Today, a significant portion of individuals’ time is spent indoors, estimated at approximately 90% of their total time. This raises concerns about the transmission of Coronavirus Disease 2019 (COVID-19) instigating Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and similar viruses and highlights the critical role of ventilation systems in indoor environments, which this study aims to investigate. Narrowing the focus to healthcare facilities, particularly hospitals in Sweden, the study includes the importance of ventilation systems in safeguarding the health safety and well-being of patients, healthcare workers in hospitals. To effectively combat the spread of the SARS-CoV-2 virus, it is crucial to have a thorough understanding of its viral characteristics, with a specific centre on airborne transmission size, virus longevity, and quantum of infection. Furthermore, it is essential to recognize the major impact of ventilation rate, thermodynamic factors such as temperature and humidity, as well as pollutants in effectively mitigating the transmission of SARS-CoV-2 and similar pathogens.  The comprehensive findings of this literature review underscore that, for hospitals in Sweden, a Heat Recovery Ventilation (HRV) system incorporating a plate heat exchanger is the most suitable ventilation system for this specific objective. Moreover, the recommended ventilation strategy is specifically tailored for implementation in wards or isolated rooms, where it is ideal for the incoming air to originate from the patient’s room floor, while the exit point is preferably located near or at the ceiling. In the context of this study, identified effective solutions involve the utilization and combination of high-efficiency filters and ultraviolet (UV) technology installed within ventilation system unit, particularly when an air recirculation system is used. Additionally, the implementation of RM3 (Rheem’s third generation products) UV-C technology for indoor use can be achieved without considerable intervention in ventilation system, depending on the type of ventilation system being utilized. In summary, this study enhances understanding of the complex relationship between ventilation systems, COVID-19 transmission and similar diseases, the optimization of thermodynamic factors, and selection of effective and practical measures. It provides valuable insights for designing effective ventilation strategies across various indoor environments, with a specific attention on healthcare facilities. / Idag spenderas en betydande del av individers tid inomhus, uppskattningsvis cirka 90% av deras totala tid. Detta väcker oro över överföringen av Coronavirussjukdom 2019 (COVID-19), som initierar Svårt Akut Respiratoriskt Syndrom Coronavirus 2 (SARS-CoV-2) och liknande virus och belyser den kritiska rollen som ventilationssystem spelar i inomhusmiljöer, som denna studie syftar till att undersöka. Genom att begränsa fokus till vårdinrättningar, särskilt sjukhus i Sverige, inkluderar studien vikten av ventilationssystem för att säkerställa hälsosäkerheten och välbefinnandet för patienter och vårdpersonal på sjukhus. För att effektivt bekämpa spridningen av SARS-CoV-2-viruset är det viktigt att ha en grundlig förståelse för dess virala egenskaper, med ett specifikt fokus på luftburen överföringsstorlek, viruslivslängd och infektionsmängd. Dessutom är det viktigt att identifiera den stora inverkan av luftomsättningstakt, termodynamiska faktorer som temperatur och fuktighet samt föroreningar för att effektivt minska överföringen av SARS-CoV-2 och liknande patogener. De omfattande resultaten av denna litteraturstudie understryker att värmeåtervinningsventilationssystem (HRV) med plattvärmeväxlare är det mest lämpliga ventilationssystemet för sjukhus i Sverige för detta specifika mål. Dessutom är den rekommenderade ventilationsstrategin speciellt anpassad för implementering på avdelningar eller isolerade rum, där den är idealisk för att inkommande luft ska komma från patientens rumsgolv, medan utgångspunkten företrädesvis är placerad nära eller i taket. I samband med denna studie involverar identifierade effektiva lösningar, användning och kombination av högeffektiva filter och ultraviolett (UV)-teknik installerad i ventilationssystemenhet, särskilt när ett luftcirkulationssystem används. Dessutom kan implementeringen av RM3 (Rheems tredje generationens produkter) UV-C-teknik för inomhusbruk uppnås utan betydande ingrepp i ventilationssystem, beroende på vilken typ av ventilationssystem som används. Sammanfattningsvis ökar denna studieförståelse för det komplexa förhållandet mellan ventilationssystem, COVID-19-överföring och liknande sjukdomar, optimering av termodynamiska faktorer och val av effektiva och praktiska åtgärder. Den ger värdefulla insikter för att utforma effektiva ventilationsstrategier i olika inomhusmiljöer, med särskild uppmärksamhet på vårdinrättningar.

The clinical severity of Covid-19 varies greatly between individuals, and all underlying risk factors are not yet well understood. Previous studies have shown Covid patients to be enriched with autoantibodies against type I interferons, suggesting autoimmunity may be an underlying factor of susceptibility to severe disease. In this project, the interplay between severe Covid-19 and autoimmunity was investigated in 114 Swedish patients, sampled in April- May 2020 as well as longitudinal re-samplings 4 and 8 months later, using the infrastructure of the Human Protein Atlas and the SciLife lab autoimmunity and serology profiling unit. First, 16 patients with few comorbidities were analyzed for autoantibodies at a near proteome-wide scale using planar microarrays, after which a custom antigen panel was assembled based on observed reactivities and literature studies. The antigen panel was implemented in a 384-plex suspension bead array which was run for all patient samples and a control group. Among the Covid patients, 23 antigens were called as differentially reactive and 8 of them were proposed as relevant to immunoregulation or Covid pathogenesis. The results partially replicated previous findings of autoimmunity directed to type I interferons and offer a list of candidate autoantigens for further inquiries. / Allvarlighetsgraden av sjukdomen Covid-19 varierar kraftigt mellan individer och alla underliggande riskfaktorer är ännu inte förstådda. Tidigare studier har påvisat Covidpatienter som överrepresenterade med autoantikroppar mot typ I interferoner, vilket förespråkar autoimmunitet som en möjlig underliggande riskfaktor till att utveckla allvarlig Covid. I detta projekt användes infrastrukturen av det mänskliga proteinatlasprojektet och enheten för autoimmunitets- och serologiprofilering på SciLife lab för att undersöka samspelet mellan allvarlig Covid-19 och autoimmunitet i 114 st svenska patienter inlagda under april-maj 2020, samt från uppföljningsprover 4 resp. 8 månader senare. Till en början undersöktes 16 patienter med låg grad av samsjukdom för förekomst av autoan- tikroppar mot proteomet i stort med hjälp av mikroarrayer. En panel av antigen sammanställdes därefter baserat på resultaten och litteraturstudier. Panelen implementerades som en 384-plex kulsuspensionsarray vilken kördes för alla patientprover samt en kontrollgrupp. Ibland Covidpatienterna klassades 23 st antigen som överrepresenterade, varav 8 st avsågs relevanta för immunoreglering eller sjukdomsförlopp. Resultaten visades delvis återskapa tidigare fynd av autoimmunitet riktad mot typ I interferoner och erbjuda en lista av potentiella autoantigen för vidare efterforskningar.

Background: Sphingosine 1-phosphat receptor modulators (S1PRMs) have been linked to attenuated immune response to SARS-CoV-2 vaccines. Objective: To characterize differences in the immune response to SARS-CoV-2 vaccines in patients on selective versus unselective S1PRMs. Methods: Monocentric, longitudinal study on people with multiple sclerosis (pwMS) on fingolimod (FTY), siponimod (SIP), ozanimod (OZA), or without disease-modifying therapy (DMT) following primary and booster SARS-CoV-2 vaccination. Anti-SARS-CoV-2 antibodies and T-cell response was measured with electro-chemiluminescent immunoassay and interferon-γ release assay. Results: Primary vaccination induced a significant antibody response in pwMS without DMT while S1PRM patients exhibited reduced antibody titers. The lowest antibodies were found in patients on FTY, whereas patients on OZA and SIP presented significantly higher levels. Booster vaccinations induced increased antibody levels in untreated patients and comparable titers in patients on OZA and SIP, but no increase in FTY-treated patients. While untreated pwMS developed a T-cell response, patients on S1PRMs presented a diminished/absent response. Patients undergoing SARS-CoV-2 vaccination before onset of S1PRMs presented a preserved, although attenuated humoral response, while T-cellular response was blunted. Conclusion: Our data confirm differential effects of selective versus unselective S1PRMs on T- and B-cell response to SARS-CoV-2 vaccination and suggest association with S1PRM selectivity rather than lymphocyte redistribution.

Undoubtedly, the rapid development of information systems and the widespread use of electronic means and social networks have played a significant role in accelerating the pace of events worldwide, such as, in the 2012 Gaza conflict (the 8-day war), in the pro-secessionist rebellion in the 2013-2014 conflict in Eastern Ukraine, in the 2016 US Presidential elections, and in conjunction with the COVID-19 outbreak pandemic since the beginning of 2020. As the number of daily shared data grows quickly on various social networking platforms in different languages, techniques to carry out automatic classification of this huge amount of data timely and correctly are needed. Of the many social networking platforms, Twitter is of the most used ones by netizens. It allows its users to communicate, share their opinions, and express their emotions (sentiments) in the form of short blogs easily at no cost. Moreover, unlike other social networking platforms, Twitter allows research institutions to access its public and historical data, upon request and under control. Therefore, many organizations, at different levels (e.g., governmental, commercial), are seeking to benefit from the analysis and classification of the shared tweets to serve in many application domains, for examples, sentiment analysis to evaluate and determine user’s polarity from the content of their shared text, and misleading information detection to ensure the legitimacy and the credibility of the shared information. To attain this objective, one can apply numerous data representation, preprocessing, natural language processing techniques, and machine/deep learning algorithms. There are several challenges and limitations with existing approaches, including issues with the management of tweets in multiple languages, the determination of what features the feature vector should include, and the assignment of representative and descriptive weights to these features for different mining tasks. Besides, there are limitations in existing performance evaluation metrics to fully assess the developed classification systems. In this dissertation, two novel frameworks are introduced; the first is to efficiently analyze and classify bilingual (Arabic and English) textual content of social networks, while the second is for evaluating the performance of binary classification algorithms. The first framework is designed with: (1) An approach to handle Arabic and English written tweets, and can be extended to cover data written in more languages and from other social networking platforms, (2) An effective data preparation and preprocessing techniques, (3) A novel feature selection technique that allows utilizing different types of features (content-dependent, context-dependent, and domain-dependent), in addition to (4) A novel feature extraction technique to assign weights to the linguistic features based on how representative they are in in the classes they belong to. The proposed framework is employed in performing sentiment analysis and misleading information detection. The performance of this framework is compared to state-of-the-art classification approaches utilizing 11 benchmark datasets comprising both Arabic and English textual content, demonstrating considerable improvement over all other performance evaluation metrics. Then, this framework is utilized in a real-life case study to detect misleading information surrounding the spread of COVID-19. In the second framework, a new multidimensional classification assessment score (MCAS) is introduced. MCAS can determine how good the classification algorithm is when dealing with binary classification problems. It takes into consideration the effect of misclassification errors on the probability of correct detection of instances from both classes. Moreover, it should be valid regardless of the size of the dataset and whether the dataset has a balanced or unbalanced distribution of its instances over the classes. An empirical and practical analysis is conducted on both synthetic and real-life datasets to compare the comportment of the proposed metric against those commonly used. The analysis reveals that the new measure can distinguish the performance of different classification techniques. Furthermore, it allows performing a class-based assessment of classification algorithms, to assess the ability of the classification algorithm when dealing with data from each class separately. This is useful if one of the classifying instances from one class is more important than instances from the other class, such as in COVID-19 testing where the detection of positive patients is much more important than negative ones. / Graduate

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IntroductionSars-CoV-2 (severe acute respiratory syndrome coronavirus-2) is an RNA virus that causes Covid-19 disease. This disease started in the city of Wuhan in China. This is not the first time that the coronavirus has caused an outbreak, in the last twenty years the coronavirus SARS-CoV (Severe Acute Respiratory Syndrome coronavirus) and MERSCoV (Middle East Respiratory Syndrome coronavirus) have caused two major outbreaks. The main structure of SARS-CoV-2 is built from membrane (M), envelope (E), nucleocapsid (N) and spike (S). When the body is infected by the virus, the virus enters the host cells by binding to the ACE2 receptor. Once the virus has entered the cell, it releases viral RNA. The virus's particles multiply inside the cell. New virus particles from the infected cell are produced, which in its turn infect new cells. The immune system against SARS-CoV-2 involves both the cellular and humoral arms, with neutralizing antibodies directed primarily against the S antigen.ObjectivesThis research aimed to study the litteratur describing how long the antibodies and memory cells remain in the blood, and how long the protection against the virus lasts.MethodThis work was based on six scientific articles to answer the question: How long protective antibody levels last in the plasma after resolution of a Covid-19 infection? To answer the question, the levels of the antibodies of different classes, IgG, IgM and IgA, against the receptorbinding domain (RBD)/ S, N protein were analysed as reported in litterature, as well as the reported amounts of T memory cells and B memory cells.ResultsHumans produce SARS-CoV-2-specific antibodies, especially IgM and IgG antibodies, and T cells response to SARS-CoV-2 infection. IgG and IgM antibody levels were higher in patients whith severe Covid-19 than in mild cases. Studies cohort included patients from 18 to 90 years old. The studies lasted on from three months and up to one year.

Contexte : Partout dans le monde, la population âgée en hébergement a été la plus lourdement affectée par la pandémie de COVID-19, du point de vue des infections et des décès. Or, ces mêmes personnes ont été exclues d’une grande partie de la littérature scientifique. Ce mémoire décrit l’évolution des éclosions dans 17 CHSLD publics de Montréal, dont certains ont été fortement atteints alors que d’autres ont été épargnés pendant la première vague (23 février au 11 juillet 2020), en cherchant à élucider les facteurs associés à l’incidence et à la létalité de la COVID-19. Méthodes : Des données institutionnelles ont été recueillies sur les 17 CHSLD du CIUSSS Centre-Sud-de-l'Île-de-Montréal et des données individuelles ont été obtenues grâce à une révision des 1197 dossiers de patients atteints de la COVID-19 en première vague. Dans l’analyse ARIMA, des séries chronologiques ont été construites pour les cas incidents bruts chez les résidents en CHSLD et dans la ville de Montréal, afin d’évaluer l’impact de deux interventions, soit le port généralisé du masque de procédure et le dépistage élargi des résidents et des employés. Dans l’analyse des infections par CHSLD, des modèles de régression de type binomial négatif ont été construits pour estimer l’effet des facteurs de risque institutionnels sur l’incidence de la COVID-19 chez les résidents. Dans l’analyse de surmortalité, les excès de décès durant la période de février à juillet ont été évalués avec des tests t et des ratios de taux entre l’année 2020 et la moyenne des quatre années précédentes (2016-2019). Enfin, pour l’analyse de mortalité dans la cohorte rétrospective de résidents atteints de la COVID-19, des modèles de régression logistique à effets mixtes ont été utilisés pour évaluer les facteurs institutionnels et les traitements associés à la mortalité dans les 30 jours suivant un diagnostic de COVID-19, en contrôlant pour les facteurs de risque individuels. Résultats : Dans l’analyse de série chronologique ARIMA, chaque augmentation d’un cas incident quotidien par 100 000 à Montréal était associée avec une augmentation de 0,051 (IC95% 0,044 à 0,058) fois l’incidence quotidienne en CHSLD la semaine suivante, chez les résidents à risque. De plus, en contrôlant pour la transmission communautaire, chaque palier d’intensification du dépistage était associé à une diminution de l’incidence de 11,8 fois (IC95% -15,1 à -8,5) dans les deux semaines suivantes, chez les résidents à risque. Dans le modèle explicatif des infections au niveau des CHSLD, la pénurie sévère d’infirmières auxiliaires (IRR 3,2; IC95% 1,4 à 7,2), la mauvaise performance aux audits ministériels (IRR 3,0; IC95% 1,1 à 7,8) et un score moyen d’autonomie plus faible (IRR 2,1; IC95% 1,4 à 3,1) étaient associés au taux d’incidence par centre. En revanche, la présence de zone chaude dédiée aux patients COVID-19 (IRR; 0,56 IC95% 0,34 à 0,92) était protectrice. Pour l’ensemble des 17 CHSLD avec 2670 lits, l’excès de décès de février à juillet 2020 était de 428 (IC95% 409 à 447). Comparé aux quatre années précédentes, il y a eu plus que le double (IRR 2,3; IC95% 2,1 à 2,5) de décès en 2020 pendant la période de la première vague. Pour 12 CHSLD qui ont vécu des éclosions importantes, les excès de décès en 2020 variaient de 5,2 à 41,9 décès par 100 lits, avec une surmortalité par rapport aux années précédentes allant de 1,9 à 3,8. Selon l’analyse de mortalité dans la cohorte rétrospective, les facteurs individuels associés à la mortalité dans les 30 jours suivant le diagnostic de COVID-19 étaient l’âge (OR 1,58; IC95% 1,35 à 1,85 par tranche additionnelle de 10 ans), le sexe masculin (OR 2,37; IC95% 1,70 à 3,32), la perte d’autonomie (OR 1,12; IC95% 1,05 à 1,20 pour chaque augmentation d’un point à l’Iso-SMAF), le niveau d’intervention médicale C (OR 3,43; IC95% 1,57 à 7,51) et D (OR 3,61; IC95% 1,47 à 8,89) comparé au niveau A, ainsi que les diagnostics de trouble neurocognitif (OR 1,54; IC95% 1,04 à 2,29) et d’insuffisance cardiaque (OR 2,36; IC95% 1,45 à 3,85). Le traitement avec une thromboprophylaxie (OR 0,42; IC95% 0,29 à 0,63) et l’infection tardive après le 20 avril 2020 (OR 0,46; IC95% 0,33 à 0,65) étaient associés à la survie à 30 jours. Pour les facteurs institutionnels, la pénurie sévère de 25% ou plus d’infirmières auxiliaires (OR 1,91; IC95% 1,14 à 3,21 par rapport à une pénurie légère < 15%) et la taille du centre (OR 1,77; IC95% 1,17 à 2,68 pour chaque 100 lits additionnels) étaient associés au décès dans les 30 jours. Conclusion : Ce mémoire a relevé plusieurs facteurs de risque modifiables au niveau institutionnel associés aux infections et aux décès COVID-19, dont le dépistage, l’adhérence aux directives ministérielles de prévention et contrôle des infections, la pénurie d’infirmières auxiliaires et le nombre de lits par centre. Ces enjeux cruciaux devront être au cœur des futures orientations et politiques touchant les centres d’hébergement, pour cette pandémie et au-delà. / Background: In the midst of the COVID-19 pandemic, the population of long-term care residents has been the hardest hit by infections and deaths all around the world. Yet, these same individuals have been excluded from vast segments of the scientific literature. This thesis describes the evolution of outbreaks in 17 public long-term care facilities (“CHSLD”) in Montreal, some of which were severely affected and others were relatively spared during the first wave (February 23 to July 12, 2020), in search of risk factors associated with COVID-19 cases and deaths. Methods: Institutional-level data on the 17 CHSLDs were collected from relevant administrative departments within the establishment (CIUSSS Centre-Sud-de-l'Île-de-Montréal), and individual-level data was obtained from the chart reviews of 1,197 first wave COVID-19 patients. For the ARIMA analysis, time series were built using the crude incidence rates among CHSLD residents and in the city of Montreal, in order to assess the impact of two interventions – introduction of the mask-wearing policy and generalized testing among residents and staff. For the analysis of facility-level infection rates, negative binomial regression models were built to estimate the effects of several institutional risk factors on incident cases. As for the excess mortality analysis, excess death and relative mortality were estimated using one-sample t-tests and rate ratio tests to compare 2020 deaths with average deaths in the previous four years (2016-2019), for the period of February to July. Lastly, for the survival analysis of the retrospective cohort, mixed-effects logistic regression models were used to identify institutional factors and treatments associated with 30-day mortality after a COVID-19 diagnosis, while controlling for individual risk factors. Results: In the ARIMA time series analysis, each additional case per 100,000 per day in Montreal was associated with a 0.051 (95%CI 0.044 to 0.058) increase in CHSLD daily incidence a week later, among at-risk residents. In addition, while controlling for community transmission, increased testing intensity was associated with a 11.8 (95%CI -15.1 to -8.5) decrease in CHSLD daily incidence two weeks later, among at-risk residents. In the negative binomial regression model for facility-level COVID-19 infections, poor performance on ministry audits (IRR 3.0 95%CI 1.1 to 7.8), severe shortage of auxiliary nurses (IRR 3.2 95%CI 1.4 to 7.2) and lower average autonomy scores (IRR 2.1 95%CI 1.4 to 3.1) were associated with incident cases, while the presence of a COVID-19 unit or “red zone” (IRR 0.56 95%CI 0.34 to 0.92) was inversely associated with infections. For the 17 CHSLDs, excess deaths from February to July 2020 was 428 (95%CI 409 to 447). Compared to the same period in the previous four years, 2020 mortality during the first wave was 2.3 (IRR 95%CI 2.1 to 2.5) times higher. For a subset of 12 facilities that experienced substantial outbreaks, excess deaths in 2020 varied from 5.2 to 41.9 deaths per 100 beds, with significant excess mortality between 1.9 and 3.8, relative to previous years. According to the mortality analysis by mixed-effects logistic regression, individual risk factors associated with 30-day mortality after a COVID-19 diagnosis were age (OR 1.58 95%CI 1.35 to 1.85 per additional 10 years), male sex (OR 2.37 95%CI 1.70 to 3.32), loss of autonomy (OR 1.12 95%CI 1.05 to 1.20 per unit increase of Iso-SMAF profile), C-level (OR 3.43 95%CI 1.57 to 7.51) or D-level (OR 3.61 95%CI 1.47 to 8.89) medical intervention compared to A-level, as well as being diagnosed with a neurocognitive disorder (OR 1.54 95%CI 1.04 to 2.29) or congestive heart failure (OR 2.36 95%CI 1.45 to 3.85). Treatment with thromboprophylaxis (OR 0.42 95%CI 0.29 to 0.63) and diagnosis after April 20, 2020 (OR 0.46 95%CI 0.33 to 0.65) were associated with 30-day survival. As for institutional risk factors, severe shortage of auxiliary nurses (OR 1.91 95%CI 1.14 to 3.21) and facility size (OR 1.77 95%CI 1.17 to 2.68 per 100 beds) increased the odds of dying within 30 days. Conclusion: This study identified several modifiable risk factors at the institutional level associated with COVID-19 infections and deaths, including testing strategies, adherence to ministry directives for infection prevention, auxiliary nurse shortages, and number of beds per facility. Future policies and regulations targeting long-term care facilities will need to tackle these critical issues, for this pandemic and beyond.