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: Tiny antibody component highly effective against SARS-COV-2 in animal studies

Abstract:
AIMS: A new human coronavirus (HCoV), which has been designated SARS-CoV-2, began spreading in December 2019 in Wuhan City, China causing pneumonia called COVID-19.
The spread of SARS-CoV-2 has been faster than any other coronaviruses that have succeeded in crossing the animal-human barrier.
There is concern that this new virus will spread around the world as did the previous two HCoVs-Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS)-each of which caused approximately 800 deaths in the years 2002 and 2012, respectively.
Thus far, 11,268 deaths have been reported from the 258,842 confirmed infections in 168 countries.
MAIN METHODS: In this study, the RNA-dependent RNA polymerase (RdRp) of the newly emerged coronavirus is modeled, validated, and then targeted using different anti-polymerase drugs currently on the market that have been approved for use against various viruses.
KEY FINDINGS: The results suggest the effectiveness of Ribavirin, Remdesivir, Sofosbuvir, Galidesivir, and Tenofovir as potent drugs against SARS-CoV-2 since they tightly bind to its RdRp.
In addition, the results suggest guanosine derivative (IDX-184), Setrobuvir, and YAK as top seeds for antiviral treatments with high potential to fight the SARS-CoV-2 strain specifically.
SIGNIFICANCE: The availability of FDA-approved anti-RdRp drugs can help treat patients and reduce the danger of the mysterious new viral infection COVID-19.
The drugs mentioned above can tightly bind to the RdRp of the SARS-CoV-2 strain and thus may be used to treat the disease.
No toxicity measurements are required for these drugs since they were previously tested prior to their approval by the FDA.

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


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: Furin cleavage of sars-cov-2 spike promotes but is not essential for infection and cell-cell fusion

Abstract:
Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infects cells by binding to the host cell receptor ACE2 and undergoing virus-host membrane fusion.
Fusion is triggered by the protease TMPRSS2, which processes the viral Spike (S) protein to reveal the fusion peptide.
SARS-CoV-2 has evolved a multibasic site at the S1-S2 boundary, which is thought to be cleaved by furin in order to prime S protein for TMPRSS2 processing.
Here we show that CRISPR-Cas9 knockout of furin reduces, but does not prevent, the production of infectious SARS-CoV-2 virus.
Comparing S processing in furin knockout cells to multibasic site mutants reveals that while loss of furin substantially reduces S1-S2 cleavage it does not prevent it.
SARS-CoV-2 S protein also mediates cell-cell fusion, potentially allowing virus to spread virion-independently.
We show that loss of furin in either donor or acceptor cells reduces, but does not prevent, TMPRSS2-dependent cell-cell fusion, unlike mutation of the multibasic site that completely prevents syncytia formation.
Our results show that while furin promotes both SARS-CoV-2 infectivity and cell-cell spread it is not essential, suggesting furin inhibitors may reduce but not abolish viral spread.

Answer: {"verdict": "SUPPORT", "evidence": ["Here we show that CRISPR-Cas9 knockout of furin reduces, but does not prevent, the production of infectious SARS-CoV-2 virus.", "Comparing S processing in furin knockout cells to multibasic site mutants reveals that while loss of furin substantially reduces S1-S2 cleavage it does not prevent it.", "We show that loss of furin in either donor or acceptor cells reduces, but does not prevent, TMPRSS2-dependent cell-cell fusion, unlike mutation of the multibasic site that completely prevents syncytia formation."]}


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 significantly increases coronavirus sufferers risk of dying

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": "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: 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:
Here we report on the most recent updates on experimental drugs successfully em- ployed in the treatment of the disease caused by SARS-CoV-2 coronavirus, also referred to as COVID-19 (COronaVIrus Disease 19).
In particular, several cases of recovered patients have been reported after being treated with lopinavir/ritonavir (which is widely used to treat human immunodeficiency virus (HIV) infection) in combination with the anti-flu drug oseltamivir.
In addition, remdesivir, which has been previously administered to Ebola virus patients, has also proven effective in the U.S. against coronavirus, while antimalarial chloroquine and hydroxy- chloroquine, favipiravir and co-administered darunavir and umifenovir (in patient therapies) were also recently recorded as having anti-SARS-CoV-2 effects.
Since the recoveries/deaths ratio in the last weeks significantly increased, especially in China, it is clear that the experi- mental antiviral therapy, together with the availability of intensive care unit beds in hospitals and rigorous government control measures, all play an important role in dealing with this vi- rus.
This also stresses the urgent need for the scientific community to devote its efforts to the development of other more specific antiviral strategies.

Answer: {"verdict": "SUPPORT", "evidence": ["In addition, remdesivir, which has been previously administered to Ebola virus patients, has also proven effective in the U.S. against coronavirus, while antimalarial chloroquine and hydroxy- chloroquine, favipiravir and co-administered darunavir and umifenovir (in patient therapies) were also recently recorded as having anti-SARS-CoV-2 effects."]}


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: camostat mesylate cure coronavirus.

Abstract:
Different treatments are currently used for clinical management of SARS-CoV-2 infection, but little is known about their efficacy yet.
Here we present ongoing results to compare currently available drugs for a variety of diseases to find out if they counteract SARS-CoV-2-induced cytopathic effect in vitro.
Our goal is to prioritize antiviral activity to provide a solid evidence-driven rationale for forthcoming clinical trials.
Since the most effective antiviral approaches are usually based on combined therapies that tackle the viral life cycle at different stages, we are also testing combinations of drugs that may be critical to reduce the emergence of resistant viruses.
We will provide results as soon as they become available, so data should be interpreted with caution, clearly understanding the limitations of the in vitro model, that may not always reflect what could happen in vivo.
Thus, our goal is to test the most active antivirals identified in adequate animal models infected with SARS-CoV-2, to add more information about possible in vivo efficacy.
In turn, successful antivirals could be tested in clinical trials as treatments for infected patients, but also as pre-exposure prophylaxis to avoid novel infections until an effective and safe vaccine is developed.

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: the mechanism behind the protective effects of smoking could be found in nicotine

Abstract:
Objectives: To investigate whether there is a causal effect of cardiometabolic traits on risk of sepsis and severe covid-19.
Design: Mendelian randomisation analysis.
Setting: UK Biobank and HUNT study population-based cohorts for risk of sepsis, and genome-wide association study summary data for risk of severe covid-19 with respiratory failure.
Participants: 12,455 sepsis cases (519,885 controls) and 1,610 severe covid-19 with respiratory failure cases (2,205 controls).
Exposure: Genetic variants that proxy body mass index (BMI), lipid traits, systolic blood pressure, lifetime smoking score, and type 2 diabetes liability - derived from studies considering between 188,577 to 898,130 participants.
Main outcome measures: Risk of sepsis and severe covid-19 with respiratory failure.
Results: Higher genetically proxied BMI and lifetime smoking score were associated with increased risk of sepsis in both UK Biobank (BMI: odds ratio 1.38 per standard deviation increase, 95% confidence interval [CI] 1.27 to 1.51; smoking: odds ratio 2.81 per standard deviation increase, 95% CI 2.09-3.79) and HUNT (BMI: 1.41, 95% CI 1.18 to 1.69; smoking: 1.93, 95% CI 1.02-3.64).
Higher genetically proxied BMI and lifetime smoking score were also associated with increased risk of severe covid-19, although with wider confidence intervals (BMI: 1.75, 95% CI 1.20 to 2.57; smoking: 3.94, 95% CI 1.13 to 13.75).
There was limited evidence to support associations of genetically proxied lipid traits, systolic blood pressure or type 2 diabetes liability with risk of sepsis or severe covid-19.
Similar findings were generally obtained when using Mendelian randomization methods that are more robust to the inclusion of pleiotropic variants, although the precision of estimates was reduced.
Conclusions: Our findings support a causal effect of elevated BMI and smoking on risk of sepsis and severe covid-19.
Clinical and public health interventions targeting obesity and smoking are likely to reduce sepsis and covid-19 related morbidity, along with the plethora of other health-related outcomes that these traits adversely affect.

Answer: {"verdict": "CONTRADICT", "evidence": ["Results: Higher genetically proxied BMI and lifetime smoking score were associated with increased risk of sepsis in both UK Biobank (BMI: odds ratio 1.38 per standard deviation increase, 95% confidence interval [CI] 1.27 to 1.51; smoking: odds ratio 2.81 per standard deviation increase, 95% CI 2.09-3.79) and HUNT (BMI: 1.41, 95% CI 1.18 to 1.69; smoking: 1.93, 95% CI 1.02-3.64).", "Conclusions: Our findings support a causal effect of elevated BMI and smoking on risk of sepsis and severe 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: Exploratory analysis of immunization records highlights higher 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: 4 x-linked agammaglobulinemia patients develop pneumonia as covid-19 manifestation but recover

Abstract:
BACKGROUND: The recent SARS-CoV-2 pandemic, which has recently affected Italy since February 21, constitutes a threat to normal subjects, as the coronavirus disease-19 (COVID-19) can manifest with a broad spectrum of clinical phenotypes ranging from asymptomatic cases to pneumonia or even death.
There is evidence that older age and several comorbidities can affect the risk to develop severe pneumonia and possibly the need of mechanic ventilation in subjects infected with SARS-CoV-2.
Therefore, we evaluated the outcome of SARS-CoV-2 infection in patients with inborn errors of immunity (IEI) such as X-linked agammaglobulinemia (XLA).
METHODS: When the SARS-CoV-2 epidemic has reached Italy, we have activated a surveillance protocol of patients with IEI, to perform SARS-CoV-2 search by nasopharyngeal swab in patients presenting with symptoms that could be a manifestation of COVID-19, such as fever, cough, diarrhea, or vomiting.
RESULTS: We describe two patients with X-linked agammaglobulinemia (XLA) aged 34 and 26 years with complete absence of B cells from peripheral blood who developed COVID-19, as diagnosed by SARS-CoV-2 detection by nasopharyngeal swab, while receiving immunoglobulin infusions.
Both patients developed interstitial pneumonia characterized by fever, cough, and anorexia and associated with elevation of CRP and ferritin, but have never required oxygen ventilation or intensive care.
CONCLUSION: Our report suggests that XLA patients might present with high risk to develop pneumonia after SARS-CoV-2 infection, but can recover from infection, suggesting that B-cell response might be important, but is not strictly required to overcome the disease.
However, there is a need for larger observational studies to extend these conclusions to other patients with similar genetic immune defects.

Answer: {"verdict": "CONTRADICT", "evidence": ["Therefore, we evaluated the outcome of SARS-CoV-2 infection in patients with inborn errors of immunity (IEI) such as X-linked agammaglobulinemia (XLA)."]}


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: Coronaviruses cause respiratory illnesses, so the lungs are usually affected first. Early symptoms include fever, cough, and shortness of breath.

Abstract:
Several related human coronaviruses (HCoVs) are endemic in the human population, causing mild respiratory infections1.
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiologic agent of Coronavirus disease 2019 (COVID-19), is a recent zoonotic infection that has quickly reached pandemic spread2,3.
Zoonotic introduction of novel coronaviruses is thought to occur in the absence of pre-existing immunity in the target human population.
Using diverse assays for detection of antibodies reactive with the SARS-CoV-2 Spike (S) glycoprotein, we demonstrate the presence of pre-existing immunity in uninfected and unexposed humans to the new coronavirus.
SARS-CoV-2 S-reactive antibodies, exclusively of the IgG class, were readily detectable by a sensitive flow cytometry-based method in SARS-CoV-2-uninfected individuals with recent HCoV infection and targeted the S2 subunit.
In contrast, SARS-CoV-2 infection induced higher titres of SARS-CoV-2 S-reactive IgG antibodies, as well as concomitant IgM and IgA antibodies throughout the observation period of 6 weeks since symptoms onset.
HCoV patient sera also variably reacted with SARS-CoV-2 S and nucleocapsid (N), but not with the S1 subunit or the receptor binding domain (RBD) of S on standard enzyme immunoassays.
Notably, HCoV patient sera exhibited specific neutralising activity against SARS-CoV-2 S pseudotypes, according to levels of SARS-CoV-2 S-binding IgG and with efficiencies comparable to those of COVID-19 patient sera.
Distinguishing pre-existing and de novo antibody responses to SARS-CoV-2 will be critical for serology, seroprevalence and vaccine studies, as well as for our understanding of susceptibility to and natural course of SARS-CoV-2 infection.

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


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: Mortality in sars-cov-2 viral rna identified in wastewater 48 hours before covid-19 clinical tests and 96 hours before hospitalizations

Abstract:
Curtailing the Spring 2020 COVID-19 surge required sweeping and stringent interventions by governments across the world.
Wastewater-based COVID-19 epidemiology programs have been initiated in many countries to provide public health agencies with a complementary disease tracking metric and facile surveillance tool.
However, their efficacy in prospectively capturing resurgence following a period of low prevalence is unclear.
In this study, the SARS-CoV-2 viral signal was measured in primary clarified sludge harvested every two days at the City of Ottawa's water resource recovery facility during the summer of 2020, when clinical testing recorded daily percent positivity below 1%.
In late July, increases of >400% in normalized SARS-CoV-2 RNA signal in wastewater were identified 48 hours prior to reported >300% increases in positive cases that were retrospectively attributed to community-acquired infections.
During this resurgence period, SARS-CoV-2 RNA signal in wastewater preceded the reported >160% increase in community hospitalizations by approximately 96 hours.
This study supports wastewater-based COVID-19 surveillance of populations in augmenting the efficacy of diagnostic testing, which can suffer from sampling biases or timely reporting as in the case of hospitalization census.

Answer: {"verdict": "CONTRADICT", "evidence": ["In late July, increases of >400% in normalized SARS-CoV-2 RNA signal in wastewater were identified 48 hours prior to reported >300% increases in positive cases that were retrospectively attributed to community-acquired infections.", "During this resurgence period, SARS-CoV-2 RNA signal in wastewater preceded the reported >160% increase in community hospitalizations by approximately 96 hours."]}


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: Get this checked by your doctor and use the correct dose to stay healthy. Get this checked by your doctor and use the correct dose to stay healthy.

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": "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: a small number of pets worldwide, including cats and dogs, can be infected with the virus that causes COVID-19, mostly after close contact with people with COVID-19.

Abstract:
BACKGROUND: The Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March.
It remains difficult to quantify the impact this had in reducing the spread of the virus.
METHODS: Bayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed.
Our models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively.
CONCLUSION: This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.

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

Abstract:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which originated in Wuhan, China, in 2019, is responsible for the COVID-19 pandemic.
It is now accepted that the wild fauna, probably bats, constitute the initial reservoir of the virus, but little is known about the role pets can play in the spread of the disease in human communities, knowing the ability of SARS-CoV-2 to infect some domestic animals.
We tested 21 domestic pets (9 cats and 12 dogs) living in close contact with their owners (belonging to a veterinary community of 20 students) in which two students tested positive for COVID-19 and several others (n = 11/18) consecutively showed clinical signs (fever, cough, anosmia, etc.) compatible with COVID-19 infection.
Although a few pets presented many clinical signs indicative for a coronavirus infection, no animal tested positive for SARS-CoV-2 by RT-PCR and no antibodies against SARS-CoV-2 were detectable in their blood using an immunoprecipitation assay.
These original data can serve a better evaluation of the host range of SARS-CoV-2 in natural environment exposure conditions.

Answer: {"verdict": "CONTRADICT", "evidence": ["Although a few pets presented many clinical signs indicative for a coronavirus infection, no animal tested positive for SARS-CoV-2 by RT-PCR and no antibodies against SARS-CoV-2 were detectable in their blood using an immunoprecipitation assay."]}


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: The d614g mutation in the sars-cov2 spike protein increases infectivity in an ace2 receptor dependent manner

Abstract:
The SARS-CoV2 coronavirus responsible for the current COVID19 pandemic has been reported to have a relatively low mutation rate.
Nevertheless, a few prevalent variants have arisen that give the appearance of undergoing positive selection as they are becoming increasingly widespread over time.
Most prominent among these is the D614G amino acid substitution in the SARS-CoV2 Spike protein, which mediates viral entry.
The D614G substitution, however, is in linkage disequilibrium with the ORF1b P314L mutation where both mutations almost invariably co-occur, making functional inferences problematic.
In addition, the possibility of repeated new introductions of the mutant strain does not allow one to distinguish between a founder effect and an intrinsic genetic property of the virus.
Here, we synthesized and expressed the WT and D614G variant SARS-Cov2 Spike protein, and report that using a SARS-CoV2 Spike protein pseudotyped lentiviral vector we observe that the D614G variant Spike has >1/2 log(10) increased infectivity in human cells expressing the human ACE2 protein as the viral receptor.
The increased binding/fusion activity of the D614G Spike protein was corroborated in a cell fusion assay using Spike and ACE2 proteins expressed in different cells.
These results are consistent with the possibility that the Spike D614G mutant increases the infectivity of SARS-CoV2.

Answer: {"verdict": "SUPPORT", "evidence": ["Most prominent among these is the D614G amino acid substitution in the SARS-CoV2 Spike protein, which mediates viral entry.", "The increased binding/fusion activity of the D614G Spike protein was corroborated in a cell fusion assay using Spike and ACE2 proteins expressed in different cells.", "These results are consistent with the possibility that the Spike D614G mutant increases the infectivity of SARS-CoV2."]}


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 new coronavirus can infect your skin

Abstract:
The novel coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), first appeared in December 2019, in Wuhan, China and evolved into a pandemic.
As Angiotensin-Converting Enzyme 2 (ACE2) is one of the potential target receptors for SARS-CoV-2 in human body, which is expressed in different tissues, multiple organs might become affected.
In the initial phase of the current pandemic, a handful of post-mortem case-series revealed COVID-19-related pathological changes in various organs.
Although pathological examination is not a feasible method of diagnosis, it can elucidate pathological changes, pathogenesis of the disease, and the cause of death in COVID-19 cases.
Herein, we thoroughly reviewed multiple organs including lung, gastrointestinal tract, liver, kidney, skin, heart, blood, spleen, lymph nodes, brain, blood vessels, and placenta in terms of COVID-19-related pathological alterations.
Also, these findings were compared with SARS and MERS infection, wherever applicable.
We found a diverse range of pathological changes, some of which resemble those found in SARS and MERS.

Answer: {"verdict": "SUPPORT", "evidence": ["Herein, we thoroughly reviewed multiple organs including lung, gastrointestinal tract, liver, kidney, skin, heart, blood, spleen, lymph nodes, brain, blood vessels, and placenta in terms of COVID-19-related pathological alterations."]}


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: A report indicates that Acetaminophen (Tylenol) may be preferred over Ibuprofen (Advil) for coronavirus (fever)

Abstract:
BACKGROUND AND AIMS: Multiple issues in management of COVID have emerged, but confusion persists regarding rational interpretation.
Aim of this brief review is to review these issues based on current literature.
METHODS: This is a narrative review with Pubmed and Google Scholar search till 23 March 2020.
Search terms were, COVID-19, treatment of coronavirus, COVID 19 and following terms; chloroquine, hydroxychloroquine, ibuprofen, ACE-inhibitors or angiotensin receptor blockers, cardiovascular disease, diarrhoea, liver, testis and gastrointestinal disease.
RESULTS: We discuss evidence regarding role of chloroquine and hydroxychloroquine in treatment and prophylaxis, use of inhibitors of the renin angiotensin system, safety of ibuprofen, unusual clinical features like gastrointestinal symptoms and interpretation of tests for cardiac enzymes and biomarkers.
CONCLUSIONS: While our conclusions on management of COVID-19 patients with co-morbidities are based on current evidence, however, data is limited and there is immediate need for fast track research.

Answer: {"verdict": "CONTRADICT", "evidence": ["RESULTS: We discuss evidence regarding role of chloroquine and hydroxychloroquine in treatment and prophylaxis, use of inhibitors of the renin angiotensin system, safety of ibuprofen, unusual clinical features like gastrointestinal symptoms and interpretation of tests for cardiac enzymes and biomarkers."]}


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: Tiny antibody component highly effective against SARS-COV-2 in animal studies

Abstract:
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative representative of a severe respiratory illness resulted in widespread human infections and deaths in nearly all of the countries since late 2019.
There is no therapeutic FDA-approved drug against SARS-CoV-2 infection, although a combination of anti-viral drugs is directly being practiced in some countries.
A broad-spectrum of antiviral agents are being currently evaluated in clinical trials, and in this review, we specifically focus on the application of Remdesivir (RVD) as a potential anti-viral compound against Middle East respiratory syndrome (MERS) -CoV, SARS-CoV and SARS-CoV-2.
First, we overview the general information about SARS-CoV-2, followed by application of RDV as a nucleotide analogue which can potentially inhibits RNA-dependent RNA polymerase of COVs.
Afterwards, we discussed the kinetics of SARS- or MERS-CoV proliferation in animal models which is significantly different compared to that in humans.
Finally, some ongoing challenges and future perspective on the application of RDV either alone or in combination with other anti-viral agents against CoVs infection were surveyed to determine the efficiency of RDV in preclinical trials.
As a result, this paper provides crucial evidence of the potency of RDV to prevent SARS-CoV-2 infections.
Communicated by Ramaswamy H. Sarma.

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: The new coronavirus can damage the lungs, heart and brain, which increases the risk of long-term health problems.

Abstract:
Importance: Increasing numbers of confirmed cases and mortality rates of coronavirus disease 2019 (COVID-19) are occurring in several countries and continents.
Information regarding the impact of cardiovascular complication on fatal outcome is scarce.
Objective: To evaluate the association of underlying cardiovascular disease (CVD) and myocardial injury with fatal outcomes in patients with COVID-19.
Design, Setting, and Participants: This retrospective single-center case series analyzed patients with COVID-19 at the Seventh Hospital of Wuhan City, China, from January 23, 2020, to February 23, 2020.
Analysis began February 25, 2020.
Main Outcomes and Measures: Demographic data, laboratory findings, comorbidities, and treatments were collected and analyzed in patients with and without elevation of troponin T (TnT) levels.
Result: Among 187 patients with confirmed COVID-19, 144 patients (77%) were discharged and 43 patients (23%) died.
The mean (SD) age was 58.50 (14.66) years.
Overall, 66 (35.3%) had underlying CVD including hypertension, coronary heart disease, and cardiomyopathy, and 52 (27.8%) exhibited myocardial injury as indicated by elevated TnT levels.
The mortality during hospitalization was 7.62% (8 of 105) for patients without underlying CVD and normal TnT levels, 13.33% (4 of 30) for those with underlying CVD and normal TnT levels, 37.50% (6 of 16) for those without underlying CVD but elevated TnT levels, and 69.44% (25 of 36) for those with underlying CVD and elevated TnTs.
Patients with underlying CVD were more likely to exhibit elevation of TnT levels compared with the patients without CVD (36 [54.5%] vs 16 [13.2%]).
Plasma TnT levels demonstrated a high and significantly positive linear correlation with plasma high-sensitivity C-reactive protein levels (ß = 0.530, P < .001) and N-terminal pro-brain natriuretic peptide (NT-proBNP) levels (ß = 0.613, P < .001).
Plasma TnT and NT-proBNP levels during hospitalization (median [interquartile range (IQR)], 0.307 [0.094-0.600]; 1902.00 [728.35-8100.00]) and impending death (median [IQR], 0.141 [0.058-0.860]; 5375 [1179.50-25695.25]) increased significantly compared with admission values (median [IQR], 0.0355 [0.015-0.102]; 796.90 [401.93-1742.25]) in patients who died (P = .001; P < .001), while no significant dynamic changes of TnT (median [IQR], 0.010 [0.007-0.019]; 0.013 [0.007-0.022]; 0.011 [0.007-0.016]) and NT-proBNP (median [IQR], 352.20 [174.70-636.70]; 433.80 [155.80-1272.60]; 145.40 [63.4-526.50]) was observed in survivors (P = .96; P = .16).
During hospitalization, patients with elevated TnT levels had more frequent malignant arrhythmias, and the use of glucocorticoid therapy (37 [71.2%] vs 69 [51.1%]) and mechanical ventilation (41 [59.6%] vs 14 [10.4%]) were higher compared with patients with normal TnT levels.
The mortality rates of patients with and without use of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers was 36.8% (7 of 19) and 25.6% (43 of 168).
Conclusions and Relevance: Myocardial injury is significantly associated with fatal outcome of COVID-19, while the prognosis of patients with underlying CVD but without myocardial injury is relatively favorable.
Myocardial injury is associated with cardiac dysfunction and arrhythmias.
Inflammation may be a potential mechanism for myocardial injury.
Aggressive treatment may be considered for patients at high risk of myocardial injury.

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

Abstract:
The Coronavirus Disease 2019 (COVID-19) has swept the whole world with high mortality.
Since droplet transmission is the main route of transmission, wearing a mask serves as a crucial preventive measure.
However, the virus has spread quite quickly, causing severe mask shortage.
Finding alternative materials for homemade masks while ensuring the significant performance indicators will help alleviate the shortage of masks.
Referring to the national standard for the "Surgical Mask" of China, 17 materials to be selected for homemade masks were tested in four key indicators: pressure difference, particle filtration efficiency, bacterial filtration efficiency and resistance to surface wetting.
Eleven single-layer materials met the standard of pressure difference ([≤]49 Pa), of which 3 met the standard of resistance to surface wetting ([≥]3), 1 met the standard of particle filtration efficiency ([≥]30%), but none met the standard of bacterial filtration efficiency ([≥]95%).
Based on the testing results of single-layer materials, fifteen combinations of paired materials were tested.
The results showed that three double-layer materials including double-layer medical non-woven fabric, medical non-woven fabric plus non-woven shopping bag, and medical non-woven fabric plus granular tea towel could meet all the standards of pressure difference, particle filtration efficiency, and resistance to surface wetting, and were close to the standard of the bacterial filtration efficiency.
In conclusion, if resources are severely lacking and medical masks cannot be obtained, homemade masks using available materials, based on the results of this study, can minimize the chance of infection to the maximum extent.

Answer: {"verdict": "SUPPORT", "evidence": ["Since droplet transmission is the main route of transmission, wearing a mask serves as a crucial preventive measure."]}


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: Salt-coated masks achieve reduced viral deactivation rate

Abstract:
Aerosolized pathogens are a leading cause of respiratory infection and transmission.
Currently used protective measures pose potential risk of primary/secondary infection and transmission.
Here, we report the development of a universal, reusable virus deactivation system by functionalization of the main fibrous filtration unit of surgical mask with sodium chloride salt.
The salt coating on the fiber surface dissolves upon exposure to virus aerosols and recrystallizes during drying, destroying the pathogens.
When tested with tightly sealed sides, salt-coated filters showed remarkably higher filtration efficiency than conventional mask filtration layer, and 100% survival rate was observed in mice infected with virus penetrated through salt-coated filters.
Viruses captured on salt-coated filters exhibited rapid infectivity loss compared to gradual decrease on bare filters.
Salt-coated filters proved highly effective in deactivating influenza viruses regardless of subtypes and following storage in harsh environmental conditions.
Our results can be applied in obtaining a broad-spectrum, airborne pathogen prevention device in preparation for epidemic and pandemic of respiratory diseases.

Answer: {"verdict": "CONTRADICT", "evidence": ["Here, we report the development of a universal, reusable virus deactivation system by functionalization of the main fibrous filtration unit of surgical mask with sodium chloride salt.", "Salt-coated filters proved highly effective in deactivating influenza viruses regardless of subtypes and following storage in harsh environmental conditions."]}


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: De novo design of ace2 protein decoys to neutralize sars-cov-2

Abstract:
There is an urgent need for the ability to rapidly develop effective countermeasures for emerging biological threats, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes the ongoing coronavirus disease 2019 (COVID-19) pandemic.
We have developed a generalized computational design strategy to rapidly engineer de novo proteins that precisely recapitulate the protein surface targeted by biological agents, like viruses, to gain entry into cells.
The designed proteins act as decoys that block cellular entry and aim to be resilient to viral mutational escape.
Using our novel platform, in less than ten weeks, we engineered, validated, and optimized de novo protein decoys of human angiotensin-converting enzyme 2 (hACE2), the membrane-associated protein that SARS-CoV-2 exploits to infect cells.
Our optimized designs are hyperstable de novo proteins (∼18-37 kDa), have high affinity for the SARS-CoV-2 receptor binding domain (RBD) and can potently inhibit the virus infection and replication in vitro.
Future refinements to our strategy can enable the rapid development of other therapeutic de novo protein decoys, not limited to neutralizing viruses, but to combat any agent that explicitly interacts with cell surface proteins to cause disease.

Answer: {"verdict": "SUPPORT", "evidence": ["The designed proteins act as decoys that block cellular entry and aim to be resilient to viral mutational escape.", "Future refinements to our strategy can enable the rapid development of other therapeutic de novo protein decoys, not limited to neutralizing viruses, but to combat any agent that explicitly interacts with cell surface proteins to cause disease."]}


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: A headache is a potential symptom of COVID-19.

Abstract:
OBJECTIVE To analyze the epidemiological characteristics and clinical features of the patients with coronavirus disease 2019 (COVID-19), so as to provide basis for clinical diagnosis.
METHODS The epidemiology, clinical symptoms, laboratory and radiologic data of 23 patients with COVID-19 admitted to the Fifth People's Hospital of Xinyang City from January 22nd to January 29th, 2020 were retrospectively analyzed.
RESULTS There was 23 patients with COVID-19, with 15 men and 8 women, and the median age was 46.0 (40.5, 52.0) years old (ranged from 27 years old to 80 years old).
Nine patients had basic disease (39.1%), including hypertension (17.4%), cardiovascular diseases (17.4%), diabetes (8.7%), hypothyroidism (4.3%) and past history of tuberculosis (4.3%).
All the 23 patients had contact history in Wuhan area or with confirmed cases.
Clinical symptoms included fever (100%), cough (69.6%), expectoration (43.5%), myalgia (26.1%), headache (17.4%) and dyspnea (17.4%), and the less common symptom was diarrhea (4.3%).
Blood routine tests showed leukocytopenia in 11 patients (47.8%), normal leukocyte counts in 10 patients (43.5%), and leukocytosis in 2 patients (8.7%); lymphopenia was found in 13 patients (56.5%).
All 23 patients had different degrees of infective lesions in chest CT, with 7 patients (30.4%) on one side and 16 patients (69.6%) on both sides.
There were 19 mild patients, 4 severe patients, and no critical or death case.
Complications included acute respiratory distress syndrome (17.4%).
No patient was reported with liver, kidney or heart dysfunction or secondary infection.
CONCLUSIONS Epidemic history of contact, fever, pneumonia signs of chest CT, normal or decreased count of leukocyte and lymphopenia are the clinical basis for diagnosis of COVID-19.
However, at present, the treatment of patients has not been completed, and the effective treatment strategy and final prognosis are unclear.

Answer: {"verdict": "SUPPORT", "evidence": ["Clinical symptoms included fever (100%), cough (69.6%), expectoration (43.5%), myalgia (26.1%), headache (17.4%) and dyspnea (17.4%), and the less common symptom was diarrhea (4.3%)."]}


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: Enjoy ginger, but it's not a 'cure' for COVID-19

Abstract:
In late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).
So far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza virus.
In this article, we have tried to reach a therapeutic window of drugs available to patients with COVID-19.
Cathepsin L is required for entry of the 2019-nCoV virus into the cell as target teicoplanin inhibits virus replication.
Angiotensin-converting-enzyme 2 (ACE2) in soluble form as a recombinant protein can prevent the spread of coronavirus by restricting binding and entry.
In patients with COVID-19, hydroxychloroquine decreases the inflammatory response and cytokine storm, but overdose causes toxicity and mortality.
Neuraminidase inhibitors such as oseltamivir, peramivir, and zanamivir are invalid for 2019-nCoV and are not recommended for treatment but protease inhibitors such as lopinavir/ritonavir (LPV/r) inhibit the progression of MERS-CoV disease and can be useful for patients of COVID-19 and, in combination with Arbidol, has a direct antiviral effect on early replication of SARS-CoV. Ribavirin reduces hemoglobin concentrations in respiratory patients, and remdesivir improves respiratory symptoms.
Use of ribavirin in combination with LPV/r in patients with SARS-CoV reduces acute respiratory distress syndrome and mortality, which has a significant protective effect with the addition of corticosteroids.
Favipiravir increases clinical recovery and reduces respiratory problems and has a stronger antiviral effect than LPV/r.
currently, appropriate treatment for patients with COVID-19 is an ACE2 inhibitor and a clinical problem reducing agent such as favipiravir in addition to hydroxychloroquine and corticosteroids.

Answer: {"verdict": "SUPPORT", "evidence": ["In late December 2019 in Wuhan, China, several patients with viral pneumonia were identified as 2019 novel coronavirus (2019-nCoV).", "So far, there are no specific treatments for patients with coronavirus disease-19 (COVID-19), and the treatments available today are based on previous experience with similar viruses such as severe acute respiratory syndrome-related coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and Influenza 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: VITAMIN D HEALTHY LEVELS decrease COVID-19 MORTALITY RATES

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": "SUPPORT", "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: Most children with COVID-19 have mild symptoms or have no symptoms at all.

Abstract:
BACKGROUND The new Coronavirus identified in Whuan at the end of 2019 (SARS-CoV-2) belongs to the Beta Coronavirus genus and is responsible for the new Coronavirus 2019 pandemia (COVID-19).
Infected children may be asymptomatic or present fever, dry cough, fatigue or gastrointestinal symptoms.
The CDC recommends that clinicians should decide to test patients based on the presence of signs and symptoms compatible with COVID-19.
MATERIAL AND METHODS 42 children (the majority < 5 years of age) were referred, to our Pediatric Department, as possible cases of COVID-19 infection.
Blood analysis, chest X-ray, and naso-oropharyngeal swab specimens for viral identification of COVID-19 were requested.
RESULTS None of the screened children resulted positive for COVID-19 infection.
At first presentation, the most frequent signs and symptoms were: fever (71.4%), fatigue (35.7%) and cough (30.9%).
An high C-reactive protein value and abnormalities of chest X-ray (bronchial wall thickening) were detected in 26.2% and 19% of patients, respectively.
Almost half of patients (45.2%) required hospitalization in our Pediatric Unit and one patient in Intensive Care Unit.
CONCLUSIONS Testing people who meet the COVID-19 suspected case definition criteria is essential for clinical management and outbreak control.
Children of all ages can get COVID-19, although they appear to be affected less frequently than adults, as reported in our preliminary survey.
Further studies are needed to confirm our observations.

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


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: Development and validation of the 4c deterioration model for adults hospitalised with covid-19

Abstract:
Prognostic models to predict the risk of clinical deterioration in acute COVID-19 are required to inform clinical management decisions.
Among 75,016 consecutive adults across England, Scotland and Wales prospectively recruited to the ISARIC Coronavirus Clinical Characterisation Consortium (ISARIC4C) study, we developed and validated a multivariable logistic regression model for in-hospital clinical deterioration (defined as any requirement of ventilatory support or critical care, or death) using 11 routinely measured variables.
We used internal-external cross-validation to show consistent measures of discrimination, calibration and clinical utility across eight geographical regions.
We further validated the final model in held-out data from 8,252 individuals in London, with similarly consistent performance (C-statistic 0.77 (95% CI 0.75 to 0.78); calibration-in-the-large 0.01 (-0.04 to 0.06); calibration slope 0.96 (0.90 to 1.02)).
Importantly, this model demonstrated higher net benefit than using other candidate scores to inform decision-making.
Our 4C Deterioration model thus demonstrates unprecedented clinical utility and generalisability to predict clinical deterioration among adults hospitalised with COVID-19.

Answer: {"verdict": "SUPPORT", "evidence": ["Our 4C Deterioration model thus demonstrates unprecedented clinical utility and generalisability to predict clinical deterioration among adults hospitalised 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: wearing a mask helps stop infected people from spreading the new coronavirus to others.

Abstract:
We identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness.
Surgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.
Our results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.

Answer: {"verdict": "SUPPORT", "evidence": ["Surgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets.", "Our results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals."]}


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 reduce the spread of the virus: try to stay at least 2 metres (3 steps) away from anyone you do not live with (or anyone not in your support bubble); wash your hands with soap and water often - do this for at least 20 seconds; use hand sanitiser gel if soap and water are not available; wash your hands as soon as you get home; cover your mouth and nose with a tissue or your sleeve (not your hands) when you cough or sneeze; put used tissues in the bin immediately and wash your hands afterwards

Abstract:
The infectious diseases are spreading due to human interactions enabled by various social networks.
Therefore, when a new pathogen such as SARS-CoV-2 causes an outbreak, the non-pharmaceutical isolation strategies (e.g., social distancing) are the only possible response to disrupt its spreading.
To this end, we introduce the new epidemic model (SICARS) and compare the centralized (C), decentralized (D), and combined (C+D) social distancing strategies, and analyze their efficiency to control the dynamics of COVID-19 on heterogeneous complex networks.
Our analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading.
The decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one.
Indeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values.
Our results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy.
Finally, we study the impact of proactive versus reactive isolation strategies, as well as their delayed enforcement.
We find that the reactive response to the pandemic is less efficient, and delaying the adoption of isolation measures by over one month (since the outbreak onset in a region) can have alarming effects; thus, our study contributes to an understanding of the COVID-19 pandemic both in space and time.
We believe our investigations have a high social relevance as they provide insights into understanding how different degrees of social distancing can reduce the peak infection ratio substantially; this can make the COVID-19 pandemic easier to understand and control over an extended period of time.

Answer: {"verdict": "SUPPORT", "evidence": ["Our analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading.", "The decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one.", "Indeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values.", "Our results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy."]}


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: The sars-cov-2 cytopathic effect is compatible with autophagy modulators

Abstract:
SARS-CoV-02 is a new type of coronavirus capable of rapid transmission and causing severe clinical symptoms; much of which has unknown biological etiology.
It has prompted researchers to rapidly mobilize their efforts towards identifying and developing anti-viral therapeutics and vaccines.
Discovering and understanding the virus’ pathways of infection, host-protein interactions, and cytopathic effects will greatly aid in the design of new therapeutics to treat COVID-19.
While it is known that chloroquine and hydroxychloroquine, extensively explored as clinical agents for COVID-19, have multiple cellular effects including inhibiting autophagy, there are also dose-limiting toxicities in patients that make clearly establishing their potential mechanisms-of-action problematic.
Therefore, we evaluated a range of other autophagy modulators to identify an alternative autophagy-based drug repurposing opportunity.
In this work, we found that 6 of these compounds blocked the cytopathic effect of SARS-CoV-2 in Vero-E6 cells with EC(50) values ranging from 2.0 to 13 μM and selectivity indices ranging from 1.5 to >10-fold.
Immunofluorescence staining for LC3B and LysoTracker dye staining assays in several cell lines indicated their potency and efficacy for inhibiting autophagy correlated with the measurements in the SARS-CoV-2 cytopathic effect assay.
Our data suggest that autophagy pathways could be targeted to combat SARS-CoV-2 infections and become an important component of drug combination therapies to improve the treatment outcomes for COVID-19.

Answer: {"verdict": "CONTRADICT", "evidence": ["While it is known that chloroquine and hydroxychloroquine, extensively explored as clinical agents for COVID-19, have multiple cellular effects including inhibiting autophagy, there are also dose-limiting toxicities in patients that make clearly establishing their potential mechanisms-of-action problematic.", "Immunofluorescence staining for LC3B and LysoTracker dye staining assays in several cell lines indicated their potency and efficacy for inhibiting autophagy correlated with the measurements in the SARS-CoV-2 cytopathic effect assay."]}


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: if you are low risk (healthy, young), you do not need social distancing.

Abstract:
The infectious diseases are spreading due to human interactions enabled by various social networks.
Therefore, when a new pathogen such as SARS-CoV-2 causes an outbreak, the non-pharmaceutical isolation strategies (e.g., social distancing) are the only possible response to disrupt its spreading.
To this end, we introduce the new epidemic model (SICARS) and compare the centralized (C), decentralized (D), and combined (C+D) social distancing strategies, and analyze their efficiency to control the dynamics of COVID-19 on heterogeneous complex networks.
Our analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading.
The decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one.
Indeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values.
Our results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy.
Finally, we study the impact of proactive versus reactive isolation strategies, as well as their delayed enforcement.
We find that the reactive response to the pandemic is less efficient, and delaying the adoption of isolation measures by over one month (since the outbreak onset in a region) can have alarming effects; thus, our study contributes to an understanding of the COVID-19 pandemic both in space and time.
We believe our investigations have a high social relevance as they provide insights into understanding how different degrees of social distancing can reduce the peak infection ratio substantially; this can make the COVID-19 pandemic easier to understand and control over an extended period of time.

Answer: {"verdict": "CONTRADICT", "evidence": ["Our analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading.", "The decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one.", "Indeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values.", "Our results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy."]}


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: Is there an airborne component to the development of covid-19?

Abstract:
Objectives While COVID-19 is known to be spread by respiratory droplets (which travel <2m horizontally), much less is known about its transmission via aerosols, which can become airborne and be widely distributed throughout room spaces.
In order to quantify the risk posed by COVID-19 infectors exhaling respiratory aerosols in enclosed spaces, we undertook a computer modelling study to simulate transmission in an office building.
Methods Respiratory droplet data from four published datasets were analysed to quantify the number and volume of droplets <100m diameter produced by a typical cough and speaking event (i.e. counting from 1 to 100).
This was used in a stochastic model to simulate (10,000 simulations) the number of respiratory particles, originating from a COVID-19 infector, that would be inhaled in one hour by a susceptible individual practicing socially distancing in a 5 x 5 x 2.75m office space.
Several scenarios were simulated that mimicked the presence of both symptomatic and asymptomatic COVID-19 infectors.
Results On average, each cough and speaking event produced similar numbers of droplets <100 m diameter (median range = 955-1010).
Computer simulations (at ventilation rate = 2AC/h) revealed that sharing the office space with a symptomatic COVID-19 infector (4 coughs per hour) for one hour resulted in the inhalation of 187.3 (median value) respiratory droplets, whereas sharing with an asymptomatic COVID-19 positive person (10 speaking events per hour) resulted in the inhalation of 482.9 droplets.
Increasing the ventilation rate resulted in only modest reductions in particle numbers inhaled.
Conclusions Given that live SARS-CoV-2 virions are known to be shed in high concentrations from the nasal cavity of both symptomatic and asymptomatic COVID-19 patients, the results suggest that individuals who share enclosed spaces with an infector may be at risk of contracting COVID-19 by the aerosol route, even when practicing social distancing.

Answer: {"verdict": "CONTRADICT", "evidence": ["In order to quantify the risk posed by COVID-19 infectors exhaling respiratory aerosols in enclosed spaces, we undertook a computer modelling study to simulate transmission in an office building."]}


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: Severe COVID-19 outcomes decreases as the pandemic progressed from winter to the warmer months

Abstract:
The 2020 coronavirus pandemic is developing at different paces throughout the world.
Some areas, like the Caribbean Basin, have yet to see the virus strike at full force.
When it does, there is reasonable evidence to suggest the consequent COVID-19 outbreaks will overwhelm healthcare systems and economies.
This is particularly concerning in the Caribbean as pandemics can have disproportionately higher mortality impacts on lower and middle-income countries.
Preliminary observations from our team and others suggest that temperature and climatological factors could influence the spread of this novel coronavirus, making spatiotemporal predictions of its infectiousness possible.
This review studies geographic and time-based distribution of known respiratory viruses in the Caribbean Basin in an attempt to foresee how the pandemic will develop in this region.
This review is meant to aid in planning short- and long-term interventions to manage outbreaks at the international, national, and subnational levels in the region.

Answer: {"verdict": "SUPPORT", "evidence": ["Preliminary observations from our team and others suggest that temperature and climatological factors could influence the spread of this novel coronavirus, making spatiotemporal predictions of its infectiousness possible."]}


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: Vitamins C and D boost our immune systems, aiding in the fight against infectious diseases;

Abstract:
Coronaviruses are a genetically highly variable family of viruses that infect vertebrates and have succeeded in infecting humans many times by overcoming the species barrier.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initially appeared in China at the end of 2019, exhibits a high infectivity and pathogenicity compared to other coronaviruses.
As the viral coat and other viral components are recognized as being foreign by the immune system, this can lead to initial symptoms, which are induced by the very efficiently working immune defense system via the respiratory epithelium.
During severe courses a systemically expressed proinflammatory cytokine storm and subsequent changes in the coagulation and complement systems can occur.
Virus-specific antibodies, the long-term expression of which is ensured by the formation of B memory cell clones, generate a specific immune response that is also detectable in blood (seroconversion).
Specifically effective cytotoxic CD8+ Tcell populations are also formed, which recognize viral epitopes as pathogen-specific patterns in combination with MHC presentation on the cell surface of virus-infected cells and destroy these cells.
At the current point in time it is unclear how regular, robust and durable this immune status is constructed.
Experiences with other coronavirus infections (SARS and Middle East respiratory syndrome, MERS) indicate that the immunity could persist for several years.
Based on animal experiments, already acquired data on other coronavirus types and plausibility assumptions, it can be assumed that seroconverted patients have an immunity of limited duration and only a very low risk of reinfection.
Knowledge of the molecular mechanisms of viral cycles and immunity is an important prerequisite for the development of vaccination strategies and development of effective drugs.

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: Hot weather can spread the virus more as it may get you more out there, make you more mobile and you would actually interact with more people

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

Answer: {"verdict": "CONTRADICT", "evidence": ["Results indicated that temperatures had a negative linear relationship with the number of confirmed cases."]}