Research gaps and priorities for quantitative microbial risk assessment (QMRA).

Publisher: Blackwell Publishers Country of Publication: United States NLM ID: 8109978 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1539-6924 (Electronic) Linking ISSN: 02724332 NLM ISO Abbreviation: Risk Anal Subsets: MEDLINE

Publication: 2002- : Malden, MA : Blackwell Publishers
Original Publication: New York : Plenum Press, c1981-

COVID-19*/prevention & control; Risk Assessment/methods ; Humans ; Public Health ; SARS-CoV-2 ; Pandemics ; Research ; Evidence Gaps

The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.
(© 2024 The Author(s). Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis.)

Aboelela, S. W., Larson, E., Bakken, S., Carrasquillo, O., Formicola, A., Glied, S. A., Haas, J., & Gebbie, K. M. (2007). Defining interdisciplinary research: Conclusions from a critical review of the literature. Health Services Research, 42(1 Pt 1), 329–346. https://doi.org/10.1111/j.1475‐6773.2006.00621.x.
Adhikari, U., Chabrelie, A., Weir, M., Boehnke, K., McKenzie, E., Ikner, L., Wang, M., Wang, Q., Young, K., Haas, C. N., Rose, J., & Mitchell, J. (2019). A case study evaluating the risk of infection from middle eastern respiratory syndrome coronavirus (MERS‐CoV) in a hospital setting through bioaerosols. Risk Analysis, 39(12), 2608–2624. https://doi.org/10.1111/risa.13389.
Amoueyan, E., Ahmad, S., Eisenberg, J. N. S., Pecson, B., & Gerrity, D. (2017). Quantifying pathogen risks associated with potable reuse: A risk assessment case study for Cryptosporidium. Water Research, 119, 252–266. https://doi.org/10.1016/j.watres.2017.04.048.
Andrade‐Mogrovejo, D. A., Gonzales‐Gustavson, E., Ho‐Palma, A. C., Prada, J. M., Bonnet, G., Pizzitutti, F., Gomez‐Puerta, L. A., Arroyo, G., O'Neal, S. E., Garcia, H. H., Guitian, J., Gonzalez, A., & Cysticercosis Working Group in Peru. (2022). Development of a dose–response model for porcine cysticercosis. PLoS ONE, 17(3), e0264898. https://doi.org/10.1371/journal.pone.0264898.
Arden, S., Morelli, B., Schoen, M., Cashman, S., Jahne, M., Ma, X. (Cissy), & Garland, J. (2020). Human health, economic and environmental assessment of onsite non‐potable water reuse systems for a large, mixed‐use urban building. Sustainability, 12(13), 13. https://doi.org/10.3390/su12135459.
Arnold, B. F., Schiff, K. C., Ercumen, A., Benjamin‐Chung, J., Steele, J. A., Griffith, J. F., Steinberg, S. J., Smith, P., McGee, C. D., Wilson, R., Nelsen, C., Weisberg, S. B., & Colford, J. M. (2017). Acute illness among surfers after exposure to seawater in dry‐ and wet‐weather conditions. American Journal of Epidemiology, 186(7), 866–875. https://doi.org/10.1093/aje/kwx019.
Ashbolt, N. J., Schoen, M. E., Soller, J. A., & Roser, D. J. (2010). Predicting pathogen risks to aid beach management: The real value of quantitative microbial risk assessment (QMRA). Water Research, 44(16), 4692–4703. https://doi.org/10.1016/j.watres.2010.06.048.
Aven, T. (2016). Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research, 253(1), 1–13. https://doi.org/10.1016/j.ejor.2015.12.023.
Beatrice, F., & Calleja, N. (2021). Early warning indicators of COVID‐19 burden for a prosilient European pandemic response. European Journal of Public Health, 31(Suppl4), iv21–iv26. https://doi.org/10.1093/eurpub/ckab154.
Bergion, V., Lindhe, A., Sokolova, E., & Rosén, L. (2018). Risk‐based cost‐benefit analysis for evaluating microbial risk mitigation in a drinking water system. Water Research, 132, 111–123. https://doi.org/10.1016/j.watres.2017.12.054.
Boehm, A. B., & Soller, J. A. (2020). Refined ambient water quality thresholds for human‐associated fecal indicator HF183 for recreational waters with and without co‐occurring gull fecal contamination. Microbial Risk Analysis, 16, 100139. https://doi.org/10.1016/j.mran.2020.100139.
Brouwer, A. F., Weir, M. H., Eisenberg, M. C., Meza, R., & Eisenberg, J. N. S. (2017). Dose–response relationships for environmentally mediated infectious disease transmission models. PLoS Computational Biology, 13(4), e1005481. https://doi.org/10.1371/journal.pcbi.1005481.
Bundesinstitut für Risikobewertung. (2023). Predictive microbial modelling and QMRA software directory—FoodRisk‐labs. Bundesinstitut für Risikobewertung. https://foodrisklabs.bfr.bund.de/predictive‐microbial‐modelling‐and‐qmra‐software‐directory/.
Burch, T. R. (2020). Outbreak‐based Giardia dose–response model using Bayesian hierarchical Markov chain Monte Carlo analysis. Risk Analysis, 40(4), 705–722. https://doi.org/10.1111/risa.13436.
Burch, T. R., Spencer, S. K., Stokdyk, J. P., Kieke, B. A., Larson, R. A., Firnstahl, A. D., Rule, A. M., & Borchardt, M. A. (2017). Quantitative microbial risk assessment for spray irrigation of dairy manure based on an empirical fate and transport model. Environmental Health Perspectives, 125(8), 087009. https://doi.org/10.1289/EHP283.
California State Water Resources Control Board. (2022). Division of drinking water response to “expert panel preliminary findings, recommendations, and comments on draft DPR criteria (dated August 17, 2021)” in the memorandum of findings submitted by NWRI dated March 14, 2022. California State Water Resources Control Board. https://www.waterboards.ca.gov/drinking_water/certlic/drinkingwater/documents/direct_potable_reuse/ddw_ep_draftmemo.pdf.
Capone, D., Adriano, Z., Cumming, O., Irish, S. R., Knee, J., Nala, R., & Brown, J. (2023). Urban onsite sanitation upgrades and synanthropic flies in Maputo, Mozambique: Effects on enteric pathogen infection risks. Environmental Science & Technology, 57(1), 549–560. https://doi.org/10.1021/acs.est.2c06864.
Chabrelie, A., Mitchell, J., Rose, J., Charbonneau, D., & Ishida, Y. (2018). Evaluation of the influenza risk reduction from antimicrobial spray application on porous surfaces. Risk Analysis, 38(7), 1502–1517. https://doi.org/10.1111/risa.12952.
Collineau, L., Boerlin, P., Carson, C. A., Chapman, B., Fazil, A., Hetman, B., McEwen, S. A., Parmley, E. J., Reid‐Smith, R. J., Taboada, E. N., & Smith, B. A. (2019). Integrating whole‐genome sequencing data into quantitative risk assessment of foodborne antimicrobial resistance: A review of opportunities and challenges. Frontiers in Microbiology, 10, 1–18. https://doi.org/10.3389/fmicb.2019.01107.
Collineau, L., Phillips, C., Chapman, B., Agunos, A., Carson, C., Fazil, A., Reid‐Smith, R. J., & Smith, B. A. (2020). A within‐flock model of Salmonella heidelberg transmission in broiler chickens. Preventive Veterinary Medicine, 174, 104823. https://doi.org/10.1016/j.prevetmed.2019.104823.
Crank, K., Petersen, S., & Bibby, K. (2019). Quantitative microbial risk assessment of swimming in sewage impacted waters using crassphage and pepper mild mottle virus in a customizable model. Environmental Science & Technology Letters, 6(10), 571–577. https://doi.org/10.1021/acs.estlett.9b00468.
De Angelis, D., Presanis, A. M., Birrell, P. J., Tomba, G. S., & House, T. (2015). Four key challenges in infectious disease modelling using data from multiple sources. Epidemics, 10, 83–87. https://doi.org/10.1016/j.epidem.2014.09.004.
Delignette‐Muller, M., & Dutang, C. (2015). Fitdistrplus: An R package for fitting distributions. Journal of Statistical Software, 64(4), 1–34.
Diamond, M. B., Keshaviah, A., Bento, A. I., Conroy‐Ben, O., Driver, E. M., Ensor, K. B., Halden, R. U., Hopkins, L. P., Kuhn, K. G., Moe, C. L., Rouchka, E. C., Smith, T., Stevenson, B. S., Susswein, Z., Vogel, J. R., Wolfe, M. K., Stadler, L. B., & Scarpino, S. V. (2022). Wastewater surveillance of pathogens can inform public health responses. Nature Medicine, 28(10), 10. https://doi.org/10.1038/s41591‐022‐01940‐x.
Drechsel, P., & Seidu, R. (2011). Cost‐effectiveness of options for reducing health risks in areas where food crops are irrigated with treated or untreated wastewater. Water International, 36(4), 535–548. https://doi.org/10.1080/02508060.2011.594549.
Enger, K. S., Nelson, K. L., Clasen, T., Rose, J. B., & Eisenberg, J. N. S. (2012). Linking quantitative microbial risk assessment and epidemiological data: Informing safe drinking water trials in developing countries. Environmental Science & Technology, 46(9), 5160–5167. https://doi.org/10.1021/es204381e.
Evers, E. G., & Bouwknegt, M. (2016). Combining QMRA and epidemiology to estimate campylobacteriosis incidence. Risk Analysis: An Official Publication of the Society for Risk Analysis, 36(10), 1959–1968. https://doi.org/10.1111/risa.12538.
Gambhir, M., Bozio, C., O'Hagan, J. J., Uzicanin, A., Johnson, L. E., Biggerstaff, M., & Swerdlow, D. L. (2015). Infectious disease modeling methods as tools for informing response to novel influenza viruses of unknown pandemic potential. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America, 60(Suppl1), S11–S19. https://doi.org/10.1093/cid/civ083.
Gupta, M., & Haas, C. N. (2004). The Milwaukee Cryptosporidium outbreak: Assessment of incubation time and daily attack rate. Journal of Water and Health, 2(2), 59–69. https://doi.org/10.2166/wh.2004.0006.
Gurian, P. L., Galada, H., Joe, A., Kumar, A., Olson, B., Richter, E., Teng, J., Zhang, H., Xagoraraki, I., Casman, E., Gerba, C., & Pepper, I. (2012). Site specific risk assessment tools for land applied biosolids. WERF. https://iwaponline.com/ebooks/book/635/.
Guzmán Barragán, B. L., de Souza Lauretto, M., Razzolini, M. T. P., Nardocci, A. C., & Marimón Sibaja, K. V. (2022). Assessment microbial risks for the presence of Cryptosporidium spp. And Giardia spp. Based on the surveillance of the water supply systems in Colombia, 2014–2018. Water Environment Research, 94(8), e10776. https://doi.org/10.1002/wer.10776.
Haas, C. N., Rose, J. B., & Gerba, C. P. (1999). Quantitative microbial risk assessment. John Wiley & Sons.
Haas, C. N., Rose, J. B., & Gerba, C. P. (2014). Quantitative microbial risk assessment (2nd ed.). Wiley Blackwell; Scopus. https://doi.org/10.1002/9781118910030.
Hamilton, K. A., Ahmed, W., Toze, S., & Haas, C. N. (2017). Human health risks for Legionella and Mycobacterium avium complex (MAC) from potable and non‐potable uses of roof‐harvested rainwater. Water Research, 119, 288–303. https://doi.org/10.1016/j.watres.2017.04.004.
Hamilton, K. A., Chen, A., de‐Graft Johnson, E., Gitter, A., Kozak, S., Niquice, C., Zimmer‐Faust, A. G., Weir, M. H., Mitchell, J., & Gurian, P. L. (2018). Salmonella risks due to consumption of aquaculture‐produced shrimp. Microbial Risk Analysis, 9, 22–32. https://doi.org/10.1016/j.mran.2018.04.001.
Hamilton, K. A., Weir, M. H., & Haas, C. N. (2017). Dose response models and a quantitative microbial risk assessment framework for the Mycobacterium avium complex that account for recent developments in molecular biology, taxonomy, and epidemiology. Water Research, 109, 310–326. https://doi.org/10.1016/j.watres.2016.11.053.
Hayashi, M. A. L., Eisenberg, M. C., & Eisenberg, J. N. S. (2019). Linking decision theory and quantitative microbial risk assessment: Tradeoffs between compliance and efficacy for waterborne disease interventions. Risk Analysis, 39(10), 2214–2226. https://doi.org/10.1111/risa.13381.
Head, M. G., Brown, R. J., Newell, M.‐L., Scott, J. A. G., Batchelor, J., & Atun, R. (2020). The allocation of US$105 billion in global funding from G20 countries for infectious disease research between 2000 and 2017: A content analysis of investments. The Lancet Global Health, 8(10), e1295–e1304. https://doi.org/10.1016/S2214‐109X(20)30357‐0.
Heida, A., Mraz, A., Hamilton, M. T., Weir, M. H., & Hamilton, K. A. (2022). Computational framework for evaluating risk trade‐offs in costs associated with legionnaires’ disease risk, energy, and scalding risk for hospital hot water systems. Environmental Science: Water Research & Technology, 8(1), 76–97. https://doi.org/10.1039/D1EW00397F.
Hong, T., & Gurian, P. L. (2015). Updating a B. anthracis risk model with field data from a bioterrorism incident. Environmental Science & Technology, 49(11), 6701–6711. https://doi.org/10.1021/acs.est.5b00010.
Hultquist, B. (2016). Basis for California's 12‐10‐10 log removal requirements. In 20th Annual Water Reuse and Desalination Research Conference.
Jahne, M. A., Rogers, S. W., Holsen, T. M., & Grimberg, S. J. (2015). Quantitative microbial risk assessment of bioaerosols from a manure application site. Aerobiologia, 31(1), 73–87. https://link.springer.com/article/10.1007/s10453‐014‐9348‐0.
Jahne, M. A., Schoen, M. E., Garland, J. L., & Ashbolt, N. J. (2017). Simulation of enteric pathogen concentrations in locally‐collected greywater and wastewater for microbial risk assessments. Microbial Risk Analysis, 5, 44–52. https://doi.org/10.1016/j.mran.2016.11.001.
Jahne, M. A., Schoen, M. E., Kaufmann, A., Pecson, B. M., Olivieri, A., Sharvelle, S., Anderson, A., Ashbolt, N. J., & Garland, J. L. (2023). Enteric pathogen reduction targets for onsite non‐potable water systems: A critical evaluation. Water Research, 233, 119742. https://doi.org/10.1016/j.watres.2023.119742.
Karanth, S., & Pradhan, A. K. (2023). Development of a novel machine learning‐based weighted modeling approach to incorporate Salmonella enterica heterogeneity on a genetic scale in a dose–response modeling framework. Risk Analysis, 43(3), 440–450. https://doi.org/10.1111/risa.13924.
Karanth, S., Tanui, C. K., Meng, J., & Pradhan, A. K. (2022). Exploring the predictive capability of advanced machine learning in identifying severe disease phenotype in Salmonella enterica. Food Research International, 151, 110817. https://doi.org/10.1016/j.foodres.2021.110817.
Li, S., Eisenberg, J. N. S., Spicknall, I. H., & Koopman, J. S. (2009). Dynamics and control of infections transmitted from person to person through the environment. American Journal of Epidemiology, 170(2), 257–265. https://doi.org/10.1093/aje/kwp116.
Machdar, E., van der Steen, N. P., Raschid‐Sally, L., & Lens, P. N. L. (2013). Application of quantitative microbial risk assessment to analyze the public health risk from poor drinking water quality in a low income area in Accra, Ghana. Science of The Total Environment, 449, 134–142. https://doi.org/10.1016/j.scitotenv.2013.01.048.
McBride, G. B., Stott, R., Miller, W., Bambic, D., & Wuertz, S. (2013). Discharge‐based QMRA for estimation of public health risks from exposure to stormwater‐borne pathogens in recreational waters in the United States. Water Research, 47(14), 5282–5297. https://doi.org/10.1016/j.watres.2013.06.001.
Mitchell‐Blackwood, J., Gurian, P. L., & O'Donnell, C. (2011). Finding risk‐based switchover points for response decisions for environmental exposure to Bacillus anthracis. Human and Ecological Risk Assessment: An International Journal, 17(2), 489–509. https://doi.org/10.1080/10807039.2011.552401.
Mokhtari, A., & Doren, J. M. (2019). An agent‐based model for pathogen persistence and cross‐contamination dynamics in a food facility. Risk Analysis, 39(5), 992–1021. https://doi.org/10.1111/risa.13215.
Morgan, M. G., Henrion, M., & Small, M. (1990). Uncertainty: A guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press.
Mraz, A. L., Nappier, S., Haas, C. N., & Weir, M. H. (2020). Dose response models for Eastern US, Western US and Venezuelan equine encephalitis viruses in mice—Part I: Standard dose response model and inference of host age. Microbial Risk Analysis, 14, 100087. https://doi.org/10.1016/j.mran.2019.100087.
Mraz, A. L., & Weir, M. H. (2018). Knowledge to predict pathogens: Legionella pneumophila lifecycle critical review Part I uptake into host cells. Water, 10(2), 2. https://doi.org/10.3390/w10020132.
Naughton, C. C., Holm, R. H., Lin, N. J., James, B. P., & Smith, T. (2023a). Online dashboards for SARS‐CoV‐2 wastewater data need standard best practices: an environmental health communication agenda. Journal of water and health, 21(5), 615–624.
Naughton, C. C., Roman, F. A., Alvarado, A. G. F., Tariqi, A. Q., Deeming, M. A., Kadonsky, K. F., Bibby, K., Bivins, A., Medema, G., Ahmed, W., & Katsivelis, P. (2023b). Show us the data: global COVID‐19 wastewater monitoring efforts, equity, and gaps. FEMS microbes, 4.
Natural Resource Management Ministerial Council (NRMMC), Environment Protection and Heritage Council, & Australian Health Ministers’ Conference. (2006). National guidelines for water recycling: Managing health and environmental risks (phase 1). Natural Resource Management Ministerial Council (NRMMC), Environment Protection and Heritage Council. http://www.ephc.gov.au/pdf/water/WaterRecyclingGuidelines‐02_Nov06_.pdf.
Olivieri, A. W., Crook, J., Anderson, M. A., Bull, R. J., Drewes, J. E., Haas, C. N., Jakubowski, W., McCarty, P. L., Nelson, K. L., Rose, J. B., & Sedlak, D. L. (2016). Evaluation of the feasibility of developing uniform water recycling criteria for direct potable reuse. California State Water Resources Control Board, Fountain Valley, CA.
Opsteegh, M., Prickaerts, S., Frankena, K., & Evers, E. G. (2011). A quantitative microbial risk assessment for meatborne Toxoplasma gondii infection in The Netherlands. International Journal of Food Microbiology, 150(2), 103–114. https://doi.org/10.1016/j.ijfoodmicro.2011.07.022.
Parhizkar, H., Van Den Wymelenberg, K. G., Haas, C. N., & Corsi, R. L. (2022). A quantitative risk estimation platform for indoor aerosol transmission of COVID‐19. Risk Analysis, 42(9), 2075–2088.
Petterson, S. R. (2016). Application of a QMRA framework to inform selection of drinking water interventions in the developing context. Risk Analysis, 36(2), 203–214. https://doi.org/10.1111/risa.12452.
Pluchino, A., Biondo, A. E., Giuffrida, N., Inturri, G., Latora, V., Le Moli, R., Rapisarda, A., Russo, G., & Zappalà, C. (2021). A novel methodology for epidemic risk assessment of COVID‐19 outbreak. Scientific Reports, 11(1), 1. https://doi.org/10.1038/s41598‐021‐82310‐4.
Pouillot, R., & Delignette‐Muller, M. L. (2010). Evaluating variability and uncertainty separately in microbial quantitative risk assessment using two R packages. International Journal of Food Microbiology, 142(3), 330–340. https://doi.org/10.1016/j.ijfoodmicro.2010.07.011.
Prasad, B., Ryan, M. O., & Haas, C. N. (2017). A method for incorporating a time‐dose–response model into a Giardia lamblia outbreak. Journal of Water and Health, 15(4), 490–504. https://doi.org/10.2166/wh.2017.001.
Regli, S., Rose, J. B., Haas, C. N., & Gerba, C. P. (1991). Modeling the risk from Giardia and viruses in drinking water. Journal—American Water Works Association, 83(11), 76–84. https://doi.org/10.1002/j.1551‐8833.1991.tb07252.x.
Reynolds, K. A., Verhougstraete, M. P., Mena, K. D., Sattar, S. A., Scott, E. A., & Gerba, C. P. (2022). Quantifying pathogen infection risks from household laundry practices. Journal of Applied Microbiology, 132(2), 1435–1448. https://doi.org/10.1111/jam.15273.
Rocha‐Melogno, L., Crank, K., Bibby, K., Gray, G., & Deshusses, M. (2021). Aerosol‐Mediated Infectious Disease Risk Assessments (AMIDRA). https://rapidqmra.shinyapps.io/Rapid_QMRA/.
Rosen, M. B., Pokhrel, L. R., & Weir, M. H. (2017). A discussion about public health, lead and Legionella pneumophila in drinking water supplies in the United States. Science of the Total Environment, 590, (Suppl C), 843–852. https://doi.org/10.1016/j.scitotenv.2017.02.164.
Ryan, M. O., Haas, C. N., Gurian, P. L., Gerba, C. P., Panzl, B. M., & Rose, J. B. (2014). Application of quantitative microbial risk assessment for selection of microbial reduction targets for hard surface disinfectants. American Journal of Infection Control, 42(11), 1165–1172. https://doi.org/10.1016/j.ajic.2014.07.024.
Salveson, A., & Soller, J. (2019). Widespread potable reuse requires thorough understanding of water quality and public health challenges. Journal AWWA, 111(7), 78–80. https://doi.org/10.1002/awwa.1328.
Schijven, J. F., van Veen, T., Delmaar, C., Kos, J., Vermeulen, L., Roosien, R., Verhoeven, F., Schipper, M., Peerlings, B., Duizer, E., Derei, J., Lammen, W., Bartels, O., van der Ven, H., Maas, R., & de Roda Husman, A. M. (2023). Quantitative microbial risk assessment of contracting COVID‐19 derived from measured and simulated aerosol particle transmission in aircraft cabins. Environmental Health Perspectives, 131(8), 087011. https://doi.org/10.1289/EHP11495.
Schmidt, P. J. (2015). Norovirus dose–Response: Are currently available data informative enough to determine how susceptible humans are to infection from a single virus? Risk Analysis: An Official Publication of the Society for Risk Analysis, 35(7), 1364–1383. https://doi.org/10.1111/risa.12323.
Schoen, M. E., Peckham, T. K., Shirai, J. H., Kissel, J. C., Thapaliya, D., Smith, T. C., & Meschke, J. S. (2020). Risk of nasal colonization of methicillin‐resistant Staphylococcus aureus during preparation of contaminated retail pork meat. Microbial Risk Analysis, 16, 100136. https://doi.org/10.1016/j.mran.2020.100136.
Schoen, M. E., Xue, X., Hawkins, T. R., & Ashbolt, N. J. (2014). Comparative human health risk analysis of coastal community water and waste service options. Environmental Science & Technology, 48(16), 9728–9736. https://doi.org/10.1021/es501262p.
Siettos, C. I., & Russo, L. (2013). Mathematical modeling of infectious disease dynamics. Virulence, 4(4), 295–306. https://doi.org/10.4161/viru.24041.
Sinclair, M., O'Toole, J., Gibney, K., & Leder, K. (2015). Evolution of regulatory targets for drinking water quality. Journal of Water and Health, 13(2), 413–426. https://doi.org/10.2166/wh.2014.242.
S McClary‐Gutierrez, J., Aanderud, Z. T., Al‐faliti, M., Duvallet, C., Gonzalez, R., Guzman, J., Holm, R. H., Jahne, M. A., Kantor, R. S., Katsivelis, P., Gaardbo Kuhn, K., Langan, L. M., Mansfeldt, C., McLellan, S. L., Grijalva, L. M. M., Murnane, K. S., Naughton, C. C., Packman, A. I., Paraskevopoulos, S., … Vela, J. D. (2021). Standardizing data reporting in the research community to enhance the utility of open data for SARS‐CoV‐2 wastewater surveillance. Environmental Science: Water Research & Technology, 7(9), 1545–1551. https://doi.org/10.1039/D1EW00235J.
Soller, J. A., Eftim, S., Wade, T. J., Ichida, A. M., Clancy, J. L., Johnson, T. B., Schwab, K., Ramirez‐Toro, G., Nappier, S., & Ravenscroft, J. E. (2016). Use of quantitative microbial risk assessment to improve interpretation of a recreational water epidemiological study. Microbial Risk Analysis, 1, 2–11. https://doi.org/10.1016/j.mran.2015.04.001.
Soller, J. A., Schoen, M., Steele, J. A., Griffith, J. F., & Schiff, K. C. (2017). Incidence of gastrointestinal illness following wet weather recreational exposures: Harmonization of quantitative microbial risk assessment with an epidemiologic investigation of surfers. Water Research, 121, 280–289. https://doi.org/10.1016/j.watres.2017.05.017.
Tang, L., Zhou, Y., Wang, L., Purkayastha, S., Zhang, L., He, J., Wang, F., & Song, P. X.‐K. (2020). A review of multi‐compartment infectious disease models. International Statistical Review, 88(2), 462–513. https://doi.org/10.1111/insr.12402.
Tanui, C. K., Karanth, S., Njage, P. M. K., Meng, J., & Pradhan, A. K. (2022). Machine learning‐based predictive modeling to identify genotypic traits associated with Salmonella enterica disease endpoints in isolates from ground chicken. Lebensmittel‐Wissenschaft & Technologie, 154(15), 112701.
Teunis, P. F. M., & Havelaar, A. H. (2000). The beta Poisson dose–response model is not a single‐hit model. Risk Analysis, 20(4), 513–520. https://doi.org/10.1111/0272‐4332.204048.
Teunis, P. F. M., Ogden, I. D., & Strachan, N. J. C. (2008). Hierarchical dose response of E. coli O157:H7 from human outbreaks incorporating heterogeneity in exposure. Epidemiology and Infection, 136(6), 761–770. https://doi.org/10.1017/S0950268807008771.
Van Abel, N., Schoen, M. E., Kissel, J. C., & Meschke, J. S. (2017). Comparison of risk predicted by multiple norovirus dose–response models and implications for quantitative microbial risk assessment: Comparison of risk predicted by multiple norovirus dose–response models. Risk Analysis, 37(2), 245–264. https://doi.org/10.1111/risa.12616.
Verbyla, M. E., Pitol, A. K., Navab‐Daneshmand, T., Marks, S. J., & Julian, T. R. (2019). Safely managed hygiene: A risk‐based assessment of handwashing water quality. Environmental Science & Technology, 53(5), 2852–2861. https://doi.org/10.1021/acs.est.8b06156.
Watanabe, T., Bartrand, T. A., Weir, M. H., Omura, T., & Haas, C. N. (2010). Development of a dose–response model for SARS coronavirus. Risk Analysis, 30(7), 1129–1138. https://doi.org/10.1111/j.1539‐6924.2010.01427.x.
Weir, M. H. (2020). A data simulation method to optimize a mechanistic dose–response model for viral loads of hepatitis A. Microbial Risk Analysis, 15, 100102. https://doi.org/10.1016/j.mran.2019.100102.
Weir, M. H., Mitchell, J., Flynn, W., & Pope, J. M. (2017). Development of a microbial dose response visualization and modelling application for QMRA modelers and educators. Environmental Modelling & Software, 88, 74–83. https://doi.org/10.1016/j.envsoft.2016.11.011.
World Health Organization. (2023). Global research and innovation for health emergencies: Building the world's resilience against future outbreaks and pandemics. WHO. https://cdn.who.int/media/docs/default‐source/documents/r‐d‐blueprint‐meetings/global‐research‐and‐innovation‐for‐health‐emergencies_report‐2023.pdf?sfvrsn=9341366_2.
World Health Organization. (2024). Research response to pathogen X during a pandemic. WHO. https://www.who.int/news‐room/events/detail/2024/01/19/default‐calendar/Research‐response‐to‐pathogen‐X‐during‐a‐pandemic.
Wigginton, K., Lahr, R., Bardha, A., & Rockey, N. (2021). Feasibility of collecting pathogens in wastewater during outbreaks (4990). Water Research Foundation.
World Health Organization. (2006). Guidelines for the safe use of wastewater, excreta and greywater in agriculture and aquaculture. World Health Organization. https://apps.who.int/iris/handle/10665/78265.
Xiao, S., An, W., Chen, Z., Zhang, D., Yu, J., & Yang, M. (2012). The burden of drinking water‐associated cryptosporidiosis in China: The large contribution of the immunodeficient population identified by quantitative microbial risk assessment. Water Research, 46(13), 4272–4280. https://doi.org/10.1016/j.watres.2012.05.012.
Zhang, X., Ji, Z., Yue, Y., Liu, H., & Wang, J. (2020). Infection risk assessment of COVID‐19 through aerosol transmission: A case study of South China seafood market. Environmental Science & Technology, 55(7), 4123–4133. https://doi.org/10.1021/acs.est.0c02895.

R25 GM135058 United States GM NIGMS NIH HHS; R25GM135058-01 QMRA-IV

Keywords: coronavirus disease 2019; environmental health; pathogens; quantitative microbial risk assessment; risk analysis; safety

Date Created: 20240521 Date Completed: 20241109 Latest Revision: 20241114

20241114

10.1111/risa.14318

38772724