Dataset Information

Simulation of COVID-19 Propagation Scenarios in the Madrid Metropolitan Area.

ABSTRACT: 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.

SUBMITTER: Singh DE

PROVIDER: S-EPMC8007867 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ImportanceThe COVID-19 pandemic and the containment and mitigation measures taken were feared to be associated with increased child abuse.ObjectiveTo investigate the trend of abusive head trauma (AHT) incidence and severity in infants during the COVID-19 pandemic.Design, setting, and participantsIn a time-series analysis of a longitudinal, population-based, cohort study, all consecutive cases of AHT in infants younger than 12 months old referred between January 2017 and December 2021 to Necker Hospital for Sick Children, the single regional pediatric neurosurgery center for the Paris metropolitan area, were included. AHT was defined as 1 or more subdural hemorrhage and a positive multidisciplinary evaluation after a social, clinical, biological, and radiological workup. Data were analyzed from January to March 2022.Main outcomes and measuresThe primary outcome was the monthly incidence of AHT, which was analyzed using Poisson regression modeling. Secondary outcomes included mortality and severe morbidity and were studied with logistic and linear regressions. The monthly incidence of neurosurgical interventions for hydrocephalus was used as a control series.ResultsAmong the 99 included infants with AHT (median [IQR] age, 4 [3-6] months; 64 boys [65%]), 86 of 99 (87%) had bridging vein thrombosis, 74 of 99 (75%) had retinal hemorrhages, 23 of 72 (32%) had fractures, 26 of 99 (26%) had status epilepticus, 20 of 99 (20%) had skin injuries, 53 of 99 (54%) underwent neurosurgical interventions, and 13 of 99 (13%) died. Compared with the prepandemic period (2017-2019), AHT incidence was stable in 2020 (adjusted incidence rate ratio, 1.02; 95% CI, 0.59-1.77) and then significantly increased in 2021 (adjusted incidence rate ratio, 1.92; 95% CI, 1.23-2.99). The severity of AHT worsened in 2021 in terms of mortality (odds ratio 9.39; 95% CI, 1.88-47.00). Other secondary outcomes and the control series were not significantly modified.Conclusions and relevanceIn this cohort study, a marked increase in AHT incidence and severity occurred during the COVID-19 pandemic in the Paris metropolitan area. These results suggest the need for clinical awareness and preventive actions.

Project description:The impact of the COVID-19 pandemic has been immense, while the epidemiology and pathophysiology remain unclear. Despite many casualties in many countries, there have been less than 1,000 deaths in Japan as of end of June, 2020. In this study, we analyzed the cases of COVID-19 patients admitted to our institution located in the Tokyo metropolitan area where the survival rate is higher than those in other cities in the world. Medical records of COVID-19 patients that were admitted to a single Japanese tertiary university hospital in the Tokyo metropolitan area between March 10th and June 2nd, 2020 were retrospectively reviewed. The identified COVID-19 cases were subdivided into two groups (severe and mild) depending on the need for mechanical ventilation. Those in the severe group required mechanical ventilation as opposed to those in the mild group. The data were analyzed using nonparametric tests expressed by median [interquartile range (IQR)]. A total of 45 COVID-19 patients were included, consisting of 22 severe cases (Group S) and 23 mild cases (Group M). Male sex (Group S, 95.5% vs. Group M, 56.5%, p<0.01), high body mass index (Group S, 24.89 [22.44-27.15] vs. Group M, 21.43 [19.05-23.75], p<0.01), and hyperlipidemia (Group S, 36.4% vs. Group M, 0%, p<0.01) were more seen in Group S. Five (22.7%) cases in Group S underwent extracorporeal membranous oxygenation (ECMO). On admission, lymphopenia, decreased albumin, and elevated fibrinogen, lactate dehydrogenase, transaminases, creatine kinase, C-reactive protein, and procalcitonin were observed in Group S. The median ICU and hospital stay were 13.5 [10.3-22.3] days and 23.0 [16.3-30.5] days, respectively, in Group S. As of June 28th, 2020, in Group S, 19 (86.4%) patients have survived, of which 17 (77.3%) were discharged, and 2 are still in treatments. Three died of multiple organ failure. All 23 patients in Group M have recovered. Male sex, high body mass index, and hyperlipidemia can be risk factors for severe COVID-19 pneumonia, and its overall short-term survival rate was between 77.3% and 86.4% in this study.

Dataset Information

Simulation of COVID-19 Propagation Scenarios in the Madrid Metropolitan Area.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets