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suck abstract from ncbi


10.1016/j.scitotenv.2020.143056

http://scihub22266oqcxt.onion/10.1016/j.scitotenv.2020.143056
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33268249!7560110!33268249
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suck abstract from ncbi

pmid33268249      Sci+Total+Environ 2021 ; 762 (?): 143056
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  • Quantifying the risk of indoor drainage system in multi-unit apartment building as a transmission route of SARS-CoV-2 #MMPMID33268249
  • Shi KW; Huang YH; Quon H; Ou-Yang ZL; Wang C; Jiang SC
  • Sci Total Environ 2021[Mar]; 762 (?): 143056 PMID33268249show ga
  • The COVID-19 pandemic has had a profound impact on human society. The isolation of SARS-CoV-2 from patients' feces on human cell line raised concerns of possible transmission through human feces including exposure to aerosols generated by toilet flushing and through the indoor drainage system. Currently, routes of transmission, other than the close contact droplet transmission, are still not well understood. A quantitative microbial risk assessment was conducted to estimate the health risks associated with two aerosol exposure scenarios: 1) toilet flushing, and 2) faulty connection of a floor drain with the building's main sewer pipe. SARS-CoV-2 data were collected from the emerging literature. The infectivity of the virus in feces was estimated based on a range of assumption between viral genome equivalence and infectious unit. The human exposure dose was calculated using Monte Carlo simulation of viral concentrations in aerosols under each scenario and human breathing rates. The probability of COVID-19 illness was generated using the dose-response model for SARS-CoV-1, a close relative of SARS-CoV-2, that was responsible for the SARS outbreak in 2003. The results indicate the median risks of developing COVID-19 for a single day exposure is 1.11 x 10(-10) and 3.52 x 10(-11) for toilet flushing and faulty drain scenario, respectively. The worst case scenario predicted the high end of COVID-19 risk for the toilet flushing scenario was 5.78 x 10(-4) (at 95th percentile). The infectious viral loads in human feces are the most sensitive input parameter and contribute significantly to model uncertainty.
  • |*COVID-19[MESH]
  • |*SARS-CoV-2[MESH]
  • |Aerosols[MESH]
  • |Humans[MESH]
  • |Pandemics[MESH]


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