Intellectual Merit 

The ongoing COVID-19 pandemic highlights the essential role of mathematical models in understanding the spread of the virus and providing a quantifiable, science-based prediction of the impact of various mitigation measures. However, such models cannot be used as off-the-shelf tools - they need careful calibration for a specific virus as well as a specific region under consideration. Using the fact that the Hawaiian Islands represent a largely controlled environment due to the geographically isolated  borders and mostly uniform pandemic-induced governmental controls and restrictions, this project studied whether demographic and spatial heterogeneity of Hawaiian counties have a significant effect on the spread of the disease. In addition, we investigated the question of what type of a mathematical model is most appropriate for a given situation. 

First, our work highlights that despite being a part of the same archipelago and having similar protocols for mitigation measures, different Hawaiian counties exhibit quantifiably different dynamics of the spread of the disease. It also shows interesting similarities between some Hawaiian counties and other archipelagos and islands. We pay special attention to details of the heterogeneity effects to discern what level of granularity and detail is appropriate for making policy decisions related to curtailing disease spread. Our results suggest that mitigation measures should be more localized, that is, targeting the county level rather than the state level if the counties are reasonably insulated from one another. We also notice that the spread of the disease is very sensitive to unexpected events and certain changes in mitigation measures. An important conclusion of our research is the identification of patterns that change extremely rapidly. This is due primarily to the nonlinear behavior of the underlying equations that simulate the spread of the pandemic. We find that it is critical to ensure that heterogeneity is included in modeling and, consequently, in decision making for adequate and effective pandemic control. We have received the Best Paper Award from a well-known International Conference (9th Global Health 2020) where we remotely presented our first results early on in the pandemic.

Second, in the literature numerous types of models have been employed with various levels of success.  We focus on two widely used types of models: equation-based models (such as standard compartmental epidemiological models) and agent-based models. We show that when it comes to information crucial to decision making, both models produce very similar results. Rather than treating the two models as two distinct ways to obtain the same results we should exploit advantages of both throughout a pandemic simulation, particularly when the simulation is used in a predictive real-time fashion.  We consider what can be learned from running both models side-by-side; taking  and  applying  the  best  of  each  model  using  the  measured  data. We  also analyze the conceptual and computational requirements of each of the models for this particular test-bed population. Consequently, we conclude that choosing the model should be mostly guided by available computational and human resources. 

Broader Impact

A large number of students have contributed to our research throughout the year, ranging from a high school student to advanced graduate students, with 6 graduate, 8 undergraduate students and 1 high schooler in total. They were exposed to a research experience driven by real data and developed extremely desirable profiles toward future employment in data and computational sciences related jobs. Two of the graduate students from this project have been hired as research assistants by the Pacific Health Analytics Collaborative Lab at the University of Hawaii to explore the introduction of behavioral health data into epidemiological models. PI Chyba and Co-PI Mileyko have been awarded a 2021 Faculty Mentoring Grant for Summer Undergraduate Research and Creative Work sponsored by the Undergraduate Research Opportunities Program in the Office of the Vice Chancellor for Research to continue the work with four undergraduate students over summer 2021. We have also developed a strong team which includes members of the Math Department and the Hawaii Data Science Institute, and are actively looking for ways to continue these important collaborations that have been primarily conducted through this RAPID grant. In addition, the PI is now a member of the Hawaiʻi Pandemic Applied Modeling Work Group. 

During the duration of this award, we have made a large number of publicly available videos for the people of Hawaii at various stages in the pandemic and tutorial videos. Through our simulation-driven research, we were able to give health officials and government important information and predictions helping to shape Hawaii’s pandemic response. We are also creating educational material, which was already partially used in an introductory Math course in Spring 20201 (Math 100 - Survey of Mathematics), to further engage the general population. 

Last Modified: 07/19/2021
Modified by: Monique Chyba