Testing our way out of pandemics

Objectives: In the face of pandemics, a viable global strategy, beyond relying on the fast discovery of a vaccine or a cure, is needed. We study quantitatively the feasibility and effectiveness of mass testing to contain an epidemic. We also explore the implications of various smart testing strategies to decrease the needed testing rates. Methods: We use a modified SIR model with testing and extend the model to incorporate mobility patterns in a densely populated area. Results: For a pandemic like COVID-19, model simulations show that the rate of testing needed to squash the curve within a month varies between 20–30 percent of the population randomly tested daily to less than 5 percent, combining periodic and group testing. We also show that mobility restrictions can enhance the efficacy of testing. Scale could be as important as accuracy in testing, implying that an epidemiological rather than clinical approach for the approval of tests is needed. The estimated cost of testing is dwarfed by its return, mitigating the economic fallout of the pandemic. Conclusions: Without a vaccine or a cure, mass testing is the only viable and less costly strategy to indefinitely "squash the curve” while allowing for major economic activities to resume. Planning and executing a testing strategy is necessary and urgent as an insurance policy against future pandemics. It should be considered as an investment to build a testing and isolation infrastructure, which should be maintained as part of the pandemic preparedness. © 2022

Testing our way out of pandemics

Objectives: In the face of pandemics, a viable global strategy, beyond relying on the fast discovery of a vaccine or a cure, is needed. We study quantitatively the feasibility and effectiveness of mass testing to contain an epidemic. We also explore the implications of various smart testing strategies to decrease the needed testing rates. Method(s): We use a modified SIR model with testing and extend the model to incorporate mobility patterns in a densely populated area. Result(s): For a pandemic like COVID-19, model simulations show that the rate of testing needed to squash the curve within a month varies between 20-30 percent of the population randomly tested daily to less than 5 percent, combining periodic and group testing. We also show that mobility restrictions can enhance the efficacy of testing. Scale could be as important as accuracy in testing, implying that an epidemiological rather than clinical approach for the approval of tests is needed. The estimated cost of testing is dwarfed by its return, mitigating the economic fallout of the pandemic. Conclusion(s): Without a vaccine or a cure, mass testing is the only viable and less costly strategy to indefinitely "squash the curve" while allowing for major economic activities to resume. Planning and executing a testing strategy is necessary and urgent as an insurance policy against future pandemics. It should be considered as an investment to build a testing and isolation infrastructure, which should be maintained as part of the pandemic preparedness. Copyright © 2022

Industrial policy against pandemics

The coronavirus disease 2019 (COVID-19) pandemic illustrated the inability of the market to meet the needed production scale and speed of essential medical products. The state should adopt a risk-based approach, allowing for experimentation with various technological solutions such as vaccines and tests, while ramping up their production. The intervention should resolve uncertainty, combine resources, coordinate technological choices, lift barriers to entry, ensure knowledge sharing, and support the value chain. The cost of this strategy is dwarfed by the economic fallout of a pandemic. Universal testing, an overlooked solution, is a key component of an infrastructure against future pandemics.

COVID-19 detection from Xray and CT scans using transfer learning

Since the novel coronavirus SARS-CoV-2 outbreak, intensive research has been conducted to find suitable tools for diagnosis and identifying infected people in order to take appropriate action. Chest imaging plays a significant role in this phase where CT and Xrays scans have proven to be effective in detecting COVID-19 within the lungs. In this research, we propose deep learning models using Transfer learning to detect COVID-19. Both X-ray and CT scans were considered to evaluate the proposed methods. © 2021 IEEE.