Since its discovery in late 2019 the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created a global pandemic of coronavirus disease 2019 (COVID-19). As of July of 2022, more than 6.5 million SARS-CoV-2 deaths have been reported worldwide to the World Health Organization (WHO). Limited access to testing in underserved communities and incomplete reporting of COVID-19 contribute to official numbers being a fraction of the total infections and deaths from the COVID-19 pandemic. In addition, novel viral variants including of Omicron have higher transmission rates than the original virus and B.1.1.529 omicron even higher than the delta variant. Due to the vaccination efforts to reduce viral replication around the world, there are less severe cases. Outbreak management has been hindered by high transmission, especially among those with mild to no symptoms, and limitations in testing capacity for SARS-CoV-2. To limit spread of SARS-CoV-2, effective public health tools are needed for rapid and early identification of infections. While the current diagnostic standard is quantitative real time polymerase chain reaction (qRT-PCR), its high cost and long turnaround times constrain its utility for widespread surveillance use. Lack of such surveillance has resulted in the closing of businesses and schools, and severe economic disruption. We present novel ways to apply mathematics, probability theory and statistics using image analysis of COVID-19 rapid antigen tests, and show examples of powerful reporting systems, data and result Interpretation from Real World Data obtained from Chelsea, MA. USA.
1. Describe the situation of antigen tests' high bar approval process in the US market.
2. Describe novel methodologies for image processing and analysis of antigen tests enabling competitiveness and acceptance in diagnostics.
3. Classify the need for rapid and frequent antigen testing as essential for control of the spread of emergent diseases.