The ongoing coronavirus disease 2019 (COVID-19) pandemic is of global concern and has recently emerged in the US. In this paper, we construct a stochastic variant of the SEIR model to make a quasi-worst-case scenario prediction of the COVID-19 outbreak in the US West and East Coasts. The model is then fitted to current data and implemented using Runge-Kutta methods. Our computation results predict that the number of new cases would peak around mid-April and begin to abate by July, and that the number of cases of COVID-19 might be significantly mitigated by having greater numbers of functional testing kits available for screening. The model also showed how small changes in variables can make large differences in outcomes and highlights the importance of healthcare preparedness during pandemics.
The ongoing coronavirus disease 2019 (COVID-19) pandemic is of global concern and has recently emerged in the US. In this paper, we construct a stochastic variant of the SEIR model to estimate a quasi-worst-case scenario prediction of the COVID-19 outbreak in the US West and East Coast population regions by considering the different phases of response implemented by the US as well as transmission dynamics of COVID-19 in countries that were most affected. The model is then fitted to current data and implemented using Runge-Kutta methods. Our computation results predict that the number of new cases would peak around mid-April 2020 and begin to abate by July provided that appropriate COVID-19 measures are promptly implemented and followed, and that the number of cases of COVID-19 might be significantly mitigated by having greater numbers of functional testing kits available for screening. The model is also sensitive to assigned parameter values and reflects the importance of healthcare preparedness during pandemics.
Objectives: We aim to investigate the effects of head model inaccuracies on signal and source reconstruction accuracies for various sensor array distances to the head. This allows for the assessment of the importance of head modeling for next-generation magnetoencephalography (MEG) sensors, optically-pumped magnetometers (OPM). Approach: A 1-shell boundary element method (BEM) spherical head model with 642 vertices was defined. The vertices were randomly perturbed radially up to 2% - 10% of the radius, and the forward signal was calculated for dipolar sources located at 2 cm to 8 cm from the center of the sphere with a 324 sensor array located at 10 cm to 15 cm from the center of the sphere. Source localization was performed for each of these forward signals. The signal for each perturbed spherical head case was analyzed in the spatial frequency domain, and the signal and source localization errors were quantified relative to the unperturbed case. Main results: In the noiseless and high signal-to-noise ratio (SNR) case of approximately >= 6 dB, inaccuracies in our spherical BEM head model led to increased signal and source localization inaccuracies when sensor arrays were closer to the head, especially for deep and superficial sources. In the noisy case however, the higher SNR for closer sensor arrays led to an improved ECD fit and outweighed the effects of head geometry inaccuracies. Significance: OPMs may be placed directly on the head, as opposed to the more commonly used superconducting quantum interference device (SQUID) sensors which must be placed a few centimeters away from the head. OPMs thus allow for signals of higher spatial resolution to be captured, resulting in potentially more accurate source localizations. Our results suggest that an increased emphasis on accurate head modeling for OPMs may be necessary to fully realize its improved source localization potential.
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