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

> **NIH NIH R35** · BOSTON CHILDREN'S HOSPITAL · 2021 · $318,600

## Abstract

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.

## Key facts

- **NIH application ID:** 10463952
- **Project number:** 3R35GM133725-04S1
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Kwonmoo Lee
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $318,600
- **Award type:** 3
- **Project period:** 2019-09-15 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10463952

## Citation

> US National Institutes of Health, RePORTER application 10463952, Spatiotemporal forecasting of COVID-19 by integrating machine learning and epidemiological modeling (3R35GM133725-04S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10463952. Licensed CC0.

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