Data Mining and Analysis of Scientific Research Data Records on COVID-19 Mortality, Immunity, and Vaccine Development - In the First Wave of the COVID-19 Pandemic

Radanliev, Petar; Roure, David C. De; Walton, Robert

doi:10.2139/ssrn.3692590

Cited by 15 publications

(19 citation statements)

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“…1 ) [1] , [2] . To date, the world is still fighting to halt the virus from spreading and the cure is still nowhere in sight [3] . As in July 2020, 13,042,340 has been infected with COVID-19 and 571,689 deaths are reported worldwide [4] .…”

Section: Introductionmentioning

confidence: 99%

Potential application of biomass derived graphene for COVID-19 pandemic

Arifin

Nordin

Jaafar

et al. 2021

Materials Today: Proceedings

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Section: Introductionmentioning

confidence: 99%

Potential application of biomass derived graphene for COVID-19 pandemic

Arifin

Nordin

Jaafar

et al. 2021

Materials Today: Proceedings

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“…The fast propagation of the COVID-19 pandemic has attracted unprecedented attention from both the public and the scientific community. This has resulted in much research and funding getting redirected towards COVID-19 and stimulated strong research collaboration between countries 34 . Due to the high fatality rate of SARS-CoV-2 37 , governments throughout the world have adopted measures such as lock-down, stay-at-home, and shelter-in-place, which in turn have led to substantial economic losses, see e.g., 24 .…”

Section: Introductionmentioning

confidence: 99%

Data-driven optimized control of the COVID-19 epidemics

Shirin

Lin

Sorrentino

2021

Sci Rep

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Optimizing the impact on the economy of control strategies aiming at containing the spread of COVID-19 is a critical challenge. We use daily new case counts of COVID-19 patients reported by local health administrations from different Metropolitan Statistical Areas (MSAs) within the US to parametrize a model that well describes the propagation of the disease in each area. We then introduce a time-varying control input that represents the level of social distancing imposed on the population of a given area and solve an optimal control problem with the goal of minimizing the impact of social distancing on the economy in the presence of relevant constraints, such as a desired level of suppression for the epidemics at a terminal time. We find that with the exception of the initial time and of the final time, the optimal control input is well approximated by a constant, specific to each area, which contrasts with the implemented system of reopening ‘in phases’. For all the areas considered, this optimal level corresponds to stricter social distancing than the level estimated from data. Proper selection of the time period for application of the control action optimally is important: depending on the particular MSA this period should be either short or long or intermediate. We also consider the case that the transmissibility increases in time (due e.g. to increasingly colder weather), for which we find that the optimal control solution yields progressively stricter measures of social distancing. We finally compute the optimal control solution for a model modified to incorporate the effects of vaccinations on the population and we see that depending on a number of factors, social distancing measures could be optimally reduced during the period over which vaccines are administered to the population.

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“…The fast propagation of the COVID-19 pandemic has attracted unprecedented attention from both the public and the scientific community. This has resulted in much research and funding getting redirected towards COVID-19 and stimulated strong research collaboration between countries [34]. Due to the high fatality rate of SARS-CoV-2 [37], governments throughout the world have adopted measures such as lock-down, stay-at-home, and shelter-in-place, which in turn have led to substantial economic losses, see e.g., [24].…”

Section: Introductionmentioning

confidence: 99%

Data-driven Optimized Control of the COVID-19 Epidemics

Shirin

Lin

Sorrentino

2020

Preprint

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Optimizing the impact on the economy of control strategies aiming at containing the spread of COVID-19 is a critical challenge. We use daily new case counts of COVID-19 patients reported by local health administrations from different Metropolitan Statistical Areas (MSAs) within the US to parametrize a simple model that well describes the propagation of the disease in each area. We then introduce a time-varying control input that represents the level of social distancing imposed on the population of a given area and solve an optimal control problem with the goal of minimizing the impact of social distancing on the economy in the presence of relevant constraints, such as a desired level of suppression for the epidemics at a terminal time. We find that with the exception of the initial time and of the final time, the optimal control input is well approximated by a constant variable which is specific to each area. For all the areas considered, this optimal level corresponds to stricter social distancing than the `current' level estimated from data. Proper selection of the time period for application of the control action optimally is important: depending on the particular MSA this period should be either short or long or intermediate. Finally, we see that small deviations from the optimal solution can lead to dramatic violations of the constraints.

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Data Mining and Analysis of Scientific Research Data Records on COVID-19 Mortality, Immunity, and Vaccine Development - In the First Wave of the COVID-19 Pandemic

Cited by 15 publications

References 0 publications

Potential application of biomass derived graphene for COVID-19 pandemic

Potential application of biomass derived graphene for COVID-19 pandemic

Data-driven optimized control of the COVID-19 epidemics

Data-driven Optimized Control of the COVID-19 Epidemics

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