Droplets provide a well-known transmission media in the COVID-19 epidemic, and the particle size is closely related to the classification of the transmission route. However, the term “aerosol” covers most particle sizes of suspended particulates because of information asymmetry in different disciplines, which may lead to misunderstandings in the selection of epidemic prevention and control strategies for the public. In this review, the time when these droplets are exhaled by a patient was taken as the initial time. Then, all available viral loads and numerical distribution of the exhaled droplets was analyzed, and the evaporation model of droplets in the air was combined with the deposition model of droplet nuclei in the respiratory tract. Lastly, the perspective that physical spread affects the transmission risk of different size droplets at different times was summarized for the first time. The results showed that although the distribution of exhaled droplets was dominated by small droplets, droplet volume was proportional to the third power of particle diameter, meaning that the viral load of a 100 μm droplet was approximately 10 6 times that of a 1 μm droplet at the initial time. Furthermore, the exhaled droplets are affected by heat and mass transfer of evaporation, water fraction, salt concentration, and acid-base balance (the water fraction > 98%), which lead them to change rapidly, and the viral survival condition also deteriorates dramatically. The time required for the initial diameter (d o ) of a droplet to shrink to the equilibrium diameter (d e , about 30% of d o ) is approximately proportional to the second power of the particle diameter, taking only a few milliseconds for a 1 μm droplet but hundreds of milliseconds for a 10 μm droplet; in other words, the viruses carried by the large droplets can be preserved as much as possible. Finally, the infectious droplet nuclei maybe inhaled by the susceptible population through different and random contact routes, and the droplet nuclei with larger d e decompose more easily into tiny particles on account of the accelerated collision in a complex airway, which can be deposited in the higher risk alveolar region. During disease transmission, the infectious droplet particle size varies widely, and the transmission risk varies significantly at different time nodes; therefore, the fuzzy term “aerosol” is not conducive to analyzing disease exposure risk. Recommendations for epidemic prevention and control strategies are: 1) Large droplets are the main conflict in disease transmission; thus, even if they are blocked by a homemade mask initially, it significantly contains the epidemic. 2) The early phase of contact, such as close-contact and short-range transmission, has the highest infection risk; therefore, social distancing can effectively keep the susceptible population from inhaling active viruses. 3) The risk of the fomite route depends on ...

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The novel human coronavirus SARS-CoV-2 has been responsible for a worldwide pandemic. Although media transmission through contaminated surfaces is one of the most recognized ways of transmission, the study on the number and viability of viruses surviving on a surface after leaving the host represents a “blind spot” in current research. In this paper we have reviewed studies on the physical process of droplet evaporation on media surfaces, and analyzed the recent literature related to experiments on the decay of the viral concentration and infectious activity of SARS-CoV-2 and other viruses on those surface and in the air. The huge differences in the risk of media transmission of large saliva and sputum droplets were analyzed in terms of time elapsed. Due to the rapid decrease of water content in the evaporated droplets and the increased concentration of each component, the living environment of the virus tended to deteriorate sharply, and virus concentration plummeted within a few minutes. Although a virus can be detected in a matter of hours, tens of hours, or days, the risk of transmission is negligible compared to when it first left the host. This study suggests that the key to prevention and control is to start from the source, the earlier the better. It is extremely important to develop good public health habits, wear masks, and wash hands frequently. That said, excessive disinfection and sterilization of surfaces during a later period may have adverse effects.

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 air-conditioning filter was then determined. Thus, the transmission risk during the operation of centralized air-conditioning system is evaluated. The results show that the indoor temperature and humidity are controlled in the range of 20-25℃ 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 air-conditioning filter increases with the increase of 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.

Graphical abstract 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.

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Distributed optimization methods with probabilistic local updates have recently gained attention for their provable ability to communication acceleration. Nevertheless, this capability is effective only when the loss function is smooth and the network is sufficiently well-connected. In this paper, we propose the first linear convergent method MG-SKIP with probabilistic local updates for nonsmooth distributed optimization. Without any extra condition for the network connectivity, MG-SKIP allows for the multipleround gossip communication to be skipped in most iterations, while its iteration complexity is O(κ log 1 /ϵ) and communication complexity is only O( κ /(1−ρ) log 1 /ϵ), where κ is the condition number of the loss function and ρ reflects the connectivity of the network topology. To the best of our knowledge, MG-SKIP achieves the best communication complexity when the loss function has the smooth (strongly convex)+nonsmooth (convex) composite form.