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: Poolkeh finds the first pooling strategy for a population-wide covid-19 testing

Abstract:
Viral respiratory diseases (VRDs), such as influenza and COVID-19, are thought to spread faster over winter than during summer.
It has been previously argued that cold and dry conditions were more conducive to the transmission of VRD than warm and humid climates, although this relationship appears restricted to temperate regions, and the causal relationship is not well understood.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing COVID-19 has emerged as a serious global public health problem after the first COVID-19 reports in Wuhan, China, in late 2019.
It is still unclear whether this novel respiratory disease will ultimately prove to be a seasonal endemic disease.
Here, we suggest that Air Drying Capacity (ADC;an atmospheric state-variable known to control the fate/evolution of the virus-laden droplets) and ultraviolet radiation (UV) are probable environmental determinants in shaping the transmission of COVID-19 at the seasonal time scale.
These variables, unlike temperature and humidity, provide a physically-based framework consistent with the apparent seasonal variability in COVID-19 prevalence across a broad range of climates (e.g., Germany and India).
Since this disease is known to be influenced by the compounding effect of social, biological, and environmental determinants, this study does not claim that these environmental determinants exclusively shape the seasonality of COVID-19.
However, we argue that ADC and UV play a significant role in COVID-19 dynamics at the seasonal scale.
These findings could help guide the development of a sound adaptation strategy against the pandemic over the coming seasons.
Plain Language Summary There is growing scientific interest in the potential seasonality of COVID-19 and its links to climate variables.
This study aims to determine whether four environmental variables, namely temperature, humidity, Air Drying Capacity (ADC), and ultraviolet radiation (UV), are probable environmental determinants for the observed seasonal dynamics of COVID-19 prevalence, based on extensive country-level data spanning the first year of the pandemic.
Although the influence of socio-economic factors may be dominant, we here suggest that ADC and UV are key environmental determinants of COVID-19 and can potentially affect the transmission and seasonality of the disease across a wide range of climates.
Key Points ### Competing Interest Statement The authors have declared no competing interest.
### Funding Statement We acknowledge support from the Breene M Kerr chair at MIT.
### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: N/A All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.
Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov.
I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.
Yes 6-hourly temperature, dew point temperature and surface pressure data, and hourly UV fields are taken from the ERA5 reanalysis (Hersbach et al. 2018) at 0.25 by 0.25 horizontal resolution, for the period ranging from March 1st, 2020 to January 13th, 2021.
With this data we calculate daily mean temperature, specific humidity, and ADC (see Section 2.2).
Since population is not uniformly distributed within each country, we avoid simple country-averaged climate variables which do not correctly reflect the climate conditions to which the population is exposed.
Instead, we calculate population-weighted average temperature, specific humidity, ADC, and UV across all grid cells contained in a given country, based on weights obtained from a gridded distribution of population density, following Carleton et al. (2021).
Population density data is taken from the gridded population of the world (GPW) v4 dataset (CIESIN, 2018).
Daily data on confirmed COVID-19 cases, number of tests, stringency index (i.e., a composite index of political measures) and population for each country are from the Our World in Data database (available at https://ourworldindata.org/).
Subnational-level COVID-19 epidemiological data for the Australia, China, and Canada are available at the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE;https://data.humdata.org/).
Daily COVID-19 data at the scale of different states within the United States are provided at the COVID Tracking Project (available at https://covidtracking.com/).
A threshold of at least 10,000 cumulative COVID-19 tests per 1 million people was retained to discard countries with unrepresentative data.
This criterion can somewhat ensure the reliability of the data, although it still has severe limitations (see discussion).
This approach yields 54 countries in the temperate Northern Hemisphere, and six tropical countries (Table 1), which are predominantly influenced by tropical monsoon systems with hot-humid summers.
To isolate the role of environmental factors in modulating the spread and potential seasonality of COVID-19, five representative countries are carefully selected, which have different climate conditions and constant social controls (i.e., stringency index does not change significantly) over the analysis period (Fig. S1).
The list of analyzed countries are provided in Table 1.
To consider the incubation period of COVID-19 which is generally regarded as 4 to 7 days but can be as long as 10 days and uncommonly even longer, we mainly use biweekly average or biweekly cumulative values (Li et al., 2020). &lt;https://ourworldindata.org/&gt; &lt;https://data.humdata.org/&gt; &lt;https://covidtracking.com/&gt;
{"verdict": "CONTRADICT", "evidence": ["Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable."]}