Knowledge-Distilled Graph Neural Networks for Personalized Epileptic Seizure Detection

Wearable devices for seizure monitoring detection could significantly improve the quality of life of epileptic patients. However, existing solutions that mostly rely on full electrode set of electroencephalogram (EEG) measurements could be inconvenient for every day use. In this paper, we propose a novel knowledge distillation approach to transfer the knowledge from a sophisticated seizure detector (called the teacher) trained on data from the full set of electrodes to learn new detectors (called the student). They are both providing lightweight implementations and significantly reducing the number of electrodes needed for recording the EEG. We consider the case where the teacher and the student seizure detectors are graph neural networks (GNN), since these architectures actively use the connectivity information. We consider two cases (a) when a single student is learnt for all the patients using preselected channels; and (b) when personalized students are learnt for every individual patient, with personalized channel selection using a Gumbelsoftmax approach. Our experiments on the publicly available Temple University Hospital EEG Seizure Data Corpus (TUSZ) show that both knowledge-distillation and personalization play significant roles in improving performance of seizure detection, particularly for patients with scarce EEG data. We observe that using as few as two channels, we are able to obtain competitive seizure detection performance. This, in turn, shows the potential of our approach in more realistic scenario of wearable devices for personalized monitoring of seizures, even with few recordings

Estimating the burden and modeling mitigation strategies of pork-related hepatitis E virus foodborne transmission in representative European countries

Hepatitis E virus (HEV) is an emerging zoonotic pathogen posing global health burden, and the concerns in Europe are tremendously growing. Pigs serve as a main reservoir, contributing to pork-related foodborne transmission. In this study, we aim to specifically simulate this foodborne transmission route and to assess potential interventions. We firstly established a dose-response relationship between the risk of transmission to human and the amount of ingested viruses. We further estimated the incidence of HEV infection specifically attributed to pork-related foodborne transmission in four representative European countries. Finally, we demonstrated a proof-of-concept of mitigating HEV transmission by implementing vaccination in human and pig populations. Our modeling approach bears essential implications for better understanding the transmission of pork-related foodborne HEV and for developing mitigation strategies

Clinical Features, Antiviral Treatment, and Patient Outcomes:A Systematic Review and Comparative Analysis of the Previous and the 2022 Mpox Outbreaks

Background:This study aims to comparatively analyze clinical features, treatment, and patient outcomes between the previous and the 2022 mpox (monkeypox) outbreaks. Methods:Five bibliographic databases were searched for studies reporting clinical features, management, and patient outcomes of mpox. Systematic review and meta-analysis were performed. Results:In total, 73 studies were included in the systematic review, of which 33 studies were subjected to meta-analysis. Previous outbreaks substantially affected children, whereas the 2022 outbreak primarily affected male adults, of which 94.66% (95% confidence interval [CI], 88.03–98.95) were men who have sex with men. Furthermore, 72.47% (95% CI, 51.04–89.71) reported high-risk sexual activity and the overall human immunodeficiency virus (HIV) prevalence was 37.65% (95% CI, 30.09–45.50). Skin lesions remain the typical symptom; however, their anatomic distribution differed. Systemic manifestations were common, but rectal pain was unique to the 2022 outbreak. The estimated overall fatality during past outbreaks in Africa was 4.61% (95% CI, 2.39%–7.35%), whereas 6.34% (95% CI, 3.35%–10.10%) of patients from the 2022 outbreak required hospitalization. Antiviral treatment, in particular tecovirimat, has been prescribed for a subset of patients, but the efficacy remains inconclusive.Conclusions:These findings are important for better understanding the disease and guiding adequate response to mpox outbreaks.</p

A multi-regional, hierarchical-tier mathematical model of the spread and control of COVID-19 epidemics from epicentre to adjacent regions

Epicentres are the focus of COVID-19 research, whereas emerging regions with mainly imported cases due to population movement are often neglected. Classical compartmental models are useful, however, likely oversimplify the complexity when studying epidemics. This study aimed to develop a multi-regional, hierarchical-tier mathematical model for better understanding the complexity and heterogeneity of COVID-19 spread and control. By incorporating the epidemiological and population flow data, we have successfully constructed a multi-regional, hierarchical-tier SLIHR model. With this model, we revealed insight into how COVID-19 was spread from the epicentre Wuhan to other regions in Mainland China based on the large population flow network data. By comprehensive analysis of the effects of different control measures, we identified that Level 1 emergency response, community prevention and application of big data tools significantly correlate with the effectiveness of local epidemic containment across different provinces of China outside the epicentre. In conclusion, our multi-regional, hierarchical-tier SLIHR model revealed insight into how COVID-19 spread from the epicentre Wuhan to other regions of China, and the subsequent control of local epidemics. These findings bear important implications for many other countries and regions to better understand and respond to their local epidemics associated with the ongoing COVID-19 pandemic

Optimal strategy for a dose-escalation vaccination against COVID-19 in refugee camps

An immunogenic and safe vaccine against COVID-19 for use in the healthy population will become available in the near future. In this paper, we aim to determine the optimal vaccine administration strategy in refugee camps considering maximum daily administration and limited total vaccine supply. For this purpose, extended SEAIRD compartmental models are established to describe the epidemic dynamics with both single-dose and double-dose vaccine administration. Taking the vaccination rates in different susceptible compartments as control variables, the optimal vaccine administration problems are then solved under the framework of nonlinear constrained optimal control problems. To the best of our knowledge, this is the first paper that addresses an optimal vaccine administration strategy considering practical constraints on limited medical care resources. Numerical simulations show that both the single-dose and double-dose strategies can successfully control COVID-19. By comparison, the double-dose vaccination strategy can achieve a better reduction in infection and death, while the single-dose vaccination strategy can postpone the infection peak more efficiently. Further studies of the influence of parameters indicate that increasing the number of medical care personnel and total vaccine supply can greatly contribute to the fight against COVID-19. The results of this study are instructive for potential forthcoming vaccine administration. Moreover, the work in this paper provides a general framework for developing epidemic control strategies in the presence of limited medical resources

Distinct effectiveness in containing COVID-19 epidemic: Comparative analysis of two cities in China by mathematical modeling.

For better preparing future epidemic/pandemic, important lessons can be learned from how different parts of China responded to the early COVID-19 epidemic. In this study, we comparatively analyzed the effectiveness and investigated the mechanistic insight of two highly representative cities of China in containing this epidemic by mathematical modeling. Epidemiological data of Wuhan and Wenzhou was collected from local health commission, media reports and scientific literature. We used a deterministic, compartmental SEIR model to simulate the epidemic. Specific control measures were integrated into the model, and the model was calibrated to the recorded number of hospitalized cases. In the epicenter Wuhan, the estimated number of unisolated or unidentified cases approached 5000 before the date of city closure. By implementing quarantine, a 40% reduction of within-population contact was achieved initially, and continuously increased up to 70%. The expansion of emergency units has finally reduced the mean duration from disease onset to hospital admission from 10 to 3.2 days. In contrast, Wenzhou is characterized as an emerging region with large number of primarily imported cases. Quick response effectively reduced the duration from onset to hospital admission from 20 to 6 days. This resulted in reduction of R values from initial 2.3 to 1.6, then to 1.1. A 40% reduction of contact through within-population quarantine further decreased R values until below 1 (0.5; 95% CI: 0.4-0.65). Quarantine contributes to 37% and reduction of duration from onset to hospital admission accounts for 63% to the effectiveness in Wenzhou. In Wuhan, these two strategies contribute to 54% and 46%, respectively. Thus, control measures combining reduction of duration from disease onset to hospital admission and within-population quarantine are effective for both epicenters and settings primarily with imported cases

Planning for the optimal vaccination sequence in the context of a population-stratified model

We explore strategies for optimally controlling the pandemic through practical vaccination planning across different subpopulations, with the aiming of striking a balance between minimizing mortalities and conserving vaccines. To achieve this objective, the total population is categorized into four subpopulations: health workers, young individuals, middle-aged individuals, and the elderly, based on immunity and exposure risk. We assume a heterogeneous pattern of social contacts among these subpopulations. Accordingly, we formulate a population-stratified SPMILHRD model with 32 compartments that accounts for different statuses of detection and symptoms to define the epidemic dynamics. Numerical simulations demonstrate a staged optimization-based, population-stratified vaccination approach, where health workers, the middle-aged, the elderly, and the young receive priority in succession. The analysis reveals that our optimal vaccination control strategy can significantly reduce the number of infections, including fatalities, compared to scenarios involving proportional policies or no control in the numerical experiment. It has demonstrated that well-designed vaccination planning could substantially curb dissemination and reduce mortality. The findings of this study are instructive for the potential implementation of future vaccine administration.</p