Contagion dynamics on higher-order networks (preprint)

Understanding the dissemination of diseases, information, and behavior stands as a paramount research challenge in contemporary network and complex systems science. The COVID-19 pandemic and the proliferation of misinformation are relevant examples of the importance of these dynamic processes, which have recently gained more attention due to the potential of higher-order networks to unlock new avenues for their investigation. Despite being in its early stages, the examination of social contagion in higher-order networks has witnessed a surge of novel research and concepts, revealing different functional forms for the spreading dynamics and offering novel insights. This review presents a focused overview of this body of literature and proposes a unified formalism that covers most of these forms. The goal is to underscore the similarities and distinctions among various models, to motivate further research on the general and universal properties of such models. We also highlight that while the path for additional theoretical exploration appears clear, the empirical validation of these models through data or experiments remains scant, with an unsettled roadmap as of today. We therefore conclude with some perspectives aimed at providing possible research directions that could contribute to a better understanding of this class of dynamical processes, both from a theoretical and a data-oriented point of view.

Characterising the role of human behaviour in the effectiveness of contact-tracing applications (preprint)

Albeit numerous countries relied on contact-tracing (CT) applications as an epidemic control measure against the COVID-19 pandemic, the debate around their effectiveness is still open. Most studies indicate that very high levels of adoption are required to stop disease progression, placing the main interest of policymakers in promoting app adherence. However, other factors of human behaviour, like delays in adherence or heterogeneous compliance, are often disregarded. To characterise the impact of human behaviour on the effectiveness of CT apps we propose a multilayer network model reflecting the co-evolution of an epidemic outbreak and the app adoption dynamics over a synthetic population generated from survey data. The model was initialised to produce epidemic outbreaks resembling the first wave of the COVID-19 pandemic and was used to explore the impact of different changes in behavioural features in peak incidence and maximal prevalence. The results corroborate the relevance of the number of users for the effectiveness of CT apps but also highlight the need for early adoption and, at least, moderate levels of compliance, which are factors often not considered by most policymakers. The insight obtained was used to identify a bottleneck in the implementation of several apps, such as the Spanish CT app, where we hypothesise that a simplification of the reporting system could result in increased effectiveness through a rise in the levels of compliance.

An informational approach to uncover the age group interactions in epidemic spreading from macro analysis (preprint)

We investigate the use of transfer entropy (TE) as a proxy to detect the contact patterns of the population in epidemic processes. We first apply the measure to a classical age-stratified SIR model and observe that the recovered patterns are consistent with the age-mixing matrix that encodes the interaction of the population. We then apply the TE analysis to real data from the COVID-19 pandemic in Spain and show that it can provide information on how the behavior of individuals changed through time. We also demonstrate how the underlying dynamics of the process allow us to build a coarse-grained representation of the time series that provides more information than raw time series. The macro-level representation is a more effective scale for analysis, which is an interesting result within the context of causal analysis across different scales. These results open the path for more research on the potential use of informational approaches to extract retrospective information on how individuals change and adapt their behavior during a pandemic, which is essential for devising adequate strategies for an efficient control of the spreading.

Digital cities and the spread of COVID-19: characterizing the impact of non-pharmaceutical interventions in five cities in Spain (preprint)

Mathematical modeling has been fundamental to achieving near real-time accurate forecasts of the spread of COVID-19. Similarly, the design of non-pharmaceutical interventions has played a key role in the application of policies to contain the spread. However, there is less work done regarding quantitative approaches to characterize the impact of each intervention, which can greatly vary depending on the culture, region, and specific circumstances of the population under consideration. In this work, we develop a high-resolution, data-driven agent-based model of the spread of COVID-19 among the population in five Spanish cities. These populations synthesize multiple data sources that summarize the main interaction environments leading to potential contacts. We simulate the spreading of COVID-19 in these cities and study the effect of several non-pharmaceutical interventions. We illustrate the potential of our approach through a case study and derive the impact of the most relevant interventions through scenarios where they are suppressed. Our framework constitutes a first tool to simulate different intervention scenarios for decision-making.

The unequal effects of the health-economy tradeoff during the COVID-19 pandemic (preprint)

The potential tradeoff between health outcomes and economic impact has been a major challenge in the policy making process during the COVID-19 pandemic. Epidemic-economic models designed to address this issue are either too aggregate to consider heterogeneous outcomes across socio-economic groups, or, when sufficiently fine-grained, not well grounded by empirical data. To fill this gap, we introduce a data-driven, granular, agent-based model that simulates epidemic and economic outcomes across industries, occupations, and income levels with geographic realism. The key mechanism coupling the epidemic and economic modules is the reduction in consumption demand due to fear of infection. We calibrate the model to the first wave of COVID-19 in the New York metropolitan area, showing that it reproduces key epidemic and economic statistics, and then examine counterfactual scenarios. We find that: (a) both high fear of infection and strict restrictions similarly harm the economy but reduce infections; (b) low-income workers bear the brunt of both the economic and epidemic harm; (c) closing non-customer-facing industries such as manufacturing and construction only marginally reduces the death toll while considerably increasing unemployment; and (d) delaying the start of protective measures does little to help the economy and worsens epidemic outcomes in all scenarios. We anticipate that our model will help designing effective and equitable non-pharmaceutical interventions that minimize disruptions in the face of a novel pandemic.

Impact of vaccine hesitancy on secondary COVID-19 outbreaks in the US (preprint)

The COVID-19 outbreak has become the worst pandemic in at least a century. To fight this disease, a global effort led to the development of several vaccines at an unprecedented rate. There have been, however, several logistic issues with its deployment, from their production and transport, to the hesitancy of the population to be vaccinated. For different reasons, an important amount of individuals is reluctant to get the vaccine, something that hinders our ability to control and - eventually - eradicate the disease. In this work, we analyze the impact that this hesitancy might have in a context in which a more transmissible SARS-CoV-2 variant of concern spreads through a partially vaccinated population. We use age-stratified data from surveys on vaccination acceptance, together with age-contact matrices to inform an age-structured SIR model set in the US. Our results clearly show that higher vaccine hesitancy ratios led to larger outbreaks. A closer inspection of the stratified infection rates also reveals the important role played by the youngest groups. Our results could shed some light on the role that hesitancy will play in the near future and inform policy-makers and the general public of the importance of reducing it.

Unraveling the COVID-19 hospitalization dynamics in Spain using publicly available data (preprint)

Summary Background One of the main challenges of the ongoing COVID-19 pandemic is to be able to make sense of available, but often heterogeneous and noisy data, to characterize the evolution of the SARS-CoV-2 infection dynamics, with the additional goal of having better preparedness and planning of healthcare services. This contribution presents a data-driven methodology that allows exploring the hospitalization dynamics of COVID-19, exemplified with a study of 17 autonomous regions in Spain. Methods We use data on new daily cases and hospitalizations reported by the Ministry of Health of Spain to implement a Bayesian inference method that allows making short and mid-term predictions of bed occupancy of COVID-19 patients in each of the autonomous regions of the country. Findings We show how to use given and generated temporal series for the number of daily admissions and discharges from hospital to reproduce the hospitalization dynamics of COVID-19 patients. For the case-study of the region of Aragon, we estimate that the probability of being admitted to hospital care upon infection is 0·090 [0·086-0·094], (95% C.I.), with the distribution governing hospital admission yielding a median interval of 3·5 days and an IQR of 7 days. Likewise, the distribution on the length of stay produces estimates of 12 days for the median and 10 days for the IQR. A comparison between model parameters for the regions analyzed allows to detect differences and changes in policies of the health authorities. Interpretation The amount of data that is currently available is limited, and sometimes unreliable, hindering our understanding of many aspects of this pandemic. We have observed important regional differences, signaling that to properly compare very different populations, it is paramount to acknowledge all the diversity in terms of culture, socio-economic status and resource availability. To better understand the impact of this pandemic, much more data, disaggregated and properly annotated, should be made available.

A significant increase in Tuberculosis diagnosis is required to mitigate the impact of COVID-19 on its future burden (preprint)

The ongoing COVID-19 pandemic has greatly disrupted our everyday life, forcing the adoption of non-pharmaceutical interventions in many countries worldwide and putting public health services and healthcare systems worldwide under stress. These circumstances are leading to unintended effects such as the increase in the burden of other diseases. Here, using a data-driven epidemiological model for Tuberculosis (TB) spreading, we describe the expected rise in TB incidence and mortality that can be attributable to the impact of COVID-19 on TB surveillance and treatment in four high-burden countries. Our calculations show that the reduction in diagnosis of new TB cases due to the COVID-19 pandemic could result in 824250 (CI 702416-940873) excess deaths in India, 288064 (CI 245932-343311) in Indonesia, 145872 (CI 120734-171542) in Pakistan, and 37603 (CI 27852-52411) in Kenya. Furthermore, we show that it is possible to revert such unflattering TB burden scenarios by increasing the pre-covid diagnosis capabilities at least a 75% during 2 to 4 years. This would prevent almost all TB-related excess mortality caused by the COVID-19 pandemic, which will be observed if nothing is done to prevent it. Our work therefore provides guidelines for mitigating the impact of COVID-19 on tuberculosis epidemic in the years to come.

Data-driven estimate of SARS-CoV-2 herd immunity threshold in populations with individual contact pattern variations (preprint)

The development of efficacious vaccines has made it possible to envision mass vaccination programs aimed at suppressing SARS-CoV-2 transmission around the world. Here we use a data-driven age-structured multilayer representation of the population of 34 countries to estimate the disease induced immunity threshold, accounting for the contact variability across individuals. We show that the herd immunization threshold of random (un-prioritized) mass vaccination programs is generally larger than the disease induced immunity threshold. We use the model to test two additional vaccine prioritization strategies, transmission-focused and age-based, in which individuals are inoculated either according to their behavior (number of contacts) or infection fatality risk, respectively. Our results show that in the case of a sterilizing vaccine the behavioral strategy achieves herd-immunity at a coverage comparable to the disease-induced immunity threshold, but it appears to have inferior performance in averting deaths than the risk vaccination strategy. The presented results have potential use in defining the effects that the heterogeneity of social mixing and contact patterns has on herd immunity levels and the deployment of vaccine prioritization strategies.

Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants (preprint)

The COVID-19 pandemic has greatly affected us all, from individuals to the world economy. Whereas great advances have been achieved in record time, a lot remains to be learned about the infection mechanisms of its causative agent, the SARS-CoV-2 coronavirus. The Spike protein interacts with the human angiotensin converting enzyme 2 receptor as part of the viral entry mechanism. To do so, the receptor binding domain (RBD) of Spike needs to be in an open state conformation. Here we utilise coarse-grained normal mode analyses to model the dynamics of the SARS-CoV-2 Spike protein and the transition probabilities between open and closed conformations for the wild type, the D614G mutant as well other variants isolated experimentally. We proceed to perform several possible in silico single mutations of Spike, 17081 in total, to determine positions and specific Spike mutations that may affect the occupancy of the open and closed states. We estimate transition probabilities between the open and closed states from the calculated normal modes. Transition probabilities are employed in a Markov model to determine conformational state occupancies. Our results correctly model a shift in occupancy of the more infectious D614G strain towards higher occupancy of the open state via an increase of flexibility of the closed state and concomitant decrease of flexibility of the open state. Our results also suggest that the N501Y mutation recently observed, drastically increases the occupancy of the open state. We utilize global vibrational entropy differences to select candidate single point mutations that affect the flexibility of the open and closed states and confirm that these lead to shifts in occupancies for the most critical mutations. Among those, we observe a number of mutations on Glycine residues (404, 416, 504) and G252 in particular accepting a number of mutations. Other residues include K417, D467 and N501. This is, to our knowledge, the first use of normal mode analysis to model conformational state transitions and the effect of mutations thereon. The specific mutations of Spike identified here, while still requiring experimental validation, may guide future studies to increase our understanding of SARS-CoV-2 infection mechanisms as well as guide public health in their surveillance efforts.

Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas (preprint)

Detailed characterization of SARS-CoV-2 transmission across different settings can help design less disruptive interventions. We used real-time, privacy-enhanced mobility data in the New York City and Seattle metropolitan areas to build a detailed agent-based model of SARS-CoV-2 infection to estimate the where, when, and magnitude of transmission events during the pandemics first wave. We estimate that only 18% of individuals produce most infections (80%), with about 10% of events that can be considered super-spreading events (SSEs). Although mass-gatherings present an important risk for SSEs, we estimate that the bulk of transmission occurred in smaller events in settings like workplaces, grocery stores, or food venues. The places most important for transmission change during the pandemic and are different across cities, signaling the large underlying behavioral component underneath them. Our modeling complements case studies and epidemiological data and indicates that real-time tracking of transmission events could help evaluate and define targeted mitigation policies.

Suppression of miR-155 attenuates lung cytokine storm induced by SARS-CoV-2 infection in human ACE2-transgenic mice (preprint)

Coronavirus disease 2019 (COVID-19) is a recent global pandemic. It is a deadly human viral disease, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with a high rate of infection, morbidity and mortality. Therefore, there is a great urgency to develop new therapies to control, treat and prevent this disease. Endogenous microRNAs (miRNAs, miRs) of the viral host are key molecules in preventing viral entry and replication, and building an antiviral cellular defense. Here, we have analyzed the role of miR-155, one of the most powerful drivers of host antiviral responses including immune and inflammatory responses, in the pathogenicity of SARS-CoV-2 infection. Subsequently, we have analyzed the potency of anti-miR-155 therapy in a COVID-19 mouse model (mice transgenic for human angiotensin I- converting enzyme 2 receptor (tg-mice hACE2)). We report for the first time that miR-155 expression is elevated in COVID-19 patients. Further, our data indicate that the viral load as well as miR-155 levels are higher in male relative to female patients. Moreover, we find that the delivery of anti-miR-155 to SARS-CoV-2-infected tg-mice hACE2 effectively suppresses miR-155 expression, and leads to improved survival and clinical scores. Importantly, anti-miR-155-treated tg-mice hACE2 infected with SARS-CoV-2 not only exhibit reduced levels of pro-inflammatory cytokines, but also have increased anti-viral and anti-inflammatory cytokine responses in the lungs. Thus, our study suggests anti-miR-155 as a novel therapy for mitigating the lung cytokine storm induced by SARS-CoV-2 infection.

Molecular diversity analysis of the spike glycoprotein (S) gene from Hong Kong - China (preprint)

In this work, 37 haplotypes of spike glycoprotein of SARS-CoV-2 from Hong Kong, China, were used. All sequences were publicly available on the Platform of the National Center for Biotechnology Information (NCBI) and were analyzed for their Molecular Variance (AMOVA), haplotypic diversity, mismatch, demographic and spatial expansion, molecular diversity and time of evolutionary divergence. The results suggested that there was a low diversity among haplotypes, with very low numbers of transitions, transversions, indels-type mutations and with total absence of population expansion perceived in the neutrality tests. The estimators used in this study supported the uniformity among all the results found and confirm the evolutionary conservation of the gene, as well as its protein product, a fact that stimulates the use of therapies based on neutralizing antibodies, such as vaccines based on protein S.

Ruling the Roost: Avian Species Reclaim Urban Habitat During India's COVID Lockdown (preprint)

As we retreated to our dwellings in the "anthropause" of spring 2020, did other species return to our urban centres? We leverage an increase in balcony birdwatching, a million eBird entries, and difference-in-difference techniques to test if avian species richness rose during Indias COVID lockdown. We find that birdwatchers in Indias 20 most populous cities observed 8-17% more species during the lockdown. Most additional observations occurred after a two-week lag, signaling greater abundance instead of improved detection. More frequent appearances of at-risk, rare, and common species were recorded, implying that making our cities more wildlife friendly can protect threatened species in addition to urban specialists. Our contributions are: 1) to isolate and estimate a causal impact of reducing human activity on avian diversity, 2) to improve the external validity of this literature in rapidly urbanizing bio-diverse developing countries, and, 3) to illustrate a method separating abundance from detection in observational avian surveys.

The rapid, massive infection of the scientific literature and authors by COVID-19 (preprint)

ImportanceCOVID-19 is a major global crisis and the scientific community has been mobilized to deal with this crisis. ObjectiveTo estimate the extent to which the scientific workforce in different fields has been engaged publishing papers relative to the COVID-19 pandemic. Design, setting, and participantsWe evaluated Scopus (data cut, December 1, 2020) for all indexed published papers and preprints relevant to COVID-19. We mapped this COVID-19 literature in terms of its authors across 174 subfields of science according to the Science Metrix classification. We also evaluated the extent to which the most influential scientists across science (based on a composite citation indicator) had published COVID-19-related research. Finally, we assessed the features of authors who published the highest number of COVID-19 publications and of those with the highest impact in the COVID-19 field based on the composite citation indicator limited to COVID-19 publications. Main outcomes and measuresPublishing scientists (authors) and their published papers and citation impact. Results84,180 indexed publications were relevant to COVID-19 including 322,279 unique authors. The highest rates of COVID-19 publications were seen for authors classified in Public Health and in Clinical Medicine, where 11.3% (6,388/56,516) and 11.1% (92,570/833,060) of authors, respectively, had published on COVID-19. Almost all (173/174) subfields (except for Automobile Design & Engineering) had some authors publishing on COVID-19. Among active scientists at the top 2% of citation impact, 15,803 (13.3%) had published on COVID-19 in their publications in the first 11 months of 2020. The rates were the highest in the fields of Clinical Medicine (27.7%) and Public Health (26.8%). In 83 of the 174 subfields of science, at least one in ten active, influential authors in that field had authored something on COVID-19. 65 authors had already at least 30 (and up to 133) COVID-19 publications each. Among the 300 authors with the highest composite citation indicator for COVID-19 publications, 26 were journalists or editors publishing news stories or editorials in prestigious journals; most common countries for the remaining were China (n=77), USA (n=66), UK (n=27), and Italy (n=20). Conclusions and relevanceThe scientific literature and publishing scientists have been rapidly and massively infected by COVID-19 creating opportunities and challenges. There is evidence for hyper-prolific productivity.

Age differential analysis of COVID-19 second wave in Europe reveals highest incidence among young adults (preprint)

Most of the western nations have been unable to suppress the COVID-19 and are currently experiencing second or third surges of the pandemic. Here, we analyze data of incidence by age groups in 25 European countries, revealing that the highest incidence of the current second wave is observed for the group comprising young adults (aged 18-29 years old) in all but 3 of the countries analyzed. We discuss the public health implications of our findings.

Impact of the accuracy of case-based surveillance data on the estimation of time-varying reproduction numbers (preprint)

Studies aimed at characterizing the evolution of COVID-19 disease often rely on case-based surveillance data publicly released by health authorities, that can be incomplete and prone to errors. Here, we quantify the biases caused by the use of inaccurate data in the estimation of the Time-Varying Reproduction Number R(t). By focusing on Italy and Spain, two of the hardest-hit countries in Europe and worldwide, we show that if the symptoms’ onset time-series is inferred from the notification date series, the R(t) curve cannot capture nor describe accurately the early dynamics of the epidemic. Furthermore, the effectiveness of the containment measures that were implemented, such as national lockdowns, can be properly evaluated only when R(t) is estimated using the real time-series of dates of symptoms’ onset. Our findings show that extreme care should be taken when a pivotal quantity like R(t) is used to make decisions and to evaluate different alternatives.

Modeling the impact of social distancing, testing, contact tracing and household quarantine on second-wave scenarios of the COVID-19 epidemic (preprint)

The new coronavirus disease 2019 (COVID-19) has required the implementation of severe mobility restrictions and social distancing measures worldwide. While these measures have been proven effective in abating the epidemic in several countries, it is important to estimate the effectiveness of testing and tracing strategies to avoid a potential second wave of the COVID-19 epidemic. We integrate highly detailed (anonymized, privacy-enhanced) mobility data from mobile devices, with census and demographic data to build a detailed agent-based model to describe the transmission dynamics of SARS-CoV-2 in the Boston metropolitan area. We find that enforcing strict social distancing followed by a policy based on a robust level of testing, contact-tracing and household quarantine, could keep the disease at a level that does not exceed the capacity of the health care system. Assuming the identification of 50% of the symptomatic infections, and the tracing of 40% of their contacts and households, which corresponds to about 9% of individuals quarantined, the ensuing reduction in transmission allows the reopening of economic activities while attaining a manageable impact on the health care system. Our results show that a response system based on enhanced testing and contact tracing can play a major role in relaxing social distancing interventions in the absence of herd immunity against SARS-CoV-2.

A data-driven assessment of early travel restrictions related to the spreading of the novel COVID-19 within mainland China (preprint)

Two months after it was firstly reported, the novel coronavirus disease COVID-19 has already spread worldwide. However, the vast majority of reported infections have occurred in China. To assess the effect of early travel restrictions adopted by the health authorities in China, we have implemented an epidemic metapopulation model that is fed with mobility data corresponding to 2019 and 2020. This allows to compare two radically different scenarios, one with no travel restrictions and another in which mobility is reduced by a travel ban. Our findings indicate that i) travel restrictions are an effective measure in the short term, however, ii) they are ineffective when it comes to completely eliminate the disease. The latter is due to the impossibility of removing the risk of seeding the disease to other regions. Our study also highlights the importance of developing more realistic models of behavioral changes when a disease outbreak is unfolding.

Are people excessively pessimistic about the risk of coronavirus infection? (preprint)

The recent emergence of the SARS-CoV-2 in China has raised the spectre of a novel, potentially catastrophic pandemic in both scientific and lay communities throughout the world. In this particular context, people have been accused of being excessively pessimistic regarding the future consequences of this emerging health threat. However, consistent with previous research in social psychology, a large survey conducted in Europe in the early stage of the COVID-19 epidemic shows that the majority of respondents was actually overly optimistic about the risk of infection.