- Social distancing and mobility
- Infections and testing
- Hospital capacity
- About the project
What has changed with the IHME COVID-19 projections?
The forecast IHME released on March 26, 2020, was geared to helping hospitals plan for a surge in demand for their resources (e.g., beds, ICUs, ventilators) to fight COVID-19. With many locations passing their first peak of COVID-19 deaths, attention is now on how best to prevent and manage a resurgence of the disease while safely enabling people to get back to work and school. On May 4, 2020, we expanded our model to account for factors that could contribute to or contain the spread of the disease. This enables IHME to project the possibility of resurgence, which government and public health officials may find helpful as they face difficult health and economic decisions. On May 29, we updated our death model. The model improves our ability to analyze data that fluctuates substantially, and allows us to make more accurate predictions for locations with smaller epidemics.
Why do the numbers keep changing?
The world’s understanding of and response to COVID-19 is rapidly evolving. On May 4, 2020, we posted a major expansion to our model to include factors that could contribute to or contain the spread of the disease. This resulted in a substantial increase in forecasted COVID-19 deaths in certain locations. This expansion incorporates planned and actual easing of social distancing, changes in mobility, and variability in testing. It also factors in the observation that the number of deaths in certain US states has been staying at a higher level for a longer period of time than seen in other countries that experienced earlier epidemics. Our initial May 4 update only covered the US. On May 12, we began using this updated model to project trends for all countries. However, Ecuador and Peru use a different model. Also on May 29, we updated our death model. The model improves our ability to analyze data that fluctuates substantially, and allows us to make more accurate predictions for locations with smaller epidemics.
We strive to incorporate new evidence as soon as it becomes available, and our estimates will change in response to these data. We do this to make the tool as useful as possible to governments and hospital administrators, who need to know how the situation is changing in real time. Our aim is to produce the best possible predictions given what we know today – and to continually improve these estimates tomorrow. Also, we improve our methods based on feedback that we receive. If you have questions or suggestions, please contact us here. Learn more about these changes by visiting our estimation updates page.
Your projections for COVID-19 look very different. Could you orient me?
IHME is tracking information about the spread and containment of the disease. Mobility and social distancing indicate the amount of personal contact in a place. When these numbers are high, COVID-19 may be likely to spread. However, as mobility increases, taking precautions such as maintaining at least six feet between individuals in any gathering, and wearing cloth masks or face coverings in public areas could reduce the risk of disease transmission.
Testing indicates the number of tests that a location is carrying out in a given day, while estimated infections captures how much coronavirus is spreading each day. If testing in a location falls below estimated infections, the risk of undetected community transmission increases, which could lead to resurgence.
What are you trying to show on the social distancing and mobility chart?
We are showing what social distancing measures have been implemented (in green) and which ones have not been implemented (in gray). For those measures that have been implemented and lifted, or are scheduled to be lifted, we indicate on the timeline when they were lifted, or are scheduled to be lifted. In the mobility chart, we are showing how movement has changed relative to background levels for each location. These movement patterns have changed as social distancing measures have been implemented and/or eased. Individual decision-making also factors into movement patterns, as individuals in certain locations choose to increase or decrease their movement regardless of government mandates.
What do the metrics around testing and infections tell us?
Comparing these two metrics, which represent testing and estimated infections on a given day, shows how well the public health system is tracking the epidemic. If testing in a particular location falls below estimated infections, the risk of undetected community transmission and resurgence increases. Social distancing can be used to drive down infections so that testing, isolation, and contact tracing have a chance of working to contain the spread of the virus. Public health officials can use these two indicators to determine when social distancing measures can be eased. In locations where social distancing measures are not currently in effect, public health officials can use these indicators to consider if and when social distancing measures might need to be imposed.
Why does the dotted line for infections grow more rapidly in certain places?
Increases in mobility and loosening of social distancing measures can drive up infections. However, as mobility increases, taking precautions such as maintaining at least six feet between individuals in any gathering, and wearing cloth masks or face coverings in public areas could reduce the risk of disease transmission. Insufficient testing (testing that doesn’t keep pace with infections) as well as insufficient contact tracing capabilities, or lack of isolation for known infections, could also contribute to this rise.
What do you mean by "mobility"?
Mobility refers to personal movement by a population and is based on anonymous cellphone data several technology companies have made available for the purposes of fighting COVID-19. Mobility is an indicator of greater potential for personal contact, which can contribute to the spread of the disease. When mobility is high, the risk of COVID-19 spreading may also be high. However, as mobility increases, taking precautions such as maintaining at least six feet between individuals in any gathering, and wearing cloth masks or face coverings in public areas could reduce the risk of disease transmission.
Many locations are easing social distancing measures. How could this impact the trajectory of the COVID-19 pandemic in these locations?
In locations where the pandemic has yet to be curbed, and sufficient testing and contact tracing are not in place, easing social distancing could prolong the pandemic and lead to a greater number of deaths. It also could lead to a higher risk of resurgence compared to locations that have maintained social distancing. However, as mobility increases, taking precautions such as maintaining at least six feet between individuals in any gathering, and wearing cloth masks or face coverings in public areas could reduce the risk of disease transmission.
On May 4, 2020, we made a major update to our model to incorporate the impact of easing social distancing. Several locations have eased social distancing restrictions before curbing their COVID-19 pandemics and ramping up testing to keep pace with infections. This is a primary reason why our death estimates have increased for many locations.
Social distancing measures have not yet been eased in my location. Why does your tool show mobility increasing?
We are using anonymized mobile phone data to measure mobility, which indicates that mobility has increased in your location despite government-mandated social distancing measures.
Why have you stopped showing the point at which it is safe to ease social distancing?
The model IHME released on May 4, 2020, enables us to forecast infections and testing on a daily basis, which are the indicators public health officials can use to determine when social distancing measures can be eased or reimposed. Social distancing can be used to drive down infections so that testing, isolation, and contact tracing have a chance of working to contain the spread of the virus. This precise forecast replaces our general assumption that it would be safe to lower social distancing when infections passed below one per one million people.
How are you measuring government-mandated social distancing in the tool?
We evaluate government-mandated social distancing measures on a case-by-case basis, classifying them based on the New Zealand Government 4-level alert system:
- Educational facilities closed
All levels of educational instruction (primary, secondary, and higher education) are required to implement distance learning and are closed for in-person teaching activities.
- Non-essential businesses ordered to close
Only locally defined “essential services” are in operation. Typically, this results in closure of public spaces such as stadiums, cinemas, shopping malls, museums, and playgrounds. It also includes restrictions on bars and restaurants (they may provide take-away and delivery services only), closure of general retail stores, and services (like nail salons, hair salons, and barber shops) where appropriate social distancing measures are not practical. There is an enforceable consequence for non-compliance such as fines or prosecution.
- People ordered to stay at home
All individuals are ordered to stay at home unless traveling to essential services. Physical contact is only allowed between residents of the same household. Exercise may be permitted, as a solitary, distanced exercise, or with members of the same household. There is an enforceable consequence for non-compliance such as fines or prosecution.
- Severe travel restrictions
Location borders are closed to all incoming traffic except for those in provision of essential services and returning residents isolated in foreign territories. Automobile travel is restricted to accessing and working at essential services. Public transit is closed.
- Any gathering restrictions
Mandatory restrictions on gatherings of individuals of any number are in place. These can apply to public and private gatherings.
- Any business closures
The mandatory closure of any businesses is in effect. These restrictions need not apply to all businesses but can apply to just a specific subset (like bars and restaurants).
If a government mandate does not apply to the entire population in a specific location, we do not count it. Examples of mandates that we exclude are:
• Stay-at-home orders issued for the elderly only
• School closures ordered at the city level only
How do social distancing measures factor into your model?
We use social distancing measures in our model to project COVID-19 deaths. Data from Germany, Italy, the US, China, and Spain have demonstrated how social distancing measures can save lives. Using data from these countries helps us understand how quickly we can expect deaths to decline after a location implements social distancing measures.
Starting May 4, we began modeling the correlation between increases in mobility and disease transmission, which has allowed us to project how deaths from COVID-19 may increase in places where the pandemic has not yet been curbed. It is important to note that we may not fully understand the effect of easing social distancing mandates on disease transmission for a few weeks given the lag between exposure, infection, and onset of symptoms. Also, if people are being more cautious as they move around, maintaining six feet of distance and wearing masks, this could reduce the risk of disease transmission as mobility increases.
We updated our assumptions about social distancing when we updated our model on May 4. Our model now assumes that mandates that are currently still in place and have not been scheduled to be relaxed will stay in place through at least August 4. For locations where social distancing policies have been eased or clear plans have been instituted for their easement, we used those dates for the predictions.
How many overall deaths will there be?
Our model is updated to account for new data and information, and the estimates may change as a result. For the latest estimate, visit our COVID-19 projections tool. At present, the forecast only goes until the beginning of August.
Why do your results show a wide range in the forecast for deaths?
Larger uncertainty intervals – or the range within which estimated deaths are likely to fall – can occur because of limited data availability, small studies, and conflicting data. A smaller range generally reflects extensive data availability, large studies, and data that are consistent across sources.
How should I interpret the range of forecasts?
For government officials and hospital administrators, it is important to pay attention to the full range of values in our forecast, especially the upper values. Many hospital systems prepared for the upper range of values to ensure that their facilities were not overwhelmed at the last minute.
Why did the estimates for my location change?
On May 4, 2020, we made a major update to our model that led to a substantial increase in forecasted COVID-19 deaths in certain locations. In this model update, we factored in easing of social distancing, rising mobility, and increased testing. Also, our updated model better accounts for the observation that deaths in certain US states have been staying at a higher level for a longer period of time than seen in other countries that experienced earlier epidemics. Our initial May 4 update only covered the US. On May 12, we began using this updated model to project trends for all countries. However, Ecuador and Peru use a different model.
On May 29, we updated our death model. The model improves our ability to analyze data that fluctuates substantially, and allows us to make more accurate predictions for locations with smaller epidemics.
Also, as the pandemic unfolds and we receive more data on COVID-19 deaths, our projections adjust to follow new trends that emerge. The largest changes tend to be seen in locations with little data, which are in the early phases of the pandemic. We have also revised our methods to better incorporate data from locations where estimates of deaths fluctuate substantially from day to day, which we believe to be the result of reporting practices instead of actual death patterns. We have also added data that some locations are reporting on presumptive COVID-19 deaths.
Along with data on reported deaths, we are now using trends in reported cases and hospitalizations to project deaths, which we believe improves the accuracy of our forecasts. Other updates to our model include incorporating anonymized mobile phone data along with data on testing, population density, and temperature to understand transmission of the virus.
In some locations in the US, COVID-19 deaths have remained at a high level for a longer period than seen in countries with earlier epidemics. This has increased the projected death toll in those places.
To learn more, please visit our update page.
Why is the peak for daily deaths still forecast in the future when it looks like it has already occurred in my location?
The date of peak daily deaths depends on the model’s projections. If the model projects that the number of daily deaths will continue to rise, then the peak will be projected for a future date. It is important to note that the data on daily deaths may fluctuate dramatically due to inconsistencies in reporting. Health care workers are extremely busy caring for COVID-19 patients, so they may fall behind on reporting deaths. Once health care workers catch up on their reporting, however, it may appear as though there has been a spike in daily deaths.
Why are the “actual deaths” shown in your results for my location different from what is shown on the government’s official page?
For deaths, we primarily use the COVID-19 death data aggregated by the Johns Hopkins University data repository. The JHU repository uses UTC time, which means new days start at 8 p.m. Eastern time. The JHU counts may differ slightly from local government data as a result of these timing differences.
Also, the JHU repository is not necessarily synchronized to the update schedule of every location, so there may be a short lag that is reflected in a difference between our recorded daily deaths in a given location and those ultimately reported on government websites. Although this will be corrected when we update our analysis, in some cases, these differences may persist for several days.
We have added additional data from government websites for Brazil, Canada, France, Germany, Italy, Japan, Mexico, Spain, United Kingdom, Wuhan City, and the US states of Indiana, Illinois, Maryland, and Washington. For New York, we use data from the New York City Department of Health and Mental Hygiene and the New York Times GitHub repository.
Do you have the number of deaths by age group?
We do not currently have the number of deaths estimated to occur within each age group (for example, the number of deaths in individuals between 15 and 49), but we are working on making these numbers available as part of our regular updates to the model and results.
News reports have described probable under-reporting of COVID-19 deaths, particularly in the early data from Wuhan – does your model account for this?
Our model is now much less reliant on data from Wuhan since we now have COVID-19 death data from multiple locations. In particular, we have data from many more locations that have passed the peak of daily deaths, which provides patterns we use to tune our predictions.
There are reports of deaths being under-reported in places. How does this impact your forecast?
We are learning that not all deaths due to COVID-19 that occur at home or in nursing homes have been attributed to COVID-19. As awareness increases, the number of reported deaths is growing, with some locations now reporting presumptive COVID-19 deaths. Another challenge is that COVID-19 death data fluctuate substantially each day, with some locations reporting more deaths on Tuesdays than on Sundays and Mondays. We believe this variation is due to data reporting practices instead of actual death patterns. To mitigate the impact of inconsistent reporting on our forecasts, our published predictions are based on averaging multiple iterations of projections. As new data emerges, we incorporate it into our model, and our projections will shift up or down in response to the data. To learn more, see our estimation updates.
For Ecuador and Peru in particular, the number of reported deaths due to COVID-19 appears to be improbably low. Instead of using reported COVID-19 deaths for these countries, we are approximating deaths from COVID-19. To approximate COVID-19 deaths, we used the number of excess deaths occurring in Ecuador and Peru during the COVID-19 pandemic and observations from other countries where we had weekly reports of total deaths and high-quality data on COVID-19 deaths.
Why do the forecasts show zero deaths for May and June in my location?
Our model predicts that deaths from COVID-19 will be near zero by this date in your location. Note that for those locations with social distancing measures currently in place, our model assumes that they will stay in place through August 4, 2020.
For my location, it looks like we have already reached the number of total deaths that you have projected will occur by August 4. Can I expect even more deaths to occur?
Our model predicts that your location has reached or will soon reach its total COVID-19 death count. However, if mobility increases, and if testing fails to keep pace with estimated infections, a resurgence may occur. Should this happen, we will update our projections to reflect this new data, which will change the death projections.
Why are you modeling tests and estimated infections?
We are modeling testing because it is significantly related to disease transmission. As testing increases, the contact tracing and isolation associated with it help decrease disease transmission.
Estimated infections are a key input to the disease transmission component of our hybrid model. Estimated infections are also useful for understanding when the pandemic is likely to be contained in different locations.
Comparing testing to estimated infections allows government officials to understand how well testing is keeping pace with infections. If testing in a particular location falls below estimated infections on a given day, the risk of undetected community transmission and resurgence increases.
How are you defining estimated infections versus confirmed infections?
We define estimated infections as prevalent infections – that is, all cases that exist in a location on a given day, not just new ones. Confirmed infections are those infections that have been identified through testing.
How are you modeling estimated and confirmed infections?
For estimated infections, we start with death estimates, then work backward, using infection fatality ratios to estimate infections based on deaths. Confirmed infections are based on case data from the Johns Hopkins University (JHU) data repository on Github, averaged over the last 3 days to account for delays in reporting.
How did you determine the infection fatality ratio?
We reviewed data from all locations where extensive COVID-19 testing has occurred, then used the infection fatality ratio from the locations where the ratios were lowest: the Diamond Princess cruise ship and New Zealand.
What data are you using to model tests?
Our primary sources for US testing data are compiled by the COVID Tracking Project. For other locations, we rely primarily on data reported by Our World in Data. However, for Brazil and the Dominican Republic, we use government data.
How are you estimating tests and projected tests?
For tests, we average the data points from the last 3 days to account for delays in reporting. In cases where there are gaps in reported testing data, we extrapolate testing levels back to the date of the first confirmed case. We model projected tests based on past trends observed in the testing data.
Our primary sources for US testing data are compiled by the COVID Tracking Project. For other locations, we rely primarily on data reported by Our World in Data. However, for Brazil and the Dominican Republic, we use government data.
Why are you estimating hospital capacity?
These forecasts were developed to help hospitals and health systems prepare for the surge of COVID-19 patients.
Why have your estimates for hospital resource needs changed?
As data continue to come in, our estimates may change. Specifically, new death data and new information about the number of COVID-19 patients who need hospital beds have changed our projections.
Do changes in your projections mean that hospitals may have overprepared?
We believe that it is better for health systems to prepare for the upper range of values in the forecast, instead of the middle or lower range, to ensure that sufficient resources are in place. Many planners also prefer to utilize the upper range in their preparations.
Are your estimates for the number of available beds and ICU beds just for COVID-19 patients or for all patients?
The hospital resources shown are those estimated to be available for COVID-19 patients. We have excluded non-COVID patient needs, that is, the typical percentage of hospital beds occupied by other patients and emergencies.
Why do your projections for number of hospital beds needed for COVID-19 patients not match what I see in my location?
These discrepancies typically stem from the limitations of the datasets that we are using to estimate hospital and ICU beds needed for COVID-19 patients. For the US, our projections are based on data from the American Hospital Association. For countries in the European Economic Area (EEA), we obtained data from sources such as government websites, the Organisation for Economic Co-operation and Development, EUROSTAT, WHO, and published studies. As hospitals rapidly increase their capacity to deal with the influx of COVID-19 patients, we do not have access to data that reflect how bed counts are changing in real time. Given these limitations, government staff and health system administrators should continue to compare any available local hospital capacity data against our projections.
Are “invasive ventilators needed” additional ventilators needed for COVID-19 patients?
These are total ventilators needed for COVID-19 patients, excluding those needed by non-COVID-19 patients. These numbers do not reflect shortfalls in ventilators needed for COVID-19 patients because we lack data on the number of ventilators that states and countries currently have available.
Why doesn’t the number of beds needed in your visualization tool match the hospitalization admissions I’m seeing reported elsewhere?
Public records of the number of hospitalizations on a particular day do not account for the number of people who are already occupying beds.
Do you have models predicting health workforce needs at the peak of the outbreak?
We do not currently have this information. Having a patient in every bed, or running out of beds entirely, may mean that a hospital is facing substantial human resource shortages.
How will hospitals get more supplies? Is there a time when they will just run out?
Whether hospitals will be able to obtain sufficient supplies depends on the number of patients as well as any extra support that they may receive from governments or others.
What are the strategies that hospitals have to deal with the shortfall in capacity?
There are a variety of options for hospitals facing shortfalls in resources, including canceling elective procedures, setting up additional beds, constructing temporary facilities, and using mobile military resources. Scaling up production of ventilators, masks, and other personal protective equipment is likely to be needed to ensure these resources are available to hospitals as demand grows.
Why are your estimates different than those produced by other organizations?
Our model is designed to be a planning tool for government officials who need to know about the likely trajectory of COVID-19 deaths and infections in their location, taking into account mobility, testing, temperature, and population density. Our model is also designed to help hospital administrators and government officials understand when demand on health system resources will be greatest. To learn more, see our estimation updates page.
What kind of model does IHME use to produce these estimates?
IHME uses a hybrid modeling approach to generate our forecasts, which incorporates elements of statistical and disease transmission models, as described in our May 4 estimation update. Our model is grounded primarily in real-time data instead of assumptions about how the disease will spread. We update our model frequently to respond to new data and new information, describing the changes on our estimation updates page. For example, on May 29, we launched a death projection model that helps us better analyze data that fluctuates dramatically, and produces more accurate predictions for locations with smaller epidemics. More detail on the prediction models can be found in the methods of our article published as a preprint here, which describes all model changes and improvements through April 17, as well as our estimation update from May 29, which describes our revised death projection model. For those who are interested in comparing our older projection methods to our newer methods, the original version of our preprint paper released on March 27 is available here.
Why did you make substantial revisions to your model on May 4, and again on May 29, 2020?
Launched on May 4, our new hybrid model approach allows us to better model the impact of recent developments on COVID-19 disease transmission and deaths, including easing of social distancing, increasing mobility, and increased testing.
We unveiled a new death model on May 29 that enables us to expand our projections to additional locations. The model has improved our ability to analyze data that fluctuates substantially, and allows us to make more accurate predictions for locations with smaller epidemics.
What are the advantages of your modeling approach?
- Incorporates data on deaths, hospitalizations, and cases adjusted for scale-ups in testing and populations tested (i.e., symptomatic individuals and active case detection efforts among high-risk populations in factories, prisons, nursing homes, and homeless shelters).
- Factors in mobility (decreased mobility reduces COVID-19 transmission and deaths)
- Relies primarily on real-world data instead of assumptions about how the disease will spread
- Takes into account variation in transmission across locations and over time
- Makes sense of data that fluctuates frequently
- Performs well for smaller epidemics
You have expressed concerns about disease transmission models overestimating the number of likely COVID-19 deaths. Why are you now using one?
Our current model is not a disease transmission model. It is a hybrid model that combines both a statistical modeling approach and a disease transmission approach, leveraging the strengths of both types of models, and scaling the results of the disease transmission model to the results of the statistical model.
Why aren’t you using the hybrid model to make projections for Ecuador and Peru?
The number of reported deaths due to COVID-19 in Ecuador and Peru appear to be improbably low. Instead of using reported COVID-19 deaths, we are approximating deaths from COVID-19 in these countries. To do this, we used the number of excess deaths occurring in Ecuador and Peru during the COVID-19 pandemic along with observations from other countries (13 European countries and the US) about the number of excess deaths that were caused by COVID-19.
Why does uncertainty interval (range of values) shrink to zero for future estimates?
As the mean estimate (the point where it is equally likely for the estimate to be above or below) approaches zero, the uncertainty interval automatically shrinks because a range of values below zero is not possible.
Why did you decide to produce these forecasts?
We were initially asked by colleagues at the University of Washington School of Medicine to develop models to help in planning their response to COVID-19. As other hospital systems and governments around the world reached out for help in determining the impacts of COVID-19 on their health systems, we developed forecasts for all 50 US states, and are continuing to add other regions of the world facing similar questions about COVID-19.
Where does IHME obtain its data?
These forecasts include data from local and national governments, hospital networks and associations, the World Health Organization, third-party aggregators, and a range of other sources. We use the Johns Hopkins University (JHU) data repository on Github to collate daily death numbers due to COVID-19. We supplement this dataset as needed to improve the accuracy of our projections. For example, we use data from government websites for Brazil, Canada, France, Germany, Italy, Japan, Mexico, Spain, United Kingdom, Wuhan City, and the US states of Indiana, Illinois, Maryland, and Washington. For New York, we use data from the New York City Department of Health and Mental Hygiene website and the New York Times GitHub repository. We obtain subnational data from government websites. We obtain testing data from Our World in Data, COVID Tracking Project, and government websites. Our models are updated regularly as new data are available, to provide the most up-to-date planning tool possible.
Why are you only making projections through August 4?
We do plan to project COVID-19 trends past August. Currently, however, our priority is to capture what is happening right now as certain locations ease social distancing measures, mobility increases, and testing rises.
Why aren’t you modeling a second wave?
We are working to determine how to model what may happen should a second wave occur. It is challenging to make predictions about a second wave for two main reasons. First, as the pandemic unfolds, we may see a larger impact of temperature on disease transmission than we have to date. A second major challenge is that we do not know how governments are likely to respond to a second wave. We would prefer to have greater insight from the data before we model what is likely to happen in a second wave.
What case data do you use in your models?
We use the Johns Hopkins University (JHU) data repository on Github to collate daily COVID-19 case data. We supplement this dataset as needed to improve the accuracy of our projections. For example, for Washington state and New York, we use case data from the New York Times. We use case data from government websites for Wuhan City, Japan, Brazil, Canada, France, Germany, Italy, Mexico, Spain, and the UK, and for Illinois, Indiana, and Maryland.
Since you use case data in your models, are you accounting for greater case detection as locations scale up testing?
Yes – we adjust trends in cases to account for scale ups in testing.
Why are you using mobility, testing, temperature, and population density to model disease transmission?
In our analysis, we found that these different factors were significantly correlated with transmission.
What temperature data do you use in your models?
We use daily data on temperature (in Kelvin) from the Physical Sciences Laboratory NCEP/NCAR Reanalysis dataset.
What population density data do you use in your models?
We use gridded population count estimates for 2020 at the 1 x 1 kilometer (km) level from WorldPop.
Can you make estimates for my country/county?
We have heard from many hospitals, government officials, and other organizations asking us to generate projections for other locations. We plan to add different locations to the tool, including additional countries, as well as major metropolitan areas in the US. Based on our current resources, we cannot meet all of the requests we are receiving. However, you are welcome to submit requests by completing our Inquiry Form, and we will evaluate whether we have the resources to address them on a case-by-case basis.
Where can I find explanations for changes in specific forecasts?
Regular updates on changes to the underlying data and model structure accompany each iteration of the model. These can be found on our estimation updates page.
Where can I download previous versions of the projections?
You can download previous versions on our estimate downloads page.
How can I get a username and password to access your data?
No username or password is required to access our COVID-19 projections. You can download our current and past projections on our estimate downloads page.
The tool isn’t loading correctly in my web browser. What should I do?
Troubleshooting tips include:
- Use the latest version of Chrome, Internet Explorer (currently IE11), Edge, or Firefox.
- Make sure to disable compatibility mode in Internet Explorer. In some cases, it renders a site unusable
- Use Ctrl+0 to reset the browser zoom. Sometimes having the browser zoomed in or out interferes with the layout or a few mouse-dependent properties.
- If the visualization isn’t loading or looks distorted, try clearing your cache. See http://www.wikihow.com/Clear-Your-Browser's-Cache for pointers.
Where may I find the technical appendix for your projections?
For technical appendices, see the supplementary appendices of our COVID-19 papers, which are located here. On April 21, we released detailed technical appendices with the article “Forecasting the impact of the first wave of the COVID-19 pandemic on hospital demand and deaths for the USA and European Economic Area countries.” Changes to the model are also regularly communicated through our estimation updates page.
How frequently do you update your projections?
We aim to release as frequently as possible and will be working to make sure the model reflects what we’re learning each day about the pandemic, and that our forecast reflects the most up-to-date information available from all locations we track.
Can I use your projections in my report/publication?
Please refer to our terms and conditions of use page.
Why did you publish estimates for the US, Europe, and Canada before creating estimates for the rest of the world?
Our goal is to produce COVID-19 estimates for all nations as quickly as possible to assist decision-makers. We were initially asked by colleagues at the University of Washington School of Medicine to develop models to help in planning their response to COVID-19. As other US hospital systems asked for our help, we recognized the need to develop forecasts for all 50 US states. After producing estimates for the US, we then expanded the analysis to Europe since we found readily available data and were already using data from Italy and Spain in our model to understand the progression of the epidemic in contexts beyond Wuhan, China. We have now published projections for more than 10 countries in Latin America, and additional countries around the world reporting 50 or more COVID-19 deaths. We will publish findings for other countries as soon as we adapt our models to best reflect and respond to the data available and the needs of these contexts. Also, in order to be useful and relevant to policymakers and those planning responses to COVID-19, models for countries with more complicated data landscapes require different approaches than those with robust and publicly available records of deaths.
Coming up next in our research pipeline, we plan to release projections for other countries as soon as possible. We are focusing on expanding our forecasts to locations that have reported a minimum of 50 deaths to date since estimation in locations with few reported deaths can be less reliable.
Why is Puerto Rico listed separately from the US in the drop-down menu in the data visualization?
In our work at IHME, we measure outcomes for territories all over the world. In our dropdown menus, we list these territories at the same level as nations. To be consistent, we have used the same approach for Puerto Rico.