mteb/CovidQA huggingface dataset:
If you are a healthcare provider or a public health responder caring for a COVID-19 patient, please take care of yourself and follow recommended infection control procedures.
People who get a fever or cough should consider whether they might have COVID-19, depending on where they live, their travel history or other exposures. More than half of the U.S. is seeing some level of community spread of COVID-19. Testing for COVID-19 may be accessed through medical providers or public health departments, but there is no treatment for this virus. Most people have mild illness and are able to recover at home without medical care.
For people who are ill with COVID-19, but are not sick enough to be hospitalized, please follow CDC guidance on how to reduce the risk of spreading your illness to others. People who are mildly ill with COVID-19 are able to isolate at home during their illness.
If you have been in China or another affected area or have been exposed to someone sick with COVID-19 in the last 14 days, you will face some limitations on your movement and activity. Please follow instructions during this time. Your cooperation is integral to the ongoing public health response to try to slow spread of this virus.
COVID-19 Emergence
COVID-19 is caused by a coronavirus. Coronaviruses are a large family of viruses that are common in people and many different species of animals, including camels, cattle, cats, and bats. Rarely, animal coronaviruses can infect people and then spread between people such as with MERS-CoV, SARS-CoV, and now with this new virus (named SARS-CoV-2).
The SARS-CoV-2 virus is a betacoronavirus, like MERS-CoV and SARS-CoV. All three of these viruses have their origins in bats. The sequences from U.S. patients are similar to the one that China initially posted, suggesting a likely single, recent emergence of this virus from an animal reservoir.
Early on, many of the patients at the epicenter of the outbreak in Wuhan, Hubei Province, China had some link to a large seafood and live animal market, suggesting animal-to-person spread. Later, a growing number of patients reportedly did not have exposure to animal markets, indicating person-to-person spread. Person-to-person spread was subsequently reported outside Hubei and in countries outside China, including in the United States. Some international destinations now have ongoing community spread with the virus that causes COVID-19, as do some parts of the United States. Community spread means some people have been infected and it is not known how or where they became exposed. Learn more about the spread of this newly emerged coronavirus.
Severity
The complete clinical picture with regard to COVID-19 is not fully known. Reported illnesses have ranged from very mild (including some with no reported symptoms) to severe, including illness resulting in death. While information so far suggests that most COVID-19 illness is mild, a reportexternal icon out of China suggests serious illness occurs in 16% of cases. Older people and people of all ages with severe chronic medical conditions — like heart disease, lung disease and diabetes, for example — seem to be at higher risk of developing serious COVID-19 illness. A CDC Morbidity & Mortality Weekly Report that looked at severity of disease among COVID-19 cases in the United States by age group found that 80% of deaths were among adults 65 years and older with the highest percentage of severe outcomes occurring in people 85 years and older.

Learn more about the symptoms associated with COVID-19.
COVID-19 Pandemic
A pandemic is a global outbreak of disease. Pandemics happen when a new virus emerges to infect people and can spread between people sustainably. Because there is little to no pre-existing immunity against the new virus, it spreads worldwide.

The virus that causes COVID-19 is infecting people and spreading easily from person-to-person. Cases have been detected in most countries worldwide and community spread is being detected in a growing number of countries. On March 11, the COVID-19 outbreak was characterized as a pandemic by the WHOexternal icon.
This is the first pandemic known to be caused by the emergence of a new coronavirus. In the past century, there have been four pandemics caused by the emergence of novel influenza viruses. As a result, most research and guidance around pandemics is specific to influenza, but the same premises can be applied to the current COVID-19 pandemic. Pandemics of respiratory disease follow a certain progression outlined in a “Pandemic Intervals Framework.” Pandemics begin with an investigation phase, followed by recognition, initiation, and acceleration phases. The peak of illnesses occurs at the end of the acceleration phase, which is followed by a deceleration phase, during which there is a decrease in illnesses. Different countries can be in different phases of the pandemic at any point in time and different parts of the same country can also be in different phases of a pandemic.
There are ongoing investigations to learn more. This is a rapidly evolving situation and information will be updated as it becomes available.

Risk Assessment
Risk depends on characteristics of the virus, including how well it spreads between people; the severity of resulting illness; and the medical or other measures available to control the impact of the virus (for example, vaccines or medications that can treat the illness) and the relative success of these. In the absence of vaccine or treatment medications, nonpharmaceutical interventions become the most important response strategy. These are community interventions that can reduce the impact of disease.

The risk from COVID-19 to Americans can be broken down into risk of exposure versus risk of serious illness and death.

Risk of exposure:
The immediate risk of being exposed to this virus is still low for most Americans, but as the outbreak expands, that risk will increase. Cases of COVID-19 and instances of community spread are being reported in a growing number of states.
People in places where ongoing community spread of the virus that causes COVID-19 has been reported are at elevated risk of exposure, with the level of risk dependent on the location.
Healthcare workers caring for patients with COVID-19 are at elevated risk of exposure.
Close contacts of persons with COVID-19 also are at elevated risk of exposure.
Travelers returning from affected international locations where community spread is occurring also are at elevated risk of exposure, with level of risk dependent on where they traveled.
Risk of Severe Illness:

Early information out of China, where COVID-19 first started, shows that some people are at higher risk of getting very sick from this illness. This includes:
Older adults, with risk increasing by age.
People who have serious chronic medical conditions like:
Heart disease
Diabetes
Lung disease
CDC has developed guidance to help in the risk assessment and management of people with potential exposures to COVID-19.

What May Happen
More cases of COVID-19 are likely to be identified in the United States in the coming days, including more instances of community spread. CDC expects that widespread transmission of COVID-19 in the United States will occur. In the coming months, most of the U.S. population will be exposed to this virus.
Widespread transmission of COVID-19 could translate into large numbers of people needing medical care at the same time. Schools, childcare centers, and workplaces, may experience more absenteeism. Mass gatherings may be sparsely attended or postponed. Public health and healthcare systems may become overloaded, with elevated rates of hospitalizations and deaths. Other critical infrastructure, such as law enforcement, emergency medical services, and sectors of the transportation industry may also be affected. Healthcare providers and hospitals may be overwhelmed. At this time, there is no vaccine to protect against COVID-19 and no medications approved to treat it. Nonpharmaceutical interventions will be the most important response strategy to try to delay the spread of the virus and reduce the impact of disease.
CDC Response
Global efforts at this time are focused concurrently on lessening the spread and impact of this virus. The federal government is working closely with state, local, tribal, and territorial partners, as well as public health partners, to respond to this public health threat.
Highlights of CDC’s Response
CDC established a COVID-19 Incident Management System on January 7, 2020. On January 21, CDC activated its Emergency Operations Center to better provide ongoing support to the COVID-19 response.
The U.S. government has taken unprecedented steps with respect to travel in response to the growing public health threat posed by this new coronavirus:
Foreign nationals who have been in China, Iran, the United Kingdom, Ireland and any one of the 26 European countries in the Schengen Area within the past 14 days cannot enter the United States.
U.S. citizens, residents, and their immediate family members who have been any one of those countries within in the past 14 days can enter the United States, but they are subject to health monitoring and possible quarantine for up to 14 days.
People at higher risk of serious COVID-19 illness avoid cruise travel and non-essential air travel.
CDC has issued additional specific travel guidance related to COVID-19.
CDC has issued clinical guidance, including:
Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19).
Infection Prevention and Control Recommendations for Patients, including guidance on the use of personal protective equipment (PPE) during a shortage.
CDC also has issued guidance for other settings, including:
Preparing for COVID-19: Long-term Care Facilities, Nursing Homes
Discontinuation of Home Isolation for Persons with COVID-19
CDC has deployed multidisciplinary teams to support state health departments in case identification, contact tracing, clinical management, and public communications.
CDC has worked with federal partners to support the safe return of Americans overseas who have been affected by COVID-19.
An important part of CDC’s role during a public health emergency is to develop a test for the pathogen and equip state and local public health labs with testing capacity.
CDC developed an rRT-PCR test to diagnose COVID-19.
As of the evening of March 17, 89 state and local public health labs in 50 states, the District of Columbia, Guam, and Puerto Rico have successfully verified and are currently using CDC COVID-19 diagnostic tests.
Commercial manufacturers are now producing their own tests.
CDC has grown the COVID-19 virus in cell culture, which is necessary for further studies, including for additional genetic characterization. The cell-grown virus was sent to NIH’s BEI Resources Repositoryexternal icon for use by the broad scientific community.
CDC also is developing a serology test for COVID-19.
Other Available Resources
The following resources are available with information on COVID-19

World Health Organization, Coronavirusexternal icon
Identifying Locations with Possible Undetected Imported Severe Acute Respiratory Syndrome Coronavirus 2 Cases by Using Importation Predictions,
https://wwwnc.cdc.gov/eid/article/26/7/20-0250_article
Volume 26, Number 7—July 2020
Research

Pablo Martinez De Salazar1Comments to Author , René Niehus, Aimee Taylor1, Caroline O’Flaherty Buckee, and Marc LipsitchComments to Author
Author affiliations: Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
Suggested citation for this article
Abstract
Cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection exported from mainland China could lead to self-sustained outbreaks in other countries. By February 2020, several countries were reporting imported SARS-CoV-2 cases. To contain the virus, early detection of imported SARS-CoV-2 cases is critical. We used air travel volume estimates from Wuhan, China, to international destinations and a generalized linear regression model to identify locations that could have undetected imported cases. Our model can be adjusted to account for exportation of cases from other locations as the virus spreads and more information on importations and transmission becomes available. Early detection and appropriate control measures can reduce the risk for transmission in all locations.
A novel coronavirus, later named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in December 2019 in the city of Wuhan, capital of Hubei Province, China, where cases were first confirmed (1). During December 2019–February 2020, the number of confirmed cases increased drastically. Model estimates suggested that >75,000 persons were infected by January 25, 2020, and the epidemic had a doubling time of ≈6 days (2). By the end of January 2020, travel restrictions were implemented for Wuhan and neighboring cities. Nonetheless, the virus spread from Wuhan to other cities in China and outside the country. By February 4, 2020, a total of 23 locations outside mainland China reported cases, 22 of which reported imported cases; Spain reported a case caused by secondary transmission (3).
Most cases imported to other locations have been linked to recent travel history from China (3), suggesting that air travel plays a major role in exportation of cases to locations outside of China. To prevent other cities and countries from becoming epicenters of the SARS-CoV-2 epidemic, substantial targeted public health interventions are required to detect cases and control local spread of the virus. We collected estimates of air travel volume from Wuhan to 194 international destinations. We then identified 49 countries that had a score of >49.2/100 on category 2, Early Detection and Reporting of Epidemics of Potential International Concern, of the Global Health Security (GHS) Index (4). We assumed these locations would be proficient at detecting SARS-CoV-2 and reporting confirmed imported cases, which we refer to as imported-and-reported cases. We ran a generalized linear regression model on this subset; based on the results, we generated predictions for the remainder of the
sample. Using these predictions, we identified locations that might not be detecting imported cases.
Methods
To identify locations reporting fewer than predicted imported SARS-CoV-2 infected cases, we fit a model to data from 49 locations outside mainland China with high surveillance capacity according to the GHS Index (4). Among these, 17 had high travel connectivity to Wuhan and 32 have low connectivity to Wuhan. We considered locations to be countries without any position on territorial claims. We performed a Poisson regression by using the cumulative number of imported-and-reported SARS-CoV-2 cases in these 49 countries and the estimated number of daily airline passengers from the Wuhan airport. We then compared predictions from this model with imported-and-reported cases across 194 locations from the GHS Index, excluding China as the epicenter of the outbreak.
The model requires data on imported-and-reported cases of SARS-CoV-2 infection, daily air travel volume, and surveillance capacity. We obtained data on imported-and-reported cases aggregated by destination from the World Health Organization technical report issued February 4, 2020 (3). We assumed a case count of 0 for locations not listed. We used February 4 as the cutoff for cumulative imported-and-reported case counts because exported cases from Hubei Province dropped rapidly after this date (3), likely because of travel restrictions for the province implement on January 23. We defined imported-and-reported cases as those with known travel history from China; of those, 83% had a travel history from Hubei Province and 17% traveled from unknown locations in China (3). We excluded reported cases likely caused by transmission outside of China or cases in which the transmission source was still under investigation (3). In addition, we excluded Hong Kong, Macau, and Taiwan from our model
because locally transmitted and imported cases were not disaggregated in these locations.
We obtained data on daily air travel from a network-based modeling study (S. Lai et al., unpub. data, https://doi.org/10.1101/2020.02.04.20020479External Link) that reported monthly air travel volume estimates for the 27 locations outside mainland China that are most connected to Wuhan. These estimates were calculated from International Air Travel Association data from February 2018, which includes direct and indirect flight itineraries from Wuhan. For these 27 locations, estimated air travel volumes are >6 passengers/day. We assumed that travel volumes for locations not among the most connected are censored by a detection limit. We used a common method of dealing with censored data from environmental sampling (5), or metabolomics (6), to set the daily air travel volume to half the minimum previously reported. Therefore, we used 3 passengers/day for estimated travel volumes for the 167 locations from the GHS Index not listed by Lai et al. We tested the robustness of our results by
using a set of alternative values of 0.1, 1, and 6 passengers/day for the censored data.
We defined high surveillance locations as those with a GHS Index for category 2 above the 75th quantile. We assessed the number of high surveillance locations, those with 0 imported-and-reported cases, and low surveillance locations, those with case counts >1 (Table).
For our model, we assumed that the cumulative imported-and-reported case counts across 49 high surveillance locations follow a Poisson distribution from the beginning of the epidemic until February 4, 2020. Then the expected case count is linearly proportional to the daily air travel volume in the following formula:where i denotes location, Ci denotes the imported-and-reported case count in a location, λi denotes the expected case count in a location, β denotes the regression coefficient, and xi denotes the daily air travel volume of a location. The Poisson model assumes cases are independent and that the variance is equal to the expected case count. Imported-and-reported cases likely meet the independence assumption because the value excludes cases with local transmission. We also checked the robustness of our results by using an over dispersed model with a negative binomial likelihood. We computed the p value of the overdispersion parameter as shown in Gelman and Hill (7).
Thumbnail of Regression plot of locations with possible undetected imported cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by air travel volume from Wuhan, China. Air travel volume measured in number of persons/day. No. cases refers to possible undetected imported SARS-CoV-2 cases. Solid line indicates the expected imported-and-reported case counts for locations. Dashed lines represent 95% prediction interval bounds smoothed for all locations. Purple dots indicate location
Figure 1. Regression plot of locations with possible undetected imported cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by air travel volume from Wuhan, China. Air travel volume measured in number of...
We used R version 3.6.1 (https://www.r-project.orgExternal Link) to compute , the maximum likelihood estimate of β, and the expected imported-and-reported case count given high surveillance (Figure 1). We also computed the 95% prediction interval (PI) bounds under this model of high surveillance for all 194 values of daily air travel volume (Figure 1). First, we generated a bootstrapped dataset by sampling n locations with replacement among high surveillance locations. Then, we reestimated β by using the bootstrapped dataset. Finally, we simulated imported-and-reported case counts for all 194 locations under our model by using the estimate of β from the bootstrapped dataset. We repeated the 3 steps 50,000 times to generate 50,000 simulated imported-and-reported case counts for each of the locations computed to the lower and upper PI bounds (PI 2.5%–97.5%). We smoothed the 95% PI bounds by using ggplot2 in R (8). We fit the imported-and-reported case counts of the 49 high surveillance
locations to the model and plotted these alongside 145 locations with low surveillance capacity (Figure 1). We noted some overlap between high and low surveillance locations (Figure 1).
Thumbnail of Analyses of imported-and-reported cases and daily air travel volume using a model to predict locations with potentially undetected cases of severe acute respiratory virus 2 (SARS-CoV-2). Air travel volume measured in number of persons/day. No. cases refers to possible undetected imported SARS-CoV-2 cases. Solid line shows the expected imported-and-reported case counts based on our model fitted to high surveillance locations, indicated by purple dots. Dashed lines indicate the 95% pr
Figure 2. Analyses of imported-and-reported cases and daily air travel volume using a model to predict locations with potentially undetected cases of severe acute respiratory virus 2 (SARS-CoV-2). Air travel volume measured in...
To assess the robustness of our results we ran 8 additional regression analyses by implementing a series of changes to the analysis. The changes included the following: set the daily air travel volume to 0.1, 1, or 6 passengers/day for locations not listed by Lai et al. (unpub. data, https://doi.org/10.1101/2020.02.04.20020479External Link) (Figure 2, panels A–C); removed all locations not listed by Lai et al. before fitting (Figure 2, panel D); defined high surveillance locations by using a more lenient GHS Index criterion, 50th quantile (Figure 2, panel E), and a more stringent criterion, 95th quantile (Figure 2, panel F); excluded Thailand from the model because it is a high-leverage point (Figure 2, panel G); or used an overdispersed Poisson likelihood with a negative-binomial likelihood (Figure 2, panel H). We provide code for these analyses on GitHub (https://github.com/c2-d2/cov19flightimportExternal Link).

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We found that daily air travel volume positively correlates with imported-and-reported case counts of SARS-CoV-2 infection among high surveillance locations (Figure 1). We noted that increasing flight volume by 31 passengers/day is associated with 1 additional expected imported-and-reported case. In addition, Singapore and India lie above the 95% PI in our model; Singapore had 12 more imported-and-reported cases (95% PI 6–17 cases) than expected and India had 3 (95% PI 1–3 cases) more than expected. Thailand has a relatively high air travel volume compared with other locations, but it lies below the 95% PI, reporting 16 (95% PI 1–40 cases) fewer imported-and-reported cases than expected under the model. Indonesia lies below the PI and has no imported-and-reported cases, but the expected case count is 5 (95% PI 1–10 cases) in our model. Across all 8 robustness regression analyses, we consistently observed that Singapore lies above the 95% PI and Thailand and Indonesia lie below (Figure
2). India remains above the 95% PI in all robustness analyses except when we used the more stringent GHS Index, 95th quantile, for fitting; then India lies on the upper bound of the 95% PI (Figure 2, panel F).
We aimed to identify locations with likely undetected or underdetected imported cases of SARS-CoV-2 by fitting a model to the case counts in locations with high surveillance capacity and Wuhan-to-location air travel volumes. Our model can be adjusted to account for exportation of cases from locations other than Wuhan as the outbreak develops and more information on importations and self-sustained transmission becomes available. One key advantage of this model is that it does not rely on estimates of incidence or prevalence in the epicenter of the outbreak. Also, we intentionally used a simple generalized linear model. The linearity of the expected case count means that we have only 1 regression coefficient in the model and no extra parameters. The Poisson likelihood then captures the many 0-counts observed for less highly connected locations but also describes the slope between case-count and flight data among more connected locations. We believe this model provides the most
parsimonious phenomenologic description of the data.