You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: COVID-19 appears to be more contagious and to spread more quickly than the flu.

Abstract:
Background The outbreak of coronavirus disease 2019 (COVID-19) in December 2019 overlaps with the flu season.
Methods We compared clinical and laboratory results from 719 influenza and 973 COVID-19 patients from January to April 2020.
We compiled laboratory results from the first 14 days of the hospitalized patients using parameters that are most significantly different between COVID-19 and influenza and hierarchically clustered COVID-19 patients based on these data.
The clinical outcomes were compared among different clusters.
Results Temporal analyses of laboratory results revealed that compared to influenza, patients with COVID-19 exhibited a continued increase in the white blood cell count, rapid decline of hemoglobin, more rapid increase in blood urea nitrogen (BUN) and D-dimer, and higher level of alanine transaminase, C-reactive protein, ferritin, and fibrinogen.
Using these results, we sub-classified the COVID-19 patients into 5 clusters through a hierarchical clustering analysis.
We then reviewed the medical record of these patients and risk stratified them based on the clinical outcomes.
The cluster with the highest risk showed 27.8% fatality, 94% ICU admission, 94% intubation, and 28% discharge rates compared to 0%, 38%, 22%, and 88% in the lowest risk cluster, respectively.
Patients in the highest risk cluster had leukocytosis including neutrophilia and monocytosis, severe anemia, higher BUN, creatinine, D-dimer, alkaline phosphatase, bilirubin, and troponin.
Conclusions There are significant differences in the clinical and laboratory courses between COVID-19 and influenza.
Risk stratification in hospitalized COVID-19 patients using laboratory data could be useful to predict clinical outcomes and pathophysiology of these patients.
{"verdict": "NEI", "evidence": []}