An Epidemic Grid Model to Address the Spread of Covid-19: A Comparison between Italy, Germany and France

Pont, María Teresa Signes; Cortés‐Plana, José Juan; Mora, Higinio

doi:10.3390/mca26010014

Cited by 4 publications

(3 citation statements)

References 10 publications

(11 reference statements)

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“…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%

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

Jørgensen

Ghosh

Sturrock

et al. 2021

Preprint

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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.

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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

show abstract

“…Besides, China is the third largest country on the planet. By also considering international flights, it is easy to understand the rapid spread of the disease and how it reached Europe so quickly [8], being Europe one of the main tourist destinations of Chinese people. Despite the above, the first confirmed case of contagion outside of China was in Thailand, generated by 16 people who arrived by direct flight from Wuhan less than two weeks after the virus was discovered [9].…”

Section: Introductionmentioning

confidence: 99%

Contagion Modeling and Simulation in Transport and Air Travel Networks During the COVID-19 Pandemic: A Survey

2021

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The COVID-19 pandemic has generated a huge volume of research from various disciplines, such as health sciences, social sciences, mathematical modeling, social network analysis, complex systems, decision-making processes, computer simulation, economics, among many others. One of the key problems has been to understand the diffusion processes of the virus, which quickly spread worldwide through transport networks, mainly air flights. Almost two years after start the pandemic, it is necessary to collect and synthesize the existing work on this matter. This work focuses on studies related to the COVID-19 contagion simulation through transport networks. In particular, we are specially interested in the different datasets and epidemiological models used. The search methodology consists of four exhaustive searches in Google Scholar carried out between January 2020 and January 2021. Of the 1786 findings, we chose 53 articles related to Covid-19 contagion modeling and simulation through transport networks. The results show 30 different data sources for the collection of air flights and 11 additional sources for maritime and land transport. These datasets are usually complemented with other data sources, local and international, with demographic information, economic data, and statistics of traceability of the pandemic. The findings also found 15 spread models of contagion, with the SEIR model being the most widely used, followed by mathematical-based risk models. This diversity of results validates the need for these types of compilation efforts since researchers do not have a single centralized repository to collect air flight data.

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Final sizes and durations of new COVID-19 pandemic waves in Poland and Germany predicted by generalized SIR model

Nesteruk

2021

Preprint

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New waves of the COVID-19 pandemic in Europe, which began in the autumn of 2021, are a matter of great concern and the need to immediately predict the epidemic dynamics in order to assess the possible maximum values of new cases, the risk of infection and the number of deaths. The generalized SIR-model and corresponding parameter identification procedure was used to simulate and predict the dynamics of new epidemic waves in Poland and Germany. Results of calculations show that new cases in these countries will not stop to appear in 2022.

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An Epidemic Grid Model to Address the Spread of Covid-19: A Comparison between Italy, Germany and France

Cited by 4 publications

References 10 publications

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

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

Contagion Modeling and Simulation in Transport and Air Travel Networks During the COVID-19 Pandemic: A Survey

Final sizes and durations of new COVID-19 pandemic waves in Poland and Germany predicted by generalized SIR model

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