Spatiotemporal forecasting of COVID-19 by integrating machine learning and epidemiological modeling

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PROJECT SUMMARY/ABSTRACT In this ongoing COVID-19 pandemic, it is crucial to have an accurate and early prediction of the spread of highly infectious SARS-CoV-2. A correct prediction of the pandemic situation and future trends enables effective resource allocation and government policies to reduce the detrimental effect of COVID-19 on public health and economics. Although various epidemiological models have facilitated the prediction of the infection spread, their ensemble-averaging approach largely disregards critical information within the heterogeneity. Conventional epidemiological modeling is focused on the global average trends, which is limited in analyzing dynamic local information and does not allow local prediction due to spatial heterogeneity of the pandemic situations. Given the rapid changes of the COVID-19 pandemic, it is also challenging to take urgent responses to the new epidemiological data if we solely rely on human intelligence. Recently, machine learning (ML) is making tremendous progress and has shown that computers can outperform humans in analyzing complex high-dimensional datasets. Our lab has been addressing these challenges in cell biology by developing an ML platform for fluorescence live cell image analyses at the subcellular level. We established the method to deconvolve the subcellular heterogeneity of time series of cell protrusion, which identified distinct subcellular protrusion phenotypes with differential drug susceptibility. Thus, our goal is to leverage our ML platform to address these technical challenges in epidemiological modeling for rapid forecasting of COVID-19 spread at the county level in the United States. First, we will advance our ML platform for the deconvolution of spatial heterogeneity of COVID-19 dynamics. This method will integrate epidemiological models and ML to identify the clusters of US counties sharing similar temporal patterns. Second, we will apply our deep learning-based feature learning, where the deep neural networks learn the critical features guided by prior knowledge and well-established epidemiological mathematical models. This will allow us to generate fine-grained forecasting maps of Covid-19 spread. Our ML platform will bring unprecedented prediction power to epidemiology and enable us to take urgent responses to the current COVID-19 pandemic and future other infectious disease outbreaks.