Last updated October 24, 2022
Table of contents
- Daily infections
- Hospital capacity
- General questions
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 different scenarios that can help policymakers understand how different policy decisions, along with the availability and use 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?
For the most recent information on scenarios, please see the “Projections and scenarios” section of our policy briefings.
How are vaccines incorporated into the model?
Brand- and variant-specific vaccine efficacy is updated using the latest available information from peer-reviewed publications and other reports. For more information on the assumptions about vaccine efficacy that we use in our models, see our COVID-19 vaccine efficacy summary.
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 (80% mask use). Mask use is defined as the percentage of people who say they always wear a mask when going out in public. Our data sources for mask use are listed on this page 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. Scientific studies have documented seasonal patterns for other coronaviruses, and most infectious respiratory diseases have a seasonal pattern. To learn more, watch this video.
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 about individual countries and regions, please visit our policy briefings page.
Why are the “reported" deaths shown in your results for my location different from what is shown on the government’s official page?
We obtain deaths data from a variety of sources. For some locations, we use the reported death numbers, with the vast majority of these coming from the Johns Hopkins University (JHU) data repository (see "Where does IHME obtain its data?"). Given that reported numbers are subject to frequent revision, often impacting the entire history of the pandemic, where substantial revisions have occurred and death data are temporally indexed by “day of death,” we use that time series instead. Finally, for some locations such as Mexico and Russia, where periodic cause of death data are released, we scale reported death numbers to match the final cause of death database releases. Cause of death data are usually more complete that the releases from surveillance systems; however, the trade-off is that they are released several months after the fact.
We also estimate the fraction of excess mortality in each country that is directly related to COVID-19 and the fraction that is increased mortality in individuals who did not test positive for COVID via PCR testing at the time of death. Please see our Estimation of total and excess mortality due to COVID-19 page for further details.
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.”
What factors drive up infections?
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)
- Emergence of new variants
- Lower vaccination rates
However, taking precautions such as getting vaccinated, avoiding large indoor gatherings during periods of high transmission, wearing masks in public areas, and regular handwashing and sanitation could reduce the risk of disease transmission.
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.
How did you determine the infection-fatality ratio?
For details about how we determine the infection-fatality ratio, please see our peer-reviewed article on this topic.
Why have your estimates for hospital resource needs changed?
As data continue to come in, our estimates change. Specifically, new death data and new information about the number of COVID-19 patients who need hospital beds have changed our projections.
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?”).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. Note public records of the number of hospitalizations on a particular day do not account for the number of people who are already occupying beds.
What do you mean by “mandates?”
To see what mandates are included in our current model, please see Table 2 in the latest policy briefings.
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 is 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, our special analyses, and our publications.
What kind of model does IHME use to produce these estimates? What assumptions are you making in your model?
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, and we update it frequently to respond to new data and new information. Please see our model update for total and excess mortality and our update to incorporate the Omicron variant and waning immunity. To learn more, see our policy briefings page, our special analyses, and our publications.
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)
- Corrects for errors in reported data
- Considers both reported COVID-19 deaths and total COVID-19 deaths in each population
- Factors in important drivers of trends in COVID-19, such as vaccination rates, 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
- Takes into account variation in transmission across locations and over time
- Makes sense of data that fluctuate frequently
Why do the numbers keep changing?
We strive to incorporate new evidence as soon as it becomes available, and our estimates 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 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. For some locations, we use the reported death numbers, with the vast majority of these coming from 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 a number of locations and for subnational estimates. Our models are updated regularly, as new data are available, to provide the most up-to-date planning tool possible.
Our primary source for US testing data is the US Department of Health and Human Services, through the HHS Protect Public Data Hub. For other global locations we use primarily what is reported by Our World in Data, supplemented by location-specific information typically sourced from government agencies, should such data be absent from the OWiD database.
We also use serosurvey data that evaluate the antibody-positivity of the population sampled, in order to better determine the total number of infections that are present among the population. These data are sourced from a variety of locations, but a significant proportion are sourced from SeroTracker, an open repository of published serosurvey datasets, in addition to ongoing state-sponsored serosurveys that occur at weekly or monthly frequencies, such as the US CDC's ongoing blood donor survey.
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 mobility, we use anonymized, aggregated data from Google.
Our mask use data sources: Premise (US only); The Delphi Group at Carnegie Mellon University and University of Maryland COVID-19 Trends and Impact Surveys, in partnership with Facebook; Kaiser Family Foundation; 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. Data on vaccine administration are primarily sourced from Our World in Data, supplemented by location-specific information typically sourced from government agencies, should such data be absent from the OWiD database. In particular, we use local datasets to obtain age-stratified and brand-specific distribution statistics.
Excess mortality data sources used in our estimation of total and excess mortality due to COVID-19 are available via this downloadable file.
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 Acknowledgments page.
Where can I find the technical appendix for your projections?
For technical appendices, see the supplementary appendices of our COVID-19 papers, which are located on our COVID-19 publications page. Changes to the model are also regularly communicated through our policy briefings and special analysis page.
How often are projections updated?
IHME updates its COVID-19 models and forecasts during the first half of each month. In the meantime, our researchers will keep track of any developments that might require more frequent updates.
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, Edge, or Firefox.
- 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.
Can I use your projections in my report/publication?
Please refer to our terms and conditions of use page.