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Gómez Ullate, David

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David
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Gómez Ullate
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IE University
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IE School of Science & Technology
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Applied Mathematics
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Author: Precioso, Daniel ×

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    Effectiveness of Non-pharmaceutical Interventions in Nine Fields of Activity to Decrease SARS-CoV-2 Transmission
    (Frontiers, 2023-04-12) Gómez Ullate, David; Barbeito, Inés; Precioso, Daniel; Sierra, María José; Vegas Azcárate, Susana; Fernández Balbuena, Sonia; Vitoriano, Begoña; Cao, Ricardo; Monge, Susana; Ozayr Mahomed; University of KwaZulu-Natal; South Africa; https://ror.org/02jjdwm75
    Background: We estimated the association between the level of restriction in nine different fields of activity and SARS-CoV-2 transmissibility in Spain, from 15 September 2020 to 9 May 2021. Methods: A stringency index (0-1) was created for each Spanish province (n = 50) daily. A hierarchical multiplicative model was fitted. The median of coefficients across provinces (95% bootstrap confidence intervals) quantified the effect of increasing one standard deviation in the stringency index over the logarithmic return of the weekly percentage variation of the 7-days SARS-CoV-2 cumulative incidence, lagged 12 days. Results: Overall, increasing restrictions reduced SARS-CoV-2 transmission by 22% (RR = 0.78; one-sided 95%CI: 0, 0.82) in 1 week, with highest effects for culture and leisure 14% (0.86; 0, 0.98), social distancing 13% (0.87; 0, 0.95), indoor restaurants 10% (0.90; 0, 0.95) and indoor sports 6% (0.94; 0, 0.98). In a reduced model with seven fields, culture and leisure no longer had a significant effect while ceremonies decreased transmission by 5% (0.95; 0, 0.96). Models R 2 was around 70%. Conclusion: Increased restrictions decreased COVID-19 transmission. Limitations include remaining collinearity between fields, and somewhat artificial quantification of qualitative restrictions, so the exact attribution of the effect to specific areas must be done with caution.
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    Publication
    Thresholding methods in non-intrusive load monitoring
    (Springer Nature Link, 2023-04-01) Gómez Ullate, David; Precioso, Daniel; https://ror.org/02jjdwm75
    Non-intrusive load monitoring (NILM) is the problem of predicting the status or consumption of individual domestic appliances only from the knowledge of the aggregated power load. NILM is often formulated as a classification (ON/OFF) problem for each device. However, the training datasets gathered by smart meters do not contain these labels, but only the electric consumption at every time interval. This paper addresses a fundamental methodological problem in how a NILM problem is posed, namely how the different possible thresholding methods lead to different classification problems. Standard datasets and NILM deep learning models are used to illustrate how the choice of thresholding method affects the output results. Some criteria that should be considered for the choice of such methods are also proposed. Finally, we propose a slight modification to current deep learning models for multi-tasking, i.e. tackling the classification and regression problems simultaneously. Transfer learning between both problems might improve performance on each of them.