Predicting Onset of COVID-19 with Mobility-Augmented SEIR Model

Wu, Neo; Ben, Xue; Green, Bradley; Rough, Kathryn; Venkatramanan, Srinivasan; Marathe, Madhav V.; Eastham, P. R.; Sadilek, Adam; O'Banion, Shawn

doi:10.1101/2020.07.27.20159996

Cited by 19 publications

(25 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several recent contributions have adopted SIR-type models to predict the development of COVID-19 cases at the country and regional levels (see, e.g., Atkeson 2020 ; Wang et al 2020 ). In several of these models, mobility between sub-populations has been found as a significant factor predicting the spatiotemporal dynamics of COVID-19 (e.g., Chang et al 2021 ; Liu et al 2020 ; Wu et al 2020 , among others). Complementary approaches based on network analysis and spatial econometrics have confirmed the link between human mobility and the international transmission of COVID-19 for different connectivity measures such as flight connections and international trade relations (Krisztin et al 2020 ).…”

Section: Related Literaturementioning

confidence: 99%

The propagation effect of commuting to work in the spatial transmission of COVID-19

Mitze

Kosfeld

2021

J Geogr Syst

View full text Add to dashboard Cite

This work is concerned with the spatiotemporal dynamics of the coronavirus disease 2019 (COVID-19) in Germany. Our goal is twofold: first, we propose a novel spatial econometric model of the epidemic spread across NUTS-3 regions to identify the role played by commuting-to-work patterns for spatial disease transmission. Second, we explore if the imposed containment (lockdown) measures during the first pandemic wave in spring 2020 have affected the strength of this transmission channel. Our results from a spatial panel error correction model indicate that, without containment measures in place, commuting-to-work patterns were the first factor to significantly determine the spatial dynamics of daily COVID-19 cases in Germany. This indicates that job commuting, particularly during the initial phase of a pandemic wave, should be regarded and accordingly monitored as a relevant spatial transmission channel of COVID-19 in a system of economically interconnected regions. Our estimation results also provide evidence for the triggering role of local hot spots in disease transmission and point to the effectiveness of containment measures in mitigating the spread of the virus across German regions through reduced job commuting and other forms of mobility.

show abstract

Section: Related Literaturementioning

confidence: 99%

The propagation effect of commuting to work in the spatial transmission of COVID-19

Mitze

Kosfeld

2021

J Geogr Syst

View full text Add to dashboard Cite

show abstract

“…The next advancement relates to the thorny problem faced by every modeling and simulation effort for complex systems: how can we effectively explore the vast parameter space of what-if analyses in which a large number of possibilities on the nearterm time horizon are explored quickly as small, incremental variations of scenarios over the current, large state of the complex system [6]. The scenarios to be explored become numerous due to the multitude of factors at play, which include locationspecific effects, behavioral effects, intervention measures, and so on [2,9,28]. On the one hand, massive simulations of microscopic models cannot typically be run in large numbers of scenarios.…”

Section: Motivationmentioning

confidence: 99%

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios

Perumalla

Alam

2021

Preprint

View full text Add to dashboard Cite

In simulation-based studies and analyses of epidemics, a major challenge lies in resolving the conflict between fidelity of models and the speed of their simulation. Another related challenge arises in dealing with the large number of what-if scenarios that need to be explored. Here, we describe new computational methods that together provide an approach to dealing with both challenges. A mesoscopic modeling approach is described that attempts to strike a middle ground between macroscopic models based on coupled differential equations and microscopic models built on fine-grained behaviors such as at the individual entity level. The mesoscopic approach offers the possibility of incorporating complex compositions of multiple layers of dynamics even while retaining the potential for aggregate behaviors at varying levels. It also provides an excellent match to the accelerator-based architectures of modern computing platforms in which graphical processing units (GPUs) can be exploited for fast simulation via the parallel execution mode of single instruction multiple data (SIMD). The challenge of simulating a large number of scenarios is addressed via a method of sharing model state and computation across a tree of what-if scenarios that are localized, incremental changes to a large base simulation. A combination of the mesoscopic modeling approach and the incremental what-if scenario tree evaluation has been implemented in software on modern GPUs. Synthetic simulation scenarios are explored and presented here to demonstrate the basic feasibility and computational characteristics of our approach. Results from the experiments on large population data illustrate the overall modeling methodology and computational run time performance on large numbers of synthetically generated what-if scenarios.

show abstract

“…The next advancement relates to the thorny problem faced by every modeling and simulation effort for complex systems: how can we effectively explore the vast parameter space of what-if analyses in which a large number of possibilities on the near-term time horizon are explored quickly as small, incremental variations of scenarios over the current, large state of the complex system 6 . The scenarios to be explored become numerous due to the multitude of factors at play, which include location-specific effects, behavioral effects, intervention measures, and so on 2,9,28 . On one hand, massive simulations of microscopic models cannot typically be run in large numbers of scenarios.…”

Section: Motivationmentioning

confidence: 99%

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs

Perumalla

Alam

2021

J Indian Inst Sci

View full text Add to dashboard Cite

In simulation-based studies and analyses of epidemics, a major challenge lies in resolving the conflict between fidelity of models and the speed of their simulation. Another related challenge arises in dealing with the large number of what–if scenarios that need to be explored. Here, we describe new computational methods that together provide an approach to dealing with both challenges. A mesoscopic modeling approach is described that strikes a middle ground between macroscopic models based on coupled differential equations and microscopic models built on fine-grained behaviors at the individual entity level. The mesoscopic approach offers the ability to incorporate complex compositions of multiple layers of dynamics even while retaining the potential for aggregate behaviors at varying levels. It also is an excellent match to the accelerator-based architectures of modern computing platforms in which graphical processing units (GPUs) can be exploited for fast simulation via the parallel execution mode of single instruction multiple thread (SIMT). The challenge of simulating a large number of scenarios is addressed via a method of sharing model state and computation across a tree of what–if scenarios that are localized, incremental changes to a large base simulation. A combination of the mesoscopic modeling approach and the incremental what–if scenario tree evaluation has been implemented in the software on modern GPUs. Synthetic simulation scenarios are presented to demonstrate the computational characteristics of our approach. Results from the experiments with large population data, including USA, UK, and India, illustrate the modeling methodology and computational performance on thousands of synthetically generated what–if scenarios. Execution of our implementation scaled to 8192 GPUs of supercomputing platforms demonstrates the ability to rapidly evaluate what–if scenarios several orders of magnitude faster than the conventional methods.

show abstract

Predicting Onset of COVID-19 with Mobility-Augmented SEIR Model

Cited by 19 publications

References 34 publications

The propagation effect of commuting to work in the spatial transmission of COVID-19

The propagation effect of commuting to work in the spatial transmission of COVID-19

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs

Contact Info

Product

Resources

About