Risk Of Novel Coronavirus Infection In A Semi-enclosed Space With Natural Ventilation Research

The outbreak of the novel coronavirus（COVID-19）has brought great disaster to mankind,and the prevention and control of the epidemic has once again become a matter of widespread concern and urgent solution.A number of respiratory infectious diseases,including the novel coronavirus,have been shown to be transmitted by aerosols.In this thesis,factors influencing the spread and spread of novel coronavirus aerosol in semi-enclosed space under natural ventilation conditions were studied,and relevant suggestions for epidemic prevention and control were put forward.Firstly,the flow field and aerosol concentration distribution in the semi-enclosed space were studied by numerical simulation method.When the ventilation volumes were 0.588m³/s,1.176m³/s and 1.764m³/s for 1h,the gas flow field and aerosol concentration distribution were obtained.After the air enters the room from the window jet,an asymmetrical jet is formed,the air mainly flows along the middle passage of the room,the speed on the section gradually decreases,forming a clockwise rotating vortex zone on the left side of the room,forming a counterclockwise rotating vortex area on the right side,and the strength of the right vortex is greater than that on the left.The turbulent diffusion in the space is obvious,and the aerosol cloud mass and its lateral control range are proportional to the distance from the aerosol release source.When the aerosol concentration is greater than1.11×10^-6kg/m³,the volume of aerosol clouds of different concentrations is the same undereachwindowopening.Secondly,for the novel coronavirus,a single-hit random dose-response model with the best adaptation to predict the probability of infection was studied.The fitting process was carried out in R 4.1.3 using the R programming language,and the parameter values when the minimum log-likelihood value was obtained,where the EM model parameters r=9.2×10^-3,EBPM model parametersα=7.5×10^-3,β=1.995×10^-1,and the ABPM model parametersα=1.45×10^-2,β=1.8×10^-1.The uncertainty of the EM model and the HILL-1 model is the smallest,and the probability uncertainty of the EBPM model and the ABPM model is higher than the maximum likelihood estimation probability.The results of the analysis of the Akaike Information Criterion（AIC）and Bayesian Information Criterion（BIC）show that the EM model,EBPM model and ABPM model are suitable for predicting the probability of novel coronavirus infection.Among them,the dose required for the EM model toreacha 50%probability ofinfection can be used as an estimateoftheaverageinfection doseinthetransmissionchain ofthenovelcoronavirus.Finally,three random dose-response model was used to predict the risk of novel coronavirus infection in semi-enclosed spaces under natural ventilation.This thesis proposes to use the combination of EM model and EBPM model to predict the risk of novel coronavirus infection.In naturally ventilated semi-enclosed spaces,the predicted probability of infection decreases with the distance from the host’s head.Changing the sleeping position of the host and using different sleeping positions on the upper and lower bunks can widen the distance between the heads of people in the room,thereby reducing the risk of infection.Taking the EBPM prediction results as an example,during the stretching of the distance from the host head from 2m to 2.8m,the predicted probability of infection decreased from 9.6×10^-2%to 2.01×10^-3%,which had a significanteffectonreducing therisk.