Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study (preprint)

Forecasting the short-term spread of an ongoing disease outbreak is a formidable challenge due to the complexity of contributing factors, some of which can be characterized through interlinked, multi-modality variables such as epidemiological time series data, viral biology, population demographics, and the intersection of public policy and human behavior. Existing forecasting model frameworks struggle with the multifaceted nature of relevant data and robust results translation, which hinders their performances and the provision of actionable insights for public health decision-makers. Our work introduces PandemicLLM, a novel framework with multi-modal Large Language Models (LLMs) that reformulates real-time forecasting of disease spread as a text reasoning problem, with the ability to incorporate real-time, complex, non-numerical information that previously unattainable in traditional forecasting models. This approach, through a unique AI-human cooperative prompt design and time series representation learning, encodes multi-modal data for LLMs. The model is applied to the COVID-19 pandemic, and trained to utilize textual public health policies, genomic surveillance, spatial, and epidemiological time series data, and is subsequently tested across all 50 states of the U.S. Empirically, PandemicLLM is shown to be a high-performing pandemic forecasting framework that effectively captures the impact of emerging variants and can provide timely and accurate predictions. The proposed PandemicLLM opens avenues for incorporating various pandemic-related data in heterogeneous formats and exhibits performance benefits over existing models. This study illuminates the potential of adapting LLMs and representation learning to enhance pandemic forecasting, illustrating how AI innovations can strengthen pandemic responses and crisis management in the future.

Research case study of the Discovery Museum and Tyne & Wear Archives in Newcastle-Upon-Tyne (United Kingdom). (preprint)

The Discovery Museum [(built in 1899, Grade II listed) (Science & Technology Museum category)] underwent a major renovation (2004 completion), with the addition of the Tyne & Wear Archives (2008 completion) situated within the museum’s basement level. The Discovery Museum and the Tyne & Wear Archives project (Atkins, 2023) is re-visited as a research case study, via the conceptual framework lens of International Council of Museum’s (ICOM) & Organisation for Economic Co-operation & Development’s (OECD) “Culture and Local Development: Maximising the Impact (ICOM & OECD, 2019)” guide for local governments, communities and museums and the “Management of Archive & Museum Collections” lecture series (Alici, 2023). The research paper seeks to question the functional and social role of the museum within society and it’s constantly evolving identity and image, for example Cole’s vision of “museums as a schoolroom for everyone” (Hallemann, 2019). A new metaphor for the Discovery Museum’s near future vision [post COVID-19 pandemic era] will be provided within the conclusion of the paper.

Robotic-inspired approach to multi-domain membrane receptor conformation space: theory and SARS-CoV-2 spike protein case study (preprint)

The spike protein of SARS-CoV-2 is a highly flexible membrane receptor that triggers the translocation of the virus into cells by attaching to the human receptors. Like other type I membrane receptors, this protein has several extracellular domains connected by flexible hinges. The presence of these hinges results in high flexibility, which consequently results in challenges in defining the conformation of the protein. Here, We developed a new method to define the conformational space based on a few variables inspired by the robotic field\'s methods to determine a robotic arm\'s forward kinematics. Using newly performed atomistic molecular dynamics (MD) simulations and publicly available data, we found that the Denavit-Hartenberg (DH) parameters can reliably show the changes in the local conformation. Furthermore, the rotational and translational components of the homogenous transformation matrix constructed based on the DH parameters can identify the changes in the global conformation of the spike and also differentiate between the conformation with a similar position of the spike head, which other types of parameters, such as spherical coordinates, fail to distinguish between such conformations. Finally, the new method will be beneficial for looking at the conformational heterogeneity in all other type I membrane receptors.

A Case Report of Acute Lymphoblastic Leukaemia (ALL)/Lymphoblastic Lymphoma (LBL) Following the Second Dose of Comirnaty®: An Analysis of the Potential Pathogenic Mechanism Based on of the Existing Literature (preprint)

In this report we describe the case of a healthy, young, athletic woman who developed acute lymphoblastic leukaemia (ALL)/lymphoblastic lymphoma (LBL) after receiving the second dose of the Pfizer/BioNTech modified mRNA (modRNA) COVID-19 genetic vaccine (marketed as Comirnaty®). The first dose of the genetic vaccine did not appear to illicit any noticeable side effects, but within 24 hours of the second dose the patient suffered widespread and intensifying bone pain, fever, vomiting, and general malaise. Due to the persistence of the symptoms, the patient underwent a series of tests and examinations including a full laboratory workup, a consult with a clinical immunologist and rheumatologist, a Positron Emission Tomography (PET) imaging, as well as an osteomedullary biopsy. These together led to a definitive diagnosis of ALL. A time interval of 16 weeks from the second vaccination to the diagnosis of cancer was noted. Several similar cases with identical pathology which developed after the modRNA COVID-19 vaccination, are described in case reports in the scientific literature. The massive and indiscriminate use of genetic vaccines to fight COVID-19 is raising serious concerns about their safety and about the technology platform as a whole for this purpose. Growing evidence is accumulating regarding the biodistribution and persistence of the modRNA which can reach, thanks to the lipid nanoparticles, a multitude of tissues and organs of the body, including the bone marrow and other blood-forming organs and tissues. Moreover, there is evidence that the modRNA vaccines display a particular tropism for the bone marrow, influencing the immune system at multiple levels and being able to trigger not only autoimmune-based pathologies, but also neoplastic mechanisms. The aim of this article is to assess, on the basis of the available scientific literature, the risk of developing haematopoietic cancers after modRNA vaccination, and to investigate the potential genetic mechanisms involved in the pathogenesis of disease.

Causal knowledge engineering: A case study from COVID-19 (preprint)

COVID-19 appeared abruptly in early 2020, requiring a rapid response amid a context of great uncertainty. Good quality data and knowledge was initially lacking, and many early models had to be developed with causal assumptions and estimations built in to supplement limited data, often with no reliable approach for identifying, validating and documenting these causal assumptions. Our team embarked on a knowledge engineering process to develop a causal knowledge base consisting of several causal BNs for diverse aspects of COVID-19. The unique challenges of the setting lead to experiments with the elicitation approach, and what emerged was a knowledge engineering method we call Causal Knowledge Engineering (CKE). The CKE provides a structured approach for building a causal knowledge base that can support the development of a variety of application-specific models. Here we describe the CKE method, and use our COVID-19 work as a case study to provide a detailed discussion and analysis of the method.

Creutzfeldt-Jakob Disease with Initial Typical Parkinsonism Precipitated by COVID-19? A Case Report (preprint)

Background Creutzfeldt-Jakob disease (CJD) is a rare and fatal neurodegenerative disease caused by the accumulation of PrPSc. While COVID-19-induced sporadic CJD (sCJD) with parkinsonism as the initial symptom is extremely uncommon, this report aims to raise awareness of sCJD cases that present with parkinsonism that are not associated with genetic mutations or pathological α-synuclein (α-Syn) accumulation. Case presentation This report presents the case of a 72-year-old man with probable sporadic Creutzfeldt-Jakob disease (sCJD) who initially showed symptoms of parkinsonism, which worsened rapidly after contracting COVID-19. Despite a history of responsive tremor and bradykinesia, his condition deteriorated following the viral infection, leading to rapid consciousness decline and diffuse myoclonus. Diagnostic tests, including brain MRI, cerebrospinal fluid analysis, and EEG, pointed towards prion disease. PrPSc, a marker for CJD, was detected in both the cerebrospinal fluid and skin samples using RT-QuIC, further confirming the diagnosis. Notably, skin analysis revealed PrPSc but no pathological α-synuclein deposits, ruling out typical Parkinson's disease.  Conclussion This case underscores the importance of considering sCJD in patients with parkinsonism, especially if they experience sudden neuropsychiatric symptoms, especially if they do not exhibit pathological α-Syn accumulation or have genetic mutations.

Traffic modeling using Machine Learning methods for predicting vehicle numbers at junctions: A case study in Colombo, Sri Lanka (preprint)

Traffic modeling is a fundamental tool for comprehending the complex dynamics of urban mobility. It provides vital insights for transportation planning and the construction of infrastructure. This study aims to employ sophisticated machine-learning techniques to forecast the volume of vehicles at certain main intersections in Colombo, Sri Lanka. The study emphasizes the importance of traffic modeling in influencing policy development and optimizing transportation networks, based on a thorough evaluation of relevant literature. The research utilizes machine learning methods, namely random forest regression, to analyze temporal and spatial trends in traffic flows. This analysis yields valuable insights for urban planners and transportation authorities. An examination of the dataset, utilizing methodologies such as rolling statistics and time series analysis, reveals subtle variations in traffic levels over time, including the influence of external influences like as the COVID-19 pandemic. The study's results highlight the significant impact of incorporating machine learning techniques into traffic modeling, providing a solution to improve urban mobility, decrease congestion, and create more sustainable transportation systems. This research ultimately enhances the development of data-driven solutions to tackle the changing mobility requirements of Colombo and establishes the groundwork for future research in the field of urban transportation modeling.

Spatiotemporal Patterns of Nitrogen Dioxide and Aerosol Optical Depth: A Case Study of Lahore Division (preprint)

To understand the relationships among atmospheric trace gases, aerosol variability, and climate change, as well as to inform next-generation climate change and air quality models, a precise understanding of the intricate relationships between these variables and their sources is needed. Therefore, this study aimed to investigate the spatiotemporal variability of tropospheric nitrogen dioxide (NO2), aerosol optical depth (AOD), and particulate matter (PM2.5) retrieved from both satellite and ground-based data for the period of 2006 − 2023. Tropospheric NO2, obtained from the Ozone Monitoring Instrument (OMI)/Aura, has shown that the Lahore Division frequently has high annual mean NO2 concentrations (3.87 − 6.34 x1015 molecules.cm− 2). Seasonally, winters (4.86 − 8.09x1015 molecules.cm− 2) and autumns (4.18 − 6.85 x1015 molecules.cm− 2) are mainly affected by high NO2 levels during 2021 − 2023 due to intense biomass and crop residue burning activities. Satellite AOD from data Moderate Resolution Imaging Spectroradiometer (MODIS)/Tera indicated that summers and autumns have greater AOD levels, with a mean value of 0.59 − 0.69. More variability in AOD was recorded just after the COVID − 19 lockdown. The NO2 − AOD correlation plots (maps) indicated a positive correlation coefficient R = 0.13 (0.02 to 0.19) in 2023, attributed to more NOx emissions. High concentrations of PM2.5 were recorded specifically in December and January, with the highest average AQI 374.96 µgm− 3, occurring on December 2022, which are the consequences of smog formation and other respiratory disorders during the winter season.

Neurosyphilis with Concomitant Cryptococcal Meningitis in a Patient with AIDS after COVID-19: a case report (preprint)

Background The common infections agents causing meningitis in patients with human immunodeficiency virus (HIV) include Cryptococcus neoformans and Treponema pallidum. Furthermore, there is an elevated risk of meningitis in patients with HIV concomitantly infected with SARS-CoV-2.Case presentation: A 38-year-old male presented with headache and dizziness. After hospitalization, polymerase chain reaction test for SARS-CoV-2 in nasopharyngeal swab was positive, and lumbar puncture revealed neurosyphilis with concomitant cryptococcal meningitis. He underwent Paxlovid, penicillin, antifungal and antiretroviral treatment. The patient had no other neurological symptoms and was stable during the 6-month follow-up period.Conclusions During the COVID-19 pandemic, patients with HIV, particularly those not underwent antiretroviral therapy, are at higher risk for severe infections, including central nervous system complications, due to their compromised immune systems.

Exploring the Efficacy of Shirodhara as an Add-on Therapy for Alleviating Post-COVID Anxiety and Depression: A Case Study (preprint)

Background This study explores post-COVID-19 psychological challenges in a 31-year-old female patient—manifesting as Anxiety, fatigue, weakness, irritability, anger, and concentration issues. The treatment approach combines SSRI and Clonazepam medications with Shirodhara therapy using Balashwagandhadi taila, presenting a novel and comprehensive intervention strategy.Methods The patient was evaluated using recognized scales, such as HAM-A, HDRS, PHQ-9, and QOL. Additionally, monitoring serum cortisol levels served as a potential physiological marker. The integrative treatment approach addresses psychological symptoms and potential underlying physiological mechanisms.Results Significant improvement is observed across various domains, evidenced by reduced HAM-A, HDRS, and PHQ-9 scores and enhanced QOL. Post-Shirodhara therapy, a notable increase in serum cortisol levels from 3.09 ug/dL to 11.76 ug/dL, suggesting a correlation with clinical improvements.Conclusion This case underscores Shirodhara's promising role as an adjunctive therapy for post-COVID-19 Anxiety and depression. Findings advocate further exploring integrative approaches in post-viral psychological care, emphasizing addressing psychological and potential physiological aspects for holistic recovery.

COVID-19 Impacts on the Sustainable Development of Spatial Network of UrbanTourism—a Case Study of Fujian, China (preprint)

This study examined the tourism spatial distribution of nine cities in the Fujian province and assessed the impacts of COVID-19. The modified gravity model found that it was widely dispersed, with uneven and relatively independent tourism development in different cities. The social network analysis showed that tourism connections across cities were significantly reduced after the pandemic. The impacts of brand awareness and transport accessibility on spatial networks were positive in pre-pandemic but became negative during the pandemic. In contrast, tourist volume had negative impacts on spatial networks in pre-pandemic but had positive ones during the pandemic. Tourism resources and market performance had significantly positive impacts in the post-pandemic era. These findings provide advice on tourism recovery and destination management in coping with future critical events. It also contributes to the theoretical gravity framework in tourism and the research scope of the social networks analysis at the city level.

Neuroleptic malignant syndrome in a patient with COVID-19 and the possible role of SARS-CoV-2 in its manifestation. Case report and overview of published cases (preprint)

Background: The manifestationof severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is more complex than that of pulmonary infection, and neuropsychiatric symptoms play a role in this complexity. In this paper, we present the case of a 29-year-old schizophrenic patient who suffered from neuroleptic malignant syndrome (NMS) that developed during coronavirus disease 2019 (COVID-19) infection, with an emphasis on the possible connection between these two conditions. Additionally, we provide an overview of published NMS cases in patients with COVID-19 or after vaccination against SARS-CoV-2. Case presentation: A 29-year-old patient treated for schizophrenia was admitted to the hospital for agitation and aggressivity; shortly after arrival at the hospital, laryngospasm and hypoxia occurred. The patient tested positive for SARS-CoV-2, and later, he developed pneumonia. After continuing restlessness, haloperidol was administered, and a few days later, neuroleptic malignant syndrome occurred. He was treated with bromocriptine and recovered. Conclusions: As SARS-CoV-2 is known to interact with angiotensin-converting enzyme 2 and DOPA-decarboxylase is known to be coexpressed with this receptor, we hypothesized that COVID-19 infection might playa substantial role in the development of NMS.

Delayed Fatal Pulmonary Hemorrhage Following COVID-19 Vaccination: Case Report, Batch Analysis, And Proposed Autopsy Checklist (preprint)

We present a case of a 47-year-old male who died unexpectedly from acute pulmonary hemorrhage 555 days after completing the BNT162b2 (Pfizer) COVID-19 vaccination primary series. Before death, he exhibited symptoms of a mild respiratory infection. Despite a healthy medical history and no medication use, the patient’s condition rapidly deteriorated and he experienced severe respiratory distress, followed by cardiopulmonary arrest with evidence of profuse pulmonary bleeding. Autopsy findings revealed massive lung congestion without embolism, normal heart size, moderate coronary atherosclerosis without myocardial infarction, and no evidence of other hemorrhagic events. The patient tested negative for COVID-19 and other respiratory pathogens at autopsy. Despite these findings, the medical examiner determined the cause of death was attributed to atherosclerotic and hypertensive cardiovascular disease, without considering the recent pulmonary hemorrhage and unremarkable medical history. Investigation into the vaccine batch indicated a higher-than-average number of serious adverse events, including fatalities. The patient's BNT162b2 batch was among the top 2.8% for reported deaths. Moreover, the autopsy failed to investigate potential contributions from the vaccine, such as the presence of the Spike protein or related antibodies. The evidence suggests that the pulmonary hemorrhage, exacerbated by a viral infection, was the immediate cause of death, with the COVID-19 vaccine potentially playing a role in the development of cardiopulmonary pathology and hemorrhage. We propose autopsy protocols for COVID-19 vaccine recipients to better investigate vaccine-related pathologies among those with one or more prior injections.

Promoting Fair Vaccination Strategies Through Influence Maximization: A Case Study on COVID-19 Spread (preprint)

The aftermath of the Covid-19 pandemic saw more severe outcomes for racial minority groups and economically-deprived communities. Such disparities can be explained by several factors, including unequal access to healthcare, as well as the inability of low income groups to reduce their mobility due to work or social obligations. Moreover, senior citizens were found to be more susceptible to severe symptoms, largely due to age-related health reasons. Adapting vaccine distribution strategies to consider a range of demographics is therefore essential to address these disparities. In this study, we propose a novel approach that utilizes influence maximization (IM) on mobility networks to develop vaccination strategies which incorporate demographic fairness. By considering factors such as race, social status, age, and associated risk factors, we aim to optimize vaccine distribution to achieve various fairness definitions for one or more protected attributes at a time. Through extensive experiments conducted on Covid-19 spread in three major metropolitan areas across the United States, we demonstrate the effectiveness of our proposed approach in reducing disease transmission and promoting fairness in vaccination distribution.