The outbreaks of Coronavirus Disease 2019 (COVID-19) have impacted the world significantly. Modeling the trend of infection and realtime forecasting of cases can help decision making and control of the
disease spread. However, data-driven methods such as recurrent neural
networks (RNN) can perform poorly due to limited daily samples in time.
In this work, we develop an integrated spatiotemporal model based on the
epidemic differential equations (SIR) and RNN. The former after simplification and discretization is a compact model of temporal infection trend of
a region while the latter models the effect of nearest neighboring regions.
The latter captures latent spatial information. We trained and tested our
model on COVID-19 data in Italy, and show that it out-performs existing
temporal models (fully connected NN, SIR, ARIMA) in 1-day, 3-day, and
1-week ahead forecasting especially in the regime of limited training data.
more »
« less
A Recurrent Neural Network and Differential Equation based Spatiotemporal Infectious Disease Model with Application to COVID-19
The outbreaks of Coronavirus Disease 2019 (COVID-19) have impacted the world significantly. Modeling the
trend of infection and real-time forecasting of cases can help decision making and control of the disease spread.
However, data-driven methods such as recurrent neural networks (RNN) can perform poorly due to limited
daily samples in time. In this work, we develop an integrated spatiotemporal model based on the epidemic
differential equations (SIR) and RNN. The former after simplification and discretization is a compact model
of temporal infection trend of a region while the latter models the effect of nearest neighboring regions. The
latter captures latent spatial information. We trained and tested our model on COVID-19 data in Italy, and
show that it out-performs existing temporal models (fully connected NN, SIR, ARIMA) in 1-day, 3-day, and
1-week ahead forecasting especially in the regime of limited training data.
more »
« less
- NSF-PAR ID:
- 10252842
- Date Published:
- Journal Name:
- 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
- Volume:
- Volume 1: KDIR
- Page Range / eLocation ID:
- 93-103
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). 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Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. 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null (Ed.)COVID-19 pandemic has an unprecedented impact all over the world since early 2020. During this public health crisis, reliable forecasting of the disease becomes critical for resource allocation and administrative planning. The results from compartmental models such as SIR and SEIR are popularly referred by CDC and news media. With more and more COVID-19 data becoming available, we examine the following question: Can a direct data-driven approach without modeling the disease spreading dynamics outperform the well referred compartmental models and their variants? In this paper, we show the possibility. It is observed that as COVID-19 spreads at different speed and scale in different geographic regions, it is highly likely that similar progression patterns are shared among these regions within different time periods. This intuition lead us to develop a new neural forecasting model, called Attention Crossing Time Series (ACTS), that makes forecasts via comparing patterns across time series obtained from multiple regions. The attention mechanism originally developed for natural language processing can be leveraged and generalized to materialize this idea. Among 13 out of 18 testings including forecasting newly con rmed cases, hospitalizations and deaths, ACTS outperforms all the leading COVID-19 forecasters highlighted by CDC.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/https://DOI:10.5220/0010130000930103
