Extension of probability models of the risk of infections by human enteric viruses

Costantino Masciopinto; Costantino Masciopinto

doi:10.3934/mbe.2023777

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 9: 17499-17519. doi: 10.3934/mbe.2023777

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Research article

Extension of probability models of the risk of infections by human enteric viruses

Costantino Masciopinto ^,

Consiglio Nazionale delle Ricerche, Istituto di Ricerca Sulle Acque, Bari viale F. De Blasio 5, 70132 Italia

Academic Editor: Yang Kuang

Received: 06 April 2023 Revised: 19 July 2023 Accepted: 30 July 2023 Published: 13 September 2023

This study presents a novel approach for obtaining reliable models and coefficients to estimate the probability of infection caused by common human enteric viruses. The aim is to provide guidance for public health policies in disease prevention and control, by reducing uncertainty and management costs in health risk assessments. Conventional dose-response (DR) models, based on the theory elaborated by Furumoto and Mickey ^[1], exhibit limitations stemming from the heterogeneity of individual host susceptibilities to infection resulting from ingesting aggregate viruses. Moreover, the scarcity of well-designed viral challenge experiments contributes to significant uncertainty in these DR models. To address these issues, we conducted a review of infection models used in health risk analysis, focusing on Norovirus (NoV) GI.1, pooled Enterovirus group (EV), Poliovirus 1/SM, and Echo-12 virus via contaminated water or food. Using a mechanistic approach, we reevaluated the known DR models and coefficients for the probability of individual host infection in the mentioned viruses based on dose-infection challenge experiments. Specifically, we sought to establish a relationship between the minimum infectious dose (ID) and the ID having a 50% probability of initiating host infection in the same challenge experiment. Furthermore, we developed a new formula to estimate the degree of aggregation of GI.1 NoV at the mean infectious dose. The proposed models, based on "exact" beta-Poisson DR models, effectively predicted infection probabilities from ingestion of both disaggregated and aggregate NoV GI.1. Through a numerical evaluation, we compared the results with the maximum likelihood estimation (MLE) probability obtained from a controlled challenge trial with the NoV GI.1 virus described in the literature, demonstrating the accuracy of our approach. By addressing the indetermination of the unmeasured degree of NoV aggregation in each single infectious dose, our models reduce overestimations and uncertainties in microbial risk assessments. This improvement enhances the management of health risks associated with enteric virus infections.
Citation: Costantino Masciopinto. Extension of probability models of the risk of infections by human enteric viruses[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 17499-17519. doi: 10.3934/mbe.2023777

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Abstract

This study presents a novel approach for obtaining reliable models and coefficients to estimate the probability of infection caused by common human enteric viruses. The aim is to provide guidance for public health policies in disease prevention and control, by reducing uncertainty and management costs in health risk assessments. Conventional dose-response (DR) models, based on the theory elaborated by Furumoto and Mickey ^[1], exhibit limitations stemming from the heterogeneity of individual host susceptibilities to infection resulting from ingesting aggregate viruses. Moreover, the scarcity of well-designed viral challenge experiments contributes to significant uncertainty in these DR models. To address these issues, we conducted a review of infection models used in health risk analysis, focusing on Norovirus (NoV) GI.1, pooled Enterovirus group (EV), Poliovirus 1/SM, and Echo-12 virus via contaminated water or food. Using a mechanistic approach, we reevaluated the known DR models and coefficients for the probability of individual host infection in the mentioned viruses based on dose-infection challenge experiments. Specifically, we sought to establish a relationship between the minimum infectious dose (ID) and the ID having a 50% probability of initiating host infection in the same challenge experiment. Furthermore, we developed a new formula to estimate the degree of aggregation of GI.1 NoV at the mean infectious dose. The proposed models, based on "exact" beta-Poisson DR models, effectively predicted infection probabilities from ingestion of both disaggregated and aggregate NoV GI.1. Through a numerical evaluation, we compared the results with the maximum likelihood estimation (MLE) probability obtained from a controlled challenge trial with the NoV GI.1 virus described in the literature, demonstrating the accuracy of our approach. By addressing the indetermination of the unmeasured degree of NoV aggregation in each single infectious dose, our models reduce overestimations and uncertainties in microbial risk assessments. This improvement enhances the management of health risks associated with enteric virus infections.

References

[1]	W. A. Furumoto, R. Mickey, A mathematical model for the infectivity-dilution curve of tobacco mosaic virus: Theoretical considerations, Virology, 32 (1967), 216–223. https://doi.org/10.1016/0042-6822(67)90271-1 doi: 10.1016/0042-6822(67)90271-1
[2]	C. Masciopinto, F. Visino, Strong release of viruses in fracture flow in response to a perturbation in ionic strength: Filtration/retention tests and modeling, Water Res., 126 (2017), 240–251. https://doi.org/10.1016/j.watres.2017.09.035 doi: 10.1016/j.watres.2017.09.035
[3]	C. Masciopinto, O. De Giglio, M. Scrascia, F. Fortunato, G. La Rosa, E. Suffredini, et al., Human health risk assessment for the occurrence of enteric viruses in drinking water supplied from wells: Role of flood runoff injections, Sci. Total Environ., 666 (2019), 559. https://doi.org/10.1016/j.scitotenv.2019.02.107 doi: 10.1016/j.scitotenv.2019.02.107
[4]	J. B. Rose, P. R. Epstein, E. K. Lipp, B. H. Sherman, S. M. Bernard, J. A. Patz, Climate variability and change in the United States: Potential impacts on water and foodborne diseases caused by microbiologic agents, Environ. Health Perspect., 109 (2001), 211–220. https://doi.org/10.1289/ehp.01109s2211 doi: 10.1289/ehp.01109s2211
[5]	C. K. Uejio, S. H. Yale, K. Malecki, M. A. Borchardt, H. A. Anderson, J. A. Patz, Drinking water systems, hydrology, and childhood gastrointestinal illness in central and northern Wisconsin, Am. J. Public Health, 104 (2014), 639–646. https://doi.org/10.2105/AJPH.2013.301659 doi: 10.2105/AJPH.2013.301659
[6]	US Environmental Protection Agency, Environmental Protection Agency: Federal Register / Vol. 71, No. 216 / Wednesday, November 8, 2006/ Rules and Regulations, in Part Ⅱ, 40 CFR Parts 9,141, and 142 (EPA–HQ–OW–2002–0061; FRL–8231–9).
[7]	C. Masciopinto, R. La Mantia, A. Carducci, B. Casini, A. Calvario, E. Jatta, Unsafe tap water in households supplied from groundwater in the Salento region of southern Italy, J. Water Health, 5 (2007), 129–148. https://doi.org/10.2166/wh.2006.054 doi: 10.2166/wh.2006.054
[8]	A. D. Anderson, A. G. Heryford, J. P. Sarisky, C. Higgins, S. S. Monroe, R. S. Beard, et al., A waterborne outbreak of Norwalk-like virus among snowmobilers-Wyoming, 2001, J. Infect. Dis., 187 (2003), 303–306. https://doi.org/10.1086/346239 doi: 10.1086/346239
[9]	S. U. Parshionikar, S. Willian-True, G. S. Fout, D. E. Robbins, S. A. Seys, J. D. Cassady, et al., Waterborne outbreak of gastroenteritis associated with a norovirus, Appl. Environ. Microbiol., 69 (2003), 5263–5268. https://doi.org/10.1128/AEM.69.9.5263-5268.2003 doi: 10.1128/AEM.69.9.5263-5268.2003
[10]	C. Masciopinto, T. La Mantia, V. Tandoi, C. Levantesi, M. Divizia, D. Donia, et al., Analytical solution for the modeling of the natural time-dependent reduction of waterborne viruses injected into fractured aquifers, Env. Sci. Tech., 45 (2011), 636–642. https://doi.org/10.1021/es102412z doi: 10.1021/es102412z
[11]	C. N. Haas, J. B. Rose, C. P. Gerba, Quantitative microbial risk assessment, John Wiley & Sons, Inc., Hoboken, New Jersey, 2014.
[12]	N. Ayuso-Gabella, D. Page, C. Masciopinto, A. Aharoni, M. Salgot, T. Wintgens, Quantifying the effect of Managed Aquifer Recharge on the microbiological human health risks of irrigating crops with recycled water, Agric. Water Manage., 9 (2011), 93–102. https://doi.org/10.1016/j.agwat.2011.07.014 doi: 10.1016/j.agwat.2011.07.014
[13]	B. M. Pecson, S. C. Triolo, S. Olivieri, E. C. Chen, A. N. Pisarenko, C. C. Yang, et al., Reliability of pathogen control in direct potable reuse: Performance evaluation and RA of a full-scale 1 MGD advanced treatment train, Water Res., 122 (2017), 258–268. https://doi.org/10.1016/j.watres.2017.06.014 doi: 10.1016/j.watres.2017.06.014
[14]	P. F. M. Teunis, A. H. Havelaar, The beta-poisson dose-response model is not a single-hit model. risk analysis, Risk Anal., 20 (2000), 513–520. https://doi.org/10.1111/0272-4332.204048 doi: 10.1111/0272-4332.204048
[15]	P. J. Schmidt, Norovirus dose response: Are currently available data informative enough to determine how susceptible humans are to infection from a single virus?, Risk Anal., 35 (2015), 1364–1383. https://doi.org/10.1111/risa.12323 doi: 10.1111/risa.12323
[16]	M. H. Weir, Dose-Response modeling and use: Challenges and uncertainties in environmental exposure, in Manual of Environmental Microbiology, ASM Press, 2016.
[17]	R. Lambkin-Williams, J. P. De Vincenzo, A COVID-19 human viral challenge model. Learning from experience, Influenza Other Respi. Viruses, 14 (2020), 747–756. https://doi.org/10.1111/irv.12797 doi: 10.1111/irv.12797
[18]	C. P. Gerba, W. Q. Betancourt, Viral aggregation: Impact on virus behavior in the environment. Environ, Sci. Technol., 1 (2017), 7318−7325. https://doi.org/10.1021/acs.est.6b058351 doi: 10.1021/acs.est.6b058351
[19]	P. F. M. Teunis, F. S. Le Guyader, P. Liu, J. Ollivier, C. L. Moe, Noroviruses are highly infectious but there is strong variation in host susceptibility and virus pathogenicity, Epidemics, 32 (2020), 100401. https://doi.org/10.1016/j.epidem.2020.100401 doi: 10.1016/j.epidem.2020.100401
[20]	P. F. M. Teunis, C. L. Moe, P. Liu, S. E. Miller, L. Lindesmith, R. S. Baric, et al., Norwalk virus: How infectious is it?, J. Med. Virol., 80 (2008), 1468–1476. https://doi.org/10.1002/jmv.21237 doi: 10.1002/jmv.21237
[21]	R. L. Atmar, A. R. Opekun, M. A. Gilger, M. K. Estes, S. E. Crawford, F. H. Neill, et al., Determination of the 50% human infectious dose for Norwalk virus, J. Infect. Dis., 209 (2014), 1016–1022. https://doi.org/10.1093/infdis/jit620 doi: 10.1093/infdis/jit620
[22]	J. S. Lion, D. H. Kingsley, J. S. Montes, G. P. Richards, G. M. Lyon, G. M. Abdulhafid, et al., Randomized, double-blinded clinical trial for human norovirus inactivation in oysters by high hydrostatic pressure processing, Appl Environ Microbiol., 77 (2011), 5476–5482. https://doi.org/10.1128/AEM.02801-10 doi: 10.1128/AEM.02801-10
[23]	R. Mateo, L. C. Lindesmith, S. J. Garg, K. Gottlieb, K. Lin, S. Said, et al., Production and clinical evaluation of Norwalk GI.1 virus Lot 001-09NV in Norovirus vaccine development, 221 (2020), 919. https://doi.org/10.1093/infdis/jiz540
[24]	V. Nilsen, J. Wyller, RA for drinking water: 1. Revisiting the mathematical structure of single-hit dose-response models, Risk Anal., 36 (2016), 145–162. https://doi.org/10.1111/risa.12389 doi: 10.1111/risa.12389
[25]	C. N. Haas, Conditional dose-response relationships for microorganisms: Development and application, Risk Anal., 22 (2002), 453–463. https://doi.org/10.1111/0272-4332.00035 doi: 10.1111/0272-4332.00035
[26]	F. Miura, D. Klinkenberg, J. Wallinga, Dose-response modelling of endemic coronavirus and SARS-CoV-2: human challenge trials reveal the individual variation in susceptibility, medRxiv, (2022). doi: DOI:10.1101/2022.04.07.22273549.
[27]	E. O. Caul, Small round structured viruses: airborne transmission and hospital control, Lancet, 343 (1994), 1240–1242. https://doi.org/10.1016/s0140-6736(94)92146-6 doi: 10.1016/s0140-6736(94)92146-6
[28]	N. Van Abel, M. E. Schoen, J. C. Kissel, J. S. Meschke, Comparison of risk predicted by MultIPle Norovirus dose-response models and implications for quantitative microbial risk assessment, Risk Anal., 37 (2017). https://doi.org/10.1111/risa.12616 doi: 10.1111/risa.12616
[29]	M. J. Messner, P. Berger, S. P. Nappier, Fractional poisson—A simple dose-response model for human Norovirus, Risk Anal., 34 (2014), 1820–1829. https://doi.org/10.1111/risa.12207 doi: 10.1111/risa.12207
[30]	A. Rahman, D. Munther, A. Fazil, B. Smith, J. Wu, Advancing risk assessment: mechanistic dose-response modelling of Listeria monocytogenes infection in human populations, R. Soc. Open Sci., 5 (2018). http://dx.doi.org/10.1098/rsos.180343 doi: 10.1098/rsos.180343
[31]	R. Ward, S. Krugman, J. Giles, M. Jacobs, O. Bodansky, Infectious hepatitis—Studies of its natural history and prevention, New Eng. J. Med., 258 (1958), 402–416.
[32]	R. L. Ward, D. I. Bernstein, E. C. Young, J. R. Sherwood, D. R. Knowlton, G. M. Schiff, Human rotavirus studies in volunteers: Determination of infectious dose and serological response to infection, J. Infect. Dis., 154 (1986), 871–880. https://doi.org/10.1093/infdis/154.5.871 doi: 10.1093/infdis/154.5.871
[33]	G. M. Schiff, G. M. Stefanovic, B. Young, J. K. Pennekamp, Determination of minimal infectious dose of an Enterovirus in drinking water, Health Effects Research Laboratory, 1993.
[34]	R. B. Couch, V. Knight, R. G. Douglas, S. H. Jr., Black, B. H. Hamory, The minimal infectious dose of adenovirus type 4: The case for natural transmission by viral aerosol, Trans. Am. Clin. Climatol. Assoc., 80 (1969), 205–211.
[35]	R. Frenck, D. I. Bernstein, M. Xia, P. Huang, W. Zhong, S. Parker, et al., Predicting susceptibility to norovirus GⅡ.4 by use of a challenge model involving humans, J. Infect. Dis., 206 (2012), 1386–1393. https://doi.org/10.1093/infdis/jis514 doi: 10.1093/infdis/jis514
[36]	S. R. Seitz, J. S. Leon, K. J. Schwab, G. M. Lyon, M. Dowd, M. McDaniels, et al., Norovirus infectivity in humans and persistence in water, Appl. Environ. Microbiol., (2011), 6884–6888. https://doi.org/10.1128/AEM.05806-11 doi: 10.1128/AEM.05806-11
[37]	P. Teunis, J. Schijven, S. Rutjes., A generalized dose-response relationship for adenovirus infection and illness by exposure pathway, Epidemiol. Infect., 144 (2016), 3461–3473. https://doi.org/10.1017/S0950268816001862 doi: 10.1017/S0950268816001862
[38]	N. J. C. Strachan, T. M. P. Doyle, F. Kasuga, O. Rotariu, I. D. Ogden, Dose-response modelling of Escherichia coli O157 incorporating data from foodborne and environmental outbreaks, Int. J. Food Microbiol., 103 (2005), 35–47. https://doi.org/10.1016/j.ijfoodmicro.2004.11.023 doi: 10.1016/j.ijfoodmicro.2004.11.023
[39]	C. N. Haas, J. N. S. Eisenberg, Risk assessment, in Water Quality: Guidelines, Standards & Health, IWA Publishing, London, UK, (2001), 161–374.
[40]	K. D. Crabtree, C. P. Gerba, J. B. Rose, J. B. Rose, Waterborne adenovirus: a risk assessment, Water Sci. Technol., 35 (1997), 1–6. https://doi.org/10.1016/S0273-1223(97)00225-4 doi: 10.1016/S0273-1223(97)00225-4
[41]	E. Sokolova, S. R. Petterson, O. Dienus, F. Nyströmd, P. E. Lindgren, T. J. R. Pettersson, Microbial risk assessment of drinking water based on hydrodynamic modelling of pathogen concentrations in source water, Sci. Total Environ., 526 (2015), 177–186. https://doi.org/10.1016/j.scitotenv.2015.04.040 doi: 10.1016/j.scitotenv.2015.04.040
[42]	H. de Man, H. H. J. L. van den Berg, E. J. T. M. Leenen, , J. F. Schijven, F. M. Schets, J. C. van der Vliet, F. van Knapen, A.M. de Roda Husman, Quantitative assessment of infection risk from exposure to waterborne pathogens in urban floodwater, Water Res., 48 (2014), 90–99. http://dx.doi.org/10.1016/j.watres.2013.09.022 doi: 10.1016/j.watres.2013.09.022
[43]	P. F. M. Teunis, van der Heijden, van der Giessen, A. H. Havelaar, The dose-response relation in human volunteers for gastro-intestinal pathogens, National Insitute of Public Health Inspectorate, Ministry of Public Health, The Netherland, 1996.
[44]	G. B. McBride, Norovirus dose-response in sewage-related RA: The importance of virus aggregation., in 7th International Congress on Environmental Modelling and Software, San Diego, California, USA, 2014.
[45]	C. N. Haas, J. B. Rose, C. P. Gerba, Quantitative Microbial Risk Assessment, Wiley, NY, 1999.
[46]	K. D. Mena, C. P. Gerba, C. N. Haas, J. B. Rose, Risk assessment of waterborne Coxsackievirus, Am. J. Water Works Assoc., 95 (2003), 122–131. https://doi.org/10.1002/j.1551-8833.2003.tb10413.x doi: 10.1002/j.1551-8833.2003.tb10413.x
[47]	S. Yezli, J. A. Otter, Minimum infective dose of the major human respiratory and enteric viruses transmitted through food and the environment, Food Environ. Virol., 3 (2011.), 1–30. https://doi.org/10.1007/s12560-011-9056-7 doi: 10.1007/s12560-011-9056-7
[48]	Health Canada, Public Health Agency of Canada, Pathogen Safety Data Sheets: Infectious Substances – Norovirus, 2019. Available from: https://www.canada.ca/en/public-health/services/laboratory-biosafety-biosecurity/pathogen-safety-data-sheets-risk-assessment/norovirus-pathogen-safety-data-sheet.html.
[49]	D. Y. Graham, G. R. Dufour, M. K. Estes, The minimal infective dose of rotavirus, Arch. Virol., 92 (1987), 261–271. https://doi.org/10.1007/BF01317483 doi: 10.1007/BF01317483
[50]	Health Canada, Public Health Agency of Canada. Pathogen Safety Data Sheets: Infectious Substances, Coxsackievirus, 2018. Available from: https://www.canada.ca/en/public-health/services/laboratory-biosafety-biosecurity/pathogen-safety-data-sheets-risk-assessment.html.
[51]	G. M. Schiff, G. M. Stefanovic, E. C. Young, D. S. Sander, J. K. Pennekamp, R. L. Ward, Studies of echovirus-12 in volunteers: Determination of minimal infectious dose and the effect of previous infection on infectious dose, J. Infect. Dis., 150 (1984), 858–866. https://doi.org/10.1093/infdis/150.6.858 doi: 10.1093/infdis/150.6.858
[52]	K. P. Burnham, D. R. Anderson, Model selection and multimodel inference: A practical information-theoretic approach, Springer-Verlag, 2002.
[53]	M. Abramowitz, I. Stegun, Handbook of mathematical functions, with formulas, graphs, and mathematical tables, Dover Publications, Inc., New York, US, 1972.
[54]	C. Masciopinto, M. Vurro, N. Lorusso, D. Santoro, C. N. Haas, Application of QMRA to MAR operations for safe agricultural water reuses in coastal areas, Water Res., 8 (2020), 100062; https://doi.org/10.1016/j.wroa.2020.100062 doi: 10.1016/j.wroa.2020.100062

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