A Survey on Data-driven COVID-19 and Future Pandemic Management

The COVID-19 pandemic has resulted in more than 440 million confirmed cases globally and almost 6 million reported deaths as of March 2022. Consequently, the world experienced grave repercussions to citizens' lives, health, wellness, and the economy. In responding to such a disastrous global event, countermeasures are often implemented to slow down and limit the virus's rapid spread. Meanwhile, disaster recovery, mitigation, and preparation measures have been taken to manage the impacts and losses of the ongoing and future pandemics. Data-driven techniques have been successfully applied to many domains and critical applications in recent years. Due to the highly interdisciplinary nature of pandemic management, researchers have proposed and developed data-driven techniques across various domains. However, a systematic and comprehensive survey of data-driven techniques for pandemic management is still missing. In this article, we review existing data analysis and visualization techniques and their applications for COVID-19 and future pandemic management with respect to four phases (namely, Response, Recovery, Mitigation, and Preparation) in disaster management. Data sources utilized in these studies and specific data acquisition and integration techniques for COVID-19 are also summarized. Furthermore, open issues and future directions for data-driven pandemic management are discussed. [ FROM AUTHOR]

PVT-COV19D: Pyramid Vision Transformer for COVID-19 Diagnosis (preprint)

With the outbreak of COVID-19, a large number of relevant studies have emerged in recent years. We propose an automatic COVID-19 diagnosis framework based on lung CT scan images, the PVT-COV19D. In order to accommodate the different dimensions of the image input, we first classified the images using Transformer models, then sampled the images in the dataset according to normal distribution, and fed the sampling results into the modified PVTv2 model for training. A large number of experiments on the COV19-CT-DB dataset demonstrate the effectiveness of the proposed method.

Progress in research in diagnosis of SARS-CoV-2 infection based on proteomics technique

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has swept the world in 2020, resulting in unprecedented pandemic of coronavirus disease 2019 (COVID-19). The number of infected persons and deaths increase every day at a frightening speed, threatening the health and life of people in the world and causing heavy burden to the global public health system. So far, nucleic acid detection is the main diagnostic method and gold standard for COVID-19. Meanwhile, other techniques and methods are also in developing for the diagnosis of SARS-CoV-2 infection. Proteomics technique is one of them. Proteomics technique has been widely used in the research of disease-related mechanism, development of diagnostic methods and pathogen identification. Up to now, there are mainly two applications of proteomics in the diagnosis of SARS-CoV-2 infection. First, proteomics based on virus particles has great potential in early diagnosis. Second, proteomics based on body fluids can be used not only for early diagnosis, but also for good monitoring the progress of infection, predicting the trend of disease, and evaluating the prognoses. In this paper, the research and application of proteomics technique in the diagnosis of SARS-CoV-2 infection in the world are summarized and prospected.

Conspiracy in the Time of Corona: Automatic detection of Emerging Covid-19 Conspiracy Theories in Social Media and the News (preprint)

Rumors and conspiracy theories thrive in environments of low confi- dence and low trust. Consequently, it is not surprising that ones related to the Covid-19 pandemic are proliferating given the lack of scientific consensus on the virus’s spread and containment, or on the long term social and economic ramifications of the pandemic. Among the stories currently circulating are ones suggesting that the 5G telecommunication network activates the virus, that the pandemic is a hoax perpetrated by a global cabal, that the virus is a bio-weapon released deliberately by the Chinese, or that Bill Gates is using it as cover to launch a broad vaccination program to facilitate a global surveillance regime. While some may be quick to dismiss these stories as having little impact on real-world behavior, recent events including the destruction of cell phone towers, racially fueled attacks against Asian Americans, demonstrations espousing resistance to public health orders, and wide-scale defiance of scientifically sound public mandates such as those to wear masks and practice social distancing, countermand such conclusions. Inspired by narrative theory, we crawl social media sites and news reports and, through the application of automated machine-learning methods, discover the underlying narrative frame- works supporting the generation of rumors and conspiracy theories. We show how the various narrative frameworks fueling these stories rely on the alignment of otherwise disparate domains of knowledge, and consider how they attach to the broader reporting on the pandemic. These alignments and attachments, which can be monitored in near real-time, may be useful for identifying areas in the news that are particularly vulnerable to reinterpretation by conspiracy theorists. Understanding the dynamics of storytelling on social media and the narrative frameworks that provide the generative basis for these stories may also be helpful for devising methods to disrupt their spread.