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      <source-app name="Horizon">Horizon</source-app>
      <rec-number>1</rec-number>
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      <ref-type name="Journal Article">17</ref-type>
      <work-type>ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES</work-type>
      <contributors>
        <authors>
          <author>
            <style face="normal" font="default" size="100%">Favier, C.</style>
          </author>
          <author>
            <style face="bold" font="default" size="100%">Degallier, Nicolas</style>
          </author>
          <author>
            <style face="bold" font="default" size="100%">Menkès, Christophe</style>
          </author>
        </authors>
      </contributors>
      <titles>
        <title>Analytical models approximating individual processes : a validation method</title>
        <secondary-title>Mathematical Biosciences</secondary-title>
      </titles>
      <pages>127-135</pages>
      <keywords>
        <keyword>Model upscaling</keyword>
        <keyword>Individual-based models</keyword>
        <keyword>Population-level models</keyword>
        <keyword>Statistical tests</keyword>
        <keyword>Epidemic models</keyword>
        <keyword>Cyclically feeding vectors</keyword>
      </keywords>
      <dates>
        <year>2010</year>
      </dates>
      <call-num>fdi:010052991</call-num>
      <language>ENG</language>
      <periodical>
        <full-title>Mathematical Biosciences</full-title>
      </periodical>
      <isbn>0025-5564</isbn>
      <accession-num>ISI:000285369500002</accession-num>
      <number>2</number>
      <electronic-resource-num>10.1016/j.mbs.2010.08.014</electronic-resource-num>
      <urls>
        <related-urls>
          <url>https://www.documentation.ird.fr/hor/fdi:010052991</url>
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        <pdf-urls>
          <url>https://www.documentation.ird.fr/intranet/publi/2011/01/010052991.pdf</url>
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      </urls>
      <volume>228</volume>
      <remote-database-provider>Horizon (IRD)</remote-database-provider>
      <abstract>Upscaling population models from fine to coarse resolutions, in space, time and/or level of description, allows the derivation of fast and tractable models based on a thorough knowledge of individual processes. The validity of such approximations is generally tested only on a limited range of parameter sets. A more general validation test, over a range of parameters, is proposed; this would estimate the error induced by the approximation, using the original model's stochastic variability as a reference. This method is illustrated by three examples taken from the field of epidemics transmitted by vectors that bite in a temporally cyclical pattern, that illustrate the use of the method: to estimate if an approximation over- or under-fits the original model; to invalidate an approximation; to rank possible approximations for their qualities. As a result, the application of the validation method to this field emphasizes the need to account for the vectors' biology in epidemic prediction models and to validate these against finer scale models.</abstract>
      <custom6>020 ; 052 ; 122</custom6>
      <custom1>UR203 / UR182</custom1>
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