Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave in Europe and the United States

Given the narrowness of the initial testing criteria, the SARS-CoV-2 virus spread through cryptic transmission in January and February, setting the stage for the epidemic wave experienced in March and April, 2020. We use a global metapopulation epidemic model to provide a mechanistic understanding of the global dynamic underlying the establishment of the COVID-19 pandemic in Europe and the United States (US). The model is calibrated on international case introductions at the early stage of the pandemic. We find that widespread community transmission of SARS-CoV-2 was likely in several areas of Europe and the US by January 2020, and estimate that by early March, only 1 - 3 in 100 SARS-CoV-2 infections were detected by surveillance systems. Modeling results indicate international travel as the key driver of the introduction of SARS-CoV-2 with possible importation and transmission events as early as December, 2019. We characterize the resulting heterogeneous spatio-temporal spread of SARS-CoV-2 and the burden of the first COVID-19 wave (February-July 2020). We estimate infection attack rates ranging from 0.78%-15.2% in the US and 0.19%-13.2% in Europe. The spatial modeling of SARS-CoV-2 introductions and spreading provides insights into the design of innovative, model-driven surveillance systems and preparedness plans that have a broader initial capacity and indication for testing.

Predicting clinical outcomes in the Machine Learning era: The Piacenza score a purely data driven approach for mortality prediction in COVID-19 Pneumonia

BackgroundSeveral models have been developed to predict mortality in patients with COVID-19 pneumonia, but only few have demonstrated enough discriminatory capacity. Machine-learning(ML) algorithms represent a novel approach for data-driven prediction of clinical outcomes with advantages over statistical modelling. We developed the Piacenza score, a ML-based score, to predict 30-day mortality in patients with COVID-19 pneumonia. Methods852 patients (mean age 70years, 70%males) were enrolled from February to November 2020. The dataset was randomly splitted into derivation and test. The Piacenza score was obtained through the Naive Bayes classifier and externally validated on 86 patients. Using a forward-search algorithm the following six features were identified: age; mean corpuscular haemoglobin concentration; PaO2 /FiO2 ratio; temperature; previous stroke; gender. In case one or more of the features are not available for a patient, the model can be re-trained using only the provided features. We also compared the Piacenza score with the 4C score and with a Naive Bayes algorithm with 14 variables chosen a-priori. ResultsThe Piacenza score showed an AUC of 0.78(95% CI 0.74-0.84, Brier-score 0.19) in the internal validation cohort and 0.79(95% CI 0.68-0.89, Brier-score 0.16) in the external validation cohort showing a comparable accuracy respect to the 4C score and to the Naive Bayes model with a-priori chosen features, which achieved an AUC of 0.78(95% CI 0.73-0.83, Brier-score 0.26) and 0.80(95% CI 0.75-0.86, Brier-score 0.17) respectively. ConclusionA personalized ML-based score with a purely data driven features selection is feasible and effective to predict mortality in patients with COVID-19 pneumonia.

Estimating the establishment of local transmission and the cryptic phase of the COVID-19 pandemic in the USA

We use a global metapopulation transmission model to study the establishment of sustained and undetected community transmission of the COVID-19 pandemic in the United States. The model is calibrated on international case importations from mainland China and takes into account travel restrictions to and from international destinations. We estimate widespread community transmission of SARS-CoV-2 in February, 2020. Modeling results indicate international travel as the key driver of the introduction of SARS-CoV-2 in the West and East Coast metropolitan areas that could have been seeded as early as late-December, 2019. For most of the continental states the largest contribution of imported infections arrived through domestic travel flows.

The effect of travel restrictions on the spread of the 2019 novel coronavirus (2019-nCoV) outbreak

Motivated by the rapid spread of a novel coronavirus (2019-nCoV) in Mainland China, we use a global metapopulation disease transmission model to project the impact of both domestic and international travel limitations on the national and international spread of the epidemic. The model is calibrated on the evidence of internationally imported cases before the implementation of the travel quarantine of Wuhan. By assuming a generation time of 7.5 days, the reproduction number is estimated to be 2.4 [90% CI 2.2-2.6]. The median estimate for number of cases before the travel ban implementation on January 23, 2020 is 58,956 [90% CI 40,759 - 87,471] in Wuhan and 3,491 [90% CI 1,924 - 7,360] in other locations in Mainland China. The model shows that as of January 23, most Chinese cities had already received a considerable number of infected cases, and the travel quarantine delays the overall epidemic progression by only 3 to 5 days. The travel quarantine has a more marked effect at the international scale, where we estimate the number of case importations to be reduced by 80% until the end of February. Modeling results also indicate that sustained 90% travel restrictions to and from Mainland China only modestly affect the epidemic trajectory unless combined with a 50% or higher reduction of transmission in the community.

IMMOBILIZING THE VULNERABLE APENNINE CHAMOIS (RUPICAPRA PYRENAICA ORNATA) WITH A LOW-DOSE XYLAZINE-KETAMINE COMBINATION, REVERSED WITH IDAZOXAN OR ATIPAMEZOLE.

Little information is available on chemical capture of the vulnerable subspecies within the genus Rupicapra. Low-dosage combinations of xylazine and ketamine were tested for immobilization of captive and free-ranging Apennine chamois, Rupicapra pyrenaica ornata (85 and 66 immobilizations, respectively) in a retrospective analysis. Of the six dosage groups, all of them providing an acceptable level of immobilization, the optimal trade-off between safety and efficacy was found following administration of a mean dosage of 0.24±0.03 mg/kg xylazine and 1.07±0.15 mg/kg ketamine, resulting in 7.50±3.31 min induction time, deep sedation with no or limited reaction to handling in 96% of the chamois, minimal deviation of physiologic parameters from previously reported physiologic values for anesthetized or physically restrained chamois, and no mortality. Intravenous injection of idazoxan (0.05±0.01 mg/kg) or atipamezole (0.38±0.37 mg/kg) resulted in faster reversal than intravenous injection of tolazoline (1.05±0.15 mg/kg) in 1.3 vs. 4.1 min. When free-ranging chamois were darted with similar xylazine and ketamine dosages, induction time was 8.49±5.48 min, 88% of the animals were deeply sedated, and a single animal died from respiratory arrest (1.5% mortality). Intramuscular atipamezole provided smoother reversal than intravenous idazoxan. The results of this study suggest that xylazine/ketamine combinations, at remarkably lower dosage than previously published in Caprinae, may be safely and effectively used in chemical capture protocols of Apennine chamois, to facilitate conservation-oriented relocation and research.