Research in the Kivlin Lab aims to create distribution models for terrestrial microorganisms and their functions. Our current focus is on arbuscular mycorrhizal (AM) fungi, as these plant symbionts are the main providers of nutrients and drought tolerance to agricultural plants. We are interested in where these fungi are, the ecosystem-level carbon and nutrient cycling they promote and how sensitive these plant-fungal interactions may be to climate change. To address these questions, we both compile data on AM fungal distributions worldwide, but also examine plant-AM fungal interactions along altitudinal gradients that serve as a space for time substitution for climate change and in long-term climate change experiments.
How are technological advances opening up new opportunities in your field?
MC: Over the last 20 years there have been rapid advances in sequencing and molecular techniques that have enabled amazing opportunities in microbial and ecosystem ecology. We are finally able to identify unculturable microorganisms inhabiting diverse communities using next generation sequencing and are getting clues into their function using metagenomics, metatranscriptomics, proteomics, and metabolomics. Further, using these techniques, people are developing some new strategies to culture more microbes.
SK: It is increasingly clear that the genomics revolution has impacted microbial ecology. We now can link functional genetic potential to microorganisms in environmental microbiomes and understand how interactions among microorganisms and between microorganisms and plants control expression of these functional genes and the metabolites they code for.
How does microbial ecology benefit from interdisciplinary collaboration?
MC: Microbial communities are incredibly complex, therefore understanding their role in ecosystems really requires a systems biology approach. Because of this, having an interdisciplinary team to tackle questions at various scales is really important.
SK: Microbial ecology is inherently interdisciplinary. We collaborate with earth system modelers to scale microbial function from the organism to the globe and with geneticists to understand the genetic underpinnings of those functions. Without these collaborations, our field would be siloed to case-studies of microbial communities and lack the ability to develop first-principles theory across microbial communities and environments.
What are some of the biggest unsolved questions in microbial ecology?
MC: There are so many unsolved questions in microbial ecology that it is hard to just identify a few. We still have a limited understanding of how microbial communities fluctuate through time. How stable are they within ecosystems? Are organisms within communities functionally redundant? Does this redundancy aid in resilience of the community post disturbance? How do these communities respond to fluctuations in abiotic variables? I could really go on and on.
SK: Despite all of the vital roles that microorganisms provide in the environment, we still don’t understand (1) where microorganisms even are spatially and what abiotic and biotic processes control these distributions, or (2) how temporally dynamic microbial communities are both within and among plant growing seasons. Answering these fundamental questions will allow us to understand linkages between microbial communities and plant growth, microbial composition and ecosystem carbon and nutrient cycles, and allow us to effectively manipulate microbial consortia for societal gain in agricultural and bioremediation settings.