Optimizing non-pharmaceutical intervention strategies against COVID-19 using artificial intelligence

One key task in the early fight against the COVID-19 pandemic was to plan non-pharmaceutical interventions to reduce the spread of the infection while limiting the burden on the society and economy. With more data on the pandemic being generated, it became possible to model both the infection trends and intervention costs, transforming the creation of an intervention plan into a computational optimization problem. This paper proposes a framework developed to help policy-makers plan the best combination of non-pharmaceutical interventions and to change them over time. We developed a hybrid machine-learning epidemiological model to forecast the infection trends, aggregated the socio-economic costs from the literature and expert knowledge, and used a multi-objective optimization algorithm to find and evaluate various intervention plans. The framework is modular and easily adjustable to a real-world situation, it is trained and tested on data collected from almost all countries in the world, and its proposed intervention plans generally outperform those used in real life in terms of both the number of infections and intervention costs.

Optimizing non-pharmaceutical intervention strategies against COVID-19 using artificial intelligence.

One key task in the early fight against the COVID-19 pandemic was to plan non-pharmaceutical interventions to reduce the spread of the infection while limiting the burden on the society and economy. With more data on the pandemic being generated, it became possible to model both the infection trends and intervention costs, transforming the creation of an intervention plan into a computational optimization problem. This paper proposes a framework developed to help policy-makers plan the best combination of non-pharmaceutical interventions and to change them over time. We developed a hybrid machine-learning epidemiological model to forecast the infection trends, aggregated the socio-economic costs from the literature and expert knowledge, and used a multi-objective optimization algorithm to find and evaluate various intervention plans. The framework is modular and easily adjustable to a real-world situation, it is trained and tested on data collected from almost all countries in the world, and its proposed intervention plans generally outperform those used in real life in terms of both the number of infections and intervention costs.

Size- and Time-Dependent Particle Removal Efficiency of Face Masks and Improvised Respiratory Protection Equipment Used during the COVID-19 Pandemic.

Size- and time-dependent particle removal efficiency (PRE) of different protective respiratory masks were determined using a standard aerosol powder with the size of particles in the range of an uncoated SARS-CoV-2 virus and small respiratory droplets. Number concentration of particles was measured by a scanning mobility particle sizer. Respiratory protective half-masks, surgical masks, and cotton washable masks were tested. The results show high filtration efficiency of FFP2, FFP3, and certified surgical masks for all sizes of tested particles, while protection efficiency of washable masks depends on their constituent fabrics. Measurements showed decreasing PRE of all masks over time due to transmission of nanoparticles through the mask-face interface. On the other hand, the PRE of the fabric is governed by deposition of the aerosols, consequently increasing the PRE.

Sterilization of polypropylene membranes of facepiece respirators by ionizing radiation.

Ionizing radiation has been identified as an option for sterilization of disposable filtering facepiece respirators in situations where the production of the respirators cannot keep up with demand. Gamma radiation and high energy electrons penetrate deeply into the material and can be used to sterilize large batches of masks within a short time period. In relation to reports that sterilization by ionizing radiation reduces filtration efficiency of polypropylene membrane filters on account of static charge loss, we have demonstrated that both gamma and electron beam irradiation can be used for sterilization, provided that the respirators are recharged afterwards.