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.
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Exo-SIR: an epidemiological model to analyze the impact of exogenous spread of infection
Epidemics like Covid-19 and Ebola have impacted people’s lives signifcantly. The impact of mobility of people across the
countries or states in the spread of epidemics has been signifcant. The spread of disease due to factors local to the population
under consideration is termed the endogenous spread. The spread due to external factors like migration, mobility, etc., is
called the exogenous spread. In this paper, we introduce the Exo-SIR model, an extension of the popular SIR model and a few
variants of the model. The novelty in our model is that it captures both the exogenous and endogenous spread of the virus.
First, we present an analytical study. Second, we simulate the Exo-SIR model with and without assuming contact network
for the population. Third, we implement the Exo-SIR model on real datasets regarding Covid-19 and Ebola. We found that
endogenous infection is infuenced by exogenous infection. Furthermore, we found that the Exo-SIR model predicts the peak
time better than the SIR model. Hence, the Exo-SIR model would be helpful for governments to plan policy interventions at
the time of a pandemic.
Keywords Covid-19, Ebola, Epidemic modeling, Compartment model, Exogenous infection, Endogenous infection,
SIR, Exo-SIR
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- Award ID(s):
- 2133842
- NSF-PAR ID:
- 10339013
- Date Published:
- Journal Name:
- International Journal of Data Science and Analytics
- ISSN:
- 2364-415X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s41060-022-00334-z
