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10.1038/s41598-021-02620-5

http://scihub22266oqcxt.onion/10.1038/s41598-021-02620-5
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34857815!8640038!34857815
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suck abstract from ncbi


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pmid34857815      Sci+Rep 2021 ; 11 (1): 23321
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  • New statistical model for misreported data with application to current public health challenges #MMPMID34857815
  • Morina D; Fernandez-Fontelo A; Cabana A; Puig P
  • Sci Rep 2021[Dec]; 11 (1): 23321 PMID34857815show ga
  • The main goal of this work is to present a new model able to deal with potentially misreported continuous time series. The proposed model is able to handle the autocorrelation structure in continuous time series data, which might be partially or totally underreported or overreported. Its performance is illustrated through a comprehensive simulation study considering several autocorrelation structures and three real data applications on human papillomavirus incidence in Girona (Catalonia, Spain) and Covid-19 incidence in two regions with very different circumstances: the early days of the epidemic in the Chinese region of Heilongjiang and the most current data from Catalonia.
  • |*Models, Statistical[MESH]
  • |COVID-19/epidemiology[MESH]
  • |China/epidemiology[MESH]
  • |Computer Simulation[MESH]
  • |Humans[MESH]
  • |Papillomavirus Infections/epidemiology[MESH]
  • |Public Health/*methods[MESH]
  • |Spain/epidemiology[MESH]


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