Mathematical modeling of respiratory viral infection and applications to SARS-CoV-2 progression.

Viral infection in cell culture and tissue is modeled with delay reaction-diffusion equations. It is shown that progression of viral infection can be characterized by the viral replication number, time-dependent viral load, and the speed of infection spreading. These three characteristics are determined through the original model parameters including the rates of cell infection and of virus production in the infected cells. The clinical manifestations of viral infection, depending on tissue damage, correlate with the speed of infection spreading, while the infectivity of a respiratory infection depends on the viral load in the upper respiratory tract. Parameter determination from the experiments on Delta and Omicron variants allows the estimation of the infection spreading speed and viral load. Different variants of the SARS-CoV-2 infection are compared confirming that Omicron is more infectious and has less severe symptoms than Delta variant. Within the same variant, spreading speed (symptoms) correlates with viral load allowing prognosis of disease progression.

Multi-MedVit: a deep learning approach for the diagnosis of COVID-19 with the CT images

The grim situation of novel coronavirus pneumonia 2019 (COVID-19) and its terrible spreading speed have already constituted a severe risk to human life, so it is ultimately essential to rapidly and accurately diagnose for COVID-19 pneumonia. Based on this study's 746 lung CT images, we propose Multi-MedVit, a novel auxiliary COVID-19 diagnosis framework based on the multi-input Transformer. We compare Multi-MedVit with state-of-the-art deep learning methods, such as CNN, VGG16, and ResNet50. Multi-MedVit outperformed the other methods on the benchmark dataset and proved that multiscale data input for data augmentation helped enhance model stability. Based on an interpretable analysis of the input and output of Multi-MedVit, we found that with the support of the training set data, the model has been possible to accurately focus on the lesion area for diagnosis of COVID-19 without expert annotations, which can provide initial references containing more potential information to doctors more precisely and fleetly. © 2022 IEEE.

COVID-19 pandemic: Analyzing of infection waves in Germany in comparison with common infectious diseases by using reliability methods

Since December 2019, the world is confronted with the COVID-19 pandemic, caused by the Coronavirus SARS-CoV-2. The COVID-19 pandemic with its incredible spreading speed shows the vulnerability of a globalized and networked world. The first two years of the pandemic were characterized by several infection waves, described by length, peak, and speed. The infection waves caused a heavy burden on health systems and severe restrictions on public life, like educational system shutdown, travel restrictions, limitations regarding public life, or a comprehensive lockdown within a lot of countries. The goal of the presented research study is the analysis of the development of the six dominant infection waves in Germany within the first two years of the COVID-19 pandemic (February 2020 - February 2022). The analyses are focusing on the occurrence of infection and spreading behavior, in detail on attributes like length, peak, and speed of each wave. Furthermore, various impacts of lockdown strategies (hard, soft) or virus variants are considered. The analyses of the infection waves are based on a transfer and application of methods - especially the Weibull distribution model and statistical hypothesis tests - used in reliability engineering for analyzing the upcoming failure development within product fleets in the field. The spreading behavior of a COVID-19 infection wave can be described by the Weibull distribution model in a sound way, related to a short time interval. The interpretation of the Weibull model parameters allows the assessment of the COVID-19 infection wave characteristics and generates additional information to classical infection analysis models like the SIR model [10]. Finally, the characteristics of the COVID-19 infection waves are analyzed in the context of other common infectious diseases in Germany like Influenza or Norovirus. This study continues previous research;cf. [1-3,11,12]. © 2022 Probabilistic Safety Assessment and Management, PSAM 2022. All rights reserved.

Propagation of Epidemics Along Lines with Fast Diffusion.

It has long been known that epidemics can travel along communication lines, such as roads. In the current COVID-19 epidemic, it has been observed that major roads have enhanced its propagation in Italy. We propose a new simple model of propagation of epidemics which exhibits this effect and allows for a quantitative analysis. The model consists of a classical SIR model with diffusion, to which an additional compartment is added, formed by the infected individuals travelling on a line of fast diffusion. The line and the domain interact by constant exchanges of populations. A classical transformation allows us to reduce the proposed model to a system analogous to one we had previously introduced Berestycki et al. (J Math Biol 66:743-766, 2013) to describe the enhancement of biological invasions by lines of fast diffusion. We establish the existence of a minimal spreading speed, and we show that it may be quite large, even when the basic reproduction number [Formula: see text] is close to 1. We also prove here further qualitative features of the final state, showing the influence of the line.

Impacts of geographic factors and population density on the COVID-19 spreading under the lockdown policies of China.

The outbreak of COVID-19 pandemic has a high spreading rate and a high fatality rate. To control the rapid spreading of COVID-19 virus, Chinese government ordered lockdown policies since late January 2020. The aims of this study are to quantify the relationship between geographic information (i.e., latitude, longitude and altitude) and cumulative infected population, and to unveil the importance of the population density in the spreading speed during the lockdown. COVID-19 data during the period from December 8, 2019 to April 8, 2020 were collected before and after lockdown. After discovering two important geographic factors (i.e., latitude and altitude) by estimating the correlation coefficients between each of them and cumulative infected population, two linear models of cumulative infected population and COVID-19 spreading speed were constructed based on these two factors. Overall, our findings from the models showed a negative correlation between the provincial daily cumulative COVID-19 infected number and latitude/altitude. In addition, population density is not an important factor in COVID-19 spreading under strict lockdown policies. Our study suggests that lockdown policies of China can effectively restrict COVID-19 spreading speed.