Predicting the evolution of COVID-19 mortality risk: A Recurrent Neural Network approach.

Background: In December 2020, the COVID-19 disease was confirmed in 1,665,775 patients and caused 45,784 deaths in Spain. At that time, health decision support systems were identified as crucial against the pandemic. Methods: This study applies Deep Learning techniques for mortality prediction of COVID-19 patients. Two datasets with clinical information were used. They included 2,307 and 3,870 COVID-19 infected patients admitted to two Spanish hospitals. Firstly, we built a sequence of temporal events gathering all the clinical information for each patient, comparing different data representation methods. Next, we used the sequences to train a Recurrent Neural Network (RNN) model with an attention mechanism exploring interpretability. We conducted an extensive hyperparameter search and cross-validation. Finally, we ensembled the resulting RNNs to enhance sensitivity. Results: We assessed the performance of our models by averaging the performance across all the days in the sequences. Additionally, we evaluated day-by-day predictions starting from both the hospital admission day and the outcome day. We compared our models with two strong baselines, Support Vector Classifier and Random Forest, and in all cases our models were superior. Furthermore, we implemented an ensemble model that substantially increased the system's sensitivity while producing more stable predictions. Conclusions: We have shown the feasibility of our approach to predicting the clinical outcome of patients. The result is an RNN-based model that can support decision-making in healthcare systems aiming at interpretability. The system is robust enough to deal with real-world data and can overcome the problems derived from the sparsity and heterogeneity of data.

Evaluating the policy of closing bars and restaurants in Cataluña and its effects on mobility and COVID19 incidence.

The world has gone through unprecedented changes since the global pandemic hit. During the early phase of the pandemic, the absence of known drugs or pharmaceutical treatments forced governments to introduce different policies in order to help reduce contagion rates and manage the economic consequences of the pandemic. This paper analyses the causal impact on mobility and COVID19 incidence from policy makers in Cataluña, Spain. We use anonymized phone-based mobility data together with reported incidence and apply a series of causal impact models frequently used in econometrics and policy evaluation in order to measure the policies impact. We analyse the case of Cataluña and the public policy decision of closing all bars and restaurants down for a 5 week period between 2020-16-10 and 2020-23-11. We find that this decision led to a significant reduction in mobility. It not only led to reductions in mobility but from a behavioural economics standpoint, we highlight how people responded to the policy decision. Moreover, the policy of closing bars and restaurants slowed the incidence rate of COVID19 after a time lag has been taken into account. These findings are significant since governments worldwide want to restrict movements of people in order to slow down COVID19 incidence without infringing on their rights directly.

Heterogeneous Infectivity and Pathogenesis of SARS-CoV-2 Variants Beta, Delta and Omicron in Transgenic K18-hACE2 and Wildtype Mice.

The emerging SARS-CoV-2 variants of concern (VOCs) may display enhanced transmissibility, more severity and/or immune evasion; however, the pathogenesis of these new VOCs in experimental SARS-CoV-2 models or the potential infection of other animal species is not completely understood. Here we infected K18-hACE2 transgenic mice with B.1, B.1.351/Beta, B.1.617.2/Delta and BA.1.1/Omicron isolates and demonstrated heterogeneous infectivity and pathogenesis. B.1.351/Beta variant was the most pathogenic, while BA.1.1/Omicron led to lower viral RNA in the absence of major visible clinical signs. In parallel, we infected wildtype (WT) mice and confirmed that, contrary to B.1 and B.1.617.2/Delta, B.1.351/Beta and BA.1.1/Omicron can infect them. Infection in WT mice coursed without major clinical signs and viral RNA was transient and undetectable in the lungs by day 7 post-infection. In silico modeling supported these findings by predicting B.1.351/Beta receptor binding domain (RBD) mutations result in an increased affinity for both human and murine ACE2 receptors, while BA.1/Omicron RBD mutations only show increased affinity for murine ACE2.

Clinical course impacts early kinetics,magnitude, and amplitude of SARS-CoV-2 neutralizing antibodies beyond 1 year after infection.

To understand the determinants of long-term immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the concurrent impact of vaccination and emerging variants, we follow a prospective cohort of 332 patients with coronavirus disease 2019 (COVID-19) over more than a year after symptom onset. We evaluate plasma-neutralizing activity using HIV-based pseudoviruses expressing the spike of different SARS-CoV-2 variants and analyze them longitudinally using mixed-effects models. Long-term neutralizing activity is stable beyond 1 year after infection in mild/asymptomatic and hospitalized participants. However, longitudinal models suggest that hospitalized individuals generate both short- and long-lived memory B cells, while the responses of non-hospitalized individuals are dominated by long-lived B cells. In both groups, vaccination boosts responses to natural infection. Long-term (>300 days from infection) responses in unvaccinated participants show a reduced efficacy against beta, but not alpha nor delta, variants. Multivariate analysis identifies the severity of primary infection as an independent determinant of higher magnitude and lower relative cross-neutralization activity of long-term neutralizing responses.

COVID-19 Flow-Maps an open geographic information system on COVID-19 and human mobility for Spain.

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus, which has spread all over the world leading to a global pandemic. The fast progression of COVID-19 has been mainly related to the high contagion rate of the virus and the worldwide mobility of humans. In the absence of pharmacological therapies, governments from different countries have introduced several non-pharmaceutical interventions to reduce human mobility and social contact. Several studies based on Anonymized Mobile Phone Data have been published analysing the relationship between human mobility and the spread of coronavirus. However, to our knowledge, none of these data-sets integrates cross-referenced geo-localised data on human mobility and COVID-19 cases into one all-inclusive open resource. Herein we present COVID-19 Flow-Maps, a cross-referenced Geographic Information System that integrates regularly updated time-series accounting for population mobility and daily reports of COVID-19 cases in Spain at different scales of time spatial resolution. This integrated and up-to-date data-set can be used to analyse the human dynamics to guide and support the design of more effective non-pharmaceutical interventions.

Systems Biology at the giga-scale: large multi-scale models of complex, heterogeneous multicellular systems

Agent-based modelling has proven its usefulness in several biomedical projects by explaining and uncovering mechanisms in diseases. Nevertheless, the scenarios addressed in these models usually consider a small number of cells, lack cell-specific characterisation and dynamic interactions and have a simplistic environment description. Tools that enable scalable, real-sized simulations of biological systems that require complex set-ups are needed to have simulations closer to biomedical scenarios that can capture cell-to-cell heterogeneity and system-wide emerging properties. To deliver simulations at the giga-scale (109 cells), different tools have implemented technologies to run in high-performance computing clusters. We hereby review these efforts and detail the main areas of improvement the field needs to focus on to have simulations that are a step closer to having digital twins.

Pigs are not susceptible to SARS-CoV-2 infection but are a model for viral immunogenicity studies.

Conventional piglets were inoculated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through different routes, including intranasal, intratracheal, intramuscular and intravenous ones. Although piglets were not susceptible to SARS-CoV-2 and lacked lesions or viral RNA in tissues/swabs, seroconversion was observed in pigs inoculated parenterally (intramuscularly or intravenously).

Identification of Plitidepsin as Potent Inhibitor of SARS-CoV-2-Induced Cytopathic Effect After a Drug Repurposing Screen.

There is an urgent need to identify therapeutics for the treatment of Coronavirus disease 2019 (COVID-19). Although different antivirals are given for the clinical management of SARS-CoV-2 infection, their efficacy is still under evaluation. Here, we have screened existing drugs approved for human use in a variety of diseases, to compare how they counteract SARS-CoV-2-induced cytopathic effect and viral replication in vitro. Among the potential 72 antivirals tested herein that were previously proposed to inhibit SARS-CoV-2 infection, only 18 % had an IC50 below 25 µM or 102 IU/ml. These included plitidepsin, novel cathepsin inhibitors, nelfinavir mesylate hydrate, interferon 2-alpha, interferon-gamma, fenofibrate, camostat along the well-known remdesivir and chloroquine derivatives. Plitidepsin was the only clinically approved drug displaying nanomolar efficacy. Four of these families, including novel cathepsin inhibitors, blocked viral entry in a cell-type specific manner. Since the most effective antivirals usually combine therapies that tackle the virus at different steps of infection, we also assessed several drug combinations. Although no particular synergy was found, inhibitory combinations did not reduce their antiviral activity. Thus, these combinations could decrease the potential emergence of resistant viruses. Antivirals prioritized herein identify novel compounds and their mode of action, while independently replicating the activity of a reduced proportion of drugs which are mostly approved for clinical use. Combinations of these drugs should be tested in animal models to inform the design of fast track clinical trials.

Protection against reinfection with D614- or G614-SARS-CoV-2 isolates in golden Syrian hamster.

Reinfections with SARS-CoV-2 have already been documented in humans, although its real incidence is currently unknown. Besides having a great impact on public health, this phenomenon raises the question of immunity generated by a single infection is sufficient to provide sterilizing/protective immunity to a subsequent SARS-CoV-2 re-exposure. The Golden Syrian hamster is a manageable animal model to explore immunological mechanisms able to counteract COVID-19, as it recapitulates pathological aspects of mild to moderately affected patients. Here, we report that SARS-CoV-2-inoculated hamsters resolve infection in the upper and lower respiratory tracts within seven days upon inoculation with the Cat01 (G614) SARS-CoV-2 isolate. Three weeks after the primary challenge, and despite high titres of neutralizing antibodies, half of the animals were susceptible to reinfection by both identical (Cat01, G614) and variant (WA/1, D614) SARS-CoV-2 isolates. However, upon re-inoculation, only nasal tissues were transiently infected with much lower viral replication than those observed after the first inoculation. These data indicate that a primary SARS-CoV-2 infection is not sufficient to elicit a sterilizing immunity in hamster models but protects against lung disease.

Stable neutralizing antibody levels 6 months after mild and severe COVID-19 episodes.

BACKGROUND: Understanding mid-term kinetics of immunity to SARS-CoV-2 is the cornerstone for public health control of the pandemic and vaccine development. However, current evidence is rather based on limited measurements, losing sight of the temporal pattern of these changes. METHODS: We conducted a longitudinal analysis on a prospective cohort of COVID-19 patients followed up for >6 months. Neutralizing activity was evaluated using HIV reporter pseudoviruses expressing SARS-CoV-2 S protein. IgG antibody titer was evaluated by ELISA against the S2 subunit, the receptor binding domain (RBD), and the nucleoprotein (NP). Statistical analyses were carried out using mixed-effects models. FINDINGS: We found that individuals with mild or asymptomatic infection experienced an insignificant decay in neutralizing activity, which persisted 6 months after symptom onset or diagnosis. Hospitalized individuals showed higher neutralizing titers, which decreased following a 2-phase pattern, with an initial rapid decline that significantly slowed after day 80. Despite this initial decay, neutralizing activity at 6 months remained higher among hospitalized individuals compared to mild symptomatic. The slow decline in neutralizing activity at mid-term contrasted with the steep slope of anti-RBD, S2, or NP antibody titers, all of them showing a constant decline over the follow-up period. CONCLUSIONS: Our results reinforce the hypothesis that the quality of the neutralizing immune response against SARS-CoV-2 evolves over the post-convalescent stage.

Artificial Intelligence-Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development.

Artificial intelligence (AI) technologies can play a key role in preventing, detecting, and monitoring epidemics. In this paper, we provide an overview of the recently published literature on the COVID-19 pandemic in four strategic areas: (1) triage, diagnosis, and risk prediction; (2) drug repurposing and development; (3) pharmacogenomics and vaccines; and (4) mining of the medical literature. We highlight how AI-powered health care can enable public health systems to efficiently handle future outbreaks and improve patient outcomes.

Simulating SARS-CoV-2 epidemics by region-specific variables and modeling contact tracing app containment.

Targeted contact-tracing through mobile phone apps has been proposed as an instrument to help contain the spread of COVID-19 and manage the lifting of nation-wide lock-downs currently in place in USA and Europe. However, there is an ongoing debate on its potential efficacy, especially in light of region-specific demographics. We built an expanded SIR model of COVID-19 epidemics that accounts for region-specific population densities, and we used it to test the impact of a contact-tracing app in a number of scenarios. Using demographic and mobility data from Italy and Spain, we used the model to simulate scenarios that vary in baseline contact rates, population densities, and fraction of app users in the population. Our results show that, in support of efficient isolation of symptomatic cases, app-mediated contact-tracing can successfully mitigate the epidemic even with a relatively small fraction of users, and even suppress altogether with a larger fraction of users. However, when regional differences in population density are taken into consideration, the epidemic can be significantly harder to contain in higher density areas, highlighting potential limitations of this intervention in specific contexts. This work corroborates previous results in favor of app-mediated contact-tracing as mitigation measure for COVID-19, and draws attention on the importance of region-specific demographic and mobility factors to achieve maximum efficacy in containment policies.

SARS-CoV-2 infection elicits a rapid neutralizing antibody response that correlates with disease severity.

The protective effect of neutralizing antibodies in SARS-CoV-2 infected individuals is not yet well defined. To address this issue, we have analyzed the kinetics of neutralizing antibody responses and their association with disease severity. Between March and May 2020, the prospective KING study enrolled 72 COVID-19+ participants grouped according to disease severity. SARS-CoV-2 infection was diagnosed by serological and virological tests. Plasma neutralizing responses were assessed against replicative virus and pseudoviral particles. Multiple regression and non-parametric tests were used to analyze dependence of parameters. The magnitude of neutralizing titers significantly increased with disease severity. Hospitalized individuals developed higher titers compared to mild-symptomatic and asymptomatic individuals, which together showed titers below the detection limit in 50% of cases. Longitudinal analysis confirmed the strong differences in neutralizing titers between non-hospitalized and hospitalized participants and showed rapid kinetics of appearance of neutralizing antibodies (50% and 80% of maximal activity reached after 11 and 17 days after symptoms onset, respectively) in hospitalized patients. No significant impact of age, gender or treatment on the neutralizing titers was observed in this limited cohort. These data identify a clear association of humoral immunity with disease severity and point to immune mechanisms other than antibodies as relevant players in COVID-19 protection.

Unraveling the molecular basis of host cell receptor usage in SARS-CoV-2 and other human pathogenic ß-CoVs.

The recent emergence of the novel SARS-CoV-2 in China and its rapid spread in the human population has led to a public health crisis worldwide. Like in SARS-CoV, horseshoe bats currently represent the most likely candidate animal source for SARS-CoV-2. Yet, the specific mechanisms of cross-species transmission and adaptation to the human host remain unknown. Here we show that the unsupervised analysis of conservation patterns across the ß-CoV spike protein family, using sequence information alone, can provide valuable insights on the molecular basis of the specificity of ß-CoVs to different host cell receptors. More precisely, our results indicate that host cell receptor usage is encoded in the amino acid sequences of different CoV spike proteins in the form of a set of specificity determining positions (SDPs). Furthermore, by integrating structural data, in silico mutagenesis and coevolution analysis we could elucidate the role of SDPs in mediating ACE2 binding across the Sarbecovirus lineage, either by engaging the receptor through direct intermolecular interactions or by affecting the local environment of the receptor binding motif. Finally, by the analysis of coevolving mutations across a paired MSA we were able to identify key intermolecular contacts occurring at the spike-ACE2 interface. These results show that effective mining of the evolutionary records held in the sequence of the spike protein family can help tracing the molecular mechanisms behind the evolution and host-receptor adaptation of circulating and future novel ß-CoVs.

Detection of SARS-CoV-2 in a cat owned by a COVID-19-affected patient in Spain.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of COVID-19, is considered a zoonotic pathogen mainly transmitted human to human. Few reports indicate that pets may be exposed to the virus. The present report describes a cat suffering from severe respiratory distress and thrombocytopenia living with a family with several members affected by COVID-19. Clinical signs of the cat prompted humanitarian euthanasia and a detailed postmortem investigation to assess whether a COVID-19-like disease was causing the condition. Necropsy results showed the animal suffered from feline hypertrophic cardiomyopathy and severe pulmonary edema and thrombosis. SARS-CoV-2 RNA was only detected in nasal swab, nasal turbinates, and mesenteric lymph node, but no evidence of histopathological lesions compatible with a viral infection were detected. The cat seroconverted against SARS-CoV-2, further evidencing a productive infection in this animal. We conclude that the animal had a subclinical SARS-CoV-2 infection concomitant to an unrelated cardiomyopathy that led to euthanasia.

Author Correction: COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.