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 data accuracy on the evaluation of COVID-19 mitigation policies

Evaluating the effectiveness of nonpharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic is crucial to maximize the epidemic containment while minimizing the social and economic impact of these measures. However, this endeavor crucially relies on surveillance data publicly released by health authorities that can hide several limitations. In this article, we quantify the impact of inaccurate data on the estimation of the time-varying reproduction number \( R(t) \), a pivotal quantity to gauge the variation of the transmissibility originated by the implementation of different NPIs. We focus on Italy and Spain, two European countries among the most severely hit by the COVID-19 pandemic. For these two countries, we highlight several biases of case-based surveillance data and temporal and spatial limitations in the data regarding the implementation of NPIs. We also demonstrate that a nonbiased estimation of \( R(t) \) could have had direct consequences on the decisions taken by the Spanish and Italian governments during the first wave of the pandemic. Our study shows that extreme care should be taken when evaluating intervention policies through publicly available epidemiological data and call for an improvement in the process of COVID-19 data collection, management, storage, and release. Better data policies will allow a more precise evaluation of the effects of containment measures, empowering public health authorities to take more informed decisions.

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.

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.

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.

Evaluation of the incidence of COVID-19 and of the efficacy of contention measures in Spain: a data-driven approach. (preprint)

Our society is currently experiencing an unprecedented challenge, managing and containing an outbreak of a new coronavirus disease known as COVID-19. While China - were the outbreak started - seems to have been able to contain the growth of the epidemic, different outbreaks are nowadays being detected in multiple countries. Much is currently unknown about the natural history of the disease, such as a possible asymptomatic spreading or the role of age in both the susceptibility and mortality of the disease. Nonetheless, authorities have to take action and implement contention measures, even if not everything is known. To facilitate this task, we have studied the effect of different containment strategies that can be put into effect. Our work specifically refers to the situation in Spain as of February 28th, 2020, where a few dozens of cases have been detected. We implemented an SEIR-metapopulation model that allows tracing explicitly the spatial spread of the disease through data-driven stochastic simulations. Our results are in line with the most recent recommendations from the World Health Organization, namely, that the best strategy is the early detection and isolation of individuals with symptoms, followed by interventions and public recommendations aimed at reducing the transmissibility of the disease, which although not efficacious for disease eradication, would produce as a second-order effect a delay of several days in the raise of the number of infected cases