The COVID-19 pandemic has triggered an urgent need to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of Artificial Intelligence, has enjoyed recent success in solving various complex problems in health care and has the potential to contribute to the fight of controlling COVID-19. In response to this call, computer vision researchers are putting their knowledge base at work to devise effective ways to counter COVID-19 challenge and serve the global community. New contributions are being shared with every passing day. It motivated us to review the recent work, collect information about available research resources and an indication of future research directions. We want to make it available to computer vision researchers to save precious time. This survey paper is intended to provide a preliminary review of the available literature on the computer vision efforts against COVID-19 pandemic. 1 Introduction COVID 19, an infectious disease is caused by severe acute respiratory syndrome (SARS-CoV-2) [1] and named coronavirus due to visual appearance (under an electron microscope) to solar corona (similar to a crown) [2]. The fight against COVID 19 has motivated researchers worldwide to explore, understand, and devise new diagnostic and treatment techniques to culminate this threat to our generation. In this article, we discuss how the computer vision community is fighting with this menace by proposing new types of approaches, improving efficiency, and speed of the existing efforts. Computer vision is an interdisciplinary field that deals with how computers can develop a high-level understanding by interpreting information present in digital images. It has made substantial progress in the last few years, mainly due to the success of deep learning, a sub-field of machine learning. Computer vision techniques have shown enormous scope in various applications areas, especially in healthcare and medical research. A plethora of novel computer vision approaches in healthcare applications exist that include, but not limited to, disease diagnosis, prognosis, surgery, therapy, medical image analysis and drug discovery [5]. This success has enabled computer vision scientists to take the challenge as soldiers in the fight against COVID-19 by contributing to disease diagnosis, prognosis, prevention, control, treatment and management. The scientific response to combat COVID-19 has been far quicker and widespread. According to PubMed [6], in the year 2019, 755 academic papers published including the word \coronavirus" but even in the first 80 days of 2020 alone, the number rose to astounding 1,245 articles about the family of viruses. The economist has dubbed it \science of the times" with the hope that such efforts would help speed up the development of a COVOD-19 vaccine [6]. Numerous approaches in computer vision have been proposed so far, dealing with different aspects of combat the COVID-19 pandemic. These approaches differ from each other based on the way they approach the fundamental questions: How can medical imaging facilitate faster and reliable diagnosis of COVID-19? Which image features properly classify conditions as Bacterial, Viral, COVID-19, and Pneumonia? What can we learn from imaging data acquired from disease survivors to screen critical and non-critical ill patients? How can computer vision be used to enforce social distancing and early screening of infected people? How can 3D computer vision help to maintain healthcare equipment supply and guide the development of a COVID-19 vaccine? The answers to these questions are 1/24 being explored, and preliminary work has been done. The goal of this survey is to group computer vision methods into broad categories and provide comprehensive descriptions of representative methods in each category. We aspire to give readers the ability to understand the baseline efforts and kickstart their work where others have left. Moreover, we aim to identify new trends and ideas to build a more robust and well-planned strategy in this war of our times. Fig 1. A portrayal of current increase in research articles about coronavirus related research. Adapted from [6]. Our survey will include research papers in pre-print format due to time urgency imposed by this disease. It is not an optimal approach due to the risk of lower quality and work without due validation. Many of the works have not been put into clinical trial as it is time-consuming. Nevertheless, our intention here is to share ideas from a single platform while highlighting the computer vision community efforts. We hope that our reader is aware of these contemporary challenges. We follow a bottom-up approach to describing the research problems that need addressing first. We start with disease diagnosis, discuss disease prevention and control, followed by treatment-related computer vision research work. Section 2 describes the overall taxonomy of computer vision research areas by classifying these efforts into three classes. Section 3 provides a detailed description of each research area, relevant papers and a brief description of representative work. Section 4 describes available resources including research datasets, their links, deep learning models and codes. Section 5 provides the discussion and future work directions followed by concluding remarks and