# Evaluation of the 50% Infectious Dose of Human Norovirus Cin-2 in Gnotobiotic Pigs: A Comparison of Classical and Contemporary Methods for Endpoint Estimation

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## Abstract

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_{50}) and diarrhea dose (DD

_{50}) of the GII.4/2003 variant of HuNoV (Cin-2) in the gnotobiotic pig model of HuNoV infection and disease. Using various mathematical approaches (Reed–Muench, Dragstedt–Behrens, Spearman–Karber, logistic regression, and exponential and approximate beta-Poisson dose–response models), we estimated the ID

_{50}and DD

_{50}to be between 2400–3400 RNA copies, and 21,000–38,000 RNA copies, respectively. Contemporary dose–response models offer greater flexibility and accuracy in estimating ID

_{50}. In contrast to classical methods of endpoint estimation, dose–response modelling allows seamless analyses of data that may include inconsistent dilution factors between doses or numbers of subjects per dose group, or small numbers of subjects. Although this investigation is consistent with state-of-the-art ID

_{50}determinations and offers an advancement in clinical data analysis, it is important to underscore that such analyses remain confounded by pathogen aggregation. Regardless, challenging virus strain ID

_{50}determination is crucial for identifying the true infectiousness of HuNoVs and for the accurate evaluation of protective efficacies in pre-clinical studies of therapeutics, vaccines and other prophylactics using this reliable animal model.

## 1. Introduction

_{50}) of various norovirus strains. In humans, the ID

_{50}of Norwalk virus was identified to be between 18 and 2800 genomic equivalents [25,26]. Similarly, the ID

_{50}of a GII.4/2006b variant was identified to be ≤2.74 × 10

^{3}RNA copies in newborns (4–5 days of age) and 6.43 × 10

^{4}RNA copies in older (33–34 days of age) Gn pigs [18]. Dose–response data are critically important for the standardization of the animal challenge model used in the pre-clinical evaluation of vaccine efficacy and anti-viral agents. While ID

_{50}is of primary interest to clinicians, such studies are also widely used to interpret pathogen occurrence and exposure data and translate them to health outcomes. For example, quantitative microbial risk assessments (QMRAs) for pathogen infections in humans are regularly used to develop and improve industrial and regulatory policy in the water and food safety sectors [27,28]. Utilization of dose–response approaches (experimental design and analysis) concurrently enables wider application and impact from experimentally derived data.

_{50}values are often estimated using classical methods, such as the Reed–Muench, Dragstedt–Behrens, and Spearman–Karber methods or more recently by logistic regression. In recent decades, several mechanistic models have been developed to describe plausible phenomena that are inherent to experimentally derived dose–response data [25,29]. Critically, such models provide a basis for inference about the probability of infection at any dose level (i.e., not just ID

_{50}), though low-dose extrapolation is an ever-present concern for applications, such as drinking water risk assessments in which mean doses are often less than one pathogen [30]. Contemporary dose–response models may offer greater flexibility and accuracy (i.e., managing inconsistent dilution factors between doses or numbers of subjects per dose group or small numbers of subjects) in estimating ID

_{50}.

_{50}) or develop diarrhea (DD

_{50})—when Gn pigs are infected with a pandemic strain of HuNoV. The significance of this study includes: (i) experimental evaluation of GII.4/2003 HuNoV dose–response in 33–34 day old Gn pigs; (ii) comparison of different dose–response analyses used for estimating the ID

_{50}and DD

_{50}of HuNoV in Gn pigs to determine the best-fit model; (iii) identification of the most appropriate challenge dose in 33–34 day old Gn pigs to standardize the model for HuNoV vaccine evaluation; (iv) comparison of the obtained ID

_{50}and the ID

_{50}used in human volunteer challenge studies. Comparing the infectiousness of the GII.4 variant challenge pools in Gn pigs and humans will lead to a better understanding of the zoonotic potential [31] of NoVs between different species and further validate the Gn pig model of HuNoV infection as a proper predictive tool of the future efficacy of vaccines and other antiviral strategies to control NoV diarrhea in humans.

## 2. Materials and Methods

#### 2.1. Virus Inoculum

^{6}viral genomic RNA copies/mL of stool. The challenge pool was stored at −80 °C in individual 1 mL aliquots until the day of Gn pig inoculation. The Gn pig challenge studies were conducted between July 2017 and March 2018.

#### 2.2. Gnotobiotic Pigs and Treatments

^{2}to 2 × 10

^{6}genomic RNA copies at PID (post-inoculation day) 0. These doses were determined based on doses used in human volunteer studies [7,26,32] as well as work carried out by our group using Gn pigs [18]. Diarrhea and fecal virus shedding were monitored daily until euthanasia. All pigs were euthanized on PID 7. All experiments involving the use of Gn pigs were approved by the Institutional Animal Care and Use Committee at Virginia Tech (IACUC protocol: 17-110-CVM). All experimental procedures were carried out in compliance with federal and university regulations.

#### 2.3. HBGA-Typing of Gn Pigs by Immunofluorescence Assay

^{®}Antifade Mounting Medium with 4,6-diamidino-2-phenylindole (DAPI), which was used as a nuclear counterstain (Vector Laboratories, Burlingame, CA, USA) for fluorescent microscopy. Based on this screening technique, HBGA type A

^{−}and H

^{−}pigs were excluded from this study because of their reduced susceptibility to NoV infection relative to A

^{+}or H

^{+}pigs [11,18].

#### 2.4. Assessment of Fecal Consistency and Detection of HuNoV Shedding by RT-qPCR

_{2}O. Five microliters of RNA was used in a 20 μL RT-qPCR reaction with a SensiFAST Probe No-ROX One-Step Kit (Bioline, London, UK) to detect HuNoV genomes. Primers COG2F and COG2R, and probe RING2, were used with cycling conditions as described in a previous study [36]. A standard curve was generated using COG2 amplicon-containing cDNA expression plasmids in standards serially diluted tenfold from 2 × 10

^{6}to 2 genomic RNA copies. Amplification was performed on CFX96 Real-Time System (Bio-Rad, Hercules, CA, USA), and data were collected and analyzed with Bio-Rad CFX Manager 2.0.

#### 2.5. Statistical Analysis

#### 2.5.1. Analysis of Variance among Challenge Doses

^{®}(https://www.infostat.com.ar) connected to R software (R Core Team) in R Studio (see Supplementary Materials). The variables related to virus shedding were analyzed using a generalized linear mixed model (GLMM), considering the treatment (pigs receiving different doses of HuNoV) as a fixed variable and the Gn pigs within the groups as a random variable. A varIdent structure for the variance–covariance matrix was used for modeling the heterogeneity of variance. The variables associated to diarrhea that met normality and homoscedasticity assumptions were analyzed by one-way ANOVA. In all cases, multiple post-ANOVA comparisons were carried out using the Tukey method for comparisons between the 7 different doses. Statistical significance was considered at p < 0.05 for all comparisons (Supplementary Materials and Table 1).

#### 2.5.2. ID_{50} and DD_{50} Calculations Using Different Approaches

_{50}—they were developed to facilitate ease of analysis before widespread availability of computing. Statistical methods, such as logistic regression, allow the estimation of dose–response at any dose and seamless analysis of data with doses that are not equally spaced (in logarithmic scale) or with variable numbers of subjects per group, but ultimately provide only an empirical fit. Exponential and exact beta-Poisson dose–response models share the advantages of logistic regression but also account for known mechanisms such as Poisson-distributed numbers of pathogens in a sample of known volume and concentration if the pathogens are disaggregated.

#### Reed–Muench, Dragstedt–Behrens and Spearman–Karber Methods

_{50}of a different HuNoV variant (GII.4/2006b) in Gn pigs [18].

_{50}is estimated by interpolating the line that connects the hypothetical fractions of the bracketing doses [40].

#### Exponential and Beta-Poisson Dose–Response Models

_{50}/[${2}^{\frac{1}{\alpha}}-1$]. The criteria β >> α and β >> 1 are well satisfied with the model fits obtained from this study.

_{50}is an estimate of the median infectious/diarrhea dose with units of counts (i.e., genome copies) and α is a unitless model parameter. This approximated form retains linearity at low doses, a fundamental characteristic of these microbial dose–response models [27,48].

_{50}) Server—https://www.ncbi.nlm.nih.gov/CBBresearch/Spouge/html_ncbi/html/id50/id50.cgi [49]. This tool was based on VACMAN, a computational program that calculates statistics for in vitro and in vivo infectivity data [50].

_{50}and DD

_{50}were also carried out using the R scripts published by Weir 2017 [48] for the exponential and approximate beta–Poisson models (R Core Team). Briefly, the script uses maximum likelihood estimation to fit the counting data (number of animals with virus shedding/diarrhea) to both theoretical models, either exponential or approximate beta-Poisson due to their biologic plausibility. The process calculates the probability of obtaining the observed data given a theoretical distribution by minimizing the deviance (Y) of each of these fitted models as defined by Equation (5).

^{2}(k-p,α). Credible bands illustrating uncertainty in the dose–response relationship were evaluated using Bayesian Markov Chain Monte Carlo (MCMC) in OpenBUGS (Version 3.2.3), as previously carried out elsewhere [51].

## 3. Results

#### 3.1. Assessment of Infection Status in Gn Pigs

^{6}RNA copies) amount of viruses in their feces (p < 0.0001) and shed viruses for a significantly longer duration (>3 days) than pigs in the other groups (p < 0.0001) (Figure 1A,B).

#### 3.2. Assessment of Diarrhea Status in Gn Pigs

#### 3.3. Determination of ID_{50} and DD_{50} Using Various Dose–Response Models

_{50}and DD

_{50}doses using different dose–response models. The log

_{10}ID

_{50}and log

_{10}DD

_{50}, estimated using the conventional, exponential, and beta-Poisson dose–response calculation methods are presented in Table 2.

#### 3.4. Comparison of Infectiousness of HuNoV GII.4/2003 Variants Cin-1 and Cin-2 in Humans and Gn Pigs

^{4}RNA copies of the challenge strain. In Gn pigs, a dose of 2 × 10

^{4}RNA copies infected 67% of pigs, whereas 8 × 10

^{4}and 2 × 10

^{5}RNA copies infected 100% of pigs (Table 1). It is observed that pigs inoculated with 2 × 10

^{5}RNA copies shed virus for a similar duration of days compared to those of the human volunteers in the study conducted by Frenck and colleagues [32].

#### 3.5. Comparison of Methods for Dose–response Analysis of Cin-2

_{50}(Figure 2A,B and Table 4), but the approximate beta-Poisson method, based on a lower deviance and Akaike Information Criterion (AIC), was determined to be the best-fitting model for determining the DD

_{50}of Cin-2 (Figure 2C,D and Table 4). The ID

_{50}and DD

_{50}values determined by the approximate beta-Poisson model were 2.57 × 10

^{3}RNA copies and 2.09 × 10

^{4}RNA copies, respectively.

#### 3.6. Determination of an Optimal Challenge Dose

^{5}. Our data show that pigs infected at this dose had a mean onset day of 1.3 with viruses shed in feces in large quantities (measured by AUC) for almost the whole duration of the infection period (6.3 days out of 7; Table 1). Moreover, pigs in this dose group also experienced the highest diarrhea burden among all dose groups, with diarrhea starting at 2.8 days after inoculation and occurring for a duration of 4 days. Pigs in this group also had the highest mean cumulative diarrhea score of 9.31 (Table 1).

## 4. Discussion

_{50}and DD

_{50}of HuNoV GII.4/2003 Cin-2 variants in Gn pigs, a large animal model that has proven to be capable of replicating HuNoV GII.4-associated disease in humans [17,20]. With the exception of the exponential dose–response model, all data analysis methods yielded ID

_{50}estimates between 2400–3400 RNA copies (Table 2). Relative to previous estimates from human trials with Norwalk virus, this is above 1015 RNA copies [25] and comparable to 2800 RNA copies [26]. The exponential dose–response model yielded higher ID

_{50}estimates, but also had poorer fit relative to the approximate beta-Poisson model. The DD

_{50}values for all methods except the exponential dose–response model (which had poor fit) were within a range of 21,000–38,000 RNA copies. While the data analysis approaches feature varying assumptions, limitations, and degrees of statistical complexity, the somewhat tight clustering of results indicates that the simple classical methods perform adequately for this particular dataset. Further research is required to explore scenarios in which the classical approaches may become unreliable and compare them with more statistically rigorous contemporary approaches.

_{50}). Additionally, aggregation compromises the Poisson foundation of the mechanistic exponential and (exact) beta-Poisson dose–response models. Performing these types of human or animal clinical studies with little attention to the aggregation status of the pathogen stock suspension has been the state-of-the-art. As discussed by Teunis and colleagues, the difference in the infectivity of 8ffIIa and 8ffIIb strains of Norwalk virus could be attributed to the aggregation statuses of the viruses [24]. They alluded to this aggregation with the inclusion of an electron micrograph image of a cluster, and attempted to estimate the degree of aggregation from the corresponding dose–response data [25]. However, it has been shown that such inference is not possible due to model non-identifiability limitations associated with the impossibility of concurrently evaluating dose response and estimating an unmeasured aggregation parameter [29,56].

_{50}and DD

_{50}values are specific to the degree and nature of virus aggregation in this experiment, (2) the ID

_{50}and DD

_{50}for disaggregated viruses are likely to be lower, and (3) exponential and (approximate) beta-Poisson models must be considered as empirical fits for these data without the typically asserted mechanistic meaning of parameters. It is important to note that these limitations apply to any dose–response experiment in which (1) disaggregation of administered pathogens was not confirmed or (2) aggregation was known to exist but not well quantified. For this reason, it has recently been suggested that ethics approval of future dose–response experiments should be contingent upon use of disaggregated pathogens (or pathogens aggregated to a known extent) [56].

^{4}RNA copies using the RM method, based on the infection status of 33–34 day old Gn pigs [18]. On the other hand, the Farmington Hills virus was responsible for around 64% of cruise ship outbreaks and 45% of land-based outbreaks, most of which occurred in long-term care facilities, schools, and restaurants, in the US in 2002 [60,61,62]. In the current study, using the RM method, we estimated the ID

_{50}of Cin-2, a variant of the Farmington Hills virus, to be 2.51 × 10

^{3}RNA copies in similar aged pigs (Table 2). This analysis suggests that at least a 25-fold higher virus titer is required to establish infection among 50% of the pigs inoculated with the GII.4/2006b virus as compared to the Cin-2 (Table 5), indicating that Cin-2 is a more infectious strain in Gn pigs based on the available information regarding the two HuNoV variants.

^{4}RNA copies of Cin-1 (the parent GII4/2003 variant of Cin-2) caused infection among 70% of secretor positive individuals [32]. The same challenge virus, when administered at a dose of 4.4 × 10

^{3}RNA copies, caused infection among 62.5% and disease (vomiting or diarrhea of mild or greater severity) in 37.5% of the placebo recipients in another phase I vaccine clinical trial by Bernstein et al. [7]. These data from placebo recipients compared to the slightly reduced incidence of infection (54%) and disease (20%) among the vaccine recipients was not statistically significantly different, suggesting that the challenge dose was insufficient to demonstrate the protective effect of the vaccine [7]. In the present study, only 67% of the dose group 5 (2 × 10

^{4}RNA copies) pigs shed viruses and 33% had diarrhea, whereas 100% of the pigs in dose groups 1–4 shed viruses and 75% to 100% had diarrhea. These data show rates of infection that are consistent with Frenck and Bernstein [7,32], hence, highlighting the importance of identifying the optimal virus challenge dose for the evaluation of protective efficacy of vaccines in animal models and in humans.

_{50}and DD

_{50}for non-statisticians [18,37,42,65].

_{50}calculation. On the other hand, the SK method would be applicable for calculating the DD

_{50}, since there were dose groups in which all (100%) or none (0%) of the pigs exhibited symptoms.

_{50}of the original Norwalk virus (GI.1, 8fIIa isolate) challenge pool was predicted to be around 2.8 × 10

^{3}RNA copies in secretor-positive individuals [26]. Similarly, in the current study, we estimated the ID

_{50}of Cin-2 to be around 5.75 × 10

^{3}RNA copies. These data show a similar infection potential between the prototypic GI.1 Norwalk virus and the Cin-2. With the availability of more dose–response studies and the relatability between human studies and Gn pig studies, it could eventually be possible to compare the pathogenesis of different HuNoV genogroups to help further identify similarities and/or differences between different HuNoVs.

_{50}, contemporary dose–response models can describe the full range of probability of response (including ID

_{20}or ID

_{80}) and they are accurate at low doses as well [69]. They allow greater flexibility and a wider range of understanding in the estimated probability of infection. Comparing the approximate beta-Poisson and exponential models using the AIC (Table 4) showed the suitability of both these models for calculating the ID

_{50}, though the approximate beta-Poisson model was identified to be a more suitable model for DD

_{50}estimation.

## 5. Conclusions

_{50}and DD

_{50}of HuNoVs. Determining the optimal HuNoV dose would help establish consistency in terms of the challenge dose for the evaluation of novel vaccine candidates between preclinical and clinical trials. In conclusion, we have established a reliable model that can be used to test candidate prophylactics and therapeutics prior to clinical trials in humans, and the model is ready to be used for the evaluation of immunogenicity and the protective efficacy of candidate HuNoV vaccines.

## Supplementary Materials

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Box and whisker plots showing (

**A**) virus shedding onset day, (

**B**) duration of virus shedding, (

**C**) log

_{10}AUC of virus shedding, (

**D**) diarrhea onset day, (

**E**) duration of diarrhea, and (

**F**) log

_{10}cumulative diarrhea scores, among each dose group. The maximum and minimum values are denoted by the whisker and the boundaries of each box represent the quartiles with the mean indicated by a black line.

**Figure 2.**Dose–response curves showing the probability of infection and diarrhea determined by maximum likelihood estimation and credible bands generated by Bayesian Markov Chain Monte Carlo for the exponential (

**A**) and (

**C**) and approximate beta-Poisson (

**B**) and (

**D**) models. Dashed lines depict the 95% credible bands, while dotted lines show 99% credible bands. Frequencies of infection and diarrhea determined by infection studies carried out in Gn pigs are depicted as points.

**Table 1.**HuNoV fecal shedding and diarrhea after inoculation of Gn pigs with different doses of Cin-2.

Dose Group | # of Viral Genome Copies | n | Virus Shedding | Diarrhea | ||||||
---|---|---|---|---|---|---|---|---|---|---|

(%) ^{a} | Mean Duration Days (SEM) ^{c–e} | AUC ^{d,f} | Mean Onset Day (SEM) ^{c,d} | (%) ^{b} | Mean Duration Days (SEM) ^{c–e} | AUC ^{d,f} | Mean Onset Day (SEM) ^{c,d} | |||

1 | 2 × 10^{6} | 4 | 4 (100%) | 2.5 (0.6) ^{BC} | 9506 ^{B} | 2 (0.4) ^{B} | 4 (100%) | 5.0 (0.3) ^{A} | 9.06 ^{A} | 1.5 (0.5) ^{D} |

2 | 4 × 10^{5} | 4 | 4 (100%) | 1.3 (0.3) ^{CD} | 5232 ^{B} | 4 (1.2) ^{AB} | 3 (75%) | 1.3 (0.9) ^{ABC} | 7.04 ^{A} | 5.5 (1.2) ^{ABC} |

3 | 2 × 10^{5} | 4 | 4 (100%) | 6.3 (0.5) ^{A} | 126774 ^{A} | 1.3 (0.3) ^{B} | 4 (100%) | 4.0 (0.9) ^{AB} | 9.31 ^{A} | 2.8 (0.5) ^{CD} |

4 | 8 × 10^{4} | 6 | 6 (100%) | 2.8 (0.5) ^{B} | 13495 ^{B} | 1.5 (0.3) ^{B} | 6 (100%) | 3.8 (1) ^{AB} | 7.46 ^{A} | 3.2 (0.9) ^{CD} |

5 | 2 × 10^{4} | 3 | 2 (67%) | 1 (0.6) ^{B} | 93 ^{B} | 3.3 (2.3) ^{AB} | 0 (0%) | 0.0 (0) ^{ABC} | 1.50 ^{B} | 6.3 (1.7) ^{AB} |

6 | 3.2 × 10^{3} | 3 | 2 (67%) | 1.3 (0.9) ^{BCD} | 2667 ^{B} | 4 (2.1) ^{AB} | 1 (33%) | 1.0 (0) ^{BC} | 3.17 ^{AB} | 5 (2.1) ^{BC} |

7 | 8 × 10^{2} | 4 | 1 (25%) | 0.5 (0.5) ^{D} | 2972 ^{B} | 6.8 (1.3) ^{A} | 0 (0%) | 0.0 (0) ^{C} | 2.75 ^{AB} | 8 (0) ^{A} |

Method | Log_{10}ID_{50} | ID_{50} | Log_{10}DD_{50} | DD_{50} |
---|---|---|---|---|

Reed-Muench | ||||

Hand calculation “skrmdb” R script | 3.40 | 2.51 × 10^{3} | 4.58 | 3.80 × 10^{4} |

Dragstedt-Behrens“skrmdb” R script | 3.39 | 2.45 × 10^{3} | 4.58 | 3.80 × 10^{4} |

Spearman-KarberHand calculation “skrmdb” R script online calculator | 3.52 | 3.31 × 10^{3} | 4.49 | 3.09 × 10^{4} |

Logistic Regressiononline calculator | 3.40 | 2.51 × 10^{3} | 4.34 | 2.18 × 10^{4} |

Exponential^{a}R script | 3.76 | 5.75 × 10^{3} | 4.76 | 5.75 × 10^{4} |

Approximate Beta-Poisson^{a} | ||||

R script | 3.41 | 2.57 × 10^{3} | 4.33 | 2.13 × 10^{4} |

**Table 3.**Comparison of virus shedding in unvaccinated Gn pigs and humans after inoculation with GII.4/2003 HuNoV inoculum.

Host | Age | Challenge Dose | n | Virus Shedding (%) | Mean Duration Days (Range) ^{c} | Peak Virus Shedding Day (PID) |
---|---|---|---|---|---|---|

Human ^{a} | 19–48 years | 5 × 10^{4} | 23 | 70 | 5.2 (2–30) | 3 |

Human ^{b} | 18–50 years | 4.4 × 10^{3} | 34 | 76.5 | - | - |

Human ^{b} | 18–50 years | 4.4 × 10^{3} | 48 | 62.5 | - | - |

Gn pig | 33–34 days | 2 × 10^{4} | 3 | 67 | 1.0 (1–2) | 2 |

Gn pig | 33–34 days | 8 × 10^{4} | 6 | 100 | 2.8 (2–4) | 3 |

Gn pig | 33–34 days | 2 × 10^{5} | 4 | 100 | 6.3 (5–7) | 4 |

**Table 4.**Comparison of goodness of fit for the determination of best-fitting ID50 and DD50 model for Cin-2.

Exponential Model | Approximate Beta Poisson Model | ||||||||
---|---|---|---|---|---|---|---|---|---|

r | AIC ^{a} | Chi-Squared p-Value | Minimized Deviance | α | N50 | AIC ^{a} | Chi-Squared p-Value | Minimized Deviance | |

ID50 | 1.20 × 10^{−4} | 10.71 | 0.71 | 3.74 | 0.998 | 2572 | 10.68 | 0.89 | 1.71 |

DD50 | 1.20 × 10^{−5} | 21.46 | 0.0 ^{b} | 16.09 | 0.928 | 21,340 | 17.92 | 0.06 | 10.5 |

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**MDPI and ACS Style**

Ramesh, A.K.; Parreño, V.; Schmidt, P.J.; Lei, S.; Zhong, W.; Jiang, X.; Emelko, M.B.; Yuan, L.
Evaluation of the 50% Infectious Dose of Human Norovirus Cin-2 in Gnotobiotic Pigs: A Comparison of Classical and Contemporary Methods for Endpoint Estimation. *Viruses* **2020**, *12*, 955.
https://doi.org/10.3390/v12090955

**AMA Style**

Ramesh AK, Parreño V, Schmidt PJ, Lei S, Zhong W, Jiang X, Emelko MB, Yuan L.
Evaluation of the 50% Infectious Dose of Human Norovirus Cin-2 in Gnotobiotic Pigs: A Comparison of Classical and Contemporary Methods for Endpoint Estimation. *Viruses*. 2020; 12(9):955.
https://doi.org/10.3390/v12090955

**Chicago/Turabian Style**

Ramesh, Ashwin K., Viviana Parreño, Philip J. Schmidt, Shaohua Lei, Weiming Zhong, Xi Jiang, Monica B. Emelko, and Lijuan Yuan.
2020. "Evaluation of the 50% Infectious Dose of Human Norovirus Cin-2 in Gnotobiotic Pigs: A Comparison of Classical and Contemporary Methods for Endpoint Estimation" *Viruses* 12, no. 9: 955.
https://doi.org/10.3390/v12090955