Social distancing and epidemic resurgence in agent-based susceptible-infectious-recovered models

Mukhamadiarov, Ruslan I; Deng, Shaoli; Serrao, Shannon R.; Priyanka,; Nandi, Riya; Yao, Louie Hong; Täuber, Uwe C.

doi:10.1038/s41598-020-80162-y

Cited by 14 publications

(16 citation statements)

References 27 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is therefore natural and relevant to ask next whether the infectious disease dynamics becomes qualitatively altered if considered in spatially extended models. This question was explored in the recent work by [13], where authors performed individual-based numerical simulations of stochastic Susceptible-Infectious-Recovered (SIR) model variants on four distinct spatially organized lattice and network architectures. They found that highly connected networks closely follow mean-field SIR rate equations, while the disease spread on a lattice and small-world network revealed marked correlation effects.…”

Section: Discussionmentioning

confidence: 99%

“…In this paper, we utilize dynamic and static variants of Newman-Watts smallworld network [11] on different underlying two-dimensional lattices as a suitable spatial framework for our individual-based Monte Carlo simulations of the COVID-19 epidemic propagation [24,25,13]. Below we list all three variations of the two-dimensional Newman-Watts small-world network that we consider in this work:…”

Section: Modified Stochastic Seir Model On a Newman-watts Small-world Networkmentioning

confidence: 99%

See 1 more Smart Citation

Requirements for the containment of COVID-19 disease outbreaks through periodic testing, isolation, and quarantine

Serrao

Deng

Priyanka

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

We employ individual-based Monte Carlo computer simulations of a stochastic SEIR model variant on a two-dimensional Newman--Watts small-world network to investigate the control of epidemic outbreaks through periodic testing and isolation of infectious individuals, and subsequent quarantine of their immediate contacts. Using disease parameters informed by the COVID-19 pandemic, we investigate the effects of various crucial mitigation features on the epidemic spreading: fraction of the infectious population that is identifiable through the tests; testing frequency; time delay between testing and isolation of positively tested individuals; and the further time delay until quarantining their contacts as well as the quarantine duration. We thus determine the required ranges for these intervention parameters to yield effective control of the disease through both considerable delaying the epidemic peak and massively reducing the total number of sustained infections.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Modified Stochastic Seir Model On a Newman-watts Small-world Networkmentioning

confidence: 99%

Requirements for the containment of COVID-19 disease outbreaks through periodic testing, isolation, and quarantine

Serrao

Deng

Priyanka

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this paper, we consider agents on a square lattice (see [23,26,27,90,91] for other examples of lattice-based models). In addition to having N re random encounters, each agent interacts with the agents at the eight neighbouring lattice points on a daily basis.…”

Section: Epidemic Sirds Modelmentioning

confidence: 99%

“…For this purpose, we consider two examples of real-world applications of ABMs: tumour development and the spread of infectious diseases. ABMs are used extensively in both fields [22][23][24][25][26][27]. The employed ABMs are complex enough to resemble real-world problems going beyond toy models.…”

Section: Introductionmentioning

confidence: 99%

Efficient inference for agent-based models of real-world phenomena

Jørgensen

Ghosh

Sturrock

et al. 2021

Preprint

View full text Add to dashboard Cite

The modelling of many real-world problems relies on computationally heavy simulations. Since statistical inference rests on repeated simulations to sample the parameter space, the high computational expense of these simulations can become a stumbling block. In this paper, we compare two ways to mitigate this issue based on machine learning methods. One approach is to construct lightweight surrogate models to substitute the simulations used in inference. Alternatively, one might altogether circumnavigate the need for Bayesian sampling schemes and directly estimate the posterior distribution. We focus on stochastic simulations that track autonomous agents and present two case studies of real-world applications: tumour growths and the spread of infectious diseases. We demonstrate that good accuracy in inference can be achieved with a relatively small number of simulations, making our machine learning approaches orders of magnitude faster than classical simulation-based methods that rely on sampling the parameter space. However, we find that while some methods generally produce more robust results than others, no algorithm offers a one-size-fits-all solution when attempting to infer model parameters from observations. Instead, one must choose the inference technique with the specific real-world application in mind. The stochastic nature of the considered real-world phenomena poses an additional challenge that can become insurmountable for some approaches. Overall, we find machine learning approaches that create direct inference machines to be promising for real-world applications. We present our findings as general guidelines for modelling practitioners.

show abstract

“…Specifically, the rate equation description cannot properly account for the strong number fluctuations that drive the continuous phase transition when the system is near the epidemic threshold, nor for spatially correlated clusters and spreading fronts that are induced by the disease's propagation through nearest-contact infection [6,7]. Consequently, the rate equation approximation cannot capture stochastic extinction events if the disease parameters are set below or near the epidemic threshold, and also severely underestimates the ultimate prevalence of the above-threshold epidemic in the population [8,9].…”

Section: Introductionmentioning

confidence: 99%

Requirements for the containment of COVID-19 disease outbreaks through periodic testing, isolation, and quarantine

Mukhamadiarov¹,

Deng²,

Serrao³

et al. 2021

J. Phys. A: Math. Theor.

Self Cite

View full text Add to dashboard Cite

We employ individual-based Monte Carlo computer simulations of a stochastic SEIR model variant on a two-dimensional Newman-Watts small-world network to investigate the control of epidemic outbreaks through periodic testing and isolation of infectious individuals, and subsequent quarantine of their immediate contacts. Using disease parameters informed by the COVID-19 pandemic, we investigate the effects of various crucial mitigation features on the epidemic spreading: fraction of the infectious population that is identifiable through the tests; testing frequency; time delay between testing and isolation of positively tested individuals; and the further time delay until quarantining their contacts as well as the quarantine duration. We thus determine the required ranges for these intervention parameters to yield effective control of the disease through both considerable delaying the epidemic peak and massively reducing the total number of sustained infections.

show abstract

Social distancing and epidemic resurgence in agent-based susceptible-infectious-recovered models

Cited by 14 publications

References 27 publications

Requirements for the containment of COVID-19 disease outbreaks through periodic testing, isolation, and quarantine

Requirements for the containment of COVID-19 disease outbreaks through periodic testing, isolation, and quarantine

Efficient inference for agent-based models of real-world phenomena

Requirements for the containment of COVID-19 disease outbreaks through periodic testing, isolation, and quarantine

Contact Info

Product

Resources

About