- About "herd immunity"
- Infections and testing
- Hospital capacity
- Social distancing
- About the project
Why are you showing different scenarios in the tool?
In making these forecasts, we aim to help policymakers plan for the days and months ahead, and take action to change the course of the pandemic for the better. We have developed three different scenarios that can help policymakers understand how different policy decisions, along with the availability of vaccines, could affect the trajectory of the pandemic in their location.
How should I interpret the different scenarios, and what assumptions do they include?
The assumptions for each scenario include:
New variant spread
Current projection (most likely to happen)
Mobility in the unvaccinated follows the pattern seen last year associated with seasonality. In 25% of those vaccinated, mobility returns toward pre-COVID-19 levels.
B.1.1.7 (first identified in the UK), B.1.351 (first identified in South Africa), and P.1 (first identified in Brazil) continue to spread from locations with (a) more than 5 sequenced variants, and (b) reports of community transmission and spread to adjacent locations, following the speed of variant scale-up observed in regions of the UK.
Stays at current levels
Mobility in the unvaccinated follows the pattern seen last year associated with seasonality. In 100% of those vaccinated, mobility returns toward pre-COVID-19 levels.
Variants B.1.351 or P1 begin to spread within 21 days in all locations that do not already have B.1.351 or P1 community transmission.
vaccines’ effectiveness is lower against B.1.351
|Starts declining among those vaccinated 30 days after completed vaccination|
Mobility in the unvaccinated follows the pattern seen last year associated with seasonality. In 25% of those vaccinated, mobility returns toward pre-COVID-19 levels.
|Same spread of variants of concern as current projection.||Expected pace||Increases to 95%|
|Note that scenarios assume the following about social distancing mandates. Governments adapt their response by re-imposing social distancing mandates for six weeks whenever daily deaths reach eight per million, unless a location has already spent at least seven of the last 14 days with daily deaths above this rate and not yet re-imposed social distancing mandates, in which case mandates are re-imposed when daily deaths reach 15 per million.|
Why did you choose 8 daily deaths per million as the point at which mandates are re-imposed?
We found that 90% of locations that we studied earlier this year imposed mandates when deaths reached the level 8 daily deaths per million. Re-imposing mandates at this point is crucial for making sure that hospital systems are well-prepared to handle the large influx of COVID-19 patients. When deaths reach this level, hospitals are beginning to be overwhelmed by patients. However, as states struggle to keep their economies afloat, mandates may not be imposed at all, or may be much more limited in scope.
Why does the universal mask scenario show increased deaths compared to the current projection scenario in my location?
Masks reduce the risk of transmission by at least one-third (see “How effective are masks?”), but they are not enough to bring transmission to a halt. Social distancing mandates are more effective than masks at reducing infections, and the current projection assumes that social distancing mandates are put in place for six weeks if daily deaths reach 8 per million. In your location, the 8 per million threshold is reached sooner in the current projection scenario than it is in the universal masks scenario. This is because masks delay the progression to the 8 per million threshold. Therefore, the universal mask scenario results in a greater number of deaths than if social distancing mandates were imposed sooner.
How effective are masks?
Our analysis indicates that masks, whether cloth or medical-grade, can reduce infections for mask-wearers by at least one-third. To learn more, visit our estimation update published on June 25, 2020, specifically the heading “Why masks? How effective are masks?”
What is the mask use chart showing?
This chart compares the current level of mask use in a location to the universal mask use target (95% mask use). Mask use is defined as the percentage of people who say they always wear a mask when going out in public. The target of 95% mask use is the highest level of mask use in the world, found in Singapore. Our data sources for mask use are listed here under "Where does IHME obtain its data?".
What do you mean by “seasonality”?
Seasonality refers to seasonal disease transmission patterns where COVID-19 transmission appears to increase during the fall and winter.
What scientific evidence do you have to support your claims about seasonality?
In the Southern Hemisphere, the rate of transmission for COVID-19 accelerated during the fall and winter months despite lockdowns in many countries. These trends stand in stark contrast to the Northern Hemisphere, where the rate of transmission fell during the spring and summer months, and lockdowns were much more effective at lowering transmission. In addition, scientific studies have documented seasonal patterns for other coronaviruses. Most infectious respiratory diseases have a seasonal pattern. To learn more, watch this video.
Why did infections and deaths rise so dramatically in Northern Hemisphere countries approaching the winter months?
Our model forecast large increases in infections and deaths in many countries in the Northern Hemisphere in the fall and winter due to seasonal patterns of disease transmission and declining vigilance (mask use declining and human contact increasing).
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.
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.
Why did the estimates for my location change?
To learn more, please visit our policy briefings 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 irregularities 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 “observed 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 (JHU) data repository (see "Where does IHME obtain its data?"). The JHU repository uses Coordinated Universal Time (UTC), 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.
Yet another reason why observed deaths may differ from numbers reported by governments is due to data processing. To address irregularities in the daily death data, we average model results over the last seven days to create a smooth version. To see the death data exactly as it is reported, click the “chart settings” icon in the upper right corner of the chart and turn off “smoothed data.”
There are reports of deaths being underreported in places. How does this impact your forecast?
We have learned 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 policy briefings.
For Ecuador, Peru, and Kazakhstan 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, Peru, and Kazakhstan 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.
We are continually checking countries’ data on total mortality to look for potential underreporting of COVID-19 deaths.
What is “herd immunity” and how would a herd immunity strategy affect the number of COVID-19 deaths?
Herd immunity means that a high enough percentage of a population has either had the disease, or been vaccinated against it, that the chance of it spreading is very low. Those promoting allowing populations to reach the point of herd immunity as a strategy for handling COVID-19 assert that policymakers should lift restrictions and let the disease spread through the population until herd immunity is reached, and in the meantime, protect the most vulnerable.
This strategy would actually lead to many more deaths compared to implementing measures like mask wearing and social distancing mandates while vaccination is scaled up.
We estimate that pursuing a herd immunity strategy before vaccination is scaled up could result in anywhere from 10.4 million to 15.7 million deaths from COVID-19 globally, depending on the level of total infection required to reach herd immunity.
Why does the dotted line for infections grow more rapidly in certain places?
All of these factors can drive up infections:
- Increases in human mobility
- Loosening of social distancing measures
- Seasonal disease transmission patterns (see “What do you mean by ‘seasonality’?”)
- Declining vigilance (mask use declining and human contact increasing)
However, taking precautions such as maintaining at least six feet between individuals at any gathering, wearing cloth masks or face coverings in public areas, and regular handwashing and sanitation could reduce the risk of disease transmission. Overwhelmed public health systems, where testing, tracing, and isolating every case is no longer feasible, could also contribute to this rise.
How are you defining estimated infections versus confirmed infections, and how do you model them?
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.
We estimate past daily infections in a modeling framework that leverages data from seroprevalence surveys, daily cases, daily deaths, and, where available, daily hospitalizations. For additional details on this modeling process, see the “Model updates” section of our January 22 policy briefing.
How did you determine the infection-fatality ratio?
We estimate the infection-fatality ratio (IFR) using COVID-19 seroprevalence data by age and COVID-19 death data. Our estimated IFR varies over time (declining by about one-third between March/April and September 2020, e.g.), and varies across locations as a function of obesity prevalence, the population distribution by age, corrections for potentially biased sources of seroprevalence, and other unexplained variation between locations. For further details on the evidence underlying our IFR estimates, see the “Model updates” section of our January 22 policy briefing.
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 linearly based on past trends observed in the testing data.
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.
What if hospitals end up over-preparing for the surge in demand?
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 (see “Where does IHME obtain its data?”). 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.
What are you trying to show on the social distancing chart?
We are showing how human mobility has changed relative to background levels for each location. These mobility patterns have changed as social distancing measures have been implemented and/or eased. Individual decision-making also factors into mobility patterns, as individuals in certain locations choose to increase or decrease their movement regardless of government mandates.
For the US only, we assume that school closure mandates will only reduce mobility by half as much as they did during the last school year.
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, wearing cloth masks or face coverings in public areas, and regular handwashing and sanitation could reduce the risk of disease transmission.
What do you mean by “mandates?”
Mandates include the following government orders, which are 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).
- Mask use
Any public mandate of mask wearing.
How is mobility being considered in light of changed behavior by individuals (i.e., mask wearing, not shaking hands/hugging, social distancing etc.)?
The models consider several drivers of infection and death. Mobility data is a proxy for social distancing, as are government mandates which are proving to have a pronounced effect lowering transmission. Mask use can lower transmission by more than 30%, and IHME now shares mask use in the visualizations of our COVID forecasts.
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 how different policy decisions can radically alter the trajectory of COVID-19 for better or worse.
Our model aimed at helping hospital administrators and government officials understand when demand on health system resources will be greatest. To learn more, see our policy briefings 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. 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 policy briefings page. More detail on the prediction models can be found in the published article "Modeling COVID-19 scenarios for the United States" (specifically in the Supplementary Information). Preprints of other articles describing our COVID-19 models are available here.
What assumptions are you making in your model?
These assumptions can be found in the published article "Modeling COVID-19 scenarios for the United States" (specifically in the Supplementary Information).
Why are you modeling different scenarios?
In making these forecasts, we aim to help policymakers plan for the days and months ahead, and take action to change the course of the pandemic for the better. We do not know which course of action policymakers will choose, so we decided to model the impact of three different courses of action that policymakers may be evaluating. We also factor in availability and rollout of COVID-19 vaccines. For more on the scenarios see the question, “What assumptions are included in each scenario?” above.
Why are you making projections four months into the future?
Projecting four months into the future allows us to strike a balance between providing a planning tool for policymakers, while producing the highest-quality results.
There are two basic uses for models. One is for health system planning, and a model that looks ahead about four to six weeks is useful for that. This allows hospitals and health care systems to make sure that they have enough staff and equipment to handle the likely number of patients in the coming weeks.
The second use is policymaking, such as deciding whether schools will open in person or stick to remote learning, or whether mask-wearing mandates should be put in place. These types of questions require a longer-range forecast that shows the overall trajectory of the epidemic, such as when peaks are likely to occur. As the modeling time frame increases, the uncertainty intervals associated with the estimates also increaser.
What are the advantages of your modeling approach?
- Shows how different policy decisions can impact the trajectory of COVID-19
- 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, population density, self-reported mask use, seasonal patterns of pneumonia (these patterns closely mirror transmission of COVID-19), and self-reported contacts to understand transmission of the virus
- 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 fluctuate frequently
- Performs well for smaller epidemics
How well does your model perform?
To learn more about our preliminary analysis of model performance, visit our June 25, 2020 estimation update, specifically the heading “How well is our model performing? Introducing a global framework for COVID-19 mortality forecast comparisons.” Please also see our paper titled, “Predictive performance of international COVID-19 mortality forecasting models.”
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.
The number of reported deaths due to COVID-19 in Ecuador, Peru, and Kazakhstan 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 these countries 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.
Does your model factor in the impact of new treatments for COVID-19?
To date, we have not seen a clear impact of these new treatments on the infection-fatality ratio from COVID-19. As soon as we see an effect in the data, we will factor this into our forecasts.
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 COVID-19 cases and deaths. 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, Pakistan, South Africa, Spain, United Kingdom, 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. We obtain subnational data from government websites. Our models are updated regularly, as new data are available, to provide the most up-to-date planning tool possible.
For 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, Canada, Cyprus, Dominican Republic, Honduras, India, Italy, Japan, Mexico, Moldova, Pakistan, Philippines, Russia, South Africa, and Spain, we use government data.
We obtain hospital resource data from sources such as government websites, hospital associations, the Organisation for Economic Co-operation and Development, WHO, and published studies. For population density, we use gridded population count estimates for 2020 at the 1 x 1 kilometer (km) level from WorldPop. For masks, we use data from Premise for the US, and from the Facebook Global symptom survey. (This research is based on survey results from University of Maryland Social Data Science Center.) For mobility, we use anonymized, aggregated data from Google, Facebook, and Apple. For the US, we use mobility data from Descartes and SafeGraph.
Our data on mask use come from Premise, Facebook Global Symptom Survey (research based on survey results from the University of Maryland Social Data Science Center), Kaiser Family Foundation (KFF), and YouGov COVID-19 Behaviour Tracker survey.
We obtain data on vaccine supply from Linksbridge, and data on vaccine hesitancy from a Facebook survey jointly conducted with MIT.
The dates of reports of local transmission and first sequence date were obtained from the variant-specific reports available here. Further details are available from O’Toole, Hill et al. (2021), Virological.org, here, with Pango nomenclature outlined in Rambaut et al. (2020), Nature Microbiology, “A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology” and assignment methodology further outlined in O’Toole et al. The software package for assigning SARS-CoV-2 genome sequences to global lineages, “pangolin: lineage assignment in an emerging pandemic as an epidemiological tool” is available here.
We would also like to thank the GISAID Initiative and are grateful to all of the data contributors, i.e., the authors, the originating laboratories responsible for obtaining the specimens, and the submitting laboratories for generating the genetic sequence and metadata and sharing via the GISAID Initiative, on which this research is based. GISAID data provided on this website are subject to GISAID’s Terms and Conditions. Individuals and their contributing laboratories are outlined in full at CoV-Lineages.
The following article describes GISAID’s contributions to global health: Elbe, S., and Buckland-Merrett, G. (2017). Data, disease and diplomacy: GISAID’s innovative contribution to global health. Global Challenges, 1:33-46. DOI: 10.1002/gch2.1018 PMCID: 31565258
For a complete list of our supporting organizations, please see our Acknowledgements page.
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.
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.
Why do the numbers keep changing?
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 policy briefings page.
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 policy briefings page.
Where can I download previous versions of the projections?
You can download previous versions 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?
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 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 drop-down menus, we list these territories at the same level as nations. To be consistent, we have used the same approach for Puerto Rico.