COVID-19 Viral Loads, Environment, Ventilation, Masks, Exposure Time, And Severity : A Pragmatic Guide Of Estimates

It can be shown that over 94% of COVID-19 superspreading events occurred in limited ventilation areas suggesting aerosolized transmission is a strong contributor to COVID-19 infections. This study helps answer "How long may a person safely remain within various environments?" And "What exposure levels could result in immunity without becoming ill via asymptomatic graduated inoculation?" COVID-19 infection likelihood, symptom severity, and immune response dependencies include viral load exposure amount. A better understanding of these relationships could help determine what Non-Pharmaceutical Interventions (NPI) would help reduce severe case counts and improve at-large epidemiologic responses in specific scenarios. This study references peer reviewed and published studies and uses them as data sources for an estimation model that calculates infection likelihood given exposure within several example scenarios. Information from ASHRAE office ventilation standards, typical home ventilation characteristics, and an outdoor air setting are used to establish several specific examples of indoor and outdoor scenarios. The model establishes a reference scenario using objectively measured air sample viral load concentration levels found within a carefully documented hospital environment containing 2 sick patients. The model extrapolates the reference scenario into several example scenarios that have varied exposure time duration, ventilation amount, with/without surgical mask use, activity/respiration levels, and infected subject shedding levels. It uses the reference data and scenario extrapolations to calculate an estimate of total viral load exposure dose for each scenario. The study then interprets the various scenario total exposure dose estimates using an National Institute of Health human challenge study where volunteers were exposed to multiple specific viral quantities and observed in a clinical environment to objectively determine likelihood of infection, severity level, and immune response given each specific exposure dose. To simplify pragmatic use of the results, each example scenario presents the estimated total exposure dose alongside an intuitive severity category of Not Ill, Minor Illness, Clinical Mild Illness, and Possible Severe Illness which are based on a defined interpretation of the NIH study results. Immune response data related to these categories is also provided along with discussion related to asymptomatic infection, graduated inoculation, and immunity. When appropriately interpreted for individualized applications, the estimates herein could contribute to guidance for those at low-risk for a severe case that have no obvious COVID-19 co-morbidities, with the understanding that those at higher risk should seek to avoid all exposure risk. The estimates herein may help efforts to strike a balance in developing holistic epidemiologic interventions that consider the effects of these interventions on economic, civic, social, and mental health, which have pathologies within their own realms.