Selected article for: "linear regression and multiple linear regression"

Author: Morbey, Roger; Elliot, Alex J.; Zambon, Maria; Pebody, Richard; Smith, Gillian E.
Title: Interpreting specific and general respiratory indicators in syndromic surveillance
  • Document date: 2017_5_1
  • ID: juc6dw4q_4
    Snippet: We used positive laboratory reports for the following pathogens as a proxy for community incidence in England: human metapneumovirus (HMPV), RSV, coronavirus, influenza strains, invasive haemophilus influenzae, invasive streptococcus pneumoniae, mycoplasma pneumoniae, parainfluenza and rhinovirus. Organisms were chosen that were found to be important in previous work 2 and were available from routine laboratory testing. Syndromic data included co.....
    Document: We used positive laboratory reports for the following pathogens as a proxy for community incidence in England: human metapneumovirus (HMPV), RSV, coronavirus, influenza strains, invasive haemophilus influenzae, invasive streptococcus pneumoniae, mycoplasma pneumoniae, parainfluenza and rhinovirus. Organisms were chosen that were found to be important in previous work 2 and were available from routine laboratory testing. Syndromic data included consultations with family doctors (called General Practitioners or GPs), calls to a national telephone helpline "NHS 111" and attendances at emergency departments (EDs). Associations between laboratory reports and syndromic data were examined over four winter seasons (weeks 40 to 20), between 2011 and 2015. Multiple linear regression was used to model correlations and to estimate the proportion of syndromic consultations associated with specific pathogens. Finally, burden estimates were used to infer the proportion of patients affected by specific pathogens that would be diagnosed with different symptoms.

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