Joint Analysis of the Epidemic Evolution and Human Mobility During the First Wave of COVID-19 in Spain: Retrospective Study.

BACKGROUND: The initial wave of the COVID-19 pandemic placed a tremendous strain on health care systems worldwide. To mitigate the spread of the virus, many countries implemented stringent nonpharmaceutical interventions (NPIs), which significantly altered human behavior both before and after their enactment. Despite these efforts, a precise assessment of the impact and efficacy of these NPIs, as well as the extent of human behavioral changes, remained elusive. OBJECTIVE: In this study, we conducted a retrospective analysis of the initial wave of COVID-19 in Spain to better comprehend the influence of NPIs and their interaction with human behavior. Such investigations are vital for devising future mitigation strategies to combat COVID-19 and enhance epidemic preparedness more broadly. METHODS: We used a combination of national and regional retrospective analyses of pandemic incidence alongside large-scale mobility data to assess the impact and timing of government-implemented NPIs in combating COVID-19. Additionally, we compared these findings with a model-based inference of hospitalizations and fatalities. This model-based approach enabled us to construct counterfactual scenarios that gauged the consequences of delayed initiation of epidemic response measures. RESULTS: Our analysis demonstrated that the pre-national lockdown epidemic response, encompassing regional measures and heightened individual awareness, significantly contributed to reducing the disease burden in Spain. The mobility data indicated that people adjusted their behavior in response to the regional epidemiological situation before the nationwide lockdown was implemented. Counterfactual scenarios suggested that without this early epidemic response, there would have been an estimated 45,400 (95% CI 37,400-58,000) fatalities and 182,600 (95% CI 150,400-233,800) hospitalizations compared to the reported figures of 27,800 fatalities and 107,600 hospitalizations, respectively. CONCLUSIONS: Our findings underscore the significance of self-implemented prevention measures by the population and regional NPIs before the national lockdown in Spain. The study also emphasizes the necessity for prompt and precise data quantification prior to enacting enforced measures. This highlights the critical interplay between NPIs, epidemic progression, and human behavior. This interdependence presents a challenge in predicting the impact of NPIs before they are implemented.

Behavioural response to heterogeneous severity of COVID-19 explains temporal variation of cases among different age groups.

Together with seasonal effects inducing outdoor or indoor activities, the gradual easing of prophylaxis caused second and third waves of SARS-CoV-2 to emerge in various countries. Interestingly, data indicate that the proportion of infections belonging to the elderly is particularly small during periods of low prevalence and continuously increases as case numbers increase. This effect leads to additional stress on the health care system during periods of high prevalence. Furthermore, infections peak with a slight delay of about a week among the elderly compared to the younger age groups. Here, we provide a mechanistic explanation for this phenomenology attributable to a heterogeneous prophylaxis induced by the age-specific severity of the disease. We model the dynamical adoption of prophylaxis through a two-strategy game and couple it with an SIR spreading model. Our results also indicate that the mixing of contacts among the age groups strongly determines the delay between their peaks in prevalence and the temporal variation in the distribution of cases. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.

Modeling Communicable Diseases, Human Mobility, and Epidemics: A Review

The spatiotemporal propagation patterns of recent infectious diseases, originated as localized epidemic outbreaks and eventually becoming global pandemics, are highly influenced by human mobility. Case exportation from endemic areas to the rest of the countries has become unavoidable because of the striking growth of the global mobility network, helping to overcome the physical distance existing between faraway regions. In this context, understanding the features driving contagions upon the arrival of an index case in local environments constitutes an essential task to devise policies aimed at avoiding the community transmission of these diseases and the subsequent case exportation to other unaffected areas. In this review, an overview of the different models addressing this topic is given, focusing on the movement–interaction–return model and different subsequent frameworks introduced to explain the complex interplay between the recurrent movements and contagion dynamics. [ FROM AUTHOR] Copyright of Annalen der Physik is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Emergence of protective behaviour under different risk perceptions to disease spreading.

The behaviour of individuals is a main actor in the control of the spread of a communicable disease and, in turn, the spread of an infectious disease can trigger behavioural changes in a population. Here, we study the emergence of individuals' protective behaviours in response to the spread of a disease by considering two different social attitudes within the same population: concerned and risky. Generally speaking, concerned individuals have a larger risk aversion than risky individuals. To study the emergence of protective behaviours, we couple, to the epidemic evolution of a susceptible-infected-susceptible model, a decision game based on the perceived risk of infection. Using this framework, we find the effect of the protection strategy on the epidemic threshold for each of the two subpopulations (concerned and risky), and study under which conditions risky individuals are persuaded to protect themselves or, on the contrary, can take advantage of a herd immunity by remaining healthy without protecting themselves, thanks to the shield provided by concerned individuals. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.

The interconnection between independent reactive control policies drives the stringency of local containment.

The lack of medical treatments and vaccines upon the arrival of the SARS-CoV-2 virus has made non-pharmaceutical interventions the best allies in safeguarding human lives in the face of the COVID-19 pandemic. Here we propose a self-organized epidemic model with multi-scale control policies that are relaxed or strengthened depending on the extent of the epidemic outbreak. We show that optimizing the balance between the effects of epidemic control and the associated socio-economic cost is strongly linked to the stringency of control measures. We also show that non-pharmaceutical interventions acting at different spatial scales, from creating social bubbles at the household level to constraining mobility between different cities, are strongly interrelated. We find that policy functionality changes for better or worse depending on network connectivity, meaning that some populations may allow for less restrictive measures than others if both have the same resources to respond to the evolving epidemic.

Interplay between population density and mobility in determining the spread of epidemics in cities

The increasing agglomeration of people in dense urban areas coupled with the existence of efficient modes of transportation connecting such centers, make cities particularly vulnerable to the spread of epidemics. Here we develop a data-driven approach combines with a meta-population modeling to capture the interplay between population density, mobility and epidemic spreading. We study 163 cities, chosen from four different continents, and report a global trend where the epidemic risk induced by human mobility increases consistently in those cities where mobility flows are predominantly between high population density centers. We apply our framework to the spread of SARS-CoV-2 in the United States, providing a plausible explanation for the observed heterogeneity in the spreading process across cities. Based on this insight, we propose realistic mitigation strategies (less severe than lockdowns), based on modifying the mobility in cities. Our results suggest that an optimal control strategy involves an asymmetric policy that restricts flows entering the most vulnerable areas but allowing residents to continue their usual mobility patterns.The evolution of epidemic outbreaks in urban settings is known to stem from the interplay between demographic, structural, and economical characteristics. Here, the authors combine a data driven approach with meta-population modelling to show that the epidemic vulnerability of cities hinges on the morphology of human flows, and propose how a city’s mobility backbone could be modified to minimize the epidemic risk.