ArticlePattern recognition2022
Efficient COVID-19 testing via contextual model based compressive sensing.
Article in Pattern recognition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The COVID-19 pandemic is threatening billions of people's life all over the world. As of March 6, 2021, covid-19 has confirmed in 115,653,459 people worldwide. It has also a devastating effect on businesses and social activities. Since there is still no definite cure for this disease, extensive testing is the most critical issue to determine the trend of illness, appropriate medical treatment, and make social distancing policies. Besides, testing more people in a shorter time helps to contain the contagion. The PCR-based methods are the most popular tests which take about an hour to make the output result. Obviously, it makes the number of tests highly limited and consequently, hurts the efficiency of pandemic control. In this paper, we propose a new approach to identify affected individuals with a considerably reduced No. of tests. Intuitively, saving time and resources is the main advantage of our approach. We use contextual information to make a graph-based model to be used in model-based compressive sensing (CS). Our proposed model makes the testing with fewer tests required compared to traditional testing methods and even group testing. We embed contextual information such as age, underlying disease, symptoms (i.e. cough, fever, fatigue, loss of consciousness), and social contacts into a graph-based model. This model is used in model-based CS to minimize the required test. We take advantage of Discrete Graph Signal Processing on Graph (DSP
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