Multiple partition Markov model for B.1.1.7, B.1.351, B.1.617.2, and P.1 variants of SARS-CoV 2 virus.

With tools originating from Markov processes, we investigate the similarities and differences between genomic sequences in FASTA format coming from four variants of the SARS-CoV 2 virus, B.1.1.7 (UK), B.1.351 (South Africa), B.1.617.2 (India), and P.1 (Brazil). We treat the virus' sequences as samples of finite memory Markov processes acting in A = { a , c , g , t } . We model each sequence, revealing some heterogeneity between sequences belonging to the same variant. We identified the five most representative sequences for each variant using a robust notion of classification, see Fernández et al. (Math Methods Appl Sci 43(13):7537-7549. 10.1002/mma.5705 ). Using a notion derived from a metric between processes, see García et al. (Appl Stoch Models Bus Ind 34(6):868-878. 10.1002/asmb.2346), we identify four groups, each group representing a variant. It is also detected, by this metric, global proximity between the variants B.1.351 and B.1.1.7. With the selected sequences, we assemble a multiple partition model, see Cordeiro et al. (Math Methods Appl Sci 43(13):7677-7691. 10.1002/mma.6079), revealing in which states of the state space the variants differ, concerning the mechanisms for choosing the next element in A. Through this model, we identify that the variants differ in their transition probabilities in eleven states out of a total of 256 states. For these eleven states, we reveal how the transition probabilities change from variant (group of variants) to variant (group of variants). In other words, we indicate precisely the stochastic reasons for the discrepancies.

Exploring the mediating role of government-public relationships during the COVID-19 pandemic: A model comparison approach.

This study proposed, tested, and compared three models to examine an antecedent and outcome of government-public relationships. It conducted three surveys of 9675 people in mainland China, Taiwan, and Hong Kong from August 2020 to January 2021. The results of the model comparison supported the proposed reciprocal model: not only were relational satisfaction and relational trust found to mediate the effect of perceived responsiveness on people's word-of-mouth intention to vaccinate, but they also had a reciprocal influence on each other. This study further affirmed that the relative effects between satisfaction and trust. We also found that emotion-dominant model is more powerful than cognition-dominant model, i.e., people's feeling of satisfaction happens before sense of trust, which results from their perceived organizational responsiveness and then contribute to their word-of-mouth behavioral intention. The theoretical and practical implications of this study were also discussed.

System-level effects of increased energy efficiency in global low-carbon scenarios: A model comparison

Supporting investments in energy efficiency is considered a robust strategy to achieve a successful transition to low-carbon energy systems in line with the Paris Agreement. Increased energy efficiency levels are expected to reduce the need for supply-side investments in controversial technologies, such as carbon dioxide capture and storage (CCS) and nuclear energy, and to induce a downward push on carbon prices, which may facilitate the political and societal acceptance of climate policies, without adversely affecting living comfort and sustainable development. In order to fully reap these potential benefits, economies need to design policy packages that balance emission reduction incentives on both the demand and the supply side. In this paper we carry out a model-comparison exercise, using two well-established global integrated assessment models, PROMETHEUS and TIAM-ECN, to quantitatively analyze the global system-level effects of increased energy efficiency in the context of ambitious post-COVID climate change mitigation scenarios. Our results confirm the expected benefits induced by higher energy efficiency levels, as in 2050 global carbon prices are found to decline by 10%–50% and CO2 storage from CCS plants is 13%–90% lower relative to the “default” mitigation scenarios. Similarly, enhanced energy efficiency reduces the additional average yearly system costs needed globally in 2050 to achieve emission reductions in line with the Paris Agreement. These additional costs are estimated to be of the order of 2 trillion US$ – or 1% of global GDP – in a well-below-2 °C scenario, and can be reduced by 6–30% with the adoption of higher energy efficiency standards. While the two models project broadly consistent future trends for the energy mix in the various scenarios, the effects may differ in magnitude due to intrinsic differences in how the models are set up and how sensitive they are to changes in energy efficiency and emission reduction targets. © 2022

Estimation of COVID-19 mortality in the United States using Spatio-temporal Conway Maxwell Poisson model.

Spatio-temporal Poisson models are commonly used for disease mapping. However, after incorporating the spatial and temporal variation, the data do not necessarily have equal mean and variance, suggesting either over- or under-dispersion. In this paper, we propose the Spatio-temporal Conway Maxwell Poisson model. The advantage of Conway Maxwell Poisson distribution is its ability to handle both under- and over-dispersion through controlling one special parameter in the distribution, which makes it more flexible than Poisson distribution. We consider data from the pandemic caused by the SARS-CoV-2 virus in 2019 (COVID-19) that has threatened people all over the world. Understanding the spatio-temporal pattern of the disease is of great importance. We apply a spatio-temporal Conway Maxwell Poisson model to data on the COVID-19 deaths and find that this model achieves better performance than commonly used spatio-temporal Poisson model.

Effect estimates of COVID-19 non-pharmaceutical interventions are non-robust and highly model-dependent.

OBJECTIVE: To compare the inference regarding the effectiveness of the various non-pharmaceutical interventions (NPIs) for COVID-19 obtained from different SIR models. STUDY DESIGN AND SETTING: We explored two models developed by Imperial College that considered only NPIs without accounting for mobility (model 1) or only mobility (model 2), and a model accounting for the combination of mobility and NPIs (model 3). Imperial College applied models 1 and 2 to 11 European countries and to the USA, respectively. We applied these models to 14 European countries (original 11 plus another 3), over two different time horizons. RESULTS: While model 1 found that lockdown was the most effective measure in the original 11 countries, model 2 showed that lockdown had little or no benefit as it was typically introduced at a point when the time-varying reproduction number was already very low. Model 3 found that the simple banning of public events was beneficial, while lockdown had no consistent impact. Based on Bayesian metrics, model 2 was better supported by the data than either model 1 or model 3 for both time horizons. CONCLUSION: Inferences on effects of NPIs are non-robust and highly sensitive to model specification. In the SIR modeling framework, the impacts of lockdown are uncertain and highly model-dependent.

COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data.

Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. We identify a suitable Convolutional Neural Network (CNN) model through initial comparative study of several popular CNN models. We then optimize the selected VGG19 model for the image modalities to show how the models can be used for the highly scarce and challenging COVID-19 datasets. We highlight the challenges (including dataset size and quality) in utilizing current publicly available COVID-19 datasets for developing useful deep learning models and how it adversely impacts the trainability of complex models. We also propose an image pre-processing stage to create a trustworthy image dataset for developing and testing the deep learning models. The new approach is aimed to reduce unwanted noise from the images so that deep learning models can focus on detecting diseases with specific features from them. Our results indicate that Ultrasound images provide superior detection accuracy compared to X-Ray and CT scans. The experimental results highlight that with limited data, most of the deeper networks struggle to train well and provides less consistency over the three imaging modes we are using. The selected VGG19 model, which is then extensively tuned with appropriate parameters, performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans.