Non-robustness of diffusion estimates on networks with measurement error (preprint)

Network diffusion models are used to study things like disease transmission, information spread, and technology adoption. However, small amounts of mismeasurement are extremely likely in the networks constructed to operationalize these models. We show that estimates of diffusions are highly non-robust to this measurement error. First, we show that even when measurement error is vanishingly small, such that the share of missed links is close to zero, forecasts about the extent of diffusion will greatly underestimate the truth. Second, a small mismeasurement in the identity of the initial seed generates a large shift in the locations of expected diffusion path. We show that both of these results still hold when the vanishing measurement error is only local in nature. Such non-robustness in forecasting exists even under conditions where the basic reproductive number is consistently estimable. Possible solutions, such as estimating the measurement error or implementing widespread detection efforts, still face difficulties because the number of missed links are so small. Finally, we conduct Monte Carlo simulations on simulated networks, and real networks from three settings: travel data from the COVID-19 pandemic in the western US, a mobile phone marketing campaign in rural India, and in an insurance experiment in China.

Recovery from post-COVID-19 vaccination biliary ductopenia and multiorgan inflammation: A case with insight from angiotensin II type 1 receptor antibody- mediated injury (preprint)

Background The coronavirus disease 2019 (COVID-19) pandemic has caused significant morbidity and mortality. Spike messenger RNA (mRNA)–based vaccines against severe acute respiratory syndrome coronavirus 2 may contribute to immune-mediated injuries. Here we present a case of marked cholangiopathy with multiorgan injury and investigate the potential mechanisms associated with mRNA-based vaccines. Case summary and investigation A previously healthy 47-year-old man developed progressive jaundice 2 weeks after receiving his 3rd COVID-19 vaccination (1st mRNA-based vaccine). Apart from elevated serum total bilirubin levels (peaked at >70 mg/dL), deteriorating renal (blood urea nitrogen: peak, 108.5 mg/dL; creatinine: peak, 6 mg/dL) and exocrine pancreas (amylase: peak, 1717 U/L; lipase: peak, 5784 U/L) profiles were also seen. Vanishing bile duct syndrome characterized by ductopenia and cholangiocyte vacuolation, positive C4d deposition, and high titer of anti-angiotensin II type 1 receptor antibody consistently explain the overall antibody-mediated pathogenesis resembling antibody-mediated “rejection” in the solid organ transplant setting. Corticosteroids and plasmapheresis were administered, leading to gradual resolution of the symptoms, and the jaundice completely resolved 2 months later. Conclusion Here we reported a case of antibody-mediated multiorgan injury after an mRNA COVID-19 vaccine characterized by severe cholangiopathy. The patient recovered with corticosteroids and plasmapheresis, and long-term follow-up is needed.

Constructions for Nonadaptive Tropical Group Testing (preprint)

PCR testing is an invaluable diagnostic tool that has most recently seen widespread use during the COVID-19 pandemic. A recent work by Wang, Gabrys and Vardy proposed tropical codes as a model for group PCR testing. For a known but arbitrary number of infected persons, a sufficient condition on the underlying block design of a zero-error tropical code, called double disjunction, is proposed. Despite this, the parameters for which the construction of doubly disjunct block designs is known to exist are very limited. In this paper, we define probabilistic tropical codes and consider random block designs that are doubly disjunct with high probability. We also provide a deterministic construction for a doubly disjunct block design given a disjunct block design. We show that for certain choices of parameters, our probabilistic construction has vanishing error. Our constructions, combined with existing methods, give us three different ways to construct tropical codes. We compare the number of tests required by each, and bounds on the error.

Mixed Attention with Deep Supervision for Delineation of COVID Infection in Lung CT (preprint)

The COVID-19 pandemic, with its multiple variants, has placed immense pressure on the global healthcare system. An early effective screening and grading become imperative towards optimizing the limited available resources of the medical facilities. Computed tomography (CT) provides a significant non-invasive screening mechanism for COVID-19 infection. An automated segmentation of the infected volumes in lung CT is expected to significantly aid in the diagnosis and care of patients. However, an accurate demarcation of lesions remains problematic due to their irregular structure and location(s) within the lung. A novel deep learning architecture, Mixed Attention Deeply Supervised Network (MiADS-Net), is proposed for delineating the infected regions of the lung from CT images. Incorporating dilated convolutions with varying dilation rates, into a mixed attention framework, allows capture of multi-scale features towards improved segmentation of lesions having different sizes and textures. Mixed attention helps prioritise relevant feature maps to be probed, along with those regions containing crucial information within these maps. Deep supervision facilitates discovery of robust and discriminatory characteristics in the hidden layers at shallower levels, while overcoming the vanishing gradient. This is followed by estimating the severity of the disease, based on the ratio of the area of infected region in each lung with respect to its entire volume. Experimental results, on three publicly available datasets, indicate that the MiADS-Net outperforms several state-of-the-art architectures in the COVID-19 lesion segmentation task; particularly in defining structures involving complex geometries.

A New Set-up of Vanishing Antibodies: A Biennial Follow-up of Five Different Clients’ Humoral Responses Against Sars-Cov-2 After Systemic Vaccination in an Oncology Hospital in Poland (preprint)

The humoral response of the COVID-19 vaccine varies from person to person. It largely depends on prior SARS-CoV-2 infection, obtaining an adequate immune response, and leaving a trace of changing antibody concentration over time. We retrospectively analyzed five clinical cases from selected patients and employees of the oncology hospital. All mild COVID-19 convalescents received the BNT162b2-Comirnaty mRNA vaccine three or four times. The levels of SARS-CoV-2 IgM- and IgG-specific antibodies, as well as S-RBD antibodies, were analyzed for two years. The concentration of antibodies was assessed in the laboratory using the chemiluminescent immunoassay CLIA, MAGLUMI. Results: (1) Active autoimmune disease stabilized the level of IgG-specific antibodies after systemic mRNA vaccination for at least six months. (2) Post-vaccination IgG and S-RBD levels decreased when vaccination was performed within three months of onset. (3) The booster dose administered only increased the S-RBD antibody levels. Declining IgG-specific antibodies were observed. (4) The S-RBD IgG levels were not correlated with the SARS-CoV-2 IgG levels in the vaccinated convalescents. (5) Subsequent reinfection with SARS-CoV-2 after vaccination three times released a more significant specific antibody response. Based on the collected data, we suggest that monitoring S-RBD antibodies is sensitive but not equivalent to a specific humoral response for SARS-CoV-2 IgG. We suggested that administering at least three doses of the mRNA vaccine should serve as the basis for immunization. The three-month interval may be the best alternative to an immunization schedule for non-immunocompromised people.

FULL-SCALE DEEPLY SUPERVISED ATTENTION NETWORK FOR SEGMENTING COVID-19 LESIONS (preprint)

Automated delineation of COVID-19 lesions from lung CT scans aids the diagnosis and prognosis for patients. The asymmetric shapes and positioning of the infected regions make the task extremely difficult. Capturing information at multiple scales will assist in deciphering features, at global and local levels, to encompass lesions of variable size and texture. We introduce the Full-scale Deeply Supervised Attention Network (FuDSA-Net), for efficient segmentation of corona-infected lung areas in CT images. The model considers activation responses from all levels of the encoding path, encompassing multi-scalar features acquired at different levels of the network. This helps segment target regions (lesions) of varying shape, size and contrast. Incorporation of the entire gamut of multi-scalar characteristics into the novel attention mechanism helps prioritize the selection of activation responses and locations containing useful information. Determining robust and discriminatory features along the decoder path is facilitated with deep supervision. Connections in the decoder arm are remodeled to handle the issue of vanishing gradient. As observed from the experimental results, FuDSA-Net surpasses other state-of-the-art architectures; especially, when it comes to characterizing complicated geometries of the lesions.

HLA-A*01:01 allele vanishing in COVID-19 patients population associated with non-structural epitope abundance in CD8+ T-cell repertoire (preprint)

In mid-2021, the SARS-CoV-2 Delta variant caused the third wave of the COVID-19 pandemic in several countries worldwide. The pivotal studies were aimed at studying changes in the efficiency of neutralizing antibodies to the spike protein. However, much less attention was paid to the T-cell response and the presentation of virus peptides by MHC-I molecules. In this study, we compared the features of the HLA-I genotype in symptomatic patients with COVID-19 in the first and third waves of the pandemic. As a result, we could identify the vanishing of carriers of the HLA-A*01:01 allele in the third wave and demonstrate the unique properties of this allele. Thus, HLA-A*01:01-binding immunodominant epitopes are mostly derived from ORF1ab. A set of epitopes from ORF1ab was tested, and their high immunogenicity was confirmed. Moreover, analysis of the results of single-cell phenotyping of T-cells in recovered patients showed that the predominant phenotype in HLA-A*01:01 carriers is central memory T-cells. The predominance of T-lymphocytes of this phenotype may contribute to forming long-term T-cell immunity in carriers of this allele. Our results can be the basis for highly effective vaccines based on ORF1ab peptides.

Classifying COVID-19 variants based on genetic sequences using deep learning models (preprint)

The COrona VIrus Disease (COVID-19) pandemic led to the occurrence of several variants with time. This has led to an increased importance of understanding sequence data related to COVID-19. In this chapter, we propose an alignment-free k-mer based LSTM (Long Short-Term Memory) deep learning model that can classify 20 different variants of COVID-19. We handle the class imbalance problem by sampling a fixed number of sequences for each class label. We handle the vanishing gradient problem in LSTMs arising from long sequences by dividing the sequence into fixed lengths and obtaining results on individual runs. Our results show that one- vs-all classifiers have test accuracies as high as 92.5% with tuned hyperparameters compared to the multi-class classifier model. Our experiments show higher overall accuracies for B.1.1.214, B.1.177.21, B.1.1.7, B.1.526, and P.1 on the one-vs-all classifiers, suggesting the presence of distinct mutations in these variants. Our results show that embedding vector size and batch sizes have insignificant improvement in accuracies, but changing from 2-mers to 3-mers mostly improves accuracies. We also studied individual runs which show that most accuracies improved after the 20th run, indicating that these sequence positions may have more contributions to distinguishing among different COVID-19 variants.

Dynamic group testing to control and monitor disease progression in a population (preprint)

In the context of a pandemic like COVID-19, and until most people are vaccinated, proactive testing and interventions have been proved to be the only means to contain the disease spread. Recent academic work has offered significant evidence in this regard, but a critical question is still open: Can we accurately identify all new infections that happen every day, without this being forbiddingly expensive, i.e., using only a fraction of the tests needed to test everyone everyday (complete testing)? Group testing offers a powerful toolset for minimizing the number of tests, but it does not account for the time dynamics behind the infections. Moreover, it typically assumes that people are infected independently, while infections are governed by community spread. Epidemiology, on the other hand, does explore time dynamics and community correlations through the well-established continuous-time SIR stochastic network model, but the standard model does not incorporate discrete-time testing and interventions. In this paper, we introduce a "discrete-time SIR stochastic block model" that also allows for group testing and interventions on a daily basis. Our model can be regarded as a discrete version of the continuous-time SIR stochastic network model over a specific type of weighted graph that captures the underlying community structure. We analyze that model w.r.t. the minimum number of group tests needed everyday to identify all infections with vanishing error probability. We find that one can leverage the knowledge of the community and the model to inform nonadaptive group testing algorithms that are order-optimal, and therefore achieve the same performance as complete testing using a much smaller number of tests.

CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans (preprint)

Automatic lung lesions segmentation of chest CT scans is considered a pivotal stage towards accurate diagnosis and severity measurement of COVID-19. Traditional U-shaped encoder-decoder architecture and its variants suffer from diminutions of contextual information in pooling/upsampling operations with increased semantic gaps among encoded and decoded feature maps as well as instigate vanishing gradient problems for its sequential gradient propagation that result in sub-optimal performance. Moreover, operating with 3D CT-volume poses further limitations due to the exponential increase of computational complexity making the optimization difficult. In this paper, an automated COVID-19 lesion segmentation scheme is proposed utilizing a highly efficient neural network architecture, namely CovSegNet, to overcome these limitations. Additionally, a two-phase training scheme is introduced where a deeper 2D-network is employed for generating ROI-enhanced CT-volume followed by a shallower 3D-network for further enhancement with more contextual information without increasing computational burden. Along with the traditional vertical expansion of Unet, we have introduced horizontal expansion with multi-stage encoder-decoder modules for achieving optimum performance. Additionally, multi-scale feature maps are integrated into the scale transition process to overcome the loss of contextual information. Moreover, a multi-scale fusion module is introduced with a pyramid fusion scheme to reduce the semantic gaps between subsequent encoder/decoder modules while facilitating the parallel optimization for efficient gradient propagation. Outstanding performances have been achieved in three publicly available datasets that largely outperform other state-of-the-art approaches. The proposed scheme can be easily extended for achieving optimum segmentation performances in a wide variety of applications.

Estimating effective infection fatality rates during the course of the COVID-19 pandemic in Germany (preprint)

The infection fatality rate (IFR) of the Coronavirus Disease 2019 (COVID-19) is one of the most discussed figures in the context of this pandemic. Using German COVID-19 surveillance data and age-group specific IFR estimates from multiple international studies, this work investigates time-dependent variations in effective IFR over the course of the pandemic. Three different methods for estimating (effective) IFRs are presented: (a) population-averaged IFRs based on the assumption that the infection risk is independent of age and time, (b) effective IFRs based on the assumption that the age distribution of confirmed cases approximately reflects the age distribution of infected individuals, and (c) effective IFRs accounting for age- and time-dependent dark figures of infections. Results show that effective IFRs in Germany are estimated to vary over time, as the age distributions of confirmed cases and estimated infections are changing during the course of the pandemic. In particular during the first and second waves of infections in spring and autumn/winter 2020, there has been a pronounced shift in the age distribution of confirmed cases towards older age groups, resulting in larger effective IFR estimates. The temporary increase in effective IFR during the first wave is estimated to be smaller but still remains when adjusting for age- and time-dependent dark figures. A comparison of effective IFRs with observed CFRs indicates that a substantial fraction of the time-dependent variability in observed mortality can be explained by changes in the age distribution of infections. Furthermore, a vanishing gap between effective IFRs and observed CFRs is apparent after the first infection wave, while a moderately increasing gap can be observed during the second wave. Further research is warranted to obtain timely age-stratified IFR estimates.