Dataset Information

MAM: Flexible Monte-Carlo Agent based Model for Modelling COVID-19 Spread.

ABSTRACT: In the three years since SARS-CoV-2 was first detected in China, hundreds of millions of people have been infected and millions have died. Along with the immediate need for treatment solutions, the COVID-19 epidemic has reinforced the need for mathematical models that can predict the spread of the pandemic in an ever-changing environment. The susceptible-infectious-removed (SIR) model has been widely used to model COVID-19 transmission, however, with limited success. Here, we present a novel, dynamic Monte-Carlo Agent-based Model (MAM), which is based on the basic principles of statistical physics. Using public aggregative data from Israel on three major outbreaks, we compare predictions made by SIR and MAM, and show that MAM outperforms SIR in all aspects. Furthermore, MAM is a flexible model and allows to accurately examine the effects of vaccinations in different subgroups, and the effects of the introduction of new variants.

SUBMITTER: De-Leon H

PROVIDER: S-EPMC10098313 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Publications

MAM: Flexible Monte-Carlo Agent based model for modelling COVID-19 spread.

De-Leon Hilla H Aran Dvir D

Journal of biomedical informatics 20230413

In the three years since SARS-CoV-2 was first detected in China, hundreds of millions of people have been infected and millions have died. Along with the immediate need for treatment solutions, the COVID-19 epidemic has reinforced the need for mathematical models that can predict the spread of the pandemic in an ever-changing environment. The susceptible-infectious-removed (SIR) model has been widely used to model COVID-19 transmission, however, with limited success. Here, we present a novel, dy ...[more]

PMID: 37061013

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Project description:Current CT dosimetry employing CTDI methodology has come under fire in recent years, partially in response to the increasing width of collimated x-ray fields in modern CT scanners. This study was conducted to provide a better understanding of the radiation dose distributions in CT.Monte Carlo simulations were used to evaluate radiation dose distributions along the z axis arising from CT imaging in cylindrical phantoms. Mathematical cylinders were simulated with compositions of water, polymethyl methacrylate (PMMA), and polyethylene. Cylinder diameters from 10 to 50 cm were studied. X-ray spectra typical of several CT manufacturers (80, 100, 120, and 140 kVp) were used. In addition to no bow tie filter, the head and body bow tie filters from modern General Electric and Siemens CT scanners were evaluated. Each cylinder was divided into three concentric regions of equal volume such that the energy deposited is proportional to dose for each region. Two additional dose assessment regions, central and edge locations 10 mm in diameter, were included for comparisons to CTDI100 measurements. Dose spread functions (DSFs) were computed for a wide number of imaging parameters.DSFs generally exhibit a biexponential falloff from the z=0 position. For a very narrow primary beam input (<< 1 mm), DSFs demonstrated significant low amplitude long range scatter dose tails. For body imaging conditions (30 cm diameter in water), the DSF at the center showed 160 mm at full width at tenth maximum (FWTM), while at the edge the FWTM was approximately 80 mm. Polyethylene phantoms exhibited wider DSFs than PMMA or water, as did higher tube voltages in any material. The FWTM were 80, 180, and 250 mm for 10, 30, and 50 cm phantom diameters, respectively, at the center in water at 120 kVp with a typical body bow tie filter. Scatter to primary dose ratios (SPRs) increased with phantom diameter from 4 at the center (1 cm diameter) for a 16 cm diameter cylinder to approximately 12.5 for a 32 cm diameter cylinder. The SPRs increased dramatically at the center of the phantom compared to the edge. For the three equal area regions, the edge to center SPRs for a 32 cm diameter phantom were approximately 1.8, 3.5, and 6.3, respectively.DSFs demonstrate low amplitude long ranging tails which reach considerable distances in cylindrical phantoms. The buildup that results from these long-ranged tails increases at the center of the field (at z=0) with increasing scan length. The DSF distributions lend a better understanding of the trends in CT dose deposition over a range of relevant imaging parameters. The DSFs as well as other related data are available to interested parties using EPAPS at http://www.aip.org/pubservs/epaps.html.

Dataset Information

MAM: Flexible Monte-Carlo Agent based Model for Modelling COVID-19 Spread.

Publications

MAM: Flexible Monte-Carlo Agent based model for modelling COVID-19 spread.

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