Evolution of COVID-19 Spread in the Cohort of LaLiga Football (Soccer) Professionals between May 2020 and April 2021 (preprint)

Objectives: COVID-19 pandemic interrupted the Spanish professional football competition until May 2020, when it was restarted following a surveillance protocol established by LaLiga. The aims were to describe the infective and serological status of professional football players (PLY) and staff (STF) between May 5th 2020 until April 22nd 2021, to analyze the spatial-temporal distribution of the COVID-19 disease in this cohort and its comparison to the Spanish population. Methods: a prospective observational cohort study was carried out. Differences between PLY and STF were assessed by Chi-squared test and test of equality of proportions. Pearson correlation test was used to measure the presence of an association between the percentages of positivity in population and LaLiga cohort. Results: 137,420 RT-PCR and 20,376 IgG serology tests were performed in 7,112 professionals. Positive baseline serology was detected in 10.57% of PLY and 6.38% of STF. Among those who started the follow-up as not infected and before STF vaccination, 11.87% of PLY and 5.03% of STF became positive. Before summer 2020 the prevalence of infection was similar than the observed at national level. The percentage of positivity in the Spanish population was higher than in LaLiga cohort, but both series showed a similar decreasing trend.

Epidemic Diffusion Network of Spain: a mobility model to characterize transmission routes of disease. A COVID-19 case study. (preprint)

Human mobility drives geographical diffusion of airborne infectious diseases at different scales. During the COVID-19 pandemic mobility data was made available and has been widely used, but few studies focus on mobility itself. We used public data from February 14th 2020, to May 9th 2021, in Spain to characterize mobility patterns and study geographical diffusion phenomena using network science methods. With 135 (out of 2.264) connections and the 52 provinces of Spain, a weighted, directed network was built: the Epidemic Diffusion Network (EDN). Centrality measures (degree and strength), community structure and shortest distances were obtained using the EDN. The resulting network was highly clustered (modularity: 63%) with 7 communities. Madrid, Valladolid and Araba/Álaba act as mobility hubs of their communities and the whole network. Shortest distances unveil a geographical wave-like diffusion pattern with occasional distance jumps, a small-world network characteristic, and COVID-19 cumulative incidence curves showed a pattern of proximity within provinces of the same community. These properties remain constant in time despite factors like seasonality or restrictions and could inform public health authorities in preparedness and response plans for diseases and other threads. Further studies are needed to better understand relationship between network measures and epidemiological outcomes in real life.

Perimeter confinements of basic health zones and COVID-19 incidence in Madrid, Spain.

BACKGROUND: A unique policy of perimeter closures of Basic Health Zones (small administrative health units) was implemented in the Autonomous Community of Madrid from September 21st 2020 to May 23rd 2021 to face the COVID-19 pandemic. AIM: To assess the impact of local perimeter confinements on the 14-days cumulative incidence of SARS-CoV-2 during the second wave of the pandemic in Madrid, Spain. METHODS: We compare the errors in estimation of two families of mathematical models: ones that include the perimeter closures as explanatory covariables and ones that do not, in search of a significant improvement in estimation of one family over the other. We incorporate leave-one-out cross-validation, and at each step of this process we select the best model in AIC score from a family of 15 differently tuned ones. RESULTS: The two families of models provided very similar estimations, for a 1- to 3-weeks delay in observed cumulative incidence, and also when restricting the analysis to only those Basic Health Zones that were subject to at least one closure during the time under study. In all cases the correlation between the errors yielded by both families of models was higher than 0.98 (±10- 3 95% CI), and the average difference of estimated 14-days cumulative incidence was smaller than 1.49 (±0.33 95% CI). CONCLUSION: Our analysis suggests that the perimeter closures by Basic Health Zone did not have a significant effect on the epidemic curve in Madrid.

Evaluation of Vaccination Strategies for the metropolitan area of Madrid (preprint)

[bullet] Background: This work analyses the impact of different vaccination strategies on the propagation of COVID-19 within the Madrid metropolitan area starting the 27th of December 2020 and ending in the Summer of 2021. The predictions are based on simulation using EpiGraph, an agent-based COVID-19 simulator. [bullet] Methods: We briefly summarize the different interconnected models of EpiGraph and then we provide a comprehensive description of the vaccination model. We evaluate different vaccination strategies, and we validate the simulator by comparing the simulation results with real data from the metropolitan area of Madrid during the third wave. [bullet] Results: We consider the different COVID-19 propagation scenarios on a social environment consisting of the ten largest cities in the Madrid metropolitan area, with 5 million individuals. The results show that the strategy that fares best is to vaccinate the elderly first with the two doses spaced 56 days apart; this approach reduces the final infection rate and the number of deaths by an additional 6% and 3% with respect to vaccinating the elderly first at the interval between doses recommended by the vaccine producer. [bullet] Conclusion: Results show that prioritizing the vaccination of young individuals would significantly increase the number of deaths. On the other hand, spacing out the first and second dose by 56 days would result in a slight reduction in the number of infections and deaths. The reason is the increase in the number of vaccinated individuals at any time during the simulation.

Impact of Lockdown on COVID-19 Transmissibility During the First Pandemic Wave in Spain (preprint)

Background: The analysis of the evolution of the COVID-19 epidemic can provide evidence of the impact of measures implemented to reduce its progression. Our aim was to describe the evolution of the pandemic in the different Spanish regions and to examine the effect of the non-pharmaceutical public health interventions during the first epidemic wave on these trends. Methods: Daily incidence rates of cases were calculated at national and regional level between 31th of January and 10th of May 2020. Epidemic curves, important dates of interventions and effective reproduction number (Rt) were plotted and transmissibility parameters were calculated. To summarize the geographical heterogeneity in the evolution, regional epidemic curves have been classified into homogeneous groups using a clustering procedure. Findings: The incidence rate reached 5 cases per 100,000 on March 1 and peaked at March 20. The Rt gradually decreased after the national lockdown falling below 1 on March 24. Two homogeneous groups of epidemic curves were identified among regions, mainly differentiated by the magnitude of the daily incidence rate and the evolution of the Rt in the period prior to lockdown. However, irrespectively of the previous trend, the lockdown was followed by a steep decrease in the number of cases starting 6 days after its implementation. Interpretation: Our results confirm that the restrictive national lockdown efficiently reduced the progression of the epidemic in Spain during the first wave. This effect was similar in the two regional clusters, independent of the previous dynamics of the epidemic.Funding Statement: The study was supported by Instituto de Salud Carlos III, Spain (ISCIII) grant number COV20-008Declaration of Interests: All authors declare no competing interests.

Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data (preprint)

Designing public health responses to outbreaks requires close monitoring of population-level health indicators in real-time. Thus an accurate estimation of the epidemic curve is critical. We propose an approach to reconstruct epidemic curves in near real time. We apply this approach to characterize the early SARS-CoV-2 outbreak in two Spanish regions between the months of March and April 2020. We address two data collection problems that affected the reliability of the available real-time epidemiological data, namely, the frequent missing information documenting when a patient first experienced symptoms, and the frequent retrospective revision of historical information (including right censoring). This is done by using a novel back-calculating procedure based on imputing patients dates of symptom onset from reported cases, according to a dynamically-estimated backward reporting delay conditional distribution, and adjusting for right censoring using an existing package, NobBS, to estimate in real time (nowcast) cases by date of symptom onset. This process allows us to obtain an approximation of the time-varying reproduction number (Rt) in real-time. At each step, we evaluate how different assumptions affect the recovered epidemiological events and compare the proposed approach to the alternative procedure of merely using curves of case counts, by report day, to characterize the time-evolution of the outbreak. Finally, we assess how these real-time estimates compare with subsequently documented epidemiological information that is considered more reliable and complete that became available weeks to months later in time. Our approach may help improve accuracy, quantify uncertainty, and evaluate frequently unstated assumptions when recovering the epidemic curves from limited data obtained from public health surveillance systems in other locations.

Real Time Surveillance of COVID-19 Space and Time Clusters During the Summer 2020 in Spain (preprint)

Background:On June 21st de-escalation measures and state-of-alarm ended in Spain after the COVID-19 first wave. New surveillance and control strategy was set up to detect emerging outbreaks.Aim:To detect and describe the evolution of COVID-19 clusters and cases during the 2020 summer in Spain.Methods:A near-real time surveillance system to detect active clusters of COVID-19 was developed based on Kulldorf´s prospective space-time scan statistic (STSS) to detect daily emerging active clusters. Results:Analysis were performed daily during the summer 2020 (June 21st – August 31st) in Spain, showing an increase of active clusters and municipalities affected. Spread happened in the study period from a few, low-cases, regional-located clusters in June to a nationwide distribution of bigger clusters encompassing a higher average number of municipalities and total cases by end-August.Conclusion:STSS-based surveillance of COVID-19 can be of utility in a low-incidence scenario to help tackle emerging outbreaks that could potentially drive a widespread transmission. If that happens, spatial trends and disease distribution can be followed with this method. Finally, cluster aggregation in space and time, as observed in our results, could suggest the occurrence of community transmission.

Lockdown measures and relative changes in the age-specific incidence of SARS-CoV-2 in Spain (preprint)

BackgroundThe first months of the SARS-CoV-2 epidemic in Spain resulted in high incidence and mortality. A national sero-epidemiological survey suggests higher cumulative incidence of infection in older individuals than in younger individuals. However, little is known about the epidemic dynamics in different age groups, including the relative effect of the lockdown measures introduced on March 15, and strengthened on March 30 to April 14, 2020 when only essential workers continued to work. MethodsWe used data from the National Epidemiological Surveillance Network (RENAVE in Spanish) on the daily number of reported COVID-19 cases (by date of symptom onset) in eleven 5-year age groups: 15-19y through 65-69y. For each age group g, we computed the proportion E(g) of individuals in age group g among all reported cases aged 15-69y during the pre-lockdown period (March 1-10, 2020) and the corresponding proportion L(g) during two lockdown periods (March 25-April 3 and April 8-17, 2020). For each lockdown period, we computed the proportion ratios PR(g)= L(g)/E(g). For each pair of age groups g1,g2, PR(g1)>PR(g2) implies a relative increase in the incidence of detected SARS-CoV-2 infection in the age group g1 compared with g2 for the later vs. early period. ResultsFor the first lockdown period, the highest PR values were in age groups 50-54y (PR=1.21; 95% CI: 1.12,1.30) and 55-59y (PR=1.19; 1.11,1.27). For the second lockdown period, the highest PR values were in age groups 15-19y (PR=1.26; 0.95,1.68) and 50-54y (PR=1.20; 1.09,1.31). ConclusionsOur results suggest that different outbreak control measures led to different changes in the relative incidence by age group. During the first lockdown period, when non-essential work was allowed, individuals aged 40-64y, particularly those aged 50-59y presented with higher COVID-19 relative incidence compared to pre-lockdown period, while younger adults/older adolescents (together with persons aged 50-59y) had increased relative incidence during the later, strengthened lockdown. The role of different age groups during the epidemic should be considered when implementing future mitigation efforts.