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

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 (preprint)

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.

A Simple Multiparametric Score System to Predict In-Hospital Mortality of COVID-19 Patients (preprint)

Background: Several clinical, laboratory and instrumental prognostic indicators for coronavirus disease 2019 (COVID-19) have been found. Combining all the different predictors in a score would make easier and more accurate the risk assessment of COVID-19 patients. To this purpose, we examined a large number of COVID-19 patients. First, we identified the best predictors of in-hospital mortality at admission. Then, we calculated a score system to capture the contribution of the various prognostic indicators.Methods: Prospective multicenter study (ELCOVID) referring to central-northern Italy. This project is registered on ClinicalTrials.gov (identifier: NCT04367129). COVID-19 patients admitted to the hospital in the period May-September 2020 were enrolled. Clinical, laboratory and electrocardiographic (ECG) records were collected at admission. Patients were followed-up and in-hospital mortality constituted the primary endpoint. A risk scoring system to predict prognosis was derived by independent predictors of in-hospital mortality.Findings: A total of 1014 patients fulfilled inclusion criteria. Demographic, clinical, laboratories and ECG characteristics were collected. Median age was 74 (IQR 64-82) years, and most patients were male (61%). During a median follow-up of 12 (IQR 7-22) days, 359 (35%) patients died. Age (HR 2.25, 95%CIs 1.72-2.94, p < 0.001), delirium (HR 2.03, 95%CIs 2.14-3.61, p = 0.012), platelets count (HR 0.91, 95%CIs 0.83-0.98, p = 0.018), D-dimer (HR 1.18, 95%CIs 1.01-1.31, p = 0.002), S1Q3T3 pattern and/or RBBB (HR 1.47, 95%CIs 1.02-2.13, p = 0.039) and ECG signs of previous myocardial necrosis (HR 2.28, 95%CIs 1.23-4.21, p = 0.009) were independently associated to in-hospital mortality. The risk scoring system derived had a moderate discriminatory capability and good calibration. A score value ≥4 had a sensitivity of 78,4% and specificity of 65,2% to predict in-hospital mortality.Interpretation: This score system stratifies prognosis and may be important for the management of COVID-19 patients admitted to the hospital.Trial Registration: ClinicalTrials.gov (identifier: NCT04367129).Funding Statement: None.Declaration of Interests: None declared.Ethics Approval Statement: ELCOVID is a prospective observational study approved by the local Ethics Committee and involves 15 hospitals in the Emilia Romagna and Lazio, two regions in northern and central Italy heavily affected by the pandemic.

Adapting to Mitigation Efforts: Evolving Strategies of Coordinated Link Sharing on Facebook (preprint)

In the context of a larger study on the spread of COVID-19 related mis/disinformation in Italy, we detected a network of 26 Facebook Pages that performed Coordinated Link Sharing. The potential reach of the network is significant, with a cumulative subscriber count close to 6 million users. Each month, the network publishes more than 18,000 posts. Half of the posts are type photos (64%), followed by status (22%) and links (14%). However, one third of the photos include links in the message/description of the post. The goal of the network is to drive traffic to the howtodofor.com domain, a news source that, according to NewsGuard, fails to meet several basic journalistic standards and republished articles from other media without mentioning the original source. The network is organized in different clusters that, beside various forms of links pointing to the main domain, also post the same image macros at approximately the same time. These types of posts tend to perform better in terms of volume of interaction received. Beside the obvious economic driver, one specific cluster also appears to be ideologically motivated. Over the year, the network experimented with different strategies aimed at maximizing the exposure of their content and, possibly, sidestepping Facebook’s community standard. Starting in March, a growing number of the links shared are posted in the first comment of click-bait posts (either photos or status). More recently, the network also started posting links to well-respected journalistic news sources such as La Repubblica and La Stampa.

An Italian Network of Memes, News, Spam Pages, and Dietary Supplements (preprint)

In the context of a larger study on the spread of Covid-19 related mis/disinformation in Italy, we detected a network of 34 Facebook Pages that performed Coordinated Link Sharing. The potential reach of the network is significant with a cumulative subscriber count of over 6 million users. Each month, the network publishes more than 20,000 posts. Due to the number of posts and users, the interaction rate is relatively low with less than 0.05 interaction per user in a month. Despite that, top monthly posts have been shared thousands of times. The large majority of content posted consists of inspirational sentences conveyed as image macros. However, 10% of the photo includes links in the message/description of the post. During mid-October, the network started posting links to well-respected journalistic news sources such as La Repubblica and La Stampa. We hypothesize that the aim of this strategy is to meddle with Facebook’s policy that prioritizes news from trustworthy publications and/or to mitigate the effects of circulation penalties gained by violating the platform community standards. The goal of the network is to drive traffic to the billoccino.com domain and monetize by selling online parapharmaceutical products (e.g. dietary supplements).

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

We use a global metapopulation transmission model to study the establishment of sustained and undetected community transmission of the COVID-19 epidemic 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.

Propagation of epidemics along lines with fast diffusion (preprint)

It has long been known that epidemics can travel along communication lines, such as roads. In the current COVID-19 epidemic, it has been observed that major roads have enhanced its propagation in Italy. We propose a new simple model of propagation of epidemics which exhibits this effect and allows for a quantitative analysis. The model consists of a classical $SIR$ model with diffusion, to which an additional compartment is added, formed by the infected individuals travelling on a line of fast diffusion. Exchanges between individuals on the line and in the rest of the domain are taken into account. A classical transformation allows us to reduce the proposed model to a system analogous to one we had previously introduced [5] to describe the enhancement of biological invasions by lines of fast diffusion. We establish the existence of a minimal spreading speed and we show that it may be quite large, even when the basic reproduction number $R_0$ is close to $1$. More subtle qualitative features of the final state, showing the important influence of the line, are also proved here.

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

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.