You will be shown a claim related to the COVID-19 pandemic, 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".
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.

For instance, if the model were given the claim "the COVID vaccine is safe for healthy adults", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults."]
}

Claim: Mean immunity thresholds for sars-cov-2 estimated from unfolding epidemics

Abstract:
As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spreads, the susceptible subpopulation declines causing the rate at which new infections occur to slow down.
Variation in individual susceptibility or exposure to infection exacerbates this effect.
Individuals that are more susceptible or more exposed tend to be infected and removed from the susceptible subpopulation earlier.
This selective depletion of susceptibles intensifies the deceleration in incidence.
Eventually, susceptible numbers become low enough to prevent epidemic growth or, in other words, the herd immunity threshold is reached.
Here we fit epidemiological models with inbuilt distributions of susceptibility or exposure to SARS-CoV-2 outbreaks to estimate basic reproduction numbers (R_0) alongside coefficients of individual variation (CV) and the effects of containment strategies.
Herd immunity thresholds are then calculated as 1-(1/R_0 )^(1/((1+CV^2 ) )) or 1-(1/R_0 )^(1/((1+2CV^2 ) )), depending on whether variation is on susceptibility or exposure.
Our inferences result in herd immunity thresholds around 10-20%, considerably lower than the minimum coverage needed to interrupt transmission by random vaccination, which for R_0 higher than 2.5 is estimated above 60%.
We emphasize that the classical formula, 1-1/R_0 , remains applicable to describe herd immunity thresholds for random vaccination, but not for immunity induced by infection which is naturally selective.
These findings have profound consequences for the governance of the current pandemic given that some populations may be close to achieving herd immunity despite being under more or less strict social distancing measures.
{"verdict": "CONTRADICT", "evidence": ["These findings have profound consequences for the governance of the current pandemic given that some populations may be close to achieving herd immunity despite being under more or less strict social distancing measures."]}