• FluTrackers.com Inc. does not provide medical advice. Information on this web site is collected from various internet resources, and the FluTrackers board of directors makes no warranty to the safety, efficacy, correctness or completeness of the information posted on this site by any author or poster. The information collated here is for instructional and/or discussion purposes only and is NOT intended to diagnose or treat any disease, illness, or other medical condition. Every individual reader or poster should seek advice from their personal physician/healthcare practitioner before considering or using any interventions that are discussed on this website. By continuing to access this website you agree to consult your personal physican before using any interventions posted on this website, and you agree to hold harmless FluTrackers.com Inc., the board of directors, the members, and all authors and posters for any effects from use of any medication, supplement, vitamin or other substance, device, intervention, etc. mentioned in posts on this website, or other internet venues referenced in posts on this website.
  • We are not asking for any donations. Do not donate to any entity who says they are raising funds for us.

Cureus . Carbon Dioxide Levels as a Key Indicator for Managing SARS-CoV-2 Airborne Transmission Risks Across 10 Indoor Scenarios

tetano

Editor, Senior Moderator
Cureus


. 2024 Nov 25;16(11):e74429.
doi: 10.7759/cureus.74429. eCollection 2024 Nov. Carbon Dioxide Levels as a Key Indicator for Managing SARS-CoV-2 Airborne Transmission Risks Across 10 Indoor Scenarios

Narumichi Iwamura[SUP] 1 [/SUP], Kanako Tsutsumi[SUP] 1 [/SUP], Takafumi Hamashoji[SUP] 1 [/SUP], Yui Arita[SUP] 1 [/SUP], Takashi Deguchi[SUP] 1 [/SUP]



Affiliations
Abstract

Background The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019 has led to a global pandemic through contact, droplets, and aerosolized particles. Aim This study aimed to quantify the airborne transmission risk of SARS-CoV-2 in various indoor environments. Methods Using indoor carbon dioxide (CO[SUB]2[/SUB]) levels, we estimated the probability of airborne transmission and the basic reproduction number (R[SUB]0[/SUB]) across 10 hypothetical indoor scenarios, including a college classroom, restaurant, classical music concert, live event, city bus, crowded train, hospital room, home, shogi match, and business meeting, using an analysis based on the modified Wells-Riley model. Results The relationship between airborne transmission rates and indoor CO[SUB]2[/SUB] concentrations was visualized with and without the use of masks. Without masks, at an indoor CO[SUB]2[/SUB] concentration of 1,000 ppm, airborne transmission rates were high in a home (100%), business meeting (100%), and hospital room (95%); however, they were moderate in a restaurant (55%), at a shogi match (22%), and at a live concert (21%); and low in a college classroom (1.7%), on a city bus (1.3%), at a classical music concert (1.0%), and on a crowded train (0.25%). In contrast, R[SUB]0[/SUB] was high at a live event (42.3), in a restaurant (15.9), in a home (3.00), and in a hospital room (2.86), indicating a greater risk of cluster infections. An examination of reduced airborne infection risk through surgical mask use and improved ventilation across various scenarios revealed that mask-wearing was highly effective in hospital rooms, in restaurants, at shogi matches, and in live concerts. Ventilation was particularly useful in hospital rooms, in restaurants, and at shogi matches. Discussion and conclusion In all indoor scenarios, a positive linear relationship existed between airborne transmission risk and indoor CO[SUB]2[/SUB] levels. The risk varied markedly across scenarios and was influenced by factors such as mask use, ventilation quality, conversation, and exposure duration. This model indicates that the risk of SARS-CoV-2 airborne transmission can be easily predicted using a CO[SUB]2[/SUB] meter.

Keywords: airborne transmission; co2 monitoring; covid-19; indoor air quality; infection probability; mask efficacy; sars-cov-2; wells–riley model.

 
Back
Top Bottom