The ongoing COVID-19 outbreak has recently reached pandemic status spreading all around the world. The severity of the pandemic, along with an enormous impact on world’s economy and society, has forced governments to introduce emergency measures. It is essential to utilize the available statistical data from trusted sources in order to model and evaluate the dynamics of the pandemic spread, to not only better understand such complex systems, but to learn and develop possible solutions to prevent further spread of current and/or similar future outbreaks. Thus, this research, devoted to the development of mathematical models of COVID-19 pandemic spread, addresses an urgent national need. Faculty and students in computer science, anthropology, and computational chemistry at New Mexico Highlands University have formed a diverse group for finding a solution to the complicated problems of the description and prediction of COVID-19 spread. This multidisciplinary project is expected to yield a better understanding of the interconnections among many factors that contribute to the spread of COVID-19. Statistical data will be collected in regions of Northern New Mexico, including San Juan and McKinley Counties in the Navajo Nation and Los Alamos county outside of the Navajo Nation. Analysis of the collected statistical data along with socio-cultural assessment from this project will be presented to New Mexico (NM) tribal and health authorities. The project will aim to provide a scientific basis for the prediction of disease spread and will consider scenarios associated with the possibility of another wave of the pandemic. Students from this minority-serving institution involved in the project will obtain valuable experience in the application of advanced machine learning models and methods in providing fast robust reaction to a national health, economic, and societal crisis.
In this study, machine learning methods will be used to analyze pandemic spread scenarios in different regions and to glean the most important features of the data characterizing the spread. The research team will use both traditional machine learning techniques and advanced methods, such as artificial neural networks, allowing development of virus incidence model capturing dependencies in both linear and nonlinear domains. The work will concentrate on understanding disease spread with regard to multiple socioeconomic factors. The problem can be treated as a sequence modeling one; so, recurrent neural networks and more complex models based on their recurrent cells might be one promising direction. The next step will be to assemble datasets for small isolated communities with different socioeconomic backgrounds and ethnicities – comparing Navajo Indians living on the Navajo reservation to Los Alamos County (NM) – and to test the applicability of the developed model to these regions. The spatiotemporal data available on the spread is heterogeneous in character. An important goal of this research is to classify the collected data with respect to the similarity in the epidemic curve behavior and then build separate models for different regions according to this classification. The proposed model will be used for prediction of future incidents and to produce the most effective non-medical recommendations for suppression and prevention of future viral outbreaks.
This research is supported by the Partnerships for Research and Education in Materials (PREM) program and the Condensed Matter and Materials Theory (CMMT) program in the Division of Materials Research in the Directorate for Mathematical and Physical Science using supplemental funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
