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.

This study uses aspects of time series methodology to model and forecast major taxes such as Personal Income Tax (PIT), Corporate Income Tax (CIT), Value Added Tax (VAT) and Total Tax Revenue(TTAXR) in the South African Revenue Service (SARS). The monthly data used for modeling tax revenues of the major taxes was drawn from January 1995 to March 2010 (in sample data) for PIT, VAT and TTAXR. Due to higher volatility and emerging negative values, the CIT monthly data was converted to quarterly data from the rst quarter of 1995 to the rst quarter of 2010. The competing ARIMA/SARIMA and Holt-Winters models were derived, and the resulting model of this study was used to forecast PIT, CIT, VAT and TTAXR for SARS fiscal years 2010/11, 2011/12 and 2012/13. The results show that both the SARIMA and Holt-Winters models perform well in modeling and forecasting PIT and VAT, however the Holt-Winters model outperformed the SARIMA model in modeling and forecasting the more volatile CIT and TTAXR. It is recommended that these methods are used in forecasting future payments, as they are precise about forecasting tax revenues, with minimal errors and fewer model revisions being necessary. / Statistics / M.Sc. (Statistics)

本研究為2000年至2005年之歷時性分析，採用個案史研究法，藉由「《明日報》的興衰」、「部落格（blog）在台灣的發展」、「周星馳想拍少林棒球？」、「SARS風暴」與「東海大學劈腿事件」五個案例的歷史紀錄，檢視網路時代新聞產製與專業呈現何種面貌。 此段期間國內媒體大環境的變遷，呈現報業經營困難、主流媒體趨於八卦化及網路個人媒體興起的現象。網路新聞的發展由最初傳統新聞媒體所成立之新聞網站，繼而出現網路原生報《明日報》，而後部落格等網路個人媒體逐漸興起，網路新聞的形式仍在演變中。 網路傳播的互動性特質提升閱聽人主動性，平民記者的概念開始出現。傳統新聞記者引用網路消息來源加以報導已不在少數，且常以獨家新聞或熱門網路話題作為新聞價值之判準，即使查證未果仍以截稿時間為由而刊登，傳統新聞專業強調真實報導、公正客觀等核心價值已被動搖。

2003年傳染病SARS流行期間，媒體在指稱此疾病時，出現許多不同的名稱，本研究認為名稱不只是用來指稱事物、也反映社會文化的想法，更可能遮蔽了真實，帶來了想像，這本論文即是嘗試描繪這個過程的痕跡。 研究者在文本中採用Norman Fairclough的批判論述分析方法，從中發展出語言、媒體論述及社會文化三個分析層次，觀察在SARS發展的過程中，媒體上出現的疾病名稱如何形成及轉變、媒體論述和整體社會氣氛如何因應隨之改變的動態過程；再輔以深度訪談法，瞭解報社編輯在處理SARS新聞時面對的情形和自身想法，而這些因素又如何影響了他/她們對疾病名稱的選擇。 研究分析發現，SARS名稱的變化可略分為三個時期：在官方尚未確定疾病名稱前，名稱強調的多是疾病的不明、外來和可怕，報導內容中有相當部分用來描述一般民眾的恐慌情緒，編輯則著重在新聞報導中找出新奇吸引讀者的觀點來形容此一新興傳染病，略為誇張也在可接受範圍；第二個時期官方名稱出現，媒體漸漸出現統一的名稱，不過在其中個別媒體組織的立場仍然可能影響名稱變化的速度，但代表專業共識和官方力量的名稱則是影響名稱最有力的因素，報導內容也以醫療專業觀點為多數；第三個時期傳染範圍擴大，媒體紛紛改以讖諱風格鮮明的「煞」字來指稱疾病，編輯除了提及諧音運用，也有將自身對疾病感知到的負面形象轉移到文字上的作用，連帶在圖片的選擇上也以除煞驅魔為主。 研究者嘗試指出，疾病名稱不是理所當然，而是新聞室內編輯在諸多考量下所做出的選擇，包括消息來源的不同、市場利潤的追求、組織意識形態的影響、社會文化概念以及個人主觀的感受等，這些都會進而影響到閱聽眾對疾病的認知。研究者試圖以此提醒新聞從業人員對事物的命名保持反思，閱聽眾在接收訊息時則必須時時保有批判性的覺察。

Based on extended research on Chinese medical and epidemiological archival material dating back to the beginning of the 20th century, and on six months of internship in epidemiology in Beijing’s Medical School and in Haidian District’s Centre of Disease Control and Prevention, this thesis explores the conjunction of three major epidemiological crises in modern Chinese history with processes of State formation: the 1911 Manchurian pneumonic plague, the 1952 germ-warfare, and the 2003 SARS outbreak. Analysing the three crises as Events in line with Alain Badiou’s epistemology it seeks to establish how different strategies of governmental fidelity to the imagined cause of each crisis have led to distinct modes of organisation and valorisation of the social: Republican China and its decline to fascism; the clash between professional revolutionaries and technocrats in Maoist China; and the emergence of the “Harmonious Society” of mass exploitation and repression today. This conjunction between State formation and epidemiological Events is explored with the use of Foucault’s genealogical method in a quest for a historical materialist approach that posits at its epicentre processes of class composition, decomposition and recomposition, and their contested enclosure by the governmental apparati of capture. The present thesis thus examines the three major epidemiological crises of modern China as forming grounds for biopolitical strategies that give rise to modes of subjectivation and circuits of debt/guilt within the context of the class struggle. And at the same time, it aims to create a new field of investigation for anthropology: the relation of State and Event, from a viewpoint that contests the accepted relation of event and structure expounded by Marshall Sahlins, proposing as the main object of this investigation the conjunction between necessity and will that can never be reduced either to the naturalism of historical determinism, nor to the culturalism of subjective contingency.

Antibiotic resistant pathogens, such as methicillin-resistant Staphylococcus aureus (MRSA), and vancomycin-resistant enterococci (VRE), are an increasing burden on healthcare systems. Hospital acquired infections with these organisms leads to higher morbidity and mortality compared with the sensitive strains of the same species and both VRE and MRSA are on the rise worldwide including in Australian hospitals. Emerging community infectious diseases are also having an impact on hospitals. The Severe Acute Respiratory Syndrome virus (SARS Co-V) was noted for its propensity to spread throughout hospitals, and was contained largely through social distancing interventions including hospital isolation. A detailed understanding of the transmission of these and other emerging pathogens is crucial for their containment. The statistical inference and mathematical models used in this thesis aim to improve understanding of pathogen transmission by estimating the transmission rates of contagions and predicting the impact of interventions. Datasets used for these studies come from the Princess Alexandra Hospital in Brisbane, Australia and Shanxi province, mainland China. Epidemiological data on infection outbreaks are challenging to analyse due to the censored nature of infection transmission events. Most datasets record the time on symptom onset, but the transmission time is not observable. There are many ways of managing censored data, in this study we use Bayesian inference, with transmission times incorporated into the augmented dataset as latent variables. Hospital infection surveillance data is often much less detailed that data collected for epidemiological studies, often consisting of serial incidence or prevalence of patient colonisation with a resistant pathogen without individual patient event histories. Despite the lack of detailed data, transmission characteristics can be inferred from such a dataset using structured HiddenMarkovModels (HMMs). Each new transmission in an epidemic increases the infection pressure on those remaining susceptible, hence infection outbreak data are serially dependent. Statistical methods that assume independence of infection events are misleading and prone to over-estimating the impact of infection control interventions. Structured mathematical models that include transmission pressure are essential. Mathematical models can also give insights into the potential impact of interventions. The complex interaction of different infection control strategies, and their likely impact on transmission can be predicted using mathematical models. This dissertation uses modified or novel mathematical models that are specific to the pathogen and dataset being analysed. The first study estimates MRSA transmission in an Intensive Care Unit, using a structured four compartment model, Bayesian inference and a piecewise hazard methods. The model predicts the impact of interventions, such as changes to staff/patient ratios, ward size and decolonisation. A comparison of results of the stochastic and deterministic model is made and reason for differences given. The second study constructs a Hidden Markov Model to describe longitudinal data on weekly VRE prevalence. Transmission is assumed to be either from patient to patient cross-transmission or sporadic (independent of cross-transmission) and parameters for each mode of acquisition are estimated from the data. The third study develops a new model with a compartment representing an environmental reservoir. Parameters for the model are gathered from literature sources and the implications of the environmental reservoir are explored. The fourth study uses a modified Susceptible-Exposed-Infectious-Removed (SEIR) model to analyse data from a SARS outbreak in Shanxi province, China. Infectivity is determined before and after interventions as well as separately for hospitalised and community symptomatic SARS cases. Model diagnostics including sensitivity analysis, model comparison and bootstrapping are implemented.

This study uses aspects of time series methodology to model and forecast major taxes such as Personal Income Tax (PIT), Corporate Income Tax (CIT), Value Added Tax (VAT) and Total Tax Revenue(TTAXR) in the South African Revenue Service (SARS). The monthly data used for modeling tax revenues of the major taxes was drawn from January 1995 to March 2010 (in sample data) for PIT, VAT and TTAXR. Due to higher volatility and emerging negative values, the CIT monthly data was converted to quarterly data from the rst quarter of 1995 to the rst quarter of 2010. The competing ARIMA/SARIMA and Holt-Winters models were derived, and the resulting model of this study was used to forecast PIT, CIT, VAT and TTAXR for SARS fiscal years 2010/11, 2011/12 and 2012/13. The results show that both the SARIMA and Holt-Winters models perform well in modeling and forecasting PIT and VAT, however the Holt-Winters model outperformed the SARIMA model in modeling and forecasting the more volatile CIT and TTAXR. It is recommended that these methods are used in forecasting future payments, as they are precise about forecasting tax revenues, with minimal errors and fewer model revisions being necessary. / Statistics / M.Sc. (Statistics)

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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