Mathematical modelling to study infectious diseases: from understanding to prediction

Tesi de modalitat de compendi de publicacionsEach year 10 million people die from communicable diseases. They are infectious diseases caused by agents transmitted between individuals. Nowadays, the two infectious diseases that have the greatest impact are tuberculosis (TB) and coronavirus disease 2019 (COVID-19). According to the World Health Organization, TB killed 40 million individuals in the last 20 years. The COVID-19 pandemic has had an overwhelming effect on human life. It has caused millions of deaths and conditioned people’s life since January 2020. Mathematical and computational models are powerful tools in science to better understand, predict, and condition the dynamics of a desired system. In this thesis, we present a compendium of five publications where mathematical modelling is used to better understand and predict the dynamics of TB and COVID-19 at different spatio-temporal scales. Although TB is a disease identified many years ago, its natural history is not fully understood yet. The main objectives of this thesis in this area are related with the understanding of the factors and processes that facilitate the triggering of an active disease from a latent tuberculosis infection. We also aim at improving understanding of the human-TB coexistence for more than 70,000 years and some of the particularities that have facilitated this coexistence. We have built several models of the pulmonary TB infection at different spatial scales. At the alveolus level, we have seen that the correct balance of the immune response determines the outcome of the infection. At the secondary lobe level, we identified the distance to pulmonary membranes as an important factor to determine final lesion size. At the lung level, we have reproduced a dynamic hypothesis that explains the generation of secondary granulomas after the bronchial dissemination of the infectious bacilli from a preceding lesion. We have assessed the importance of lesion merging as a driving force for the triggering of the active disease. In addition, we have modelled human-TB coexistence in the Paleolithic and Neolithic ages, and determined that female protection against TB was crucial for the survival of the human species. In the Neolithic age, new "modern" lineages emerged, displacing "ancient" ones. Mathematical modelling yields results that explain why this emergence was not possible in the Paleolithic age. When the COVID-19 pandemic started, there was a lack of monitoring systems to help control and manage the pandemic. In this thesis, we focus on several aims related to the assessment of the real incidence during the first wave, as well as on the building and testing of a short-term prediction model. We developed a methodology to estimate the real incidence of COVID-19 based on the estimated lethality and the reported death series. We applied this to several European countries, after analyzing possible bias due to differing age structures. As well, we proposed and calibrated an empirical model based on the Gompertz growth that allows for reliable short-term forecasting at the country level. This thesis demonstrates how mathematical and computational models can be used to predict and better understand important characteristics of infectious diseases such as TB and COVID-19.Cada any 10 milions de persones es moren a causa de malalties transmissibles. Són malalties infeccioses causades per agents que es transmeten entre els diferents individus. Actualment, la tuberculosi (TB) i la COVID-19 són les dues malalties infeccioses que tenen un gran impacte. Segons les estimacions de l’Organització Mundial de la Salut, la TB ha causat la mort de gairebé 40 milions d’individus en els últims 20 anys. La pandèmia de la covid-19 ha afectat enormement la manera de viure de la població mundial. Des del gener del 2020 ha causat milions de morts i ha condicionat les vides i el comportament de les persones. Els models matemàtics i computacionals són una eina fonamental que, en ciència, es poden usar per entendre, predir i/o condicionar la dinàmica d’un sistema en concret. En aquesta tesi presentem un compendi de cinc articles on s’usen els models matemàtics per entendre i predir les dinàmiques de la tuberculosi i la COVID-19 en diferents escales espai-temporals. Tot i que la TB és una malaltia que es va identificar fa molts anys, alguns detalls de la seva història natural encara són desconeguts. L’objectiu principal d’aquesta tesi en relació a la TB és entendre els factors i processos que faciliten el pas de la infecció latent cap a malaltia activa. També hem intentat millorar el coneixement i identificar les particularitats dels 70000 anys de coexistència entre els humans i la TB. S’han creat diferents models de la infecció tuberculosa pulmonar a diferents escales espacials. Al nivell alveolar, hem identificat que el correcte balanç entre la resposta immune i la resposta inflamatòria condiciona el resultat de la infecció. A escala de lòbul secundari, hem vist que la distància entre la lesió i la membrana pulmonar és un factor important que determinarà la seva mida final. A escala del pulmó, s’ha reproduït la hipòtesi dinàmica que ens permet explicar la generació de noves lesions a partir de disseminació bronquial de les lesions inicials. S’ha identificat el procés de fusió de lesions com un dels processos més importants que fa aparèixer lesions més grans i acaba originant la malaltia activa. A més, hem modelitzat la coexistència entre els humans i la TB en el Paleolític i el Neolític. S’ha identificat que la protecció femenina envers la TB va ser crucial per la supervivència de l’espècie humana. En el neolític, van aparèixer soques "modernes" que van desplaçar les “antigues”. Amb models matemàtics s’ha pogut observar perquè aquesta aparició no va ser possible en el paleolític. Quan la pandèmia de la COVID-19 va començar, els sistemes de vigilància que havien de servir per controlar i monitoritzar la pandèmia eren inexistents o deficients. En aquesta tesi hem treballat principalment en dos aspectes per ajudar a la monitorització de la pandèmia: determinar la incidència real de la primera onada i crear un model de prediccions a curt termini. Hem desenvolupat una metodologia per determinar la incidència real que va tenir la COVID-19 basada en la letalitat de la malaltia i les sèries temporals de defuncions. Aquesta metodologia s’ha pogut aplicar a diversos països europeus, tenint en compte els possibles biaixos, per exemple, les diferents piràmides de població. S’ha proposat i calibrat un model empíric bastant en l’equació de Gompertz que ens permet fer una predicció dels casos a curt termini a nivell de país. Aquesta tesi demostra com els models computacionals i matemàtics poden ajudar a predir i entendre millor les característiques de les malalties infeccioses usant com a exemple la tuberculosi i la COVID-19.Cada año 10 millones de personas mueren a causa de enfermedades contagiosas. Son enfermedades infecciosas causadas por agentes que se transmiten entre los diferentes individuos. Hoy en día, la tuberculosis (TB) y el COVID-19 son las dos enfermedades infecciosas que tienen un mayor impacto mundial. Según las estimaciones de la Organización Mundial de la Salud, la TB ha causado la muerte de casi 40 millones de individuos en los últimos 20 años. La pandemia del COVID-19 ha cambiado por completo la forma de vivir de la población mundial. Desde enero de 2020 ha causado millones de muertos y ha condicionado las vidas y el comportamiento de las personas. Los modelos matemáticos y computacionales son una herramienta muy potente que, en ciencia, pueden ayudar a entender, predecir y/o condicionar la dinámica de un sistema en concreto. En esta tesis presentamos un compendio de cinco artículos donde se usan los modelos matemáticos para entender y predecir las dinámicas de la tuberculosis y el COVID-19 en diferentes escalas espacio-temporales. Aunque la TB es una enfermedad que se identificó hace muchos años, algunos detalles de su historia natural aún son desconocidos. El objetivo principal de esta tesis en relación a la TB es entender los factores y procesos que facilitan el paso desde una infección latente a enfermedad activa. También hemos intentado mejorar el conocimiento e identificar las particularidades de los 70000 años de coexistencia entre los humanos y la TB. Hemos creado diferentes modelos sobre la infección tuberculosa pulmonar a diferentes escalas espaciales. A nivel alveolar, hemos identificado que el correcto balance entre la respuesta inmune y la respuesta inflamatoria es determinante para el resultado de la infección. A escala del lóbulo secundario hemos visto que la distancia entre la lesión y la membrana pulmonar es un factor importante que determinará el tamaño final de la lesión. A escala del pulmón, se ha reproducido la hipótesis dinámica que nos permite explicar la generación de nuevas lesiones a partir de diseminación bronquial de las lesiones iniciales. Se ha identificado el proceso de fusión de lesiones como uno de los procesos más importantes que hace aparecer lesiones más grandes y acaba originando la enfermedad activa. Además, hemos modelizado la coexistencia entre los humanos y la TB en el Paleolítico y el Neolítico. Se ha identificado que la protección femenina ante la TB fue crucial para la supervivencia de la especie humana. En el Neolítico, aparecieron cepas ”modernas”que desplazaron las ”antiguas”. Con modelos matemáticos se ha podido observar que esta aparición no era posible en el Paleolítico. Al iniciarse la pandemia del COVID-19, los sistemas de vigilancia que debían servir para controlar y monitorizar la pandemia eran inexistentes o deficientes. En esta tesis hemos trabajado principalmente en dos aspectos para ayudar a la monitorización de la pandemia: determinar la incidencia real de la primera ola y crear un modelo de predicciones a corto plazo. Hemos desarrollado una metodología para determinar la incidencia real que tuvo el COVID-19 basada en la letalidad de la enfermedad y las series temporales de defunciones. Esta metodología se ha podido aplicar en varios países europeos, teniendo en cuenta los posibles sesgos, por ejemplo, las diferentes pirámides de población. También se ha usado y calibrado un modelo empírico basado en la ecuación de Gompertz que nos permite hacer una predicción de los casos a corto plazo a nivel de país. Esta tesis demuestra cómo los modelos computacionales y matemáticos pueden ayudar a predecir y entender mejor las características de las enfermedades infecciosas usando como ejemplo la tuberculosis y el COVID-19.Postprint (published version

A 3D computational model for understanding tuberculosis lesions dynamics in lungs

Màster Oficial en Física Avançada, Facultat de Física, Universitat de Barcelona, Curs: 2016, Tutors: Ignacio Pagonabarraga, Clara PratsTuberculosis (TB) is an infectious bacterial disease caused by Mycobacterium tuberculosis (Mtb), which most commonly affects the lungs. In healthy people, an infection with Mtb often causes no symptoms, remaining controlled as a non-contagious latent tuberculosis infection. World Health Organization estimates that one third of the world population is already infected by this bacillus. From those, a 10% will probably develop an active disease the next decade. Nowadays, over 1 million people die annually because of an active TB. The mechanisms that maintain a latent infection for a few years or that make it evolving towards an active disease are not fully understood, yet. In a previous work, the dynamics of TB lesions during an active disease in mice was described by an Agent-Based Model (ABM). This model accounted for the growth, coalescence and proliferation of lesions, showing that the most important mechanism for lesions growth during the active disease was coalescence. In a later work, the dynamics of lesions during a latent infection in minipigs was tackled by implementing a revised version of the previous ABM into a computational model of the bronchial tree. The model was fed with Computed Tomography scan data from latent infection in minipigs. In this case, the model showed that the proliferation of lesions through the bronchial tree was essential for maintaining the latent infection. In this Master thesis we propose a first approach on the evolution of a latent tuberculosis infection into an active disease. The parameter space will be explored trying to elucidate which is the role of each mechanism on the trigger for the diseas

El Museu Romàntic Can Papiol: Anàlisi de les intervencions

Treballs Finals de Grau de Conservació-Restauració de Béns Culturals. Facultat de Belles Arts. Universitat de Barcelona, Curs: 2017-2018, Tutor: Marquès Balagué, Mercè[cat] El treball neix de l’objectiu d’investigar les intervencions que s’han succeït en l’edifici que actualment acull el Museu Romàntic Can Papiol, al llarg dels seus més de 200 anys d’història. Es presenta el resultat de la recerca, sistematització i interpretació de tota la informació que s’ha pogut recollir, per tal d’entendre com era la casa en un origen, i com s’ha anat transformant per les successives intervencions, especialment en els darrers 60 anys. Per tal d’acompanyar el treball, s’ha realitzat una planimetria de la casa en els seus diversos estadis. Amb tot, es busca posar en valor tota la informació possible, per tal de tenir-la present a l’hora de portar a terme futures intervencions de conservació-restauració.[eng] This project has been written with the objective of investigating the interventions that have taken place in the building that currently houses the “Museu Romàntic Can Papiol”, which has more than 200 years of history. The result of the research, systematization and interpretation of all the information that has been collected, is presented to understand how the house was originally, and how it has been transformed by the successive interventions, especially in the last 60 years. To accompany the project, a planimetry of the house has been carried out to compare its different stages. In general, this project tries to give value to all the information, to take it into consideration if new conservation-restoration interventions are carried out in the future

Origin of tuberculosis in the Paleolithic predicts unprecedented population growth and female resistance

The project leading to these results has received funding from "la Caixa" Foundation (ID 100010434), under agreement LCF/PR/GN16/10290002Current data estimate the origin of Mycobacterium tuberculosis complex (MtbC) infection around 73,000 years before the common era (BCE), and its evolution to "modern" lineages around 46,000 BCE. Being MtbC a major killer of humanity, the question is how both species could persist. To answer this question, we have developed two new epidemiological models (SEIR type), adapted to sex dimorphism and comparing coinfection and superinfection for different MtbC lineages. We have attributed a higher resistance/tolerance to females to explain the lower incidence noted in this sex, a better health status in the Paleolithic compared to the Neolithic, and a higher dissemination of "modern" lineages compared to "ancient" ones. Our findings show the extraordinary impact caused by "modern" lineages, provoking the extinction of the groups infected. This could only be overcomed by an unprecedented population increase (x20 times in 100 years) and helped with the protection generated by previous infection with "ancient" lineages. Our findings also suggest a key role of female resistance against MtbC. This data obliges us to rethink the growth population parameters in the Paleolithic, which is crucial to understanding the survival of both MtbC and humans, and to decipher the nature of human female resistance against TB

The origin and maintenance of tuberculosis is explained by the induction of smear-negative disease in the paleolithic

Is it possible that the origin of Mycobacterium tuberculosis (Mtb) infection was around 70,000 years before the common era? At that time Homo sapiens was just another primate species with discrete growth and a very low-density geographic occupation. Therefore, it is difficult to understand the origin of a highly virulent obligate human pathogen. We have designed a new SEIR model (TBSpectr) that allows the differentiation of smear-positive and -negative tuberculosis. The model reconciles currently accepted growth rates for the Middle Paleolithic (0.003%/year) and Neolithic (0.1%/year). The obtained data link the origin of Mtb infection in the Middle Paleolithic to the induction of smear-negative TB, and reveal that its persistence required interrelations among hunter–gatherer groups, while the risk of human extinction was negligible. It also highlights the number of people infected per case and the fast progression to disease for Mtb infection maintenance, as well as the link between poor health in the Neolithic with the increased incidence of more severe forms of TB (smear-positive). In conclusion, our data support the origin of TB as a well-tolerated, highly persistent disease, even in low-density populations, showing the difficulty of its eradication and highlighting the necessity for providing better health conditions to humans to reduce its severity.Peer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i BenestarPostprint (published version

Transmissibility, hospitalization, and intensive care admissions due to omicron compared to delta variants of SARS-CoV-2 in Catalonia: A cohort study and ecological analysis

COVID-19; Cohorts; SeverityCOVID-19; Cohorts; GravetatCOVID-19; Cohortes; GravedadPurpose: We aim to compare the severity of infections between omicron and delta variants in 609,352 SARS-CoV-2 positive cases using local hospitalization, vaccination, and variants data from the Catalan Health Care System (which covers around 7. 8 million people). Methods: We performed a substitution model to establish the increase in transmissibility of omicron using variant screening data from primary care practices (PCP) and hospital admissions. In addition, we used this data from PCP to establish the two periods when delta and omicron were, respectively, dominant (above 95% of cases). After that, we performed a population-based cohort analysis to calculate the rates of hospital and intensive care unit (ICU) admissions for both periods and to estimate reduction in severity. Rate ratios (RR) and 95% confidence intervals (95% CI) were calculated and stratified by age and vaccination status. In a second analysis, the differential substitution model in primary care vs. hospitals allowed us to obtain a population-level average change in severity. Results: We have included 48,874 cases during the delta period and 560,658 during the omicron period. During the delta period, on average, 3.8% of the detected cases required hospitalization for COVID-19. This percentage dropped to 0.9% with omicron [RR of 0.46 (95% CI: 0.43 to 0.49)]. For ICU admissions, it dropped from 0.8 to 0.1% [RR 0.25 (95% CI: 0.21 to 0.28)]. The proportion of cases hospitalized or admitted to ICU was lower in the vaccinated groups, independently of the variant. Omicron was associated with a reduction in risk of admission to hospital and ICU in all age and vaccination status strata. The differential substitution models showed an average RR between 0.19 and 0.50. Conclusion: Both independent methods consistently show an important decrease in severity for omicron relative to delta. The systematic reduction happens regardless of age. The severity is also reduced for non-vaccinated and vaccinated groups, but it remains always higher in the non-vaccinated population. This suggests an overall reduction in severity, which could be intrinsic to the omicron variant. The fact is that the RR in ICU admission is systematically smaller than in hospitalization points in the same direction.MC received funding from la Caixa Foundation ID 100010434, under agreement LCF/PR/GN17/50300003. MC, CP, and SA received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00

Portable Bio-Devices: Design of Electrochemical Instruments from Miniaturized to Implantable Devices

The integration of biosensors and electronic technologies allows the development of biomedical systems able to diagnose and monitoring pathologies by detecting specific biomarkers. The chapter presents the main modules involved in the development of such devices, generically represented in Fig. 1, and focuses its attention on the essential components of these systems to address questions such as: how is the device powered? How does it communicate the measured data? What kind of sensors could be used?, and What kinds of electronics are used

Multisystem Inflammatory Syndrome in Children in Western Countries? Decreasing Incidence as the Pandemic Progresses?: An Observational Multicenter International Cross-sectional Study

COVID-19, Multisystemic inflammatory syndrome; Children; EpidemiologyCOVID-19, Síndrome inflamatorio multisistémico; Niños; EpidemiologíaCOVID-19, Síndrome inflamatòria multisistèmica; Nens; EpidemiologiaBackground: SARS-CoV-2 variations as well as immune protection after previous infections and/or vaccination may have altered the incidence of multisystemic inflammatory syndrome in children (MIS-C). We aimed to report an international time-series analysis of the incidence of MIS-C to determine if there was a shift in the regions or countries included into the study. Methods: This is a multicenter, international, cross-sectional study. We collected the MIS-C incidence from the participant regions and countries for the period July 2020 to November 2021. We assessed the ratio between MIS-C cases and COVID-19 pediatric cases in children <18 years diagnosed 4 weeks earlier (average time for the temporal association observed in this disease) for the study period. We performed a binomial regression analysis for 8 participating sites [Bogotá (Colombia), Chile, Costa Rica, Lazio (Italy), Mexico DF, Panama, The Netherlands and Catalonia (Spain)]. Results: We included 904 cases of MIS-C, among a reference population of 17,906,432 children. We estimated a global significant decrease trend ratio in MIS-C cases/COVID-19 diagnosed cases in the previous month ( P < 0.001). When analyzing separately each of the sites, Chile and The Netherlands maintained a significant decrease trend ( P < 0.001), but this ratio was not statistically significant for the rest of sites. Conclusions: To our knowledge, this is the first international study describing a global reduction in the trend of the MIS-C incidence during the pandemic. COVID-19 vaccination and other factors possibly linked to the virus itself and/or community transmission may have played a role in preventing new MIS-C cases

Teragnosis in vivo: Innovación nanomédica fomentada por la convergencia de tecnologías emergentes

El creciente desarrollo y la mejora en cuanto a innovación de dispositivos basados en la convergencia de tecnologías emergentes ha dado lugar a un uso cada vez mayor de los nanosensores en la comunidad biomédica. Sin embargo, los nanosensores implantables aún tienen que afrontar ciertos retos como la biocompatibilidad y la seguridad de datos. En este artículo se abordan el progreso y los principales desafíos para esta clase de dispositivos nanomédicos y se analizan además las principales aplicaciones médicas con especial énfasis en la teragnosis, término que integra el concepto de diagnosis y terapia en un mismo dispositivo. De este modo, se traza el proceso desde la investigación aplicada hasta la comercialización del producto, que es cuando el retorno social puede ser estimado. Finalmente, se contempla la gestión de la tecnología dentro de un ecosistema de innovación, cuya cadena de valor incluye una integración multidisciplinaria y el flujo del conocimiento