Question:
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: Hydroxychloroquine is an Effective Treatment for COVID-19

Abstract:
Background: Coronavirus pandemic is currently a global public health emergency.
At present, no pharmacological treatment is known to treat this condition, and there is a need to review the available treatments.
Objective: While there have been studies to describe the role of chloroquine and hydroxychloroquine in various viral conditions, there is limited information about the use of them in COVID-19.
This systematic review aims to summarize the available evidence regarding the role of chloroquine in treating coronavirus infection.
Methods: The preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were used for this review.
A literature search was performed using PUBMED & Google Scholar to find articles about the role of CQ in COVID-19 patients.
Results: We included 19 publications (Five published articles, three letters/correspondence, one commentary, five pre-proofs of accepted articles, one abstract of yet to be published article, and four were pre-prints (not yet peer-reviewed) articles) in this systematic review.
All the articles mentioned about the role of chloroquine and /or hydroxychloroquine in limiting the infection with SARS-CoV-2 (the virus causing COVID-19).
Conclusions: There is theoretical, experimental, preclinical and clinical evidence of the effectiveness of chloroquine in patients affected with COVID-19.
There is adequate evidence of drug safety from the long-time clinical use of chloroquine and hydroxychloroquine in other indications.
More data from ongoing and future trials will add more insight into the role of chloroquine and hydroxychloroquine in COVID-19 infection.

Answer: {"verdict": "SUPPORT", "evidence": ["Conclusions: There is theoretical, experimental, preclinical and clinical evidence of the effectiveness of chloroquine in patients affected with COVID-19."]}


Question:
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: cloth face mask with a filter can help prevent the spread of COVID-19

Abstract:
Ma's research shows N95 masks, medical masks, even homemade masks could block at least 90% of the virus in aerosols(1).
This study puts the debate on whether the public wear masks back on the table.
Recently Science interviewed Dr. Gao, director‐general of Chinese Center for Disease Control and Prevention (CDC).
This article is protected by copyright.
All rights reserved.

Answer: {"verdict": "SUPPORT", "evidence": ["Ma's research shows N95 masks, medical masks, even homemade masks could block at least 90% of the virus in aerosols(1)."]}


Question:
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: Many readers have written in to ask whether ibuprofen or other non-steroidal anti-inflammatory drugs, or NSAIDs, can worsen COVID-19, the disease caused by the novel coronavirus.

Abstract:
: The COVID-19 pandemic is challenging our cardiovascular care of patients with heart diseases.
In the setting of pericardial diseases, there are two possible different scenarios to consider: the patient being treated for pericarditis who subsequently becomes infected with SARS-CoV-2, and the patient with COVID-19 who develops pericarditis or pericardial effusion.
In both conditions, clinicians may be doubtful regarding the safety of nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroids, colchicine, and biological agents, such as anti-IL1 agents (e.g. anakinra), that are the mainstay of therapy for pericarditis.
For NSAIDs, there is no clear scientific evidence linking ibuprofen and other NSAIDs to worsening of COVID-19; however, it seems prudent to continue them, if necessary to control pericarditis, and on the other hand, to prefer paracetamol for fever and systemic symptoms related to COVID-19.
Treatments with corticosteroids, colchicine, and anakinra appear well tolerated in the context of COVID-19 infection and are currently actively evaluated as potential therapeutic options for COVID infection at different stages of the disease.
On this basis, currently most treatments for pericarditis do not appear contraindicated also in the presence of possible COVID-19 infection and should not be discontinued, and some (corticosteroids, colchicine, and anakinra) can be considered to treat both conditions.

Answer: {"verdict": "NEI", "evidence": []}


Question:
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: hydroxychloroquine cures covid-19.

Abstract:
Hydroxychloroquine has been promoted for its use in treatment of COVID-19 patients based on in-vitro evidences.
We searched the databases to include randomized and observational studies evaluating the effect of Hydroxychloroquine on mortality in COVID-19 patients.
The outcome was summarized as odds ratios (OR) with a 95% confidence interval (CI).We used the inverse-variance method with a random effect model and assessed the heterogeneity using I2 test.
We used ROBINS-I tool to assess methodological quality of the included studies.
We performed the meta-analysis using 'Review manager software version 5.3'.
We identified 6 observationalstudies satisfying the selection criteria.
In all studies, Hydroxychloroquine was given as add on to the standard care and effect was compared with the standard care alone.
A pooled analysis observed 251 deaths in 1331 participants of the Hydroxychloroquine arm and 363 deaths in 1577 participants of the control arm.
There was no difference in odds of mortality events amongst Hydroxychloroquine and supportive care arm [1.25 (95% CI: 0.65, 2.38); I2 = 80%].
A similar trend was observed with moderate risk of bias studies [0.95 (95% CI: 0.44, 2.06); I2 = 85%].
The odds of mortality were significantly higher in patients treated with Hydroxychloroquine + Azithromycin than supportive care alone [2.34 (95% CI: 1.63, 3.34); I2 = 0%].
A pooled analysis of recently published studies suggests no additional benefit for reducing mortality in COVID-19 patients when Hydroxychloroquine is given as add-on to the standard care.
Graphical Abstract.

Answer: {"verdict": "CONTRADICT", "evidence": ["The odds of mortality were significantly higher in patients treated with Hydroxychloroquine + Azithromycin than supportive care alone [2.34 (95% CI: 1.63, 3.34); I2 = 0%].", "A pooled analysis of recently published studies suggests no additional benefit for reducing mortality in COVID-19 patients when Hydroxychloroquine is given as add-on to the standard care."]}


Question:
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: To help slow the spread and reduce your risk of COVID-19, stay at least 6 feet away from others. Keeping physical distance is important, even if you are not sick.

Abstract:
Social distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19.
In this work we analyze the effect of current social distancing measures in the United States.
We quantify the reduction in doubling rate, by state, that is associated with social distancing.
We find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states.
At the same time, we do not find significant evidence that social distancing has resulted in a reduction in the number of daily confirmed cases.
Instead, social distancing has merely stabilized the spread of the disease.
We provide an illustration of our findings for each state, including point estimates of the effective reproduction number, R, both with and without social distancing.
We also discuss the policy implications of our findings.

Answer: {"verdict": "SUPPORT", "evidence": ["We find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states."]}


Question:
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: The drugs have anti-inflammatory effects "in addition to their blood pressure benefits.

Abstract:
Coronavirus disease 2019 (COVID-19) is a viral pandemic precipitated by the severe acute respiratory syndrome coronavirus 2.
Since previous reports suggested that viral entry into cells may involve angiotensin converting enzyme 2, there has been growing concern that angiotensin converting enzyme inhibitor (ACEI) and angiotensin II receptor blocker (ARB) use may exacerbate the disease severity.
In this retrospective, single-center US study of adult patients diagnosed with COVID-19, we evaluated the association of ACEI/ARB use with hospital admission.
Secondary outcomes included: ICU admission, mechanical ventilation, length of hospital stay, use of inotropes, and all-cause mortality.
Propensity score matching was performed to account for potential confounders.
Among 590 unmatched patients diagnosed with COVID-19, 78 patients were receiving ACEI/ARB (median age 63 years and 59.7% male) and 512 patients were non-users (median age 42 years and 47.1% male).
In the propensity matched population, multivariate logistic regression analysis adjusting for age, gender and comorbidities demonstrated that ACEI/ARB use was not associated with hospital admission (OR 1.2, 95% CI 0.5-2.7, p = 0.652).
CAD and CKD/ESRD remained independently associated with admission to hospital.
All-cause mortality, ICU stay, need for ventilation, and inotrope use was not significantly different between the 2 study groups.
In conclusion, among patients who were diagnosed with COVID-19, ACEI/ARB use was not associated with increased risk of hospital admission.

Answer: {"verdict": "NEI", "evidence": []}


Question:
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: Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus

Abstract:
Coronavirus disease 2019 (COVID-19) originated in the city of Wuhan, Hubei Province, Central China, and has spread quickly to 72 countries to date.
COVID-19 is caused by a novel coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [previously provisionally known as 2019 novel coronavirus (2019-nCoV)].
At present, the newly identified SARS-CoV-2 has caused a large number of deaths with tens of thousands of confirmed cases worldwide, posing a serious threat to public health.
However, there are no clinically approved vaccines or specific therapeutic drugs available for COVID-19.
Intensive research on the newly emerged SARS-CoV-2 is urgently needed to elucidate the pathogenic mechanisms and epidemiological characteristics and to identify potential drug targets, which will contribute to the development of effective prevention and treatment strategies.
Hence, this review will focus on recent progress regarding the structure of SARS-CoV-2 and the characteristics of COVID-19, such as the aetiology, pathogenesis and epidemiological characteristics.

Answer: {"verdict": "NEI", "evidence": []}


Question:
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: There's been much speculation about whether vitamin D might prevent or help survival with COVID-19, and two new studies appear to underscore the link.

Abstract:
The severity of coronavirus 2019 infection (COVID-19) is determined by the presence of pneumonia, severe acute respiratory distress syndrome (SARS-CoV-2), myocarditis, microvascular thrombosis and/or cytokine storms, all of which involve underlying inflammation.
A principal defence against uncontrolled inflammation, and against viral infection in general, is provided by T regulatory lymphocytes (Tregs).
Treg levels have been reported to be low in many COVID-19 patients and can be increased by vitamin D supplementation.
Low vitamin D levels have been associated with an increase in inflammatory cytokines and a significantly increased risk of pneumonia and viral upper respiratory tract infections.
Vitamin D deficiency is associated with an increase in thrombotic episodes, which are frequently observed in COVID-19.
Vitamin D deficiency has been found to occur more frequently in patients with obesity and diabetes.
These conditions are reported to carry a higher mortality in COVID-19.
If vitamin D does in fact reduce the severity of COVID-19 in regard to pneumonia/ARDS, inflammation, inflammatory cytokines and thrombosis, it is our opinion that supplements would offer a relatively easy option to decrease the impact of the pandemic.

Answer: {"verdict": "NEI", "evidence": []}


Question:
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 increases risk of heart attacks and stroke

Abstract:
BACKGROUND: The 2019 novel coronavirus has caused the outbreak of the acute respiratory disease in Wuhan, Hubei Province of China since December 2019.
This study was performed to analyze the clinical characteristics of patients who succumbed to and who recovered from 2019 novel coronavirus disease (COVID-19).
METHODS: Clinical data were collected from two tertiary hospitals in Wuhan.
A retrospective investigation was conducted to analyze the clinical characteristics of fatal cases of COVID-19 (death group) and we compare them with recovered patients (recovered group).
Continuous variables were analyzed using the Mann-Whitney U test.
Categorical variables were analyzed by χ test or Fisher exact test as appropriate.
RESULTS: Our study enrolled 109 COVID-19 patients who died during hospitalization and 116 recovered patients.
The median age of the death group was older than the recovered group (69 [62, 74] vs. 40 [33, 57] years, Zâ=â9.738, Pâ<â0.001).
More patients in the death group had underlying diseases (72.5% vs. 41.4%, χâ=â22.105, Pâ<â0.001).
Patients in the death group had a significantly longer time of illness onset to hospitalization (10.0 [6.5, 12.0] vs. 7.0 [5.0, 10.0] days, Zâ=â3.216, Pâ=â0.001).
On admission, the proportions of patients with symptoms of dyspnea (70.6% vs. 19.0%, χâ=â60.905, Pâ<â0.001) and expectoration (32.1% vs. 12.1%, χâ=â13.250, Pâ<â0.001) were significantly higher in the death group.
The blood oxygen saturation was significantly lower in the death group (85 [77, 91]% vs. 97 [95, 98]%, Zâ=â10.625, Pâ<â0.001).
The white blood cell (WBC) in death group was significantly higher on admission (7.23 [4.87, 11.17] vs. 4.52 [3.62, 5.88] ×10/L, Zâ=â7.618, Pâ<â0.001).
Patients in the death group exhibited significantly lower lymphocyte count (0.63 [0.40, 0.79] vs. 1.00 [0.72, 1.27] ×10/L, Zâ=â8.037, Pâ<â0.001) and lymphocyte percentage (7.10 [4.45, 12.73]% vs. 23.50 [15.27, 31.25]%, Zâ=â10.315, Pâ<â0.001) on admission, and the lymphocyte percentage continued to decrease during hospitalization (7.10 [4.45, 12.73]% vs. 2.91 [1.79, 6.13]%, Zâ=â5.242, Pâ<â0.001).
Alanine transaminase (22.00 [15.00, 34.00] vs. 18.70 [13.00, 30.38] U/L, Zâ=â2.592, Pâ=â0.010), aspartate transaminase (34.00 [27.00, 47.00] vs. 22.00 [17.65, 31.75] U/L, Zâ=â7.308, Pâ<â0.001), and creatinine levels (89.00 [72.00, 133.50] vs. 65.00 [54.60, 78.75] µmol/L, Zâ=â6.478, Pâ<â0.001) were significantly higher in the death group than those in the recovered group.
C-reactive protein (CRP) levels were also significantly higher in the death group on admission (109.25 [35.00, 170.28] vs. 3.22 [1.04, 21.80] mg/L, Zâ=â10.206, Pâ<â0.001) and showed no significant improvement after treatment (109.25 [35.00, 170.28] vs. 81.60 [27.23, 179.08] mg/L, Zâ=â1.219, Pâ=â0.233).
The patients in the death group had more complications such as acute respiratory distress syndrome (ARDS) (89.9% vs. 8.6%, χâ=â148.105, Pâ<â0.001), acute cardiac injury (59.6% vs. 0.9%, χâ=â93.222, Pâ<â0.001), acute kidney injury (18.3% vs. 0%, χâ=â23.257, Pâ<â0.001), shock (11.9% vs. 0%, χâ=â14.618, Pâ<â0.001), and disseminated intravascular coagulation (DIC) (6.4% vs. 0%, χâ=â7.655, Pâ=â0.006).
CONCLUSIONS: Compared to the recovered group, more patients in the death group exhibited characteristics of advanced age, pre-existing comorbidities, dyspnea, oxygen saturation decrease, increased WBC count, decreased lymphocytes, and elevated CRP levels.
More patients in the death group had complications such as ARDS, acute cardiac injury, acute kidney injury, shock, and DIC.

Answer: {"verdict": "SUPPORT", "evidence": ["CONCLUSIONS: Compared to the recovered group, more patients in the death group exhibited characteristics of advanced age, pre-existing comorbidities, dyspnea, oxygen saturation decrease, increased WBC count, decreased lymphocytes, and elevated CRP levels."]}


Question:
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: Imbalanced host response to sars-cov-2 drives development of covid-19

Abstract:
Viral pandemics, such as the one caused by SARS-CoV-2, pose an imminent threat to humanity.
Because of its recent emergence, there is a paucity of information regarding viral behavior and host response following SARS-CoV-2 infection.
Here we offer an in-depth analysis of the transcriptional response to SARS-CoV-2 compared with other respiratory viruses.
Cell and animal models of SARS-CoV-2 infection, in addition to transcriptional and serum profiling of COVID-19 patients, consistently revealed a unique and inappropriate inflammatory response.
This response is defined by low levels of type I and III interferons juxtaposed to elevated chemokines and high expression of IL-6.
We propose that reduced innate antiviral defenses coupled with exuberant inflammatory cytokine production are the defining and driving features of COVID-19.

Answer: {"verdict": "SUPPORT", "evidence": ["Cell and animal models of SARS-CoV-2 infection, in addition to transcriptional and serum profiling of COVID-19 patients, consistently revealed a unique and inappropriate inflammatory response.", "We propose that reduced innate antiviral defenses coupled with exuberant inflammatory cytokine production are the defining and driving features of COVID-19."]}


Question:
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: Sterilizing immunity against sars-cov-2 in hamsters conferred by a single recombinant subunit vaccine.

Abstract:
A safe and effective SARS-CoV-2 vaccine is essential to avert the on-going COVID-19 pandemic.
Here, we developed a subunit vaccine, which is comprised of CHO-expressed spike ectodomain protein (StriFK) and nitrogen bisphosphonates-modified zinc-aluminum hybrid adjuvant (FH002C).
This vaccine candidate rapidly elicited the robust humoral response, Th1/Th2 balanced helper CD4 T cell and CD8 T cell immune response in animal models.
In mice, hamsters, and non-human primates, 2-shot and 3-shot immunization of StriFK-FH002C generated 28-to 38-fold and 47-to 269-fold higher neutralizing antibody titers than the human COVID-19 convalescent plasmas, respectively.
More importantly, the StriFK-FH002C immunization conferred sterilizing immunity to prevent SARS-CoV-2 infection and transmission, which also protected animals from virus-induced weight loss, COVID-19-like symptoms, and pneumonia in hamsters.
Vaccine-induced neutralizing and cell-based receptor-blocking antibody titers correlated well with protective efficacy in hamsters, suggesting vaccine-elicited protection is immune-associated.
The StriFK-FH002C provided a promising SARS-CoV-2 vaccine candidate for further clinical evaluation.

Answer: {"verdict": "CONTRADICT", "evidence": ["More importantly, the StriFK-FH002C immunization conferred sterilizing immunity to prevent SARS-CoV-2 infection and transmission, which also protected animals from virus-induced weight loss, COVID-19-like symptoms, and pneumonia in hamsters.", "Vaccine-induced neutralizing and cell-based receptor-blocking antibody titers correlated well with protective efficacy in hamsters, suggesting vaccine-elicited protection is immune-associated."]}


Question:
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: Scientists are endeavoring to find antivirals specific to the virus. Several drugs such as chloroquine, arbidol, remdesivir, and favipiravir are currently undergoing clinical studies to test their efficacy and safety in the treatment of coronavirus disease 2019 (COVID-19) in China; some promising results have been achieved thus far.

Abstract:
The current pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has presented unprecedented challenges to the healthcare systems in almost every country around the world.
Currently, there are no proven effective vaccines or therapeutic agents against the virus.
Current clinical management includes infection prevention and control measures and supportive care including supplemental oxygen and mechanical ventilatory support.
Evolving research and clinical data regarding the virologic SARS-CoV-2 suggest a potential list of repurposed drugs with appropriate pharmacological effects and therapeutic efficacies in treating COVID-19 patients.
In this review, we will update and summarize the most common and plausible drugs for the treatment of COVID-19 patients.
These drugs and therapeutic agents include antiviral agents (remdesivir, hydroxychloroquine, chloroquine, lopinavir, umifenovir, favipiravir, and oseltamivir), and supporting agents (Ascorbic acid, Azithromycin, Corticosteroids, Nitric oxide, IL-6 antagonists), among others.
We hope that this review will provide useful and most updated therapeutic drugs to prevent, control, and treat COVID-19 patients until the approval of vaccines and specific drugs targeting SARS-CoV-2.

Answer: {"verdict": "SUPPORT", "evidence": ["Currently, there are no proven effective vaccines or therapeutic agents against the virus."]}


Question:
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 and kids: What can happen when children get the coronavirus. A rare but sometimes deadly syndrome poses extra risk for COVID's youngest victims.

Abstract:
The novel severe acute respiratory syndrome coronavirus 2 (SARS-COV 2) has rapidly spread worldwide with increasing hospitalization and mortality rate.
Ongoing studies and accumulated data are de- tailing the features and the effects of the new coronavirus disease 19 (COVID 19) in the adult population, and cardiovascular involvement is emerging as the most significant and life-threatening complication, with an in- creased risk of morbidity and mortality in patients with underlying cardiovascular disease.
At present, though the limited data on the effects of COVID 19 in pediatric patients, children seem to count for a little proportion of SARS-COV 2 infection, and present with less severe disease and effects However infants and toddlers are at risk of developing critical course.
The disease has a range of clinical presentations in children, for which the potential need for further investigation of myocardial injury and cardiovascular issues should be kept in mind to avoid misdiagnosing severe clinical entities.
Overlapping with Kawasaki disease is a concern, particularly the incomplete and atypical form.
We aim to summarize the initial considerations and potential cardiovascular implications of COVID-19 for children and patients with congenital heart disease.

Answer: {"verdict": "SUPPORT", "evidence": ["At present, though the limited data on the effects of COVID 19 in pediatric patients, children seem to count for a little proportion of SARS-COV 2 infection, and present with less severe disease and effects However infants and toddlers are at risk of developing critical course.", "The disease has a range of clinical presentations in children, for which the potential need for further investigation of myocardial injury and cardiovascular issues should be kept in mind to avoid misdiagnosing severe clinical entities."]}


Question:
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: vitamin D might be able to protect people from the coronavirus (COVID-19).

Abstract:
Importance: Vitamin D treatment has been found to decrease incidence of viral respiratory tract infection, especially in vitamin D deficiency.
It is unknown whether COVID-19 incidence is associated with vitamin D deficiency and treatment.
Objective: To examine whether vitamin D deficiency and treatment are associated with testing positive for COVID-19.
Design: Retrospective cohort study Setting: University of Chicago Medicine Participants: Patients tested for COVID-19 from 3/3/2020-4/10/2020.
Vitamin D deficiency was defined by the most recent 25-hydroxycholecalciferol <20ng/ml or 1,25-dihydroxycholecalciferol <18pg/ml within 1 year before COVID-19 testing.
Treatment was defined by the most recent vitamin D type and dose, and treatment changes between the time of the most recent vitamin D level and time of COVID-19 testing.
Vitamin D deficiency and treatment changes were combined to categorize vitamin D status at the time of COVID-19 testing as likely deficient(last-level-deficient/treatment-not-increased), likely sufficient(last-level-not-deficient/treatment-not-decreased), or uncertain deficiency(last-level-deficient/treatment-increased or last-level-not-deficient/treatment-decreased).
Main Outcomes and Measures: The main outcome was testing positive for COVID-19.
Multivariable analysis tested whether the most recent vitamin D level and treatment changes after that level were associated with testing positive for COVID-19 controlling for demographic and comorbidity indicators.
Bivariate analyses of associations of treatment with vitamin D deficiency and COVID-19 were performed.
Results: Among 4,314 patients tested for COVID-19, 499 had a vitamin D level in the year before testing.
Vitamin D status at the time of COVID-19 testing was categorized as likely deficient for 127(25%) patients, likely sufficient for 291(58%) patients, and uncertain for 81(16%) patients.
In multivariate analysis, testing positive for COVID-19 was associated with increasing age(RR(age<50)=1.05,p<0.021;RR(age[≥]50)=1.02,p<0.064)), non-white race(RR=2.54,p<0.01) and being likely vitamin D deficient (deficient/treatment-not-increased:RR=1.77,p<0.02) as compared to likely vitamin D sufficient(not-deficient/treatment-not-decreased), with predicted COVID-19 rates in the vitamin D deficient group of 21.6%(95%CI[14.0%-29.2%] ) versus 12.2%(95%CI[8.9%-15.4%]) in the vitamin D sufficient group.
Vitamin D deficiency declined with increasing vitamin D dose, especially of vitamin D3.
Vitamin D dose was not significantly associated with testing positive for COVID-19.
Conclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk.
Testing and treatment for vitamin D deficiency to address COVID-19 warrant aggressive pursuit and study.

Answer: {"verdict": "SUPPORT", "evidence": ["Conclusions and Relevance: Vitamin D deficiency that is not sufficiently treated is associated with COVID-19 risk."]}


Question:
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: Personalized prediction approach for hospitalized patients with covid-19

Abstract:
Hospitalized patients with COVID-19 experiencing respiratory symptoms have different complications (inflammatory, co-infection and thrombotic) that are identifiable by analytics patterns.
Personalized treatment decisions decreased early mortality (OR 0.144, CI 0.03-0.686; p=0.015).
Increasing age (OR 1.06; p=0.038) and therapeutic effort limitation (OR 9.684; p<0.001) were associated with higher mortality.

Answer: {"verdict": "CONTRADICT", "evidence": ["Personalized treatment decisions decreased early mortality (OR 0.144, CI 0.03-0.686; p=0.015)."]}


Question:
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: Diabetes is generally known to weaken the immune system, making it harder to protect against viral infections like COVID-19.

Abstract:
AIMS: To describe characteristics of COVID-19 patients with type 2 diabetes and to analyze risk factors for severity.
METHODS: Demographics, comorbidities, symptoms, laboratory findings, treatments and outcomes of COVID-19 patients with diabetes were collected and analyzed.
RESULTS: Seventy-fourCOVID-19 patients with diabetes were included.
Twenty-seven patients (36.5%) were severe and 10 patients (13.5%) died.
Higher levels of blood glucose, serum amyloid A (SAA), C reactive protein and interleukin 6 were associated with severe patients compared to non-severe ones (P<0.05).
Levels of albumin, cholesterol, high density lipoprotein, small and dense low density lipoprotein and CD4+T lymphocyte counts in severe patients were lower than those in non-severe patients (P<0.05).
Logistic regression analysis identified decreased CD4+T lymphocyte counts (odds ratio [OR]=0.988, 95%Confidence interval [95%CI] 0.979-0.997) and increased SAA levels (OR=1.029, 95%CI 1.002-1.058) as risk factors for severity of COVID-19 with diabetes (P<0.05).
CONCLUSIONS: Type 2 diabetic patients were more susceptible to COVID-19 than overall population, which might be associated with hyperglycemia and dyslipidemia.
Aggressive treatment should be suggested, especially when these patients had low CD4+T lymphocyte counts and high SAA levels.

Answer: {"verdict": "SUPPORT", "evidence": ["CONCLUSIONS: Type 2 diabetic patients were more susceptible to COVID-19 than overall population, which might be associated with hyperglycemia and dyslipidemia."]}


Question:
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: Exploratory analysis of immunization records highlights increased sars-cov-2 rates in individuals with recent non-covid-19 vaccinations

Abstract:
Multiple clinical studies are ongoing to assess whether existing vaccines may afford protection against SARS-CoV-2 infection through trained immunity.
In this exploratory study, we analyze immunization records from 137,037 individuals who received SARS-CoV-2 PCR tests.
We find that polio, Hemophilus influenzae type-B (HIB), measles-mumps-rubella (MMR), varicella, pneumococcal conjugate (PCV13), geriatric flu, and hepatitis A / hepatitis B (HepA-HepB) vaccines administered in the past 1, 2, and 5 years are associated with decreased SARS-CoV-2 infection rates, even after adjusting for geographic SARS-CoV-2 incidence and testing rates, demographics, comorbidities, and number of other vaccinations.
Furthermore, age, race/ethnicity, and blood group stratified analyses reveal significantly lower SARS-CoV-2 rate among black individuals who have taken the PCV13 vaccine, with relative risk of 0.45 at the 5 year time horizon (n: 653, 95% CI: (0.32, 0.64), p-value: 6.9e-05).
These findings suggest that additional pre-clinical and clinical studies are warranted to assess the protective effects of existing non-COVID-19 vaccines and explore underlying immunologic mechanisms.
We note that the findings in this study are preliminary and are subject to change as more data becomes available and as further analysis is conducted.

Answer: {"verdict": "CONTRADICT", "evidence": ["We find that polio, Hemophilus influenzae type-B (HIB), measles-mumps-rubella (MMR), varicella, pneumococcal conjugate (PCV13), geriatric flu, and hepatitis A / hepatitis B (HepA-HepB) vaccines administered in the past 1, 2, and 5 years are associated with decreased SARS-CoV-2 infection rates, even after adjusting for geographic SARS-CoV-2 incidence and testing rates, demographics, comorbidities, and number of other vaccinations.", "Furthermore, age, race/ethnicity, and blood group stratified analyses reveal significantly lower SARS-CoV-2 rate among black individuals who have taken the PCV13 vaccine, with relative risk of 0.45 at the 5 year time horizon (n: 653, 95% CI: (0.32, 0.64), p-value: 6.9e-05).", "These findings suggest that additional pre-clinical and clinical studies are warranted to assess the protective effects of existing non-COVID-19 vaccines and explore underlying immunologic mechanisms."]}


Question:
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: Seasonal human coronavirus antibodies are boosted upon sars-cov-2 infection but not associated with protection

Abstract:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread within the human population.
Although SARS-CoV-2 is a novel coronavirus, most humans had been previously exposed to other antigenically distinct common seasonal human coronaviruses (hCoVs) before the COVID-19 pandemic.
Here, we quantified levels of SARS-CoV-2-reactive antibodies and hCoV-reactive antibodies in serum samples collected from 204 humans before the COVID-19 pandemic.
We then quantified pre-pandemic antibody levels in serum from a separate cohort of 252 individuals who became PCR-confirmed infected with SARS-CoV-2.
Finally, we longitudinally measured hCoV and SARS-CoV-2 antibodies in the serum of hospitalized COVID-19 patients.
Our studies indicate that most individuals possessed hCoV-reactive antibodies before the COVID-19 pandemic.
We determined that ~23% of these individuals possessed non-neutralizing antibodies that cross-reacted with SARS-CoV-2 spike and nucleocapsid proteins.
These antibodies were not associated with protection against SARS-CoV-2 infections or hospitalizations, but paradoxically these hCoV cross-reactive antibodies were boosted upon SARS-CoV-2 infection.

Answer: {"verdict": "SUPPORT", "evidence": ["Although SARS-CoV-2 is a novel coronavirus, most humans had been previously exposed to other antigenically distinct common seasonal human coronaviruses (hCoVs) before the COVID-19 pandemic.", "We then quantified pre-pandemic antibody levels in serum from a separate cohort of 252 individuals who became PCR-confirmed infected with SARS-CoV-2.", "These antibodies were not associated with protection against SARS-CoV-2 infections or hospitalizations, but paradoxically these hCoV cross-reactive antibodies were boosted upon SARS-CoV-2 infection."]}


Question:
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: Mutation of sars-cov-2 orf8, a rapidly evolving coronavirus protein implicated in immune evasion

Abstract:
The molecular basis for the severity and rapid spread of the COVID-19 disease caused by SARS-CoV-2 is largely unknown.
ORF8 is a rapidly evolving accessory protein that has been proposed to interfere with immune responses.
The crystal structure of SARS-CoV-2 ORF8 was determined at 2.04 Å resolution by x-ray crystallography.
The structure reveals a ~60 residue core similar to SARS-CoV ORF7a with the addition of two dimerization interfaces unique to SARS-CoV-2 ORF8.
A covalent disulfide-linked dimer is formed through an N-terminal sequence specific to SARS-CoV-2, while a separate non-covalent interface is formed by another SARS-CoV-2-specific sequence, (73)YIDI(76).
Together the presence of these interfaces shows how SARS-CoV-2 ORF8 can form unique large-scale assemblies not possible for SARS-CoV, potentially mediating unique immune suppression and evasion activities.

Answer: {"verdict": "CONTRADICT", "evidence": ["Together the presence of these interfaces shows how SARS-CoV-2 ORF8 can form unique large-scale assemblies not possible for SARS-CoV, potentially mediating unique immune suppression and evasion activities."]}


Question:
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: Tobacco kills more than 8 million people globally every year. More than 7 million of these deaths are from direct tobacco use and around 1.2 million are due to non-smokers being exposed to second-hand smoke.

Abstract:
Importance.
Covid-19 infection has major international health and economic impacts and risk factors for infection are not completely understood.
Cannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.
Potential synergism between the two epidemics would represent a major public health convergence.
Cigarettes were implicated with disease severity in Wuhan, China.
Objective.
Is cannabis use epidemiologically associated with coronavirus incidence rate (CVIR)?
Design.
Cross-sectional state-based multivariable study.
Setting.
USA.
Primary and Secondary Outcome Measures.
CVIR.
Multivariable-adjusted geospatially-weighted regression models.
As the American cannabis epidemic is characterized by a recent doubling of daily cannabis use it was considered important to characterize the contribution of high intensity use.
Results.
Significant associations of daily cannabis use quintile with CVIR were identified with the highest quintile having a prevalence ratio 5.11 (95%C.I. 4.90-5.33), an attributable fraction in the exposed (AFE) 80.45% (79.61-81.25%) and an attributable fraction in the population of 77.80% (76.88-78.68%) with Chi-squared-for-trend (14,782, df=4) significant at P<10-500.
Similarly when cannabis legalization was considered decriminalization was associated with an elevated CVIR prevalence ratio 4.51 (95%C.I. 4.45-4.58), AFE 77.84% (77.50-78.17%) and Chi-squared-for-trend (56,679, df=2) significant at P<10-500.
Monthly and daily use were linked with CVIR in bivariate geospatial regression models (P=0.0027, P=0.0059).
In multivariable additive models number of flight origins and population density were significant.
In interactive geospatial models adjusted for international travel, ethnicity, income, population, population density and drug use, terms including last month cannabis were significant from P=7.3x10-15, daily cannabis use from P=7.3x10-11 and last month cannabis was independently associated (P=0.0365).
Conclusions and Relevance.
Data indicate CVIR demonstrates significant trends across cannabis use intensity quintiles and with relaxed cannabis legislation.
Recent cannabis use is independently predictive of CVIR in bivariate and multivariable adjusted models and intensity of use is interactively significant.
Cannabis thus joins tobacco as a SARS2-CoV-2 risk factor.

Answer: {"verdict": "SUPPORT", "evidence": ["Cannabis smoking is linked with poor respiratory health, immunosuppression and multiple contaminants.", "Potential synergism between the two epidemics would represent a major public health convergence.", "Cigarettes were implicated with disease severity in Wuhan, China."]}


Question:
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: Many readers have written in to ask whether ibuprofen or other non-steroidal anti-inflammatory drugs, or NSAIDs, can worsen COVID-19, the disease caused by the novel coronavirus.

Abstract:
OBJECTIVE: It was recently suggested that ibuprofen might increase the risk for severe and fatal coronavirus disease 2019 (COVID-19) and should therefore be avoided in this patient population.
We aimed to evaluate whether ibuprofen use in individuals with COVID-19 was associated with more severe disease, compared with individuals using paracetamol or no antipyretics.
METHODS: In a retrospective cohort study of patients with COVID-19 from Shamir Medical Centre, Israel, we monitored any use of ibuprofen from a week before diagnosis of COVID-19 throughout the disease.
Primary outcomes were mortality and the need for respiratory support, including oxygen administration and mechanical ventilation.
RESULTS: The study included 403 confirmed cases of COVID-19, with a median age of 45 years.
Of the entire cohort, 44 patients (11%) needed respiratory support and 12 (3%) died.
One hundred and seventy-nine (44%) patients had fever, with 32% using paracetamol and 22% using ibuprofen, for symptom-relief.
In the ibuprofen group, 3 (3.4%) patients died, whereas in the non-ibuprofen group, 9 (2.8%) patients died (p 0.95).
Nine (10.3%) patients from the ibuprofen group needed respiratory support, compared with 35 (11%) from the non-ibuprofen group (p 1).
When compared with exclusive paracetamol users, no differences were observed in mortality rates or the need for respiratory support among patients using ibuprofen.
CONCLUSIONS: In this cohort of COVID-19 patients, ibuprofen use was not associated with worse clinical outcomes, compared with paracetamol or no antipyretic.

Answer: {"verdict": "NEI", "evidence": []}


Question:
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 spreads by contact with respiratory droplets that spread when an infected person coughs or sneezes.

Abstract:
PURPOSE: To share a useful intervention to minimize risk of COVID-19 infection to both healthcare workers and patients in the eye clinic.
METHODS: We present our experience of virtual, within-clinic remote visual acuity assessment to reduce the risk of infection with COVID-19.
RESULTS: Along with standard recommendations for personal protective equipment and hand hygiene to contain viral spread and treating only urgent cases, remote within-clinic visual acuity testing and consultations can be undertaken with minimal specialist equipment and appears to provide useful information whilst being acceptable to patients.
CONCLUSION: Ophthalmology practice must adapt in order to combat COVID-19.
This measure can easily be incorporated into daily practice to reduce both patient footfall within the department and close contact between patient and healthcare practitioners.

Answer: {"verdict": "NEI", "evidence": []}


Question:
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: Some herbal medicine advocates believe that the combination of garlic, ginger and some herbs can cure Coronavirus.

Abstract:
The severity of coronavirus disease 2019 (COVID-19) infection is quite variable and the manifestations varies from asymptomatic disease to severe acute respiratory infection.
Fever, dry cough, dyspnea, myalgia, fatigue, loss of appetite, olfactory and gustatory dysfunctions are the most prevalent general symptoms.
Decreased immune system cells such as suppressed regulatory T cells, cytotoxic and helper T cells, natural killer cells, monocytes/macrophages and increased proinflammatory cytokines are the characteristic features.
Compounds derived from Allium sativum (garlic) have the potential to decrease the expression of proinflammatory cytokines and to reverse the immunological abnormalities to more acceptable levels.
Allium sativum is suggested as a beneficial preventive measure before being infected with SARS-CoV-2 virus.
Allium sativum is a functional food well-known for its immunomodulatory, antimicrobial, antiinflammatory, antimutagenic, antitumor properties.
Its antiviral efficiency was also demonstrated.
Some constituents of this plant were found to be active against protozoan parasites.
Within this context, it appears to reverse most immune system dysfunctions observed in patients with COVID-19 infection.
The relations among immune system parameters, leptin, leptin receptor, adenosin mono phosphate-activated protein kinase, peroxisome proliferator activated receptor-gamma have also been interpreted.
Leptin's role in boosting proinflammatory cytokines and in appetite decreasing suggest the possible beneficial effect of decreasing the concentration of this proinflammatory adipose tissue hormone in relieving some symptoms detected during COVID-19 infection.
In conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature.

Answer: {"verdict": "CONTRADICT", "evidence": ["In conclusion, Allium sativum may be an acceptable preventive measure against COVID-19 infection to boost immune system cells and to repress the production and secretion of proinflammatory cytokines as well as an adipose tissue derived hormone leptin having the proinflammatory nature."]}


Question:
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: hydroxychloroquine cures covid-19.

Abstract:
Backgrounds.
Since COVID-19 outbreak, various agents have been tested but no proven effective therapies have been identified.
This has led to a lot of controversies among associated researches.
Hence, in order to address the issue of using hydroxychloroquine in treating COVID-19 patients, we conducted a systematic review and meta-analysis.
Methods.
A thorough search was carried out to find relevant studies in MEDLINE, medRxiv, PubMed, Cochrane Database, China Academic Journals Full-text Database and Web of Science.
Two investigators independently reviewed 274 abstracts and 23 articles.
The trials which evaluated hydroxychloroquine for treatment of COVID-19 were included for this systematic review.
Two investigators assessed quality of the studies and data extraction was done by one reviewer and cross checked by the other.
Results.
Five trials involving 677 patients were included while conducting the meta-analysis.
Compared with the control group, hydroxychloroquine with or without azithromycin showed benefits in positive-to-negative conversion of SARS-CoV-2 (odds ratio [OR], 1.95 [95% CI,0.19 to 19.73] and a reduction in progression rate (OR, 0.89 [95% CI, 0.58 to 1.37]), but without demonstrating any statistical significance.
This systematic review has also suggested a possible synergistic effect of the combination therapy which included hydroxychloroquine and azithromycin.
However, the use of hydroxychloroquine alone was associated with increased mortality in COVID-19 patients.
Conclusion.
The use of hydroxychloroquine with or without azithromycin for treatment of COVID-19 patients, seems to be effective.
The combination of hydroxychloroquine and azithromycin has shown synergic effects.
However, mortality rate was increased when the treatment was conducted with hydroxychloroquine.

Answer: {"verdict": "CONTRADICT", "evidence": ["Since COVID-19 outbreak, various agents have been tested but no proven effective therapies have been identified.", "However, the use of hydroxychloroquine alone was associated with increased mortality in COVID-19 patients.", "However, mortality rate was increased when the treatment was conducted with hydroxychloroquine."]}


Question:
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: Ferrets can catch the coronavirus and might give it to other ferrets. But poultry and pigs don't appear to be at risk.

Abstract:
On April 22, CDC and the U.S. Department of Agriculture (USDA) reported cases of two domestic cats with confirmed infection with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19).
These are the first reported companion animals (including pets and service animals) with SARS-CoV-2 infection in the United States, and among the first findings of SARS-CoV-2 symptomatic companion animals reported worldwide.
These feline cases originated from separate households and were epidemiologically linked to suspected or confirmed human COVID-19 cases in their respective households.
Notification of presumptive positive animal test results triggered a One Health* investigation by state and federal partners, who determined that no further transmission events to other animals or persons had occurred.
Both cats fully recovered.
Although there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1).

Answer: {"verdict": "CONTRADICT", "evidence": ["Although there is currently no evidence that animals play a substantial role in spreading COVID-19, CDC advises persons with suspected or confirmed COVID-19 to restrict contact with animals during their illness and to monitor any animals with confirmed SARS-CoV-2 infection and separate them from other persons and animals at home (1)."]}


Question:
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: it is safe take Advil to bring down your temperature caused by covid-19

Abstract:
Ibuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.
A concern was raised regarding the safety of ibuprofen use because of its role in increasing ACE2 levels within the Renin-Angiotensin-Aldosterone system.
ACE2 is the coreceptor for the entry of SARS-CoV-2 into cells, and so, a potential increased risk of contracting COVID-19 disease and/or worsening of COVID-19 infection was feared with ibuprofen use.
However, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.
At this time, there is no supporting evidence to discourage the use of ibuprofen.

Answer: {"verdict": "SUPPORT", "evidence": ["Ibuprofen is an over-the-counter medication that is used widely for the treatment of pain and fever during COVID-19 pandemic.", "However, available data from limited studies show administration of recombinant ACE2 improves lung damage caused by respiratory viruses, suggesting ibuprofen use may be beneficial in COVID-19 disease.", "At this time, there is no supporting evidence to discourage the use of ibuprofen."]}


Question:
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: SARS-CoV-2 and COVID-19 are the same thing

Abstract:
The recent global outbreak of viral pneumonia designated as Coronavirus Disease 2019 (COVID-19) by coronavirus (SARS-CoV-2) has threatened global public health and urged to investigate its source.
Whole genome analysis of SARS-CoV-2 revealed ~96% genomic similarity with bat CoV (RaTG13) and clustered together in phylogenetic tree.
Furthermore, RaTGl3 also showed 97.43% spike protein similarity with SARS-CoV-2 suggesting that RaTGl3 is the closest strain.
However, RBD and key amino acid residues supposed to be crucial for human-to-human and cross-species transmission are homologues between SARS-CoV-2 and pangolin CoVs.
These results from our analysis suggest that SARS-CoV-2 is a recombinant virus of bat and pangolin CoVs.
Moreover, this study also reports mutations in coding regions of 125 SARS-CoV-2 genomes signifying its aptitude for evolution.
In short, our findings propose that homologous recombination has been occurred between bat and pangolin CoVs that triggered cross-species transmission and emergence of SARS-CoV-2, and, during the ongoing outbreak, SARS-CoV-2 is still evolving for its adaptability.

Answer: {"verdict": "CONTRADICT", "evidence": ["The recent global outbreak of viral pneumonia designated as Coronavirus Disease 2019 (COVID-19) by coronavirus (SARS-CoV-2) has threatened global public health and urged to investigate its source."]}


Question:
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: Several people, including the US president, have suggested that the novel coronavirus SARS-CoV-2 and the disease it causes, COVID-19, will go away on its own in the warmer weather that will come.

Abstract:
Background: Understanding and projecting the spread of COVID-19 requires reliable estimates of how weather components are associated with the transmission of the virus.
Prior research on this topic has been inconclusive.
Identifying key challenges to reliable estimation of weather impact on transmission we study this question using one of the largest assembled databases of COVID-19 infections and weather.
Methods: We assemble a dataset that includes virus transmission and weather data across 3,739 locations from December 12, 2019 to April 22, 2020.
Using simulation, we identify key challenges to reliable estimation of weather impacts on transmission, design a statistical method to overcome these challenges, and validate it in a blinded simulation study.
Using this method and controlling for location-specific response trends we estimate how different weather variables are associated with the reproduction number for COVID-19.
We then use the estimates to project the relative weather-related risk of COVID-19 transmission across the world and in large cities.
Results: We show that the delay between exposure and detection of infection complicates the estimation of weather impact on COVID-19 transmission, potentially explaining significant variability in results to-date.
Correcting for that distributed delay and offering conservative estimates, we find a negative relationship between temperatures above 25 degrees Celsius and estimated reproduction number ([R]), with each degree Celsius associated with a 3.1% (95% CI, 1.5% to 4.8%) reduction in [R].
Higher levels of relative humidity strengthen the negative effect of temperature above 25 degrees.
Moreover, one millibar of additional pressure increases [R] by approximately 0.8 percent (95% CI, 0.6% to 1%) at the median pressure (1016 millibars) in our sample.
We also find significant positive effects for wind speed, precipitation, and diurnal temperature on [R].
Sensitivity analysis and simulations show that results are robust to multiple assumptions.
Despite conservative estimates, weather effects are associated with a 43% change in [R] between the 5th and 95th percentile of weather conditions in our sample.
Conclusions: These results provide evidence for the relationship between several weather variables and the spread of COVID-19.
However, the (conservatively) estimated relationships are not strong enough to seasonally control the epidemic in most locations.

Answer: {"verdict": "CONTRADICT", "evidence": ["Conclusions: These results provide evidence for the relationship between several weather variables and the spread of COVID-19.", "However, the (conservatively) estimated relationships are not strong enough to seasonally control the epidemic in most locations."]}


Question:
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: Phylogenetic modeling explains differential sars cov-2 kinetics in lung and nasal passages in remdesivir treated rhesus macaques

Abstract:
Remdesivir was recently demonstrated to decrease recovery time in hospitalized patients with SARS-CoV-2 infection.
In rhesus macaques, early initiation of remdesivir therapy prevented pneumonia and lowered viral loads in the lung, but viral loads increased in the nasal passages five days after therapy.
We developed mathematical models to explain these results.
We identified that 1) drug potency is slightly higher in nasal passages than in lungs, 2) viral load decrease in lungs relative to nasal passages during therapy because of infection-dependent generation of refractory cells in the lung, 3) incomplete drug potency in the lung that decreases viral loads even slightly may allow substantially less lung damage, and 4) increases in nasal viral load may occur due to a slight blunting of peak viral load and subsequent decrease of the intensity of the innate immune response, as well as a lack of refractory cells.
We also hypothesize that direct inoculation of the trachea in rhesus macaques may not recapitulate natural infection as lung damage occurs more abruptly in this model than in human infection.
We demonstrate with sensitivity analysis that a drug with higher potency could completely suppress viral replication and lower viral loads abruptly in the nasal passages as well as the lung.
One Sentence Summary We developed a mathematical model to explain why remdesivir has a greater antiviral effect on SARS CoV-2 in lung versus nasal passages in rhesus macaques.

Answer: {"verdict": "CONTRADICT", "evidence": ["In rhesus macaques, early initiation of remdesivir therapy prevented pneumonia and lowered viral loads in the lung, but viral loads increased in the nasal passages five days after therapy.", "One Sentence Summary We developed a mathematical model to explain why remdesivir has a greater antiviral effect on SARS CoV-2 in lung versus nasal passages in rhesus macaques."]}


Question:
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: clinical trials are needed to determine if endogenously created vitamin D could be effective for the prevention and treatment of COVID-19

Abstract:
The outbreak of COVID-19 has created a global public health crisis.
Little is known about the protective factors of this infection.
Therefore, preventive health measures that can reduce the risk of infection, progression and severity are desperately needed.
This review discussed the possible roles of vitamin D in reducing the risk of COVID-19 and other acute respiratory tract infections and severity.
Moreover, this study determined the correlation of vitamin D levels with COVID-19 cases and deaths in 20 European countries as of 20 May 2020.
A significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.
However, the correlation of vitamin D with COVID-19 deaths of these countries was not significant.
Some retrospective studies demonstrated a correlation between vitamin D status and COVID-19 severity and mortality, while other studies did not find the correlation when confounding variables are adjusted.
Several studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.
These include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.
In the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.
Thus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L).
In conclusion, there is not enough evidence on the association between vitamin D levels and COVID-19 severity and mortality.
Therefore, randomized control trials and cohort studies are necessary to test this hypothesis.

Answer: {"verdict": "CONTRADICT", "evidence": ["A significant negative correlation (p=0.033) has been observed between mean vitamin D levels and COVID-19 cases per one million population in European countries.", "Several studies demonstrated the role of vitamin D in reducing the risk of acute viral respiratory tract infections and pneumonia.", "These include direct inhibition with viral replication or with anti-inflammatory or immunomodulatory ways.", "In the meta-analysis, vitamin D supplementation has been shown as safe and effective against acute respiratory tract infections.", "Thus, people who are at higher risk of vitamin D deficiency during this global pandemic should consider taking vitamin D supplements to maintain the circulating 25(OH)D in the optimal levels (75-125nmol/L)."]}


Question:
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: Some models show that if people wear masks, death rates from COVID-19 stay very low.

Abstract:
In the context of Coronavirus Disease (2019) (COVID-19) cases globally, there is a lack of consensus across cultures on whether wearing face masks is an effective physical intervention against disease transmission.
This study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context.
To achieve this goal, government should establish a risk adjusted strategy of mask use to scientifically publicize the use of masks, guarantee sufficient supply of masks, and cooperate for reducing health resources inequities.

Answer: {"verdict": "SUPPORT", "evidence": ["This study 1) illustrates transmission routes of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2); 2) addresses controversies surrounding the mask from perspectives of attitude, effectiveness, and necessity of wearing the mask with evidence that the use of mask would effectively interrupt the transmission of infectious diseases in both hospital settings and community settings; and 3) provides suggestion that the public should wear the mask during COVID-19 pandemic according to local context."]}