Infectious disease modeling in a time of COVID-19 – PLOS ONE authors’ perspectives – Part 2

In February 2020, PLOS published a Collection entitled “Mathematical Modeling of Infectious Disease Dynamics” which includes papers from PLOS ONE, PLOS Biology and PLOS Computational Biology, on a variety of topics relevant to the modeling of infectious diseases, such as disease spread, vaccination strategies and parameter estimation. As the world grappled with the effects of COVID-19 this year, the importance of accurate infectious disease modeling has become apparent. We therefore invited a few authors featured in the Collection to give their perspectives on their research during this global pandemic. We caught up with Verrah Otiende (independent researcher, Pan African University Institute of Basic Sciences Technology and Innovation), Lauren White (USAID), Jess Liebig (CSIRO) and Johnny Whitman (The Ohio State University) to hear their reflections on this collection and the time that has passed.

In this second blog post of two, we hear from Jess Liebig and Johnny Whitman, who discuss the modeling of human movement, the assumptions that go into creating a model, the virtue of simpler models, and the importance of understanding under-reporting in disease modeling.

What is your research focused on currently?

JL: Since September 2017 I am part of CSIRO’s DiMeMo (Disease Networks and Mobility) team. The aim of DiNeMo is to understand how human infectious diseases might arrive and spread in Australia. We analyse various sources of data and identify patterns of people movement both internationally and domestically in order to forecast the risk of disease spread. Initially I worked on modelling dengue importations via air travel. However, since the beginning of the pandemic the focus of my work has shifted to COVID-19. I am currently studying the effects of international travel restrictions on COVID-19 importation risk. The results of this study shed light onto how many importations a country can expect when opening its borders and can guide authorities in making decisions.

JW: My research is currently split between two main thrusts: the first is a collaboration with Battelle Memorial Institute, working on comparisons of codon usage in certain classes of proteins. The second is investigating methods of identifying parameters in biological signaling networks using the supercomputing cluster at Nationwide Children’s Hospital in Columbus, Ohio. Finally, I am finishing my PhD this spring semester and my current research for that deals with the design and verification of biological circuits for intracellular signaling, as well as developing methods to coarse-grain out complicated host-virus interactions in simulations of dendritic and epithelial cells.

We analyse various sources of data and identify patterns of people movement both internationally and domestically in order to forecast the risk of disease spread.

Jess Liebig

What do you think are the lessons we can learn from the research in your field which will help us to better model infectious diseases in the future?

JL: We need high quality datasets to accurately model the spread of infectious diseases. In reality, the datasets that are accessible for researchers are often biased, incomplete and erroneous. While the process of data collection can be tedious and expensive it can add much value to the research community when done in an organised and purposeful manner.

JW: A trend in current modeling is to hyperfocus on fitting parameters in a model in order to precisely match available data; with advances in artificial intelligence and neural networks, researchers are quick to use these overparameterization models to get very good fits to the data. I would argue that we should instead focus on identifying important qualitative features of data or populations – a difficult and careful human process – and implementing simpler models around these features. To be concrete, if a complex model of American COVID-19 cases from January to May fits the data extremely well, but offers 500 parameters to change to predict future behavior, it is very difficult to make any form of meaningful prediction or understanding of what the model is actually saying about the underlying population, whereas a simpler model with directly interpretable parameters may perform worse quantitatively, but be much more expressive overall.

I think the pandemic has (or should have) focused researchers more on making observations in real populations and taking note of how real behavior patterns can make fundamental difference in model predictions.

Johnny Whitman

Have your motivations, direction or the way you conduct or disseminate your research changed in 2020 as a consequence of the COVID-19 pandemic, either for yourself or the field as a whole?

JL: My work is motivated by several studies that have shown that the structure of the global air transport network as well as the increasing volume of international travellers has contributed to the large-scale spread of infectious diseases. The COVID-19 pandemic is an unpalatable reminder of human movement being able to rapidly spread a disease across the globe. While the motivation and direction of my work has been reinforced as a consequence of the pandemic, there have been changes to the way I disseminate my research. With travel restrictions and lockdowns in place, conferences, research meetings etc. have moved online, giving rise to new challenges. For example, it can be more difficult to clearly communicate your ideas to collaborators in a teleconference as opposed to a face-to-face meeting. What I find particularly challenging is to give online presentations where you cannot see the reaction of your audience.

JW: I think the pandemic has (or should have) focused researchers more on making observations in real populations and taking note of how real behavior patterns can make fundamental difference in model predictions. A simple example is a very good group at the University of Illinois put together an intricate and well-thought out model, which ultimately failed. The failure was due to not including the possibility that a contagious individual who knew they were contagious would continue to be social. Clearly, they are not at fault for using a rational actor assumption, but the lesson is that we should always remain grounded in the people and phenomenon we model if we hope to make any progress.

It is very important to understand what exactly these assumptions are and how they affect the results of the modelling study. Any conclusions have to be drawn carefully, taking into consideration the set of assumptions that were made.

Lauren White

If there was one thing you wished that the general public understood better about modeling infectious diseases, what would that be?

JL: Naturally, when modelling the spread of infectious disease (or any other process), scientists have to make certain assumptions due to incomplete data and knowledge gaps. It is very important to understand what exactly these assumptions are and how they affect the results of the modelling study. Any conclusions have to be drawn carefully, taking into consideration the set of assumptions that were made.

JW: Partially due to the manner in which models are presented to the public and also how researchers have positioned their work, I think that the public believes that models are intended to exactly predict the course of a disease. Rather, I wish we collectively understood the role of modeling more as a probe into the possibilities of a system; I would never trust a model to truly predict the number of COVID cases, but they can give us the possibilities of recurrent infection waves, how the dynamics depend on observable parameters like recovery time and incubation period, and other broad qualitative features that can influence public health decisions. A more technical wish would be that the public understood model predictions in the same sense that they understand weather predictions; most complex systems modeling is stochastic in some sense, so I would prefer that reporting on modeling emphasized the possibilities of events more than definitive statements. We’ve seen public support unnecessarily erode due to unrealized model predictions, and I think this could be avoided if communication was clearer.

Are there any unanswered research questions in this field that you would really like to see us make progress on?

JL: A key ingredient to modelling the spread of infectious disease is the incidence rate. Unfortunately, the incidence of most infectious diseases is under-estimated, which is due to under-reporting and under-ascertainment. Under-reporting refers to positive disease cases not being reported, for example due to mis-diagnosis. Under-ascertainment occurs when infected individuals do not report to a health professional, for example due to the absence of symptoms. Reporting and ascertainment rates vary across time and space and depend on the disease itself. A model that requires incidence rates as input can only be accurate if we have a good understanding of the level of under-estimation surrounding the incidence rates. Unfortunately, current techniques for determining the level of under-estimation are time consuming, expensive and often biased.

JW: The physics background in me would like to see a more general study of disease modeling in the spirit of field theory models; due to the much simpler nature of interactions in theoretical physics problems, we have done a careful and systematic investigation of how essentially every class of interaction type affects the macroscopic behavior of the model, e.g. if there is some symmetry, what types of particles are allowed, if this interaction is strong, it suppresses that behavior. I would like to see a similar-minded effort in disease modeling, so that researchers in this community build up a common base of tools and understanding. As it stands, the field is so fragmented in terminology and approach that it is difficult to quickly agree about what the setup of a problem is, much less the implications of the model.

About the authors:


Jess Liebig: Jessica Liebig is a postdoctoral fellow at the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia’s national science agency. She received a BSc(Hons) and a PhD in Applied Mathematics from RMIT University in 2013 and 2017, respectively. Her primary research interest lies in the area of network science and is directed towards the study of infectious disease spread. She is part of CSIRO’s Disease Networks and Mobility (DiNeMo) project, an interdisciplinary research initiative that aims to understand how human infectious diseases might arrive and spread in Australia. As part of her work she identifies patterns of people movement, both internationally and domestically, to forecast the risk of disease spread.


Johnny Whitman: John Whitman graduated from the University of Illinois in 2016, and is currently finishing his PhD in Physics at The Ohio State University with Prof. Ciriyam Jayaprakash. His research interests include stochastic modeling of systems at all scales, from intracellular signaling pathways to large scale population epidemiological modeling. He is most interested in problems which exhibit some form of complexity, since he really enjoys scientific programming and visualization/animation of processes.

Disclaimer: Views expressed by contributors are solely those of individual contributors, and not necessarily those of PLOS.

Featured Image : Spencer J. Fox, CC0

The post Infectious disease modeling in a time of COVID-19 – PLOS ONE authors’ perspectives – Part 2 appeared first on EveryONE.

Infectious disease modeling in a time of COVID-19 – PLOS ONE authors’ perspectives

In February 2020, PLOS published a Collection entitled “Mathematical Modeling of Infectious Disease Dynamics” which includes papers from PLOS ONE, PLOS Biology and PLOS Computational Biology, on a variety of topics relevant to the modeling of infectious disease, such as disease spread, vaccination strategies and parameter estimation. As the world grappled with the effects of COVID-19 this year, the importance of accurate infectious disease modeling has become apparent. We therefore invited a few authors  featured in the Collection to give their perspectives on their research during this global pandemic. We caught up with Verrah Otiende (independent researcher, Pan African University Institute of Basic Sciences Technology and Innovation), Lauren White (USAID), Jess Liebig (CSIRO) and Johnny Whitman (The Ohio State University) to hear their reflections on this collection and the time that has passed.

In this first blog post of a set of two, we hear from Verrah Otiende and Lauren White, who discuss the modeling of other infectious diseases such as HIV and TB during the COVID-19 pandemic, the importance of good data, the increasing focus of incorporating human behavior in disease models, and more. Please check back in a couple of weeks for the next installment of this blog post series.

What is your research focused on currently?

VO: Currently, I am independently researching the spatiotemporal patterns of successful TB treatment outcomes for HIV co-infected cases in Kenya. The motivation of this study is mainly the convergence of TB and HIV epidemics that threatens the management of TB treatment. This is evidenced by various spatial studies that have described how HIV co-infection propagates unsuccessful TB treatment outcomes. I am using the Bayesian Hierarchical Modeling approach to generate the estimates for each of the 47 counties of Kenya. These estimates will help identify the high-risk counties with successful TB treatment outcomes and deliberately prioritize other counties with an increased risk of unsuccessful treatment outcomes.

I believe that we will continue to improve disease models as we learn more about the ways that individual contact patterns, behaviors, and immune responses affect epidemics.

Lauren White

LW: I am a quantitative disease ecologist interested in developing and improving mathematical models of disease to assist in prediction and prevention of emerging and zoonotic infectious diseases in the context of rapidly changing, human-impacted environments. The overall objective of my research is to explore the effects of heterogeneity in behavioral and immune competence on disease modeling predictions within and across populations. I use mathematical modelling approaches, integrated with empirical data, to explore three different types of heterogeneity that can alter individual transmission rates: (i) within-host heterogeneity; (ii) contact heterogeneity and group structure within populations; and (iii) spatial heterogeneity across landscapes. My work also has broader implications for understanding human disease risk within the One Health framework, which includes human, animal, and environmental health.

What do you think are the lessons we can learn from the research in your field which will help us to better model infectious diseases in the future?

VO: Applying Bayesian algorithms to modeling multiple related infectious diseases is critical for quantifying both the joint and disease-specific risk estimates. The flexibility and informative outputs of Bayesian Hierarchical Models play a key role in clustering the geographical risk areas over a given time period. This would further provide additional insights towards the collaborative monitoring of the diseases and facilitate the comparative benefit obtained across the disease populations.

LW: Before this year, “superspreader” was considered a technical term, but COVID-19 has really highlighted the role of individual behavior in community spread.  I believe that we will continue to improve disease models as we learn more about the ways that individual contact patterns, behaviors, and immune responses affect epidemics. These are still very open questions, especially for less-studied livestock and wildlife, host-pathogen systems.

It is critical not to ignore other life-threatening infectious diseases while working towards managing COVID-19.

Verrah Otiende

Have your motivations, direction or the way you conduct or disseminate your research changed in 2020 as a consequence of the COVID-19 pandemic, either for yourself or the field as a whole?

VO: I am still enthusiastic about conducting and disseminating research work on infectious diseases. The direction has changed as a consequence of the COVID-19 pandemic, especially during dissemination. But the most positive effect of this change was reaching a wider audience virtually than I have ever thought of.

On case notifications, my worry is on underreporting and data capture processes of other infectious diseases since most efforts have been directed towards controlling and preventing the spread of COVID-19. Probably the non-pharmaceutical practices like physical distancing and lockdowns have kept some infectious diseases from spreading for now but there is still a vacuum for certain diseases to rebound and spread which could have much more severe consequences to millions of humans for a very long time. It is critical not to ignore other life-threatening infectious diseases while working towards managing COVID-19.

LW: I have just recently started a position through the AAAS Science and Technology Policy Fellowship program. This means that I am spending less time researching questions around COVID-19 directly but learning a lot more about program planning and implementation, as well as the effects of COVID-19 on other public health efforts like epidemic control for HIV/AIDS. This is an important career opportunity for me to see what makes science actionable and useful for stakeholders, policymakers, and other end users.

Disease models are only as good as the information or data that we put into them—often times in new situations we end up using “best guesses.”

Lauren White

If there was one thing you wished that the general public understood better about modeling infectious diseases, what would that be?

VO: Modeling the joint dynamics of infectious diseases and human behavior is fundamental in understanding and quantifying the risks and effects associated with their global spread.

LW: COVID-19 has highlighted some confusion in how disease models are used for decision making. Disease models come in many types, but especially those that aim to predict or forecast the future function as thought experiments, not as written-in-stone prophecies. Disease models are only as good as the information or data that we put into them—often times in new situations we end up using “best guesses.” As our information and estimates improve, so can the accuracy of our models. This is not, by default, bad science; it simply reflects an iterative process.

It is also important to note that sometimes models can show as the worst case or “do nothing” scenario. Again, such an outcome is not a forgone conclusion. Public health interventions can help us do better. So better outcomes are not necessarily a failure of modeling or an overreaction to an epidemic, rather they are an indication that we, as a society, are doing something right.

Are there any unanswered research questions in this field that you would really like to see us make progress on?

VO: Numerous unanswered research questions would be of interest to progress on. A quick one that comes to my mind would be incorporating human behavior in the spatiotemporal joint modeling of infectious diseases to understand the possible effects of such behavior. This would require rich behavioural datasets and developing unsupervised ML algorithms to automate and predict the risks of joint infections over spatial and temporal dimensions.

LW: There will always be more to discover with regards to infectious diseases, but I actually think that the most pressing question is how we, as a scientific community, will do a better job in this current crisis and during future epidemics. I have faith that we will be able to answer research questions as they arise, and in fact, we have increased our understanding of a completely novel pathogen incredibly quickly. But we need to think more critically about how we are communicating results and making our work actionable: How do we maintain and build trust in a climate where scientific expertise itself is controversial? How can we better engage with the communities that we live in and serve? Are we communicating results thoughtfully and responsibly? These are by no means “new” or “novel” research questions, but COVID-19 has starkly highlighted their importance. 

About the authors:


Verrah Otiende: My name is Verrah Otiende and I am a statistician and an ML enthusiast with proven expertise in data governance concepts and using Big Data platforms to efficiently store and manage large amounts of data. I am an independent researcher and currently working on building, evaluating, and integrating predictive models on infectious disease case notifications using unsupervised ML algorithms to optimize intervention options and public health decisions. Besides infectious disease modeling, I am also working on the Named Entity Recognition (NER) datasets to build translation models for African languages through the MASAKHANE research initiative for Natural Language Processing (NLP).


Lauren White: Dr. Lauren White is a first year AAAS Science and Technology Policy Fellow at the Office of HIV/AIDS in USAID. Dr. White has a background in infectious disease modeling and epidemiology with an interest in the intersections of human, animal, and environmental health. Most recently, she worked as a post-doctoral research fellow at the National Socio-Environmental Synthesis Center (SESYNC) at the University of Maryland. Dr. White finished her Ph.D. in 2018 at the University of Minnesota in the Department of Ecology, Evolution & Behavior.

Disclaimer: Views expressed by contributors are solely those of individual contributors, and not necessarily those of PLOS.

Disclaimer from Lauren White: The views in this interview are those of the author and do not necessarily represent the views of USAID, PEPFAR, or the United States Government.

Featured Image : Spencer J. Fox, CC0

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Introducing the Open Soft Robotics Research Collection

PLOS ONE is delighted to announce a Collection entitled Open Soft Robotics Research. This Collection consists of research articles submitted to a 2019-2020 Call for Papers on the same topic. As the Collection launches today, it consists of six research articles, while two reviews will be added at a later stage.

Largely inspired by the way many living organisms move and adapt their shape to their surroundings, soft robots have been designed and constructed with compliant, deformable and variable-stiffness materials, sensors and actuators. Biomimicry has allowed soft robots to acquire novel features such as stretchability, growth, morphing, self-reconfigurability, self-healing and edibility. Their impact has grown in a variety of sectors, from search and rescue and exploration, to rehabilitation medicine, surgery, prostheses and exoskeletons, as well as various applications that improve wellness and quality of life.

The papers published today present several exciting aspects of the latest research on the topic of soft robotics. Two papers touch on 3D printing, one for printing surgical devices [1], and one for cores [2] which can be used in a variety of applications. A second set of papers intersect with medicine, in that they provide methods for fabricating prosthetic hands [3] and artificial muscles [4], respectively. Lastly, two of the papers utilise dynamic modelling, one for dielectric elastomer actuators [5] and one for soft continuum manipulators [6]. Taken together, these papers present a fascinating snapshot of the state-of-the-art within soft robotics research.

This Collection was curated by a dedicated team of Guest Editors: Guoying Gu (Shanghai Jiao Tong University), Aslan Miriyev (EMPA, Swiss Federal Laboratories for Materials Science and Technology), Lucia Beccai, (IIT, Istituto Italiano di Tecnologia), Matteo Cianchetti (Scuola Superiore Sant’Anna, School of Advanced Studies Pisa), Barbara Mazzolai (IIT, Istituto Italiano di Tecnologia) and Dana D. Damian (University of Sheffield).

We invite you to explore the Collection starting today, and encourage you to check back in for more Open Soft Robotics Research in PLOS ONE.

References:

[1] Culmone C, Henselmans PWJ, van Starkenburg RIB, Breedveld P (2020) Exploring non-assembly 3D printing for novel compliant surgical devices. PLoS ONE 15(5): e0232952. https://doi.org/10.1371/journal.pone.0232952 

[2] Preechayasomboon P, Rombokas E (2020) Negshell casting: 3D-printed structured and sacrificial cores for soft robot fabrication. PLoS ONE 15(6): e0234354. https://doi.org/10.1371/journal.pone.0234354 

[3] Mohammadi A, Lavranos J, Zhou H, Mutlu R, Alici G, Tan Y, et al. (2020) A practical 3D-printed soft robotic prosthetic hand with multi-articulating capabilities. PLoS ONE 15(5): e0232766. https://doi.org/10.1371/journal.pone.0232766 

[4] Harjo M, Järvekülg M, Tamm T, Otero TF, Kiefer R (2020) Concept of an artificial muscle design on polypyrrole nanofiber scaffolds. PLoS ONE 15(5): e0232851. https://doi.org/10.1371/journal.pone.0232851 

[5] Huang P, Ye W, Wang Y (2020) Dynamic modeling of dielectric elastomer actuator with conical shape. PLoS ONE 15(8): e0235229. https://doi.org/10.1371/journal.pone.0235229 

[6] Tariverdi A, Venkiteswaran VK, Martinsen ØG, Elle OJ, Tørresen J, Misra S (2020) Dynamic modeling of soft continuum manipulators using lie group variational integration. PLoS ONE 15(7): e0236121. https://doi.org/10.1371/journal.pone.0236121 

Featured Image credit: UC San Diego Jacobs School of Engineering CC-BY 2.0

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