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: Ideal weather conditions may have helped coronavirus thrive

Abstract:
The coronavirus disease 2019 (COVID-19) outbreak has become a severe public health issue.
The novelty of the virus prompts a search for understanding of how ecological factors affect the transmission and survival of the virus.
Several studies have robustly identified a relationship between temperature and the number of cases.
However, there is no specific study for a tropical climate such as Brazil.
This work aims to determine the relationship of temperature to COVID-19 infection for the state capital cities of Brazil.
Cumulative data with the daily number of confirmed cases was collected from February 27 to April 1, 2020, for all 27 state capital cities of Brazil affected by COVID-19.
A generalized additive model (GAM) was applied to explore the linear and nonlinear relationship between annual average temperature compensation and confirmed cases.
Also, a polynomial linear regression model was proposed to represent the behavior of the growth curve of COVID-19 in the capital cities of Brazil.
The GAM dose-response curve suggested a negative linear relationship between temperatures and daily cumulative confirmed cases of COVID-19 in the range from 16.8 °C to 27.4 °C.
Each 1 °C rise of temperature was associated with a -4.8951% (t = -2.29, p = 0.0226) decrease in the number of daily cumulative confirmed cases of COVID-19.
A sensitivity analysis assessed the robustness of the results of the model.
The predicted R-squared of the polynomial linear regression model was 0.81053.
In this study, which features the tropical temperatures of Brazil, the variation in annual average temperatures ranged from 16.8 °C to 27.4 °C.
Results indicated that temperatures had a negative linear relationship with the number of confirmed cases.
The curve flattened at a threshold of 25.8 °C.
There is no evidence supporting that the curve declined for temperatures above 25.8 °C.
The study had the goal of supporting governance for healthcare policymakers.
{"verdict": "CONTRADICT", "evidence": ["Results indicated that temperatures had a negative linear relationship with the number of confirmed cases."]}