Simulation of Covid-19 Propagation Scenarios in the Madrid Metropolitan Area
Bibliographic Data
| ID | 22079424 |
|---|---|
| Authors | David E Singh (0000-0002-8125-0049, Universidad Carlos III de Madrid, corresponding author), Maria-Cristina Marinescu (0000-0002-6978-2974, Barcelona Supercomputing Center), Miguel Guzmán-Merino (0000-0001-9933-5537, Universidad Carlos III de Madrid), Christian Durán (Universidad Carlos III de Madrid), Concepción Delgado‐Sanz (0000-0002-2603-3471, Instituto de Salud Carlos III), Diana Gómez‐Barroso (0000-0001-7388-1767, Instituto de Salud Carlos III), Diana Gómez-Barroso, Jesús Carretero (0000-0002-1413-4793, Universidad Carlos III de Madrid) |
| Year | 2021 |
| Volume | 9 |
| Pages | 636023-636023 |
| Publication date | 2021-03-16 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2021.636023 |
| PMID | 33796497 |
| OpenAlex | W3138846853 |
| Language | EN |
| Citations received | 3 |
| References cited | 41 |
This work presents simulation results for different mitigation and confinement scenarios for the propagation of COVID-19 in the metropolitan area of Madrid. These scenarios were implemented and tested using EpiGraph, an epidemic simulator which has been extended to simulate COVID-19 propagation. EpiGraph implements a social interaction model, which realistically captures a large number of characteristics of individuals and groups, as well as their individual interconnections, which are extracted from connection patterns in social networks. Besides the epidemiological and social interaction components, it also models people's short and long-distance movements as part of a transportation model. These features, together with the capacity to simulate scenarios with millions of individuals and apply different contention and mitigation measures, gives EpiGraph the potential to reproduce the COVID-19 evolution and study medium-term effects of the virus when applying mitigation methods. EpiGraph, obtains closely aligned infected and death curves related to the first wave in the Madrid metropolitan area, achieving similar seroprevalence values. We also show that selective lockdown for people over 60 would reduce the number of deaths. In addition, evaluate the effect of the use of face masks after the first wave, which shows that the percentage of people that comply with mask use is a crucial factor for mitigating the infection's spread
Data science · Econometrics · Epigraph · Geography · Mathematical optimization · Metropolitan area · Computer Science · COVID-19 Digital Contact Tracing · COVID-19 epidemiological studies · Mathematics · Medicine · SARS-CoV-2 and COVID-19 Research · Artificial Intelligence
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| Unique citing works | 3 |
|---|---|
| Citations per year | 0,6 |
| Citation span | 2021 - 2024 (4) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |