In This Issue
Microbiomes of the Built Environment
September 15, 2022 Volume 52 Issue 3
The covid-19 pandemic suddenly directed awareness to potential health impacts of the built environment of everyday living – schools, dwellings, offices, public buildings, and other spaces. This issue explores the “microbiome” of the built environment in the postpandemic reality in terms of ventilation performance, filtration, understanding and quantification of transmission risk, protection of “benign” microbes, and the important role of equity, among others.

Estimating Indoor Microbial Risks as Applied to Covid-19

Tuesday, September 20, 2022

Author: Charles N. Haas

There is no average dose at which the estimated risk to a population is zero, so decision making must incorporate a concept of acceptable residual risk.

The estimation of risk from exposure to microorganisms in the indoor environment is useful to assess the degree to which controls, such as engineering interventions, are needed. Risk assessment is a “[s]ystematic process to comprehend the nature of risk, express and evaluate risk, with the available knowledge” (SRA 2018, p. 8). The objective of this article is to explain how quantitative microbial risk assessment could be applied to understanding and controlling risks from SARS-CoV-2.


The use of risk assessment in enabling decision making for environmental health protection is over 40 years old, since its role was elucidated in 1976 (EPA 1976). By the time of the 1983 National Research Council report, Risk Assessment in the Federal Government (NRC 1983)—often called “the Red Book”—over 150 health risk assessments had been completed (Anderson 1983).

The centrality of risk assessment to management of risks from environmental contaminants is illustrated in figure 1 (NRC 2009). The key technical activities of risk assessment (Stage 2 of Phase II) are hazard identification, dose-response assessment, exposure assessment, and risk characterization. These drive subsequent steps of the process and are discussed below.

Haas fig 1.gif

Contemporaneously with the Red Book, dose-response relationships for key waterborne pathogens were published (Haas 1983). This was then broadened into the area called quantitative microbial risk assessment (QMRA), and systematically presented in a monograph (Haas et al. 2014).

What Is Quantitative Microbial Risk Assessment?

There are at least two key differences between QMRA and the general principles of risk assessment (particularly as applied to most chemical contaminants). The first is that, since exposure to even a small number of organisms in a single event can result in an adverse effect, the component of variability due to the stochastics of small numbers becomes important. This difference is accounted for with the use of mechanistically based dose-response models. The second difference calls for estimating in vivo body burden to understand the kinetics of adverse effects, because pathogens multiply in vivo and can be emitted back into the environment, serving as an additional source of illness. This requires coupling with population-scale disease transmission models, which QMRA takes into account.

QMRA has now been applied to a broad range of venues, routes of exposure to pathogens, and types of microorganisms. The approach is now being applied to assess risks from SARS-CoV-2.

Hazard Identification

Specific pathogen(s) and adverse effects can often be identified from clinical case reports. This was certainly the case with SARS-CoV-2.

However, in some cases, the specific pathogen may not be known, at least initially. The 1976 outbreak of what became known as Legionnaires’ disease was via a previously unidentified bacterium, now called Legionella pneumophila (Fraser et al. 1977). And the phenomenon of Sick Building Syndrome (Nag 2019) may in part be mediated by microorganisms or microbial fragments or metabolites as yet not definitively confirmed.

When a pathogen is not known, it is possible to use indicators for risk assessment such as the presence of coliform or enterococci bacteria, as widely used in the management of swimming exposures in “natural recrea-tional waters” (Federigi et al. 2019).

Emission rates can be
directly measured from infected individuals or indirectly estimated based on microbial concentrations in emitted aerosols.

Dose Response

For infectious microorganisms (rather than the metabolites or toxins they may produce), for a response to occur, the pathogen must evade the host responses to proliferate, colonize at a susceptible site, and produce adverse effects in vivo. All infectious pathogens for which dose-response relationships have been developed[1] can be described by two simple models, exponential or beta Poisson, that incorporate probabilistic host-pathogen survival probabilities (Haas et al. 2014). These models, in which dose is regarded as the average exposure to a population, have two significant properties:

  • There is no average dose at which the estimated risk to a population is zero. Decision making must therefore either directly or indirectly incorporate a concept of acceptable residual risk.
  • At low dose, there is a linear relationship between dose and risk.

The exponential dose-response relationship is functionally equivalent to the more empirical Wells-Riley approach long used in indoor air infectious disease analysis (Sze To and Chao 2010). Prior to covid-19, these models were demonstrated as suitable for many other pathogens potentially transmissible by the respiratory route, including:

  • Bacteria: Legionella pneumophila, the causative agent of Legionnaires’ disease (Armstrong and Haas 2008); nontuberculosis Mycobacterium (Hamilton et al. 2017)
  • Viruses: Influenza (Watanabe et al. 2012), rhinovirus and respiratory syncytial virus (Jones and Su 2015)
  • Protozoa: Naegleria (Rasheduzzaman et al. 2019).

Following the SARS-1 outbreaks in 2003, dose-response models were developed for analogs to that coronavirus (Watanabe et al. 2010). The best models were in exponential form. For SARS-CoV-2, the routes of exposure are inhalation and (to a lesser degree) -omite exposure.[2]

By adjusting one parameter in the exponential -model to “anchor” it to the early SARS-CoV-2 cluster at a restaurant in Guangzhou (Xie et al. 2020), a revised model was found to be sufficient to explain attack rates in a variety of other clusters (Parhizkar et al. 2021). It remains to be seen whether this updated model for SARS-CoV-2 produces results that remain true for more recent and future variants of the virus. However, this does indicate that the virus responsible for covid-19 is amenable to dose-response modeling.

Exposure Assessment

Exposure assessment relies on direct measurements of pathogens at the point of exposure; modeling of the dose based on source emissions, transport, and decay; or a combination of these approaches. In this issue the article by Stephens and colleagues (2022) outlines model-ing and measurements of pathogens relevant to the indoor environment.

Emission rates are a function of the pathogens themselves. They can be directly measured from infected individuals (Coleman et al. 2022) or indirectly estimated based on microbial concentrations in emitted aerosols (Riediker and Tsai 2020).

One issue with exposure assessment of pathogens is that often data are obtained (or modeled) based on gene copies (RNA or DNA) of the pathogen. These are not synonymous with the occurrence of the viable infectious agent, and the ratio between gene copies and viable infectious organisms may depend on analytical methods and on intrinsic differences in persistence of the nucleic acid and the viable organism. This is an area where further research, for both SARS-CoV-2 and other pathogens, is needed. A QMRA may still be done, however, by incorporating probability distributions for this ratio (Pitol and Julian 2021).

Risk Characterization

Risk characterization is the integration of information from dose-response and exposure assessments to provide overall estimates of risk with attendant uncertainties. As shown in figure 1, it represents an interface to risk management and so should be presented in a way that is useful and actionable for decision makers.

There are many inputs to an exposure assessment, each with elements of uncertainty and variability. In dose response, there is typically uncertainty in the parameters of the exponential or beta-Poisson model. There may be additional uncertainties, such as in the ratio of viable infectious organisms to gene copies of organisms. These various elements of uncertainty and variability can be described by probability distributions.

To obtain the risk characterization with corresponding uncertainties a Monte Carlo approach can be used to sample repeatedly from each of the input distributions and “build up” the resultant distribution for estimated risk. Good practices for use of this method have been developed and are broadly accepted (Burmaster and Anderson 1994).

With current hardware and software, it is straightforward and rapid to run 10,000 or 100,000 trials to achieve a good prediction. As examples of risk characterization, two recent studies on covid-19 have been published, described below.

Early in the covid-19 outbreak, one of the routes of transmission was thought to be fomites (see Haas and Loftness in this issue). A comprehensive risk assessment of fomite exposure (primarily based on data collected during the early months of the outbreak with the original virus strain) indicated that exposure via the fomite route posed only a minor risk and did not justify -special cleaning procedures for surface decontamination of SARS-CoV-2 (Pitol and Julian 2021).

More recently, a comprehensive air and surface sampling program for SARS-CoV-2 nucleic acid was conducted on a university campus to compare risks from inhalation and fomite exposure (Zhang et al. 2022). The authors coupled the experimental measurements with other data in a Monte Carlo assessment and concluded that inhalation risk was orders of magnitude more significant than fomites.

Risk characterization is the integration of information from dose-response and exposure assessments
to estimate risk with
attendant uncertainties.

Advances Needed

QMRA is clearly useful in efforts to understand and characterize microbial risks in the indoor environment, including from SARS-CoV-2. However, there remain areas where this approach could advance. These include the following:

  • The exponential and beta-Poisson models describe the proportion adversely affected. The applicability of dynamic models incorporating time to effect and internal host immune response needs further exploration (Haas 2015). It may be that, by incorporating factors of the dynamics of host response (Pujol et al. 2009), impacts of multiple exposure events could be more accurately assessed.
  • Populations exposed are heterogeneous, both intrinsically and via factors influencing exposure (e.g., differential activity patterns). These patterns can be coupled with dose response to develop overall risk models. For example, differential mobility patterns might be assessed using mobile phone geolocation data, with suitable privacy protections (Grantz et al. 2020). Variations in the angiotensin-converting enzyme 2 (ACE2) receptor may modulate susceptibility to -covid-19 (SeyedAlinaghi et al. 2021), and other genetic determinants of susceptibility may be elucidated. Such information can be used in elaborations of dose response to incorporate intrinsic differential infectivity in subpopulations (such as by genetic factors or concurrent environmental exposures) in quantitative models.
  • Covid-19 is clearly a contagious disease whose transmission is mediated by the environment. There is a large literature on disease transmission models, including the incorporation of intermediate environmental stages, such as survival in air or on surfaces (Li et al. 2009). But work is needed to incorporate dose-response models (most likely in their dynamic forms) in these transmission models. For example, relationships for the infection rate and incubation parameters in disease transmission models (Godio et al. 2020) could be developed from static and dynamic dose-response data, as suggested for other pathogens (Prasad et al. 2017).


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[1]  Notably absent are dose-response models for fungi. These organisms are known to be important, and this represents a considerable data gap (Weiskerger and Brandão 2020).

[2]  For other pathogens, dose-response models are available for ingestion, dermal, and other routes.

About the Author:Charles Haas (NAE) is the LD Betz Professor of Environmental Engineering, Drexel University.