Efficient Direct and Limited Environmental Transmission of SARS-CoV-2 Lineage B.1.22 in Domestic Cats.

The susceptibility of domestic cats to infection with SARS-CoV-2 has been demonstrated by several experimental studies and field observations. We performed an extensive study to further characterize the transmission of SARS-CoV-2 between cats, through both direct and indirect contact. To that end, we estimated the transmission rate parameter and the decay parameter for infectivity in the environment. Using four groups of pair-transmission experiment, all donor (inoculated) cats became infected, shed virus, and seroconverted, while three out of four direct contact cats got infected, shed virus, and two of those seroconverted. One out of eight cats exposed to a SARS-CoV-2-contaminated environment became infected but did not seroconvert. Statistical analysis of the transmission data gives a reproduction number R0 of 2.18 (95% CI = 0.92 to 4.08), a transmission rate parameter ß of 0.23 day-1 (95% CI = 0.06 to 0.54), and a virus decay rate parameter µ of 2.73 day-1 (95% CI = 0.77 to 15.82). These data indicate that transmission between cats is efficient and can be sustained (R0 > 1), however, the infectiousness of a contaminated environment decays rapidly (mean duration of infectiousness 1/2.73 days). Despite this, infections of cats via exposure to a SARS-CoV-2-contaminated environment cannot be discounted if cats are exposed shortly after contamination. IMPORTANCE This article provides additional insight into the risk of infection that could arise from cats infected with SARS-CoV-2 by using epidemiological models to determine transmission parameters. Considering that transmission parameters are not always provided in the literature describing transmission experiments in animals, we demonstrate that mathematical analysis of experimental data is crucial to estimate the likelihood of transmission. This article is also relevant to animal health professionals and authorities involved in risk assessments for zoonotic spill-overs of SARS-CoV-2. Last but not least, the mathematical models to calculate transmission parameters are applicable to analyze the experimental transmission of other pathogens between animals.

Influence of Indoor Relative Humidity on the Number Concentration, Size Distribution, and Trajectory of Sneeze Droplets: Effects on Social Distancing Guidelines

The spread of the COVID-19 pandemic is mainly due to the direct transmission routes of SARS-CoV-2 virus-carrying aerosols in indoor environments. In this study, the effect of indoor relative humidity (RH∞) on the number concentration, size distribution, and trajectory of sneeze droplets was studied in a confined space experimentally and numerically. Computational fluid dynamics (CFD) simulations using the renormalization group k-ε turbulence model by considering the one-way and two-way (humidity) coupling models were performed to assess the effects of humidity fields on the propagation of droplets. Number concentration profiles indicated that the RH∞ affected the dispersion modes of droplets differently for the puff, droplet cloud, fully-dispersed, and dilute-dispersed droplets phases identified by the shadowgraph imaging technique. The two-way (humidity) coupling model led to a close agreement with the experimental data in all phases. In particular, the two-way coupling provided better agreement with the data in the puff phase compared to the one-way coupling model. However, the one-way coupling model was sufficient for studying the motion of airborne droplets in the other phases. The velocity fields in the droplet cloud were more sensitive to RH∞ than the puff and fully-dispersed droplets phases. Also, the effect of RH∞ on the maximum spreading distance of droplets, dmax,sp, in the puff was insignificant, while its effect became dominant in the dilute-dispersed droplets phase. A dynamic change in the velocity profile of the sneeze jet was seen at a critical relative humidity RH∞,crit of about 48%. At RH∞< RH∞,crit, the number concentration of aerosolized droplets increases, significantly affecting the size distribution and the velocity of droplets. At RH∞≥ RH∞,crit, the effect of evaporation time on the number concentration, and diameter of droplets was negligible. At RH∞ of 24 and 64%, dmax,sp was 2.14 m (7 feet) and 3.05 m (10 feet), respectively. However, a dry indoor environment led to an increase in evaporation rate and more than four times number concentration of aerosolized droplets compared to a humid environment. Thus, the risk of direct transmission of Covid-19 in a humid indoor environment was higher than the dry conditions, which suggested the requirements for incorporating the RH∞ effect in the social distancing guideline.

An epidemic model integrating direct and fomite transmission as well as household structure applied to COVID-19.

This paper stresses its base contribution on a new SIR-type model including direct and fomite transmission as well as the effect of distinct household structures. The model derivation is modulated by several mechanistic processes inherent from typical airborne diseases. The notion of minimum contact radius is included in the direct transmission, facilitating the arguments on physical distancing. As fomite transmission heavily relates to former-trace of sneezes, the vector field of the system naturally contains an integral kernel with time delay indicating the contribution of undetected and non-quarantined asymptomatic cases in accumulating the historical contamination of surfaces. We then increase the complexity by including the different transmission routines within and between households. For airborne diseases, within-household interactions play a significant role in the propagation of the disease rendering countrywide effect. Two steps were taken to include the effect of household structure. The first step subdivides the entire compartments (susceptible, exposed, asymptomatic, symptomatic, recovered, death) into the household level and different infection rates for the direct transmission within and between households were distinguished. Under predefined conditions and assumptions, the governing system on household level can be raised to the community level. The second step then raises the governing system to the country level, where the final state variables estimate the total individuals from all compartments in the country. Two key attributes related to the household structure (number of local households and number of household members) effectively classify countries to be of low or high risk in terms of effective disease propagation. The basic reproductive number is calculated and its biological meaning is invoked properly. The numerical methods for solving the DIDE-system and the parameter estimation problem were mentioned. Our optimal model solutions are in quite good agreement with datasets of COVID-19 active cases and related deaths from Germany and Sri Lanka in early infection, allowing us to hypothesize several unobservable situations in the two countries. Focusing on extending minimum contact radius and reducing the intensity of individual activities, we were able to synthesize the key parameters telling what to practice.