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The European COVID-19 drugs calculation tool: an aid for the estimation of the drugs needed during the SARS-CoV 2 pandemic

D. Leonardi Vinci, A. Meccio, A. Provenzani, ME. Faggiano, N. Miljković, D. Makridaki, P. Horák, P. Polidori

. 2022 ; 29 (e1) : e23-e29. [pub] 20210222

Language English Country Great Britain

Document type Journal Article

E-resources Online Full text

NLK PubMed Central from 2016
Europe PubMed Central from 2016 to 1 year ago
ProQuest Central from 2012-02-01 to 6 months ago
Health & Medicine (ProQuest) from 2012-02-01 to 6 months ago

OBJECTIVE: To create an informatics supportive tool, which can assist healthcare professionals in estimating potential requirements for essential drug supplies to respond to the current SARS-CoV-2 pandemic based on epidemiological forecasting. METHODS: The tool was based on a Susceptible-Infected-Removed (SIR) epidemiological model in which the population is divided into three compartments and transmission parameters are specified to define the rate at which people move between stages. Appropriate data entry was guaranteed by the creation of structured guided paths. The drugs needed for the forecasted patients were estimated according to a list of critical care drugs compiled by consulting previous published scientific works, national and international guidelines. For each drug, an estimation was made of the percentage average ICU uptake for each therapeutic group and active principle. RESULTS: The tool consists of a Microsoft Excel template that is based on the initial epidemiological situation, the non-pharmaceutical interventions applied, the risk of hospitalisation based on the population age distribution, and the hospital beds available. The tool provides a forecast of which patients with COVID-19 will need to be treated in a hospital setting. The number of patients is used to estimate the drugs needed based on the average daily dose and the treatment length of each drug. The possibility of editing the type of distribution (exponential or linear) of the number of patients at the beginning of the analysis, the percentage adherence with non-pharmaceutical interventions and their delayed effect, and all the key epidemiological parameters make the estimation tailorable to different clinical contexts and needs. CONCLUSIONS: This model might be an effective supporting tool that could be easily implemented within the workflow of health professionals. All the information reported in this paper could be useful in developing new strategies to tackle the COVID-19 pandemic.

References provided by Crossref.org

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