Author: Krivoňáková, NaÄa; Å oltýsová, Andrea; Tamáš, Michal; TakáÄ, Zdenko; Krahulec, Ján; Ficek, Andrej; Gál, Miroslav; Gall, Marián; Fehér, Miroslav; Krivjanská, Anna; Horáková, Ivana; BeliÅ¡ová, Noemi; BÃmová, Paula; Å kulcová, Andrea Butor; Mackuľak, Tomáš
Title: Mathematical modeling based on RT-qPCR analysis of SARS-CoV-2 in wastewater as a tool for epidemiology Cord-id: bzk4tb26 Document date: 2021_9_30
ID: bzk4tb26
Snippet: Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerges to scientific research and monitoring of wastewaters to predict the spread of the virus in the community. Our study investigated the COVID-19 disease in Bratislava, based on wastewater monitoring from September 2020 until March 2021. Samples were analyzed from two wastewater treatment plants of the city with reaching 0.6 million monitored inhabitants. Obtained results from
Document: Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerges to scientific research and monitoring of wastewaters to predict the spread of the virus in the community. Our study investigated the COVID-19 disease in Bratislava, based on wastewater monitoring from September 2020 until March 2021. Samples were analyzed from two wastewater treatment plants of the city with reaching 0.6 million monitored inhabitants. Obtained results from the wastewater analysis suggest significant statistical dependence. High correlations between the number of viral particles in wastewater and the number of reported positive nasopharyngeal RT-qPCR tests of infected individuals with a time lag of 2 weeks/12 days (R(2) = 83.78%/R(2) = 52.65%) as well as with a reported number of death cases with a time lag of 4 weeks/27 days (R(2) = 83.21%/R(2) = 61.89%) was observed. The obtained results and subsequent mathematical modeling will serve in the future as an early warning system for the occurrence of a local site of infection and, at the same time, predict the load on the health system up to two weeks in advance.
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