Attention-based Multi-flow Network for COVID-19 Classification and Lesion Localization from Chest CT

COVID-19 has been rapidly spreading worldwide and infected more than 1 million people with over 690k deaths reported. It is urgent and crucial to identify COVID-19-infected patients by computed tomography (CT) accurately and rapidly. However, we found that two problems, weak supervision and lack of interpretability, hindered its development. To address these challenges, we propose an attention-based multi-flow network for COVID-19 classification and lesion localization from chest CT. In the proposed model, we built a Resnet-based multi-flow network to learn the local information and the longitudinal information from the full chest sequence slice. To assist doctors in decision-making, the attention mechanism integrated into the network, which can locate the key slices and key parts from a full chest CT sequence of patients. We have systematically evaluated our method on the CT images of 1031 cases, including 420 COVID-19 cases, 311CAP cases, and 300 non-pneumonia cases. Our method could obtain an average accuracy of 82.3%, with 85.7% sensitivity and 86.4 % specificity, which outperformed previous works. © 2022 IEEE.

Corporate philanthropic response to the COVID-19 pandemic in China: a dilemma?

Purpose: This study aims to examine the corporate donations in response to the intensive outbreak of the COVID-19 pandemic in China in 2020 and proposes that the local spread of COVID-19 is negatively associated with corporate donations due to the non-trivial costs, but meanwhile, strong institutional pressures based on institutional theory are put on firms to donate, which thus creates a dilemma for firms. This study further argues that the dilemma is heterogeneous across different institutional fields. Design/methodology/approach: Using a sample of Chinese listed companies during the intensive outbreak of this pandemic, a two-stage Heckman selection model is conducted to address the potential sample selection bias. Findings: This study reveals a negative relationship between the local spread of COVID-19 and corporate donations, confirms the driving effect of various types of institutional pressure and finds that the intensity of the COVID-19 pandemic strengthens the effect of coercive pressure and mimetic pressure on philanthropic giving but weakens the effect of normative pressure. Originality/value: This study extends the knowledge on firms’ philanthropic response to natural crises, as the COVID-19 pandemic has not only led to a public health crisis but also to a global economic crisis, and how the effects of institutional pressures are affected by a situational crisis. This work enriches the literature on corporate philanthropy and crisis management and has some implications for both policymakers and business practitioners. © 2022, Emerald Publishing Limited.

Deep survival analysis with longitudinal X-rays for COVID-19

Time-to-event analysis is an important statistical tool for allocating clinical resources such as ICU beds. However, classical techniques like the Cox model cannot directly incorporate images due to their high dimensionality. We propose a deep learning approach that naturally incorporates multiple, time-dependent imaging studies as well as non-imaging data into time-to-event analysis. Our techniques are bench-marked on a clinical dataset of 1,894 COVID-19 patients, and show that image sequences significantly improve predictions. For example, classical time-to-event methods produce a concordance error of around 30-40% for predicting hospital admission, while our error is 25% without images and 20% with multiple X-rays included. Ablation studies suggest that our models are not learning spurious features such as scanner artifacts and that models which use multiple images tend to perform better than those which only use one. While our focus and evaluation is on COVID-19, the methods we develop are broadly applicable.

Optimal control strategy for COVID-19 concerning both life and economy based on deep reinforcement learning

At present, the global COVID-19 is still severe. More and more countries have experienced second or even third outbreaks. The epidemic is far from over until the vaccine is successfully developed and put on the market on a large scale. Inappropriate epidemic control strategies may bring catastrophic consequences. It is essential to maximize the epidemic restraining and to mitigate economic damage. However, the study on the optimal control strategy concerning both sides is rare, and no optimal model has been built. In this paper, the Susceptible-Infectious-Hospitalized-Recovered (SIHR) compartment model is expanded to simulate the epidemic's spread concerning isolation rate. An economic model affected by epidemic isolation measures is established. The effective reproduction number and the eigenvalues at the equilibrium point are introduced as the indicators of controllability and stability of the model and verified the effectiveness of the SIHR model. Based on the Deep Q Network (DQN), one of the deep reinforcement learning (RL) methods, the blocking policy is studied to maximize the economic output under the premise of controlling the number of infections in different stages. The epidemic control strategies given by deep RL under different learning strategies are compared for different reward coefficients. The study demonstrates that optimal policies may differ in various countries depending on disease spread and anti-economic risk ability. The results show that the more economical strategy, the less economic loss in the short term, which can save economically fragile countries from economic crises. In the second or third outbreak stage, the earlier the government adopts the control strategy, the smaller the economic loss. We recommend the method of deep RL to specify a policy which can control the epidemic while making quarantine economically viable.