Impact of presymptomatic transmission on epidemic spreading in contact networks: A dynamic message-passing analysis

Learning of networked spreading models from noisy and incomplete data

Recent years have seen a lot of progress in algorithms for learning parameters of spreading dynamics from both full and partial data. Some of the remaining challenges include model selection under the scenarios of unknown network structure, noisy data, missing observations in time, as well as an efficient incorporation of prior information to minimize the number of samples required for an accurate learning. Here, we introduce a universal learning method based on a scalable dynamic message-passing technique that addresses these challenges often encountered in real data. The algorithm leverages available prior knowledge on the model and on the data, and reconstructs both network structure and parameters of a spreading model. We show that a linear computational complexity of the method with the key model parameters makes the algorithm scalable to large network instances.

An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect

Currently, the spread of COVID-19 is running at a constant pace. The current situation is not so alarming, but every pandemic has a history of three waves. Two waves have been seen, and now expecting the third wave. Compartmental models are one of the methods that predict the severity of a pandemic. An enhanced SEIR model is expected to predict the new cases of COVID-19. The proposed model has an additional compartment of vaccination. This proposed model is the SEIRV model that predicts the severity of COVID-19 when the population is vaccinated. The proposed model is simulated with three conditions. The first condition is when social distancing is not incorporated, while the second condition is when social distancing is included. The third one condition is when social distancing is combined when the population is vaccinated. The result shows an epidemic growth rate of about 0.06 per day, and the number of infected people doubles every 10.7 days. Still, with imparting social distancing, the proposed model obtained the value of R0 is 1.3. Vaccination of infants and kids will be considered as future work.

Views and Attitudes of Blood Donors toward Blood Donation during the COVID-19 Pandemic in Thrace Region, Greece

The COVID-19 pandemic has been going on for the last two years and it has affected our society and, amongst other things, has had a negative impact on blood donation, which has led to a significant reduction in blood supplies worldwide. The imposed restrictions in terms of physical presence and transportation, and the fear of the unknown, have aggravated the situation. In Greece, after the first cases of COVID-19 were reported, the blood supplies at the blood transfusion units (BTUs) were dramatically reduced. Although the blood transfusions were lessened during the COVID-19 pandemic period, the blood stocks at all the BTUs of the country were also reduced.

The coupled dynamics of information dissemination and SEIR-based epidemic spreading in multiplex networks

The outbreak of coronavirus disease 2019 (COVID-19) threatens the health and safety of all humanity. This disease has a prominent feature: the presymptomatic and asymptomatic viral carriers can spread the disease. It is crucial to estimate the impact of this undetected transmission on epidemic outbreaks. Currently, disease-related information has been widely disseminated by the mass media. To investigate the impact of both individuals and mass media information dissemination on the epidemic spreading, we establish a new UAU-SEIR (Unaware-Aware-Unaware-Susceptible-Exposed-Infected-Recovered) model with mass media on two-layer multiplex networks. In the model, E-state individuals denote asymptomatic infections, and a single node connecting to all individuals denotes the mass media. In this work, we use the Microscopic Markovian Chain Approach (MMCA) to derive the epidemic threshold. Comparing the MMCA theoretical results with Monte Carlo (MC) simulations, we find that the MMCA has a good consistency with MC simulations. In addition, we also analyze the impact of model parameters on epidemic spreading and epidemic threshold. The results show that reducing the proportion of asymptomatic infections, accelerating the dissemination of information between individuals and the dissemination of information via the mass media can effectively inhibit the epidemic spreading and raise the epidemic threshold.