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.
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.
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.
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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