  In this worldwide spread of SARS-CoV-2 (COVID-19) infection, it is of utmost
importance to detect the disease at an early stage especially in the hot spots
of this epidemic. There are more than 110 Million infected cases on the globe,
sofar. Due to its promptness and effective results computed tomography
(CT)-scan image is preferred to the reverse-transcription polymerase chain
reaction (RT-PCR). Early detection and isolation of the patient is the only
possible way of controlling the spread of the disease. Automated analysis of
CT-Scans can provide enormous support in this process. In this article, We
propose a novel approach to detect SARS-CoV-2 using CT-scan images. Our method
is based on a very intuitive and natural idea of analyzing shapes, an attempt
to mimic a professional medic. We mainly trace SARS-CoV-2 features by
quantifying their topological properties. We primarily use a tool called
persistent homology, from Topological Data Analysis (TDA), to compute these
topological properties. We train and test our model on the "SARS-CoV-2 CT-scan
dataset" \citep{soares2020sars}, an open-source dataset, containing 2,481
CT-scans of normal and COVID-19 patients. Our model yielded an overall
benchmark F1 score of $99.42\% $, accuracy $99.416\%$, precision $99.41\%$, and
recall $99.42\%$. The TDA techniques have great potential that can be utilized
for efficient and prompt detection of COVID-19. The immense potential of TDA
may be exploited in clinics for rapid and safe detection of COVID-19 globally,
in particular in the low and middle-income countries where RT-PCR labs and/or
kits are in a serious crisis.
