Analysis on the risk of respiratory virus transmission by air conditioning system operation based on experimental evidence.

It is important to know whether SARS-CoV-2 is spread through the air conditioning systems. Taking the central air conditioning system as an example, we analyze the mechanism and potential health risk of respiratory virus transmission in air-conditioned rooms and propose a method to study the risk of virus transmission in central air conditioning systems by investigating the data from medical experiments. The virus carrying capacity and the decay characteristics of indoor pathogen droplets are studied in this research. Additionally, the effects of air temperature and relative humidity on the virus survival in the air or on surfaces are investigated. The removal efficiency of infectious droplet nuclei by using an air conditioning filter was then determined. Thus, the transmission risk during the operation of the centralized air conditioning system is evaluated. The results show that the indoor temperature and humidity are controlled in the range of 20-25 °C and 40-70% by central air conditioning during the epidemic period, which not only benefits the health and comfort of residents, but also weakens the vitality of the virus. The larger the droplet size, the longer the viruses survive. Since the filter efficiency of the air conditioning filter increases with the increase in particle size, increasing the number of air changes of the circulating air volume can accelerate the removal of potential pathogen particles. Therefore, scientific operation of centralized air conditioning systems during the epidemic period has more advantages than disadvantages.

COVID-19 epidemic prediction and the impact of public health interventions: A review of COVID-19 epidemic models.

The coronavirus disease outbreak of 2019 (COVID-19) has been spreading rapidly to all corners of the word, in a very complex manner. A key research focus is in predicting the development trend of COVID-19 scientifically through mathematical modelling. We conducted a systematic review of epidemic prediction models of COVID-19 and the public health intervention strategies by searching the Web of Science database. 55 studies of the COVID-19 epidemic model were reviewed systematically. It was found that the COVID-19 epidemic models were different in the model type, acquisition method, hypothesis and distribution of key input parameters. Most studies used the gamma distribution to describe the key time period of COVID-19 infection, and some studies used the lognormal distribution, the Erlang distribution, and the Weibull distribution. The setting ranges of the incubation period, serial interval, infectious period and generation time were 4.9-7 days, 4.41-8.4 days, 2.3-10 days and 4.4-7.5 days, respectively, and more than half of the incubation periods were set to 5.1 or 5.2 days. Most models assumed that the latent period was consistent with the incubation period. Some models assumed that asymptomatic infections were infectious or pre-symptomatic transmission was possible, which overestimated the value of R0. For the prediction differences under different public health strategies, the most significant effect was in travel restrictions. There were different studies on the impact of contact tracking and social isolation, but it was considered that improving the quarantine rate and reporting rate, and the use of protective face mask were essential for epidemic prevention and control. The input epidemiological parameters of the prediction models had significant differences in the prediction of the severity of the epidemic spread. Therefore, prevention and control institutions should be cautious when formulating public health strategies by based on the prediction results of mathematical models.