ABSTRACT
Background Severe COVID-19 has been shown to produce convulsions, encephalitis, Guillain-Barré syndrome, or cerebrovascular disease. However, only 4 case reports described subarachnoid or brain hemorrhage caused by ruptured cerebral aneurysms or pseudoaneurysms in patients with COVID-19. Cerebral pseudoaneurysms represent <1% of all intracranial aneurysms and have been related to radiation therapy, vasculitis, rupture of true saccular aneurysms, arteriovenous malformations, and infections by bacteria and viruses, such as Epstein-Bar and Herpes virus. Case presentation A 28-year-old Caucasian woman, with no medical history of interest and completely vaccinated against SARS-CoV-2, was admitted to Neurology due to progressive tetraparesis with areflexia, a cough, and a fever of 38°C. SARS-CoV2 PCR was positive while lumbar puncture, blood tests, and electromyogram showed criteria for Guillain-Barré syndrome. Despite the treatment, the patient developed dyspnea and tetraplegia requiring invasive mechanical ventilation. There was motor neurological improvement but a decreased level of consciousness was observed on day 13. A brain CT scan demonstrated an acute haematoma and cerebral arteriography showed a 4-mm pseudoaneurysm located in a branch of the left middle cerebral artery. Given the high risk of rebleeding, endovascular treatment was decided upon. Therefore, complete embolization of the pseudoaneurysm was carried out by using the synthetic glue N-butyl-cyanocrylate. Two days later, the patient was clinically and neurologically recovered and was discharged. Lastly, a new angiography showed no evidence of the pseudoaneurysm 3-weeks later. Conclusions We report, for the first time, a patient suffering a severe immune reaction caused by SARS-CoV2 infection and developing a cerebral pseudoaneurysm treated with endovascular embolization without complications.
ABSTRACT
Retrospective epidemiological models are powerful tools to understand its transmission dynamics and to assess the efficacy of different control measures. This study summarises key epidemiological parameters of COVID-19 for retrospective mathematical and clinical modeling. A review of scientific papers and preprints published in English between 1 January and 15 April 2020 in PubMed, MedRxiv and BioRxiv was performed to obtain epidemiological parameters of the initial stage of COVID-19 pandemic in Asia. After excluding articles with unacceptable risks of bias and those that remained as preprints as of 15 November 2021, meta-analyses were performed to derive summary effect estimates from the data collected using the statistical software R. Out of 4,893 articles identified, 88 provided data for 22 parameters for the overall population and 7 specifically for children. Meta-analyses were conducted considering time period as a categorical moderator when it was statistically significant. The results obtained are essential for building more reliable models to help clinicians and policymakers improve their knowledge on COVID-19 and apply it in future decisions.
Subject(s)
COVID-19ABSTRACT
Human mobility drives geographical diffusion of airborne infectious diseases at different scales. During the COVID-19 pandemic mobility data was made available and has been widely used, but few studies focus on mobility itself. We used public data from February 14th 2020, to May 9th 2021, in Spain to characterize mobility patterns and study geographical diffusion phenomena using network science methods. With 135 (out of 2.264) connections and the 52 provinces of Spain, a weighted, directed network was built: the Epidemic Diffusion Network (EDN). Centrality measures (degree and strength), community structure and shortest distances were obtained using the EDN. The resulting network was highly clustered (modularity: 63%) with 7 communities. Madrid, Valladolid and Araba/Álaba act as mobility hubs of their communities and the whole network. Shortest distances unveil a geographical wave-like diffusion pattern with occasional distance jumps, a small-world network characteristic, and COVID-19 cumulative incidence curves showed a pattern of proximity within provinces of the same community. These properties remain constant in time despite factors like seasonality or restrictions and could inform public health authorities in preparedness and response plans for diseases and other threads. Further studies are needed to better understand relationship between network measures and epidemiological outcomes in real life.
Subject(s)
COVID-19ABSTRACT
BACKGROUND: A unique policy of perimeter closures of Basic Health Zones (small administrative health units) was implemented in the Autonomous Community of Madrid from September 21st 2020 to May 23rd 2021 to face the COVID-19 pandemic. AIM: To assess the impact of local perimeter confinements on the 14-days cumulative incidence of SARS-CoV-2 during the second wave of the pandemic in Madrid, Spain. METHODS: We compare the errors in estimation of two families of mathematical models: ones that include the perimeter closures as explanatory covariables and ones that do not, in search of a significant improvement in estimation of one family over the other. We incorporate leave-one-out cross-validation, and at each step of this process we select the best model in AIC score from a family of 15 differently tuned ones. RESULTS: The two families of models provided very similar estimations, for a 1- to 3-weeks delay in observed cumulative incidence, and also when restricting the analysis to only those Basic Health Zones that were subject to at least one closure during the time under study. In all cases the correlation between the errors yielded by both families of models was higher than 0.98 (±10- 3 95% CI), and the average difference of estimated 14-days cumulative incidence was smaller than 1.49 (±0.33 95% CI). CONCLUSION: Our analysis suggests that the perimeter closures by Basic Health Zone did not have a significant effect on the epidemic curve in Madrid.
Subject(s)
COVID-19 , Humans , Incidence , Pandemics , SARS-CoV-2 , Spain/epidemiologyABSTRACT
Accurate detection of early COVID-19 cases is crucial to drastically reduce infection, hospitalization, and death rates. However, it remains a challenge and methods for identifying initial COVID-19 cases are urgently needed. Here, we used the results from a seroprevalence study in 50 US states to apply our Retrospective Methodology to Estimate Daily Infections from Deaths (REMEDID) with the aim of analyzing the initial stages and spread of SARS-CoV-2 infections across the United States (US). Our retrospective data analysis revealed that the virus likely entered the country through California on December 28, 2019, which corresponds to 16 days before the officially recognized entry date established by the CDC. Thus, REMEDID provides evidence that SARS-CoV-2 entered the U.S. earlier than previously reflected in official data. Collectively, our mathematical modeling more accurately estimates the initial COVID-19 cases in the US, may be extrapolated to other countries, and may be used to retrospectively track the progress of the pandemic. Approaches such as REMEDID may enable health authorities to accelerate preventative measures aimed at controlling pandemics within weeks of their onset.
Subject(s)
COVID-19ABSTRACT
After a year of living with the COVID-19 pandemic and its associated consequences, hope looms on the horizon thanks to vaccines. The question is what percentage of the population needs to be immune to reach herd immunity, that is to avoid future outbreaks. The answer depends on the basic reproductive number, R0, a key epidemiological parameter measuring the transmission capacity of a disease. Besides the virus itself, R0 depends on the characteristics of the population and their environment. Additionally, the estimate of R0 depends on the methodology used, the accuracy of data, and the generation time distribution. The aim of this study is to provide a herd immunity threshold for Spain, for which we considered the different combinations of these elements to obtain the R0 for the Spanish population. Estimates of R0 range from 1.39 to 3.10, with the largest differences produced by the choice of the methodology to estimate R0. With these values, the herd immunity threshold ranges from 28.1–67.7%, which makes 70% a realistic upper bound for Spain.
Subject(s)
COVID-19ABSTRACT
The number of new daily infections is one of the main parameters to understand the dynamics of an epidemic. During the COVID-19 pandemic in 2020, however, such information has been underestimated. Here, we propose a retrospective methodology to estimate daily infections from daily deaths, because those are usually more accurately documented. The methodology is applied to Spain and its 19 administrative regions. Our results showed that probable infections were between 34 and 42 times more than the official ones on 14 March, when national government decreed the national lockdown. The latter had a strong effect on the growth rate of virus transmission, which began to decrease immediately. Finally, the first infection in Spain may have occurred on 11 January 2020, around 40 days before it was officially reported. In summary, we state that our methodology is adequate to reinterpret official daily infections, being more accurate in magnitude and dates
Subject(s)
COVID-19ABSTRACT
We present a literature review and meta-analysis of relevant epidemiological parameters (24 for adults, 7 for children) of COVID-19. Standardization of these parameters is key to performing valid clinical and mathematical modeling, as well as forecasts, helping us to improve our understanding about the characteristics and impact of the pandemic.