COVID-19 Group Vaccine Status Estimation

Estimate the vaccination status of a group of people at a future date

This app estimates the numbers of fully vaccinated, partially vaccinated, and unvaccinated people in a group at a future date. These people can be from different locations and from different age groups subject to data availability.

This app consists of the following steps:

  • Step 1: Specify group members
  • Step 2: Specify estimation date and other settings
  • Step 3: Review the estimated vaccination rates per location and age group
  • Step 4: View the estimated group vaccination status

Created by Bryn Wiley. Thank you to Dr. Sarah Otto, Dr. Daniel J. McDonald, and the BC COVID-19 Modelling Group for their substantial input and suggestions.

Step 1: Add Group Members

To add or remove group members either

  • Click or right click on the table to adjust the defaults,
  • Use the dropdown menus below, or
  • Upload a csv file containing group membership information

Location: A location for individuals to originate from (eg: United States)

Region: A region of a selected location. Currently only supports US states or Canadian provinces

Age: An age group for individuals from the selected location and region. Not available for all locations or regions.

Number of People: The number of people from the selected location, region, and age group.

Maximum Portion Vaccinated: The maximum allowable portion of people vaccinated. If you have an upper limit on what you expect the maximum portion of people to be you can set this to a value between 0 and 1.

To ensure required group data is present, use this template for file upload.

to save group changes and check for data ability

to move to the next step

Step 2: Date and Estimation Settings

This is the date on which vaccination status estimation will be made. It is not recommended to specify this more than 3 weeks in the future.

This specifies how the app will estimate future vaccination rates. There are two options:

  • Linear regression assumes vaccination coverage will continue increasing at its present rate in the future. This is best used for short term estimation.
  • Logistic regression assumes vaccination coverage accelerates early then decelerates and plateaus later. This produces a more pessimistic prediction better for longer term estimation.

to move back to group members

to move to the next step

Step 3: Review Estimated Vaccination Rates

Review the past and future estimated vaccination rates per location, region, and age group

Portion with at least one dose, by category

Portion fully vaccinated, by category

to move back to date and estimation settings

to move to the next step

Dots are predictions up to the specified prediction date.

Dashed lines are locations with uncertain data, and they include approximations or estimations for past vaccination rates. This app likely overestimates the proportion with at least one dose and underestimates the proportion fully vaccinated for these locations (see Data Sources and Explanations for more information).

Locations and age groups with too few past observations cannot generate reasonable future estimations. For these, we assume the vaccination rate will be constant in the future.

Step 4: Review Estimated Group Vaccination Coverage

Review the estimated numbers or percentages of people unvaccinated, partially vaccinated, or fully vaccinated in the group

Estimated Group Vaccination Coverage

Estimated Vaccination Status Totals and Confidence Interval Bounds (rounded)

Download totals as a csv file

Estimated Vaccination Status Totals by Category (rounded)

Download totals by category as a csv file

Error bars on graphs represent the uncertainty in prediction for total numbers of each level of vaccination. They are estimated 95% confidence intervals, see Data Sources and Explanations for more information.

to move back to vaccination rate visualisation

Disclamer: Estimations of future vaccination rates are made based on available data, which can be infrequent or incomplete (see below). These predictions are made by extrapolating from recent trends, and changes in vaccination policy, vaccination availability, and anything that makes the individuals gathering less representative of the places they come from would cause errors in predictions. Projecting vaccinations into the future is overall highly uncertain, and is subject to increasing error the further the event is into the future.

Vaccination data is taken from Our World in Data, the US CDC, and the Public Health Agency of Canada. There are some countries who will primarily report total doses given instead of first or second doses. If a country has not reported a quantity for first or second doses within the past month we assume that the proportion fully vaccinated remains constant from the last date it was reported and all new reported doses are first doses. This is conservative with regards to fully vaccinated individuals but optimistic to the number partially vaccinated. Currently, the countries to which this applies are: Bonaire Sint Eustatius and Saba, Falkland Islands, Guernsey, Kuwait, Monaco, Nauru, Niue, Pitcairn, Qatar, Saint Helena, Sudan, Turkmenistan and Tuvalu

Prediction using linear regression uses available data from the past 3 weeks to estimate a linear model with the lm function in R. If there are two or less observations in this period, regression is deemed not viable and so the vaccination rate is held constant.

Prediction using logistic regression uses available data from the past 6 weeks to estimate a logistic (s-shaped) curve using the nls function in R. More recent observations are weighted stronger than observations further in the past. Starting values are generated using the SSlogis function, but if the starting estimate for the asymptote is greater than the maximum allowed value or less than the current vaccination rate, it is adjusted to the maximum allowed value or the current value respectively. In the event that the nls function fails to fit a logistic function, a linear regression is fit instead. Again, if there are two or less observations in this period, regression is deemed not viable and so the vaccination rate is held constant

95% prediction intervals for estimated vaccination rates are generated from the predict function for linear regression, or from the predFit function from the investr package in R for logistic regression. If the upper bound of the prediction interval is larger than 1 or the maximum specified amount, it is reduced to this maximum amount. For the proportion fully vaccinated this upper amount is either the maximum specified amount or the maximum predicted proportion with at least one vaccination. Similarly, if the lower bound of the prediction interval is smaller than the most recent observation it is set to be equal to the most recent observation, as vaccination rates should not decrease.

Confidence intervals for total group vaccination percentages are estimated by taking the maximum and minimum predicted totals for each vaccination status, as generated by the vaccination rate prediction intervals from each category, and then adding a safe binomial confidence interval of 1/sqrt(N), where N is the total group size.

Data sources:

Centers for Disease Control and Prevention. (2021). COVID-19 Vaccinations in the United States,Jurisdiction [Data set]. Retrieved from

Mathieu, E., Ritchie, H., Ortiz-Ospina, E. et al. A global database of COVID-19 vaccinations. Nat Hum Behav (2021).

Public Health Agency of Canada. Canadian COVID-19 vaccination coverage report. Ottawa: Public Health Agency of Canada.

All relevant code is hosted at

Questions or comments? Email Bryn Wiley at