Aerosol Model¶

class archABM.aerosol_model.AerosolModel(params: archABM.parameters.Parameters)[source]¶

Aerosol transmission estimator

name: str¶

params: archABM.parameters.Parameters¶

get_risk(inputs: archABM.parameters.Parameters) → Tuple[float, float][source]¶

Calculate the infection risk of an individual in a room and the CO₂ thrown into the air.

Parameters: inputs (Parameters) – model parameters
Returns: CO₂ concentration (ppm), and infection risk probability
Return type: Tuple[float, float]

Colorado Model¶

class archABM.aerosol_model_colorado.AerosolModelColorado(params)[source]¶

Aerosol transmission estimator

COVID-19 Airborne Transmission Estimator [MNJ+21, PBB+21, PJ21]

The model combines two submodels:

A standard atmospheric box model, which assumes that the emissions are completely mixed across a control volume quickly (such as an indoor room or other space). See for example Chapter 3 of the Jacob Atmos. Chem. textbook [Jac99], and Chapter 21 of the Cooper and Alley Air Pollution Control Engineering Textbook [CA10] for indoor applications. This is an approximation that allows easy calculation, is approximately correct as long as near-field effects are avoided by social distancing, and is commonly used in air quality modeling.
A standard aerosol infection model (Wells-Riley model), as formulated in Miller et al. 2020 [MNJ+21], and references therein [BMS20, BSM20, RMR78].

Important

The propagation of COVID-19 is only by aerosol transmission.

The model is based on a standard model of aerosol disease transmission, the Wells-Riley model. It is calibrated to COVID-19 per recent literature on quanta emission rate.

This is not an epidemiological model, and does not include droplet or contact / fomite transmission, and assumes that 6 ft / 2 m social distancing is respected. Otherwise higher transmission will result.

name: str = 'Colorado'¶

get_risk(inputs: archABM.parameters.Parameters) → Tuple[float, float][source]¶

Calculate the infection risk of an individual in a room and the CO₂ thrown into the air.

Parameters: inputs (Parameters) – model parameters
Returns: CO₂ concentration (ppm), and infection risk probability
Return type: Tuple[float, float]

MIT Model¶

class archABM.aerosol_model_mit.AerosolModelMIT(params)[source]¶

Aerosol transmission estimator

MIT COVID-19 Indoor Safety Guideline [BB21, BKC+21, RBJ+21]

Theoretical model that quantifies the extent to which transmission risk is reduced in large rooms with high air exchange rates, increased for more vigorous respiratory activities, and dramatically reduced by the use of face masks. Consideration of a number of outbreaks yields self-consistent estimates for the infectiousness of the new coronavirus.

name: str = 'MIT'¶

get_risk(inputs: archABM.parameters.Parameters) → Tuple[float, float][source]¶

Calculate the infection risk of an individual in a room and the CO₂ thrown into the air.

Parameters: inputs (Parameters) – model parameters
Returns: CO₂ concentration (ppm), and infection risk probability
Return type: Tuple[float, float]

Max-Planck Model¶

class archABM.aerosol_model_maxplanck.AerosolModelMaxPlanck(params)[source]¶

Aerosol transmission estimator

Model Calculations of Aerosol Transmission and Infection Risk of COVID-19 in Indoor Environments [LHB+20]

An adjustable algorithm to estimate the infection risk for different indoor environments, constrained by published data of human aerosol emissions, SARS-CoV-2 viral loads, infective dose and other parameters. Evaluates typical indoor settings such as an office, a classroom, choir practice, and a reception/party.

The model includes a number of modifiable environmental factors that represent relevant physiological parameters and environmental conditions. For simplicity, all subjects are assumed to be equal in terms of breathing, speaking and susceptibility to infection. The model parameters can be easily adjusted to account for different environmental conditions and activities.

name: str = 'MaxPlanck'¶

get_risk(inputs: archABM.parameters.Parameters) → Tuple[float, float][source]¶

Calculate the infection risk of an individual in a room and the CO₂ thrown into the air.

Parameters: inputs (Parameters) – model parameters
Returns: CO₂ concentration (ppm), and infection risk probability
Return type: Tuple[float, float]

BB21: Martin Z. Bazant and John W. M. Bush. A guideline to limit indoor airborne transmission of covid-19. Proceedings of the National Academy of Sciences, 2021. doi:10.1073/pnas.2018995118.
BKC+21: Martin Z. Bazant, Ousmane Kodio, Alexander E. Cohen, Kasim Khan, Zongyu Gu, and John W. M. Bush. Monitoring carbon dioxide to quantify the risk of indoor airborne transmission of covid-19. medRxiv, 2021. doi:10.1101/2021.04.04.21254903.
BMS20: G. Buonanno, L. Morawska, and L. Stabile. Quantitative assessment of the risk of airborne transmission of sars-cov-2 infection: prospective and retrospective applications. Environment International, 145:106112, 2020. doi:https://doi.org/10.1016/j.envint.2020.106112.
BSM20: G. Buonanno, L. Stabile, and L. Morawska. Estimation of airborne viral emission: quanta emission rate of sars-cov-2 for infection risk assessment. Environment International, 141:105794, 2020. doi:https://doi.org/10.1016/j.envint.2020.105794.
CA10: C David Cooper and Forrest Christopher Alley. Air pollution control: A design approach. Waveland press, 2010.
Jac99: Daniel J. Jacob. Introduction to Atmospheric Chemistry. Princeton University Press, 1999. ISBN 9780691001852. URL: http://www.jstor.org/stable/j.ctt7t8hg.
LHB+20: Jos Lelieveld, Frank Helleis, Stephan Borrmann, Yafang Cheng, Frank Drewnick, Gerald Haug, Thomas Klimach, Jean Sciare, Hang Su, and Ulrich Poschl. Model calculations of aerosol transmission and infection risk of covid-19 in indoor environments. International Journal of Environmental Research and Public Health, 2020. doi:10.3390/ijerph17218114.
MNJ+21(1,2): Shelly L. Miller, William W Nazaroff, Jose L. Jimenez, Atze Boerstra, Giorgio Buonanno, Stephanie J. Dancer, Jarek Kurnitski, Linsey C. Marr, Lidia Morawska, and Catherine Noakes. Transmission of sars-cov-2 by inhalation of respiratory aerosol in the skagit valley chorale superspreading event. Indoor Air, 31(2):314–323, 2021. doi:https://doi.org/10.1111/ina.12751.
PBB+21: Z. Peng, W. Bahnfleth, G. Buonanno, S. J. Dancer, J. Kurnitski, Y. Li, M.G.L.C. Loomans, L.C. Marr, L. Morawska, W. Nazaroff, C. Noakes, X. Querol, C. Sekhar, R. Tellier, T. Greenhalgh, L. Bourouiba, A. Boerstra, J. Tang, S. Miller, and J.L. Jimenez. Indicators for risk of airborne transmission in shared indoor environments and their application to covid-19 outbreaks. medRxiv, 2021. doi:10.1101/2021.04.21.21255898.
PJ21: Zhe Peng and Jose L. Jimenez. Exhaled co2 as a covid-19 infection risk proxy for different indoor environments and activities. Environmental Science & Technology Letters, 8(5):392–397, 2021. doi:10.1021/acs.estlett.1c00183.
RMR78: E. C. RILEY, G. MURPHY, and R. L. RILEY. AIRBORNE SPREAD OF MEASLES IN A SUBURBAN ELEMENTARY SCHOOL. American Journal of Epidemiology, 107(5):421–432, 05 1978. doi:10.1093/oxfordjournals.aje.a112560.
RBJ+21: Michael J. Risbeck, Martin Z. Bazant, Zhanhong Jiang, Young M. Lee, Kirk H. Drees, and Jonathan D. Douglas. Quantifying the tradeoff between energy consumption and the risk of airborne disease transmission for building hvac systems. medRxiv, 2021. doi:10.1101/2021.06.21.21259287.