Fractional poisson-A simple dose-response model for human norovirus

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      <subfield code="a">Messner, Michael J.</subfield>
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      <subfield code="a">Fractional poisson-A simple dose-response model for human norovirus</subfield>
      <subfield code="c">Michael J. Messner, Philip Berger , Sharon P. Nappier</subfield>
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      <subfield code="a">This study utilizes old and new Norovirus (NoV) human challenge data to model the dose-response relationship for human NoV infection. The combined data set is used to update estimates from a previously published beta-Poisson dose-response model that includes parameters for virus aggregation and for a beta-distribution that describes variable susceptibility among hosts. The quality of the beta-Poisson model is examined and a simpler model is proposed. The new model (fractional Poisson) characterizes hosts as either perfectly susceptible or perfectly immune, requiring a single parameter (the fraction of perfectly susceptible hosts) in place of the two-parameter beta-distribution. A second parameter is included to account for virus aggregation in the same fashion as it is added to the beta-Poisson model. Infection probability is simply the product of the probability of nonzero exposure (at least one virus or aggregate is ingested) and the fraction of susceptible hosts. The model is computationally simple and appears to be well suited to the data from the NoV human challenge studies. The model's deviance is similar to that of the beta-Poisson, but with one parameter, rather than two. As a result, the Akaike information criterion favors the fractional Poisson over the beta-Poisson model. At low, environmentally relevant exposure levels (<100), estimation error is small for the fractional Poisson model; however, caution is advised because no subjects were challenged at such a low dose. New low-dose data would be of great value to further clarify the NoV dose-response relationship and to support improved risk assessment for environmentally relevant exposures.</subfield>
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      <subfield code="w">MAP20077000345</subfield>
      <subfield code="t">Risk analysis : an international journal</subfield>
      <subfield code="d">McLean, Virginia : Society for Risk Analysis, 1987-2015</subfield>
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      <subfield code="g">06/10/2014 Volumen 34 Número 10 - octubre 2014 </subfield>
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