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The respiratory syncytial virus (RSV) causes 70% of bronchiolitis cases and thousands of annual deaths, particularly in the paediatric population under six months, placing a significant burden on the public health system. In October 2023, the monoclonal antibody nirsevimab was implemented for the first time in Catalonia in infants to combat the most severe clinical episodes caused by this virus. The primary objective of this study is to evaluate the impact of the first immunisation campaign with nirsevimab on the epidemiology and clinical outcomes of RSV. To this end, pre-pandemic patterns by age group have been characterized and compared with the current season, in addition to analysing the clinical outcomes of a prospective cohort of infants, observing differences between those who received nirsevimab and those who did not. The methodology followed in this study is divided into two major analyses: an epidemiological study and a patient-level study. The epidemiological analysis involves exploring the dynamics of bronchiolitis as an indirect measure of RSV and of the virus itself, conducting comparative analyses using an average pre-pandemic season and differentiating between various age groups. The patient-level analysis consists of a prospective study of clinical variables comparing a cohort that received nirsevimab with another cohort that did not. The results indicate that the pre-pandemic incidence of bronchiolitis followed a very robust pattern each season, which was significantly affected by the pandemic. It has been observed that the introduction of nirsevimab has reduced the incidence of RSV in infants under one year. Additionally, the prospective study determined that infants receiving nirsevimab are less likely to test positive for RSV and develop lower respiratory tract infections compared to those who were not immunised. In conclusion, this study demonstrates that the administration of nirsevimab in Catalonia has reduced the incidence of RSV and lower r, El virus respiratori sincicial (VRS) causa el 70% de les bronquiolitis i milers de morts anuals, particularment en la població pediàtrica menor de sis mesos, generant una càrrega significativa en el sistema públic sanitari. L'octubre de 2023 es va implementar per primera vegada a Catalunya l'anticòs monoclonal nirsevimab en lactants, per combatre els episodis clínics més greus causats per aquest virus. L'objectiu principal d'aquest estudi és avaluar l'impacte de la primera campanya d'immunització amb nirsevimab en l'epidemiologia i els resultats clínics del VRS. Per a això, s'han caracteritzat els patrons prepandèmics per franja d'edat i s'han comparat amb la temporada actual, a més d'analitzar els resultats clínics d'una cohort prospectiva de lactants, observant les diferències entre aquells que han rebut nirsevimab i els que no. La metodologia seguida en aquest estudi es divideix en dues grans anàlisis: un estudi epidemiològic i un estudi a escala pacient. L'anàlisi epidemiològica consisteix a explorar les dinàmiques de bronquiolitis com a mesura indirecta del VRS, i del mateix virus, fent anàlisis comparatives emprant una temporada mitjana prepandèmica i diferenciant entre diferents grups d'edat. L'anàlisi a escala pacient consisteix en un estudi prospectiu de variables clíniques que compara una cohort a la qual se li ha administrat nirsevimab amb una altra cohort a la qual no. Els resultats obtinguts indiquen que la incidència de bronquiolitis prepandèmica seguia un patró molt robust cada temporada, el qual es va veure afectat significativament per la pandèmia. S'ha observat que la introducció del nirsevimab ha reduït la incidència de VRS en els lactants menors d'un any. A més, l'estudi prospectiu ha determinat que els lactants que reben nirsevimab tenen menys probabilitat de donar positiu en VRS i de desenvolupar infeccions respiratòries de les vies baixes en comparació amb aquells que no han estat immunitzats. En conclusió, aquest estudi demostra que l'adminis, El virus respiratorio sincitial (VRS) causa el 70% de las bronquiolitis y miles de muertes anuales, particularmente en la población pediátrica menor de seis meses, generando una carga significativa en el sistema público sanitario. En octubre de 2023, se implementó por primera vez en Cataluña el anticuerpo monoclonal nirsevimab en lactantes, para combatir los episodios clínicos más severos causados por este virus. El objetivo principal de este estudio es evaluar el impacto de la primera campaña de inmunización con nirsevimab en la epidemiología y los resultados clínicos del VRS. Para ello, se han caracterizado los patrones prepandémicos por franja de edad y se han comparado con la temporada actual, además de analizar los resultados clínicos de una cohorte prospectiva de lactantes, observando las diferencias entre aquellos que han recibido nirsevimab y los que no. La metodología seguida en este estudio se divide en dos grandes análisis: un estudio epidemiológico y un estudio a nivel de paciente. El análisis epidemiológico consiste en explorar las dinámicas de bronquiolitis como medida indirecta del RSV, y del propio virus, realizando análisis comparativos empleando una temporada promedio prepandémica y diferenciando entre distintos grupos de edad. El análisis a nivel de paciente consiste en un estudio prospectivo de variables clínicas que compara una cohorte a la que se la ha administrado nirsevimab con otra cohorte a la que no. Los resultados obtenidos indican que la incidencia de bronquiolitis prepandémica seguía un patrón muy robusto cada temporada, el cual se vio afectado significativamente por la pandemia. Se ha observado que la introducción del nirsevimab ha reducido la incidencia de VRS en los lactantes menores de 1 año. Además, el estudio prospectivo ha determinado que los lactantes que reciben nirsevimab tienen menos probabilidad de dar positivo en VSR y de desarrollar infecciones respiratorias de las vías bajas en comparación a aquellos que no han sido inmun, Objectius de Desenvolupament Sostenible::3 - Salut i Benestar

The flu is an infectious and contagious disease that affects the respiratory system, caused by influenza viruses. In our environment, the flu behaves like a seasonal epidemic. In the northern hemisphere there is an annual flu season between the autumn and winter months. We want to know what their behavior is in other countries, and observe what the conditions are for their start in each country. The aim of this work is to understand the dynamics of the flu in the different countries that make up the European Union together with Norway, the United Kingdom and Switzerland, to see if there is a relationship between the flu epidemic and the climatology of the country, to later be able to use more accurate mathematical models. Likewise, the interaction of climate elements (such as temperature) and climate factors (such as latitude) with the transmission and seasonality of the disease caused by the influenza virus is studied, since its effects on biological systems, in this case specifically in people, are known. Finally, from the results obtained, we can say that the incidence of the flu epidemic and the temperature of each country conform to a decreasing exponential function. Although it would be necessary to be able to analyze it in other geographical regions. But, there is no evidence of a relationship between the onset of influenza epidemics and temperature, and neither for the case of latitude., La grip és una malaltia infecciosa i contagiosa, que afecta l'aparell respiratori, causada pels virus de la influença. En el nostre entorn la grip es comporta com una epidèmia estacional. A l'hemisferi nord anualment hi ha temporada de grip entre els mesos de tardor i hivern. Volem conèixer quin és el seu comportament en altres països, i observar quines són les condicions pel seu inici en cada país. L'objectiu d'aquest treball és entendre les dinàmiques de la grip en els diferents països que formen la Unió Europea juntament amb Noruega, Regne Unit i Suïssa, per veure si hi ha relació entre l'epidèmia de grip i la climatologia del país, per després poder utilitzar models matemàtics més acurats. Així mateix, s'estudia la interacció d'elements del clima (com la temperatura) i factors del clima (com la latitud) amb la transmissió i l'estacionalitat de la malaltia causada pel virus de la grip, ja que els seus efectes sobre els sistemes biològics, en aquest cas concretament en les persones, són coneguts. Finalment, dels resultats obtinguts, podem dir que la incidència de l'epidèmia de grip i la temperatura de cada país, s'ajusten a una funció exponencial decreixent. Tot i que caldria poder-ho analitzar en altres regions geogràfiques. Però, no hi ha una evidència de relació entre l'inici de les epidèmies de grip i la temperatura, i tampoc per al cas de la latitud., La gripe es una enfermedad infecciosa y contagiosa, que afecta el aparato respiratorio, causada por los virus de la influenza. En nuestro entorno la gripe se comporta como una epidemia estacional. En el hemisferio norte anualmente hay temporada de gripe entre los meses de otoño e invierno. Queremos conocer cuál es su comportamiento en otros países, y observar cuáles son las condiciones para su inicio en cada país. El objetivo de este trabajo es entender las dinámicas de la gripe en los diferentes países que forman la Unión Europea junto con Noruega, Reino Unido y Suiza, para ver si hay relación entre la epidemia de gripe y la climatología del país, para después poder utilizar modelos matemáticos más precisos. Así mismo, se estudia la interacción de elementos del clima (como la temperatura) y factores del clima (como la latitud) con la transmisión y la estacionalidad de la enfermedad causada por el virus de la gripe, puesto que sus efectos sobre los sistemas biológicos, en este caso concretamente en las personas, son conocidos. Finalmente, de los resultados obtenidos, podemos decir que la incidencia de la epidemia de gripe y la temperatura de cada país, se ajustan a una función exponencial decreciente. A pesar de que habría que poderlo analizar en otras regiones geográficas. Pero, no hay una evidencia de relación entre el inicio de las epidemias de gripe y la temperatura, y tampoco para el caso de la latitud.

In the current COVID-19 landscape dominated by Omicron subvariants, understanding the timing and efficacy of vaccination against emergent lineages is crucial for planning future vaccination campaigns, yet detailed studies stratified by subvariant, vaccination timing, and age groups are scarce. This retrospective study analyzed COVID-19 cases from December 2021 to January 2023 in Catalonia, Spain, focusing on vulnerable populations affected by variants BA.1, BA.2, BA.5, and BQ.1 and including two national booster campaigns. Our database includes detailed information such as dates of diagnosis, hospitalization and death, last vaccination, and cause of death, among others. We evaluated the impact of vaccination on disease severity by age, variant, and vaccination status, finding that recent vaccination significantly mitigated severity across all Omicron subvariants, although efficacy waned six months post-vaccination, except for BQ.1, which showed more stable levels. Unvaccinated individuals had higher hospitalization and mortality rates. Our results highlight the importance of periodic vaccination to reduce severe outcomes, which are influenced by variant and vaccination timing. Although the seasonality of COVID-19 is uncertain, our analysis suggests the potential benefit of annual vaccination in populations >60 years old, probably in early fall, if COVID-19 eventually exhibits a major peak similar to other respiratory viruses., This research was supported by the Ayudas Fundación BBVA a proyectos investigación científica 2021 under the project BBVA: Epidemiological modelling of SARS-CoV-2 in a post-pandemic surveil- lance context: an open platform for mid-term scenarios and short-term predictions; the Agència de Gestió d’Ajuts Universitaris i de Recerca, Catalonia [grant number 2021 SGR 00582]; and PID-2022- 139216NB-I00 funded by Ministerio de Ciencia e Innovación (MCIN/ AEI/10.13039/501100011033) and by ‘ERDF: A way of making Europe’, by the European Union., Postprint (published version)

Background: Countries across Europe have faced similar evolutions of SARS-CoV-2 variants of concern, including the Alpha, Delta, and Omicron variants.Materials and methods: We used data from GISAID and applied a robust, automated mathematical substitution model to study the dynamics of COVID-19 variants in Europe over a period of more than 2 years, from late 2020 to early 2023. This model identifies variant substitution patterns and distinguishes between residual and dominant behavior. We used weekly sequencing data from 19 European countries to estimate the increase in transmissibility ()Db between consecutive SARS-CoV-2 variants. In addition, we focused on large countries with separate regional outbreaks and complex scenarios of multiple competing variants.Results: Our model accurately reproduced the observed substitution patterns between the Alpha, Delta, and Omicron major variants. We estimated the daily variant prevalence and calculated Db between variants, revealing that: (i) Db increased progressively from the Alpha to the Omicron variant; (ii) Db showed a high degree of variability within Omicron variants; (iii) a higher Db was associated with a later emergence of the variant within a country; (iv) a higher degree of immunization of the population against previous variants was associated with a higher Db for the Delta variant; (v) larger countries exhibited smaller Db, suggesting regionally diverse outbreaks within the same country; and finally (vi) the model reliably captures the dynamics of competing variants, even in complex scenarios.Conclusion: The use of mathematical models allows for precise and reliable estimation of daily cases of each variant. By quantifying Db, we have tracked the OPEN ACCESSEDITED BYTamer Oraby, The University of Texas Rio Grande Valley, United StatesREVIEWED BYFulvia Pimpinelli, San Gallicano Dermatological Institute IRCCS, ItalyMd Rafiul Islam, University of the Incarnate Word, United States*CORRESPONDENCEClara Prats clara.prat, The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The research leading to these results received funding from Ayudas Fundación BBVA a proyectos investigación científica 2021 under the project BBVA: Epidemiological modeling of SARS-CoV-2 in a post-pandemic surveillance context: an open platform for mid-term scenarios and short-term predictions, and from grants 2021 SGR 00582 funded by Agència de Gestió d’Ajuts Universitaris i de Recerca, and PID-2022- 139216NB-I00 funded by Ministerio de Ciencia e Innovación (MCIN/ AEI/10.13039/501100011033) and by ‘ERDF: A way of making Europe’, by the European Union., Postprint (published version)

Accurate short-term predictions of COVID-19 cases with empirical models allow Health Officials to prepare for hospital contingencies in a two–three week window given the delay between case reporting and the admission of patients in a hospital. We investigate the ability of Gompertz-type empiric models to provide accurate prediction up to two and three weeks to give a large window of preparation in case of a surge in virus transmission. We investigate the stability of the prediction and its accuracy using bi-weekly predictions during the last trimester of 2020 and 2021. Using data from 2020, we show that understanding and correcting for the daily reporting structure of cases in the different countries is key to accomplish accurate predictions. Furthermore, we found that filtering out predictions that are highly unstable to changes in the parameters of the model, which are roughly 20%, reduces strongly the number of predictions that are way-off. The method is then tested for robustness with data from 2021. We found that, for this data, only 1–2% of the one-week predictions were off by more than 50%. This increased to 3% for two-week predictions, and only for three-week predictions it reached 10%., Te research leading to these results received funding from Ayudas Fundación BBVA a proyectos investigación científca 2021 under the project BBVA: Epidemiological modeling of SARS-CoV-2 in a post-pandemic surveillance context: an open platform for mid-term scenarios and short-term predictions, and from grants 2021 SGR 00582 funded by Agència de Gestió d’Ajuts Universitaris i de Recerca, and PID-2022-139216NB-I00 funded by Ministerio de Ciencia e Innovación (MCIN/AEI/10.13039/501100011033) and by ‘ERDF: A way of making Europe’, by the European Union., Peer Reviewed, Postprint (published version)

During the pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), mitigation policies for children have been a topic of considerable uncertainty and debate. Although some children have co-morbidities which increase their risk for severe coronavirus disease (COVID-19), and complications such as multisystem inflammatory syndrome and long COVID, most children only get mild COVID-19. On the other hand, consistent evidence shows that mass mitigation measures had enormous adverse impacts on children. A central question can thus be posed: What amount of mitigation should children bear, in response to a disease that is disproportionally affecting older people? In this review, we analyze the distinct child versus adult epidemiology, policies, mitigation trade-offs and outcomes in children in Western Europe. The highly heterogenous European policies applied to children compared to adults did not lead to significant measurable differences in outcomes. Remarkably, the relative epidemiological importance of transmission from school-age children to other age groups remains uncertain, with current evidence suggesting that schools often follow, rather than lead, community transmission. Important learning points for future pandemics are summarized., Peer Reviewed, Postprint (published version)

The research leading to these results received funding from Fundació La Marató TV3 under Grant Agreement No 202134‐30‐31., Peer Reviewed, Objectius de Desenvolupament Sostenible::3 - Salut i Benestar, Postprint (published version)

Purpose: To analyse the association between the mortality during the summer 2022 and either high temperatures or the COVID-19 wave with data from the Catalan Health Care System (7.8 million people). Methods: We performed a retrospective study using publicly available data of meteorological variables, influenza-like illness (ILI) cases (including COVID-19) and deaths. The study comprises the summer months of the years 2021 and 2022. To compare the curves of mortality, ILI and temperature we calculated the z-score of each series. We assessed the observed lag between curves using the cross-correlation function. Finally, we calculated the correlation between the z- scores using the Pearson correlation coefficient (R2). Results: During the study period, 33,967 deaths were reported in Catalonia (16,416 in the summer of 2021 and 17,551 in the summer of 2022). In 2022, the observed lag and the correlation between the z-scores of temperature and all-cause deaths was 3days and R2 =0.86, while between ILI and all-cause deaths was 22days and R2 =0.21. This high correlation between temperature and deaths increased up to 0.91 when we excluded those deaths reported as COVID-19 deaths, while the correlation between ILI and non-COVID-19 deaths decreased to -0.19. No correlation was observed between non-COVID deaths and temperature or ILI cases in 2021. Conclusion: Our study suggests that the main cause of the increase in deaths during summer 2022 in Catalonia was the high temperatures and its duration. The contribution of the COVID-19 seems to be limited., APe received funding from Fundació La Marató de TV3 through the grant 202134-31., Peer Reviewed, Objectius de Desenvolupament Sostenible::3 - Salut i Benestar, Postprint (published version)

During the pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), mitigation policies for children have been a topic of considerable uncertainty and debate. Although some children have co-morbidities which increase their risk for severe coronavirus disease (COVID-19), and complications such as multisystem inflammatory syndrome and long COVID, most children only get mild COVID-19. On the other hand, consistent evidence shows that mass mitigation measures had enormous adverse impacts on children. A central question can thus be posed: What amount of mitigation should children bear, in response to a disease that is disproportionally affecting older people? In this review, we analyze the distinct child versus adult epidemiology, policies, mitigation trade-offs and outcomes in children in Western Europe. The highly heterogenous European policies applied to children compared to adults did not lead to significant measurable differences in outcomes. Remarkably, the relative epidemiological importance of transmission from school-age children to other age groups remains uncertain, with current evidence suggesting that schools often follow, rather than lead, community transmission. Important learning points for future pandemics are summarized., SCOPUS: re.j, info:eu-repo/semantics/published

La epidemiología matemática es una disciplina que utiliza las matemáticas para comprender y predecir la dinámica de las epidemias, ayudando en la toma de decisiones de la salud pública. Este trabajo se centra en el uso del modelo de Gompertz para analizar la epidemia de viruela del mono (Monkeypox) y evaluar su eficiencia en la modelización de esta enfermedad. Se trata de una enfermedad zoonótica (que se puede transmitir entre animales y seres humanos) causada por el virus de la viruela del mono, o Monkeypox virus (MPXV) en inglés, que también afecta a los humanos. Se considera una enfermedad menos grave que la viruela, pero todavía puede ser peligrosa. Su propagación se extendió a escala mundial, hasta el punto de que el 23 de julio de 2022, la OMS declaró la viruela del mono como pandemia. Hemos utilizado una serie de herramientas estadísticas e informáticas, con la ayuda del lenguaje de programación MATLAB, para evaluar el modelo de Gompertz en la modelización de la dinámica epidemiológica de la viruela del mono y proporcionar un análisis detallado de esta dinámica, obteniendo gráficas que ilustran el ajuste del modelo de Gompertz en los datos epidemiológicos. Destacamos una alta concordancia entre el modelo y los datos, indicando un ajuste satisfactorio. En definitiva, las modificaciones realizadas en el código y las mejoras en el procesamiento de datos han permitido adaptar el modelo de Gompertz de forma efectiva a los datos epidemiológicos de la viruela del mono. Así, hemos encontrado que las características demográficas del país afectado parecen tener menor efecto en la dinámica del Monkeypox que el número de brotes en cada país. La monitorización y análisis de la epidemia de Monkeypox es esencial para entender la epidemiología del virus y poder realizar predicciones de uso a salud pública., Mathematical epidemiology is a discipline that uses mathematics to understand and predict the dynamics of epidemics, aiding public health decision-making. This paper focuses on the use of the Gompertz model to analyse the monkeypox epidemic and evaluate its efficiency in modelling this disease. Monkeypox is a zoonotic disease (a disease that can be transmitted between animals and humans) caused by the Monkeypox virus (MPXV), which also affects humans. It is considered a less serious disease than smallpox but can still be dangerous. It has spread globally, to the point that on 23 July 2022, the WHO declared monkeypox a pandemic. We have used statistical and computational tools, with the help of the MATLAB programming language, to evaluate the Gompertz model in modelling the epidemiological dynamics of monkeypox and provide a detailed analysis of these dynamics, obtaining plots that illustrate the fit of the Gompertz model on epidemiological data. We highlight a high agreement between the model and the data, indicating a satisfactory fit. Ultimately, modifications to the code and improvements in data processing have allowed us to adapt the Gompertz model effectively to monkeypox epidemiological data. Thus, we have found that the demographics of the affected country appear to have less effect on the dynamics of Monkeypox than the number of outbreaks in each country. Monitoring and analysis of the Monkeypox epidemic is essential to understand the epidemiology of the virus and to make predictions for public health use., L’epidemiologia matemàtica és una disciplina que utilitza les matemàtiques per a comprendre i predir la dinàmica de les epidèmies, ajudant en la presa de decisions de la salut pública. Aquest treball se centra en l’ús del model de Gompertz per a analitzar l’epidèmia de verola del mico (Monkeypox) i avaluar la seva eficiència en la modelització d’aquesta malaltia. Es tracta d’una malaltia zoonòtica (que es pot transmetre entre animals i éssers humans) causada pel virus de la verola del mico, o Monkeypox virus (MPXV) en anglès, que també afecta els humans. Es considera una malaltia menys greu que la verola, però encara pot ser perillosa. La seva propagació es va estendre a escala mundial, fins al punt que el 23 de juliol de 2022, l’OMS va declarar la verola del mico com a pandèmia. Hem emprat un seguit d’eines estadístiques i informàtiques, amb l’ajuda del llenguatge de programació MATLAB, per avaluar el model de Gompertz en la modelització de la dinàmica epidemiològica de la verola del mico i proporcionar una anàlisi detallada d’aquesta dinàmica, obtenint gràfiques que il·lustren l’ajustament del model de Gompertz a les dades epidemiològiques. Destaquem una alta concordança entre el model i les dades, indicant un ajust satisfactori. En definitiva, les modificacions realitzades al codi i les millores en el processament de dades han permès adaptar el model de Gompertz de manera efectiva a les dades epidemiològiques de la verola del mico. Així, hem trobat que les característiques demogràfiques del país afectat semblen tenir un menor efecte en la dinàmica del Monkeypox que el nombre de brots en cada país. El monitoratge i anàlisi de l’epidèmia de Monkeypox és essencial per entendre l’epidemiologia del virus i poder fer prediccions d’ús a salut pública., Objectius de Desenvolupament Sostenible::3 - Salut i Benestar

Background: The seasonality of respiratory diseases caused by viruses has been altered by the emergence of SARS-CoV-2. After a period of almost no bronchiolitis and influenza diagnoses, these seasonal infectious diseases are progressively recovering their pre-pandemic dynamics. We aim to describe how this process is taking place in Catalonia. Material and method: We used primary-care syndromic diagnostic data of bronchiolitis, influenza, and COVID-19 in Catalonia (Spain), which are publicly available through the new Information System for the Surveillance of Infections in Catalonia (SIVIC). We carried out a descriptive study of their dynamics from 2014 to 2023, focusing on the changes induced by the pandemic. Results: The results show that the old viral normality was significantly disrupted by SARS-CoV-2 and that we are experiencing a transition to a new viral normality where this novel infectious agent could play a role, but its precise dynamics remains unclear. Conclusions: We are slowly moving towards regular influenza and bronchiolitis seasonality. The role of SARS- CoV-2 in the viral landscape in Catalonia remains uncertain, but its effects on other pathogens are relevant and warrant further investigation., Peer Reviewed, Objectius de Desenvolupament Sostenible::3 - Salut i Benestar, Postprint (published version)

Multisystem inflammatory syndrome in children (MIS-C) is a rare but severe disease temporarily related to SARS-CoV-2. We aimed to describe the epidemiological, clinical, and laboratory findings of all MIS-C cases diagnosed in children < 18 years old in Catalonia (Spain) to study their trend throughout the pandemic. This was a multicenter ambispective observational cohort study (April 2020– April 2022). Data were obtained from the COVID-19 Catalan surveillance system and from all hospitals in Catalonia. We analyzed MIS-C cases regarding SARS-CoV-2 variants for demographics, symptoms, severity, monthly MIS-C incidence, ratio between MIS-C and accumulated COVID-19 cases, and associated rate ratios (RR). Among 555,848 SARS-CoV-2 infections, 152 children were diagnosed with MIS-C. The monthly MIS-C incidence was 4.1 (95% CI: 3.4–4.8) per 1,000,000 people, and 273 (95% CI: 230–316) per 1,000,000 SARS-CoV-2 infections (i.e., one case per 3,700 SARS-CoV-2 infections). During the Omicron period, the MIS-C RR was 8.2 (95% CI: 5.7–11.7) per 1,000,000 SARS-CoV-2 infections, which was significantly lower (p < 0.001) than that for previous variant periods in all age groups. The median [IQR] age of MIS-C was 8 [4–11] years, 62.5% male, and 80.2% without comorbidities. Common symptoms were gastrointestinal findings (88.2%) and fever > 39 °C (81.6%); nearly 40% had an abnormal echocardiography, and 7% had coronary aneurysm. Clinical manifestations and laboratory data were not different throughout the variant periods (p > 0.05). Conclusion: The RR between MIS-C cases and SARS-CoV-2 infections was significantly lower in the Omicron period for all age groups, including those not vaccinated, suggesting that the variant could be the main factor for this shift in the MISC trend. Regardless of variant type, the patients had similar phenotypes and severity throughout the pandemic., This study has received funding for the data analysis from the “Fundació la Marató TV3” with file number 202134–30-31., Peer Reviewed, Objectius de Desenvolupament Sostenible::3 - Salut i Benestar, Postprint (published version)

The flu is a contagious respiratory infectious disease caused by the influenza viruses. Annually in Catalonia there is a flu season within the months of autumn and winter, that can create a lot of stress to the healthcare system. The main goal of this study is to understand the dynamics of the flu and to be able to make predictions that allow anticipation, with enough time to respond to an increase of infected individuals that requires active action from the healthcare system managers. Computational and mathematical models are a powerful tool that help understand and predict systems behaviours. In this study different models are adapted, developed and implemented to achieve the pursued objective. Therefore, the interaction between meteorological factors and the flu seasonality and transmissivity has been studied, due to the known effects of these factors upon biological systems. The mathematical models developed in this study fit correctly the epidemiological data, they allow the explanation of flu seasonality and perform predictions at short and long term. The base model that has been used proved to be a good fit to the previous seasons and has been applied to produce weakly reports of flu evolution this season 2021-2022. From the base model new models have been created, these include meteorological factors and have been called meteomodels. They have revealed the effect of temperature and absolute humidity upon the epidemic behavior of the flu. This is one of the main results of this study, there is no knowledge of any other published model that describes flu epidemics through meteorological factors as the models in this study do, with the added value that these meteorological models have the potential to predict two weeks ahead. To sum up, predictive mathematical models have been developed, each one with a precision above 95%, for the flu epidemic in Catalonia and incorporating, for the first time, meteorological factors. Further work includes new and different co, La gripe es una enfermedad infecciosa respiratoria contagiosa causada por los virus de la influenza. En Cataluña anualmente hay una temporada de gripe en los meses de otoño e invierno, que puede llegar a crear mucha tensión en el sistema sanitario. El objetivo de este trabajo es comprender las dinámicas de la gripe y poder realizar predicciones que permitan anticiparse con tiempo a un incremento de casos que requieran de acción activa por parte de los gestores de salud pública. Los modelos matemáticos y computacionales son un potente recurso que permite comprender y predecir el comportamiento de sistemas. En este estudio se adaptan, desarrollan e implementan diferentes modelos para lograr el objetivo propuesto. Así mismo, se estudia la interacción de factores meteorológicos con la transmisión y la estacionalidad de la enfermedad de la gripe, ya que sus efectos sobre los sistemas biológicos son conocidos. Los modelos matemáticos desarrollados se ajustan correctamente a los datos epidemiológicos, permiten explicar la estacionalidad de la gripe y realizar predicciones a corto y largo plazo. El modelo base con el que se ha trabajado muestra un buen ajuste a las temporadas anteriores y se ha usado para realizar informes semanales de la evolución de la gripe esta temporada 2021-2022. Partiendo del modelo base se han creado modelos que incluyen factores meteorológicos, que hemos nombrado meteomodelos, que han conseguido mostrar el efecto de la temperatura y la humedad absoluta sobre el comportamiento epidémico de la gripe. Este es uno de los resultados principales del trabajo, no se tiene constancia de ningún otro modelo publicado que describa la evolución epidémica de la gripe mediante factores meteorológicos como lo hacen los de este estudio, con el valor añadido de que estos modelos meteorológicos tienen el potencial de predecir a dos semanas vista. En definitiva, se han elaborado modelos matemáticos predictivos, todos con una precisión superior al 95%, de la epidemia d, La grip és una malaltia infecciosa respiratòria contagiosa causada pels virus de la influença. A Catalunya anualment hi ha temporada de grip en els mesos de tardor i hivern, que pot arribar a crear molta tensió al sistema sanitari. L’objectiu d’aquest treball és entendre les dinàmiques de la grip i poder fer prediccions que permetin anticipar-se amb temps a un augment de casos que requereixin d’acció activa dels gestors de salut pública. Els models matemàtics i computacionals són un potent recurs que permeten entendre i predir comportaments de sistemes. En aquest estudi s’adapten, desenvolupen i implementen diferents models per assolir l’objectiu proposat. Així mateix, s’estudia la interacció de factors meteorològics amb la transmissió i l’estacionalitat de la malaltia de la grip, ja que els seus efectes sobre els sistemes biològics són coneguts. Els models matemàtics desenvolupats s’ajusten correctament a les dades epidemiològiques, permeten explicar l’estacionalitat de la grip i realitzar prediccions a curt i llarg termini. El model base amb el que s’ha treballat mostra un bon ajust a temporades anteriors i s’ha fet servir per realitzar informes setmanals de l’evolució de la grip aquesta temporada 2021-2022. Partint del model base s’han creat models amb la inclusió de factors meteorològics, que hem anomenat meteomodels, que han aconseguit mostrar l’efecte de la temperatura i la humitat absoluta sobre el comportament epidèmic de la grip. Aquest és un dels resultats principals del treball, no es té constància de cap altre model publicat que descrigui l’evolució epidèmica de la grip mitjançant factors meteorològics com ho fan els d’aquest estudi, amb el valor afegit que aquests models meteorològics tenen el potencial de predir a dues setmanes vista. En definitiva, s’han elaborat models matemàtics predictius, tots amb una precisió superior al 95%, de l’epidèmia de grip a Catalunya i incloent, per primer cop, factors meteorològics. Plantejaments de futur inclouen noves, Objectius de Desenvolupament Sostenible::3 - Salut i Benestar

We show that in all countries with more than 1000 monkeypox cases at the end of August 2022, the evolution of the total number of cases is described by the Gompertz growth model. Although the data collection has many temporal irregularities, we have been able to measure the order of magnitude of the number of new cases per day in each country until the end of the study period in August 2022, as well as to analyse its dynamics. In this way, it is easy to check whether the epidemiological situation is improving. If no new epidemic outbreaks appear, as of early September, the model predicts a rapid improvement in the epidemiological situation., Objectius de Desenvolupament Sostenible::3 - Salut i Benestar, Postprint (published version)

Multisystemic inflammatory syndrome temporally associated with SARS-CoV-2 infection in children (MIS-C) has been reported worldwide.1–7 The case definition of MIS-C has been estab- lished by different institutions and organizations such as the US Centers for Disease Control and Prevention (CDC) (May 14, 2020),8 the Royal College of Paediatrics and Child Health in the United Kingdom (RCPCH) (May 1, 2020)9,10 and the World Health Organi- zation (WHO) (May 15, 2020).1, Postprint (published version)

Viral coinfection presented in humans has important value in clinical and epidemiological studies. The relationship between viruses determines the consequences of viral coinfection. These consequences could be inhibition of viral growth, increased viral load, or even more severe symptoms in the patient. This work aims to understand the relationship between different viruses and the dynamics of their viral load during coinfection. In this way, it is hoped to be able to make an accurate prediction of the evolution of coinfection between respiratory viruses and, therefore, improve the protocol for the prevention of infectious respiratory diseases. On the other hand, mathematical and computational models are efficient in understanding the dynamics of viruses and predicting the trends of an epidemic. This has also been demonstrated during the SARS-CoV-2 pandemic. Therefore, in this work, a series of mathematical models are developed to study the relationship between viruses under coinfection and to understand how the infection changes in the presence of other pathogens. In addition, the results are expected to be useful for taking measures to control a pandemic or epidemic similar to those experienced in the future. This study focuses on 4 respiratory viruses: SARS-CoV-2, respiratory syncytial virus (RSV), influenza (IV), and human metapneumovirus (hMPV). The models generated in this work are derived from the Pinky and Dobrovoly1 coinfection model, which evaluates the evolution of viral load during coinfection. Viral load is an important variable that indicates what kind of interaction viruses establish: the interaction is positive when the presence of one virus increases the viral load of another virus over time, otherwise the interaction is negative. And when the viral load does not vary in coinfection, this interaction is neutral. The mathematical models developed in this work not only fit well with the experimental data of coinfection but also with those of simple in, La coinfección viral presentada en humanos tiene un importante valor en estudios clínicos y epidemiológicos. La relación entre virus condiciona las consecuencias de la coinfección viral. Estas consecuencias podrían ser inhibición del crecimiento viral, aumento de carga viral o incluso síntomas más graves en el paciente. El objetivo de este trabajo es entender la relación entre distintos virus y la dinámica de su carga viral durante la coinfección. De esta forma se espera poder hacer una predicción precisa de la evolución de una coinfección entre virus respiratorios y, por tanto, mejorar el protocolo de prevención de las enfermedades respiratorias infecciosas. Por otra parte, los modelos matemáticos y computacionales son eficientes para entender la dinámica de los virus y predecir las tendencias de una epidemia. Esto se ha demostrado también durante la pandemia del SARS-CoV-2. Por eso, en este trabajo se desarrollan una serie de modelos matemáticos para entender la relación entre virus bajo coinfección, para entender cómo cambia la infección en presencia otros patógenos. Además, se espera que los resultados puedan ser de utilidad para tomar medidas de control de una pandemia o epidemia similar a las vividas en el futuro. Este estudio se centra en 4 virus respiratorios: SARS-CoV-2, virus respiratorio sincicial (VRS), influenza (IV) y metapneumovirus humano (hMPV). Los modelos generados en este trabajo derivan del modelo de coinfección Pinky y Dobrovoly1, que evalúa la evolución de la carga viral durante la coinfección. La carga viral es una variable importante que indica qué tipo de interacción establecen los virus: la interacción es positiva cuando la presencia de un virus aumenta la carga viral de otro virus con el tiempo; de lo contrario, la interacción es negativa. Y cuando la carga viral no varía en coinfección, esa interacción es neutral. Los modelos matemáticos desarrollados en este trabajo no solo se ajustan bien a los datos experimentales de coinfección sino, La coinfecció viral presentada en humans té un valor important en estudis clínics i epidemiològics. La relació que s’estableix entre virus condiciona les conseqüències de la coinfecció viral. Aquestes conseqüències podrien ser inhibició del creixement viral, augment de la càrrega viral o fins i tot símptomes més greus en el pacient. L'objectiu d'aquest treball és entendre la relació entre diferents virus i la dinàmica de la seva càrrega viral durant la coinfecció. D’aquesta manera s’espera poder fer una predicció precisa de l’evolució d’una coinfecció entre virus respiratoris i, per tant, millorar el protocol de prevenció de les malalties respiratòries infeccioses. D’altra banda, els models matemàtics i computacionals són eficients per entendre la dinàmica dels virus i predir les tendències d’una epidèmia. Això s’ha demostrat també durant la pandèmia del SARS-CoV-2. Per això, en aquest treball es desenvolupen una sèrie de models matemàtics per estudiar la relació entre virus sota coinfecció, per tal d’entendre com canvia la infecció en presència d’altres patògens. A més, s’espera que els resultats puguin ser d’utilitat per a prendre mesures de control d’una pandèmia o epidèmia similar a les viscudes, en el futur. Aquest estudi se centra en 4 virus respiratoris: SARS-CoV-2, virus respiratori sincicial (VRS), influenza (IV) i metapneumovirus humà (hMPV). Els models generats en aquest treball deriven del model de coinfecció Pinky i Dobrovoly1, que avalua l'evolució de la càrrega viral durant la coinfecció. La càrrega viral és una variable important que indica quin tipus d'interacció estableixen els virus: la interacció és positiva quan la presència d'un virus augmenta la càrrega viral d'un altre virus amb el temps, en cas contrari, la interacció és negativa. I quan la càrrega viral no varia en coinfecció, aquesta interacció és neutral. Els models matemàtics desenvolupats en aquest treball no només s'ajusten bé a les dades experimentals de coinfecció sinó també a les d’, Objectius de Desenvolupament Sostenible::3 - Salut i Benestar

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We provide some figures and tables with several indexes and indicators as well as predictions for the next days. We also include a brief analysis of the global situation, as well as the highlights for European countries. On Fridays, we prepare a full analysis of a specific topic. As for the predictions, we employ a validated empirical model based on the Gompertz growth. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of trends. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 7-14 days later. We show an individual report with 7 graphs and a summary table with the main indicators for different countries and regions. Only reliable predictions that accomplish with the quality criteria are included. Methodological details are given in the Methods section, at the end of the report., These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00, Postprint (published version)

Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is neither always accessible nor easy to perform in children. We aimed to propose a machine learning model to assess the need for a SARS-CoV-2 test in children (<16 years old), depending on their clinical symptoms. Methods: Epidemiological and clinical data were obtained from the REDCap® registry. Overall, 4434 SARS-CoV-2 tests were performed in symptomatic children between 1 November 2020 and 31 March 2021, 784 were positive (17.68%). We pre-processed the data to be suitable for a machine learning (ML) algorithm, balancing the positive-negative rate and preparing subsets of data by age, This research has received external funding from the Fundació la Marató tv3 after being awarded in the COVID-19 research call with the expedient number 202134-30-31., We acknowledge the BitsxlaMarató hackathon organised by the computer engineering faculty of the Universitat Politècnica de Catalunya, Objectius de Desenvolupament Sostenible::3 - Salut i Benestar, Postprint (published version)

Objective: We describe and analyze the childhood (<18 years) COVID-19 incidence in Catalonia, Spain, during the first 36 weeks of the 2020-2021 school-year and to compare it with the incidence in adults. Methods: Data on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests were obtained from the Catalan Agency for Quality and Health Assessment. Overall, 7,203,663 SARS-CoV-2 tests were performed, of which 491,819 were positive (6.8%). We collected epidemiological data including age-group incidence, diagnostic effort, and positivity rate per 100,000 population to analyze the relative results for these epidemiological characteristics. Results: Despite a great diagnostic effort among children, with a difference of 1,154 tests per 100,000 population in relation to adults, the relative incidence of SARS-CoV-2 for <18 years was slightly lower than for the general population, and it increased with the age of the children. Additionally, positivity of SARS-CoV-2 in children (5.7%) was lower than in adults (7.2%), especially outside vacation periods, when children were attending school (4.9%). Conclusions: A great diagnostic effort, including mass screening and systematic whole-group contact tracing when a positive was detected in the class group, was associated with childhood SARS-CoV-2 incidence and lower positivity rate in the 2020-2021 school year. Schools have been a key tool in epidemiological surveillance rather than being drivers of SARS-CoV-2 incidence in Catalonia, Spain., Peer Reviewed, Postprint (published version)

The greatest advances in the biomedical field would not have been possible without microscopy. The higher the resolution of the image obtained, the more details one can know about the sample. In the last few decades, microscopy have progressed from differentiating objects at millimetres of distance to the nanoscale. The latter accuracy is achieved using the contemporary super resolution microscopy, including Single Molecule Localization Microscopy, examples of which are the fluorescence microscopy techniques PALM and STORM. This kind of imaging method is done by stochastically activating and deactivating a set of fluorescent molecules distributed in a sample, taking a picture of it with a CCD camera in every switch, and then processing all the frames to obtain a final image. However, nowadays precision is of tens of nanometres, which suggests that there is still room for improvement. Furthermore, the state?of?the?art super resolution techniques still exhibit certain drawbacks such as overlapping between emitters PSFs and the difficulty to make molecular tracking especially due to photobleaching. Therefore, some techniques have recently been developed to improve super resolution microscopy, such as MINFLUX and DeepSTORM. MINFLUX aims to minimize the number of emitted photons required for the localization of molecules with the main objective of being able to do molecular tracking, while DeepSTORM focuses on eliminating the overlapping of PSFs using a Deep Learning algorithm to create a phase mask that generates a suitable PSF for this purpose. In this project both techniques are to be combined expecting to obtain highly accurate estimations of the emitters? two?dimensional positions with resolutions of units of nanometres, while avoiding overlapping of PSFs in highly dense samples and minimizing the number of photons emitted per fluorophore., Los mayores avances en el campo biomédico no habrían sido posibles sin la microscopía. Cuanto más alta es la resolución de la imagen obtenida, más detalles se pueden conocer de la muestra. En las últimas décadas, la microscopía ha progresado desde la diferenciación de objetos a milímetros de distancia hasta la nanoescala. Esta última precisión se logra utilizando la microscopía contemporánea de súper resolución, incluyendo la Microscopía de Localización de Moléculas Únicas, ejemplos de las cuales son las técnicas de microscopía de fluorescencia PALM y STORM. Este tipo de método de obtención de imágenes se realiza activando y desactivando estocásticamente un conjunto de moléculas fluorescentes distribuidas en una muestra, tomando una fotografía de la misma con una cámara CCD en cada cambio, y luego procesando todos los fotogramas para obtener una imagen final. Sin embargo, hoy en día la precisión es de decenas de nanómetros, lo que sugiere que todavía hay margen de mejora. Además, las técnicas de súper resolución de última generación todavía presentan ciertos inconvenientes como la superposición entre las PSF de los emisores y la dificultad de hacer seguimiento de la trayectoria molecular, especialmente debido al efecto conocido como photobleaching. Es por eso que recientemente se han desarrollado algunas técnicas para mejorar la microscopía de súper resolución, como MINFLUX y DeepSTORM. MINFLUX tiene por objetivo reducir al mínimo el número de fotones emitidos necesarios para la localización de las moléculas con el objetivo principal de poder hacer el seguimiento molecular, mientras que DeepSTORM se centra en eliminar la superposición de las PSF utilizando un algoritmo de Deep Learning para crear una máscara de fase (phase mask) que genere una PSF adecuada para este fin. En este proyecto se combinarán ambas técnicas esperando obtener estimaciones muy precisas de las posiciones bidimensionales de los emisores con resoluciones de unidades de nanómetros, evitando al mi, Els majors avenços en el camp biomèdic no haurien estat possibles sense la microscòpia. Com més alta és la resolució de la imatge obtinguda, més detalls es poden conèixer de la mostra. En les últimes dècades, la microscòpia ha progressat des de la diferenciació d'objectes a mil·límetres de distància fins a la nanoescala. Aquesta última precisió s'aconsegueix utilitzant la microscòpia contemporània de súper resolució, incloent la Microscopia de Localització de Molècula Única, exemples de la qual són les tècniques de microscòpia de fluorescència PALM i STORM. Aquest tipus de mètode d'obtenció d'imatges es realitza activant i desactivant estocàsticament un conjunt de molècules fluorescents distribuïdes en una mostra, prenent una fotografia de la mateixa amb una càmera CCD en cada canvi, i després processant tots els fotogrames per obtenir una imatge final. No obstant això, avui dia la precisió és de desenes de nanòmetres, el que suggereix que encara hi ha marge de millora. A més, les tècniques de súper resolució d'última generació encara presenten certs inconvenients com la superposició entre les PSF dels emissors i la dificultat de fer seguiment de la trajectòria molecular, especialment a causa de l'efecte conegut com photobleaching. És per això que recentment s'han desenvolupat algunes tècniques per millorar la microscòpia de súper resolució, com MINFLUX i DeepSTORM. MINFLUX té per objectiu reduir al mínim el nombre de fotons emesos necessaris per a la localització de les molècules amb l'objectiu principal de poder fer el seguiment molecular, mentre que DeepSTORM se centra a eliminar la superposició de les PSF utilitzant un algoritme de Deep Learning per a crear una màscara de fase (phase mask) que generi una PSF adequada per a aquest fi. En aquest projecte es combinaran les dues tècniques esperant obtenir estimacions molt precises de les posicions bidimensionals dels emissors amb resolucions d'unitats de nanòmetres, evitant alhora la superposició de PSF en mostres m