By: Patricia Solís, Gautam Dasarathy, Pavan Turaga, Alexandria Drake, Kevin Jatin Vora, Akarshan Sajja, Ankith Raaman, Sarbeswar Praharaj & Robert Lattus
What is the main purpose of your study? The effects of the COVID-19 pandemic has unfolded differently across the United States. Due in no small part to a lack of a quick initial federal response to address the growing pandemic in the early days, decisions on COVID-19 prevention efforts fell on state level governments. This led our team to seek to understand how the crucial first wave response resulted in not one pandemic, but a patchwork of pandemics that confounded our collective ability to address it. This study used mathematical modeling to highlight the way COVID-19 patterns varied, partially in response to decision makers (governors, mayors, public health officials, etc.) operating in different local contexts. This research seeks to fill in our gaps about how to think about and predict pandemics by exploring the patchwork character of decision making occurred while also addressing the importance of interdisciplinary research that is sensitive to context of decision making as much as model performance.
What are the practical, day to day, implications of your study? The results of this study provides evidence of the importance of a geographic approach to understanding outbreaks. This project included researchers with a wide range of expertise – including geography, public health, computer science, engineering, and community planning. It also demonstrates the importance of critically assessing the role of decision making and how that has a spatial character, when applying mathematical modeling to pandemic research.
How does your study relate to other work on the subject? Our study seeks to redefine how research on emerging pandemics is conceived, by connecting it to interdisciplinary approaches that contextualize decision making and recognize the importance of how they unfold in place. We seek to make more visible the choices of spatial and time scale for modeling pandemics. COVID-19 models can be classified into one of two categories – forecasting models or mechanistic models (or a hybrid of the two). The models used in this study look to use this information to illustrate how patchworkiness in spatial performance should be a source of reflection in the way the pandemic unfolded, especially during the crucial first wave responses. This study expands on concepts from both forecasting and mechanistic models categories by including elements that touch on the role and scale of decision making as it relates both to behavior and outcomes of the pandemic.
This work also expands upon the plethora of COVID-19 research by adding an additional focus on the patchwork of policies enforced to mitigate the spread of infections.
What are two or three interesting findings that come from your study? Beyond decision made at the state level, the modeling shows a patchwork of clusters. This means that contextual factors including such things as a governor’s order or decision had a role in the pandemic’s presentation. This exemplifies the need to move away from a one size fits all approach to predictive modeling.
What might be some of the theoretical implications of this study? This study demonstrates the need to improve the integration of research and theoretical concepts in geography with interdisciplinary models. The framework used in this study shows the need to pay close attention to choices of spatio-temporal scale. Using this theoretical understanding of the incongruence of decisions to accountability (exemplified in the patchwork character of results) can provide an additional layer of accountability for decision makers and systems of governance. This would apply to future outbreaks as well as events such as climate change impacts, natural disasters, chemical incidents, or food recalls. To our knowledge, this work is among the first to measure the behavior of models themselves pertaining to the pandemic.
How does your research help us think about Geography? This article reminds geographers to be cognizant and explicit participants in supporting interdisciplinary teams to explore the bounds and criteria being choosing for research. It reminds us to think about the implications of our work within decision making spaces, and align our research choices with the ways in which decisions can be held accountable. We emphasize the need to build research of critical public value with scholars from diverse areas of expertise, including computer science, engineering, and mathematics.