ASHRAE Journal - July 2023 - 26

COLUMN IEQ APPLICATIONS
Limitations
R₀ assumes a homogeneous population and constant
transmission parameters, which may not be accurate
in real-world settings where populations are often
heterogeneous, and transmission rates may vary over
time.6 It does not account for factors such as the duration
of infectiousness, the presence of asymptomatic
individuals, and varying levels of susceptibility within
the population.7 R₀ can be sensitive to initial conditions
and model assumptions, making it challenging to derive
precise estimates in some cases.7
Quanta of Infection
Definition and Applications
Quanta is an abstract measure of the infectious potential
of an environment. It can be used to model the probability
of infection based on pathogen concentration,
exposure time, and the infectivity of the pathogen.8 The
concept of quanta has been applied to various airborne
infections, such as tuberculosis and COVID-19.1,9
Strengths
Quanta can provide a more detailed understanding of
transmission dynamics by accounting for factors such as
environmental conditions, host susceptibility, and varying
pathogen concentrations.1 It enables the assessment
of the effectiveness of interventions, such as ventilation
systems, air filtration, and personal protective equipment
(PPE), which can help guide the implementation
of targeted control measures.9
Quanta-based models can be used to evaluate the
risk of infection in various settings, such as hospitals,
schools, and public transportation, allowing for betterinformed
decisions about when and where to apply
interventions.1
Limitations
Quantifying quanta requires data on pathogen concentration,
exposure time, and host susceptibility, which
can be challenging to obtain in practice.8 The quanta
concept may not apply to all infectious diseases, particularly
those with non-airborne transmission routes, such
as contact and fecal-oral transmission,1 as well as largedroplet
transmission.8
Quanta-based models may be sensitive to model
assumptions and uncertainties in parameter estimates,
which can limit their accuracy and predictive
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ASHRAE JOURNAL ashrae.o rg
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capabilities.8 These models also typically assume the
transmission environment is well-mixed. Still, these
models are not overly sensitive to the well-mixed
assumption and can produce accurate results even for
large, poorly ventilated regions.10
Integration of R0 and Quanta in Infectious Disease Modeling
Given the strengths and limitations of R₀ and quanta,
integrating these two metrics in infectious disease modeling
can provide a more comprehensive understanding
of disease transmission dynamics.
For example, using R₀ in conjunction with quantabased
models can help identify high-risk settings and
populations, allowing for the design and implementation
of targeted interventions.
Some potential approaches for integrating R₀ and
quanta include:
* Developing multi-scale models that incorporate
both population-level transmission dynamics (captured
by R₀) and individual-level exposure risks (captured by
quanta).1
* Combining R₀-based models with quanta-based
risk assessments to estimate the impact of various interventions
on disease spread and inform public health
decision-making.9
* Using R0-based models to estimate the disease parameters
for quanta-based models. This can bypass the
need to measure pathogen concentrations and infectivity
directly since R0 provides insight into the ratio of
these terms.10
Conclusion and Recommendations
Both R₀ and quanta of infection provide valuable
insights into infectious disease dynamics. However, they
have distinct strengths and limitations, and their applicability
depends on the specific disease and research
question being addressed. A successful approach is to
use R₀ as a preliminary indicator of transmissibility, particularly
when comparing different diseases or emerging
pathogens, and using quanta for a more nuanced
understanding of transmission dynamics, especially in
airborne infections where typical concentrations and
infectivity are well-studied.
Integrating both R₀ and quanta in infectious disease
modeling can help provide a more comprehensive
understanding of disease transmission dynamics and
inform the design and implementation of effective
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ASHRAE Journal - July 2023

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