Evergreen Life insights

COVID-19 Insights

We are using data provided by our users to generate health and wellbeing insights throughout the COVID-19 pandemic.

Insights you have helped us discover

  • Users with symptoms are more likely to eat processed food
  • Users with symptoms are also more likely to be anxious* and less likely to be satisfied with their sleep
  • Users who are staying at home are more likely to be eating vegetables and other healthy foods like oily fish*
  • Users who are staying at home are also drinking alcohol more frequently but are drinking fewer units per day*
The below graphs and maps are based on information provided by over 40,000 Evergreen Life users. The statistical analysis is being done by Evergreen Life in partnership with the University of Manchester's Department of Mathematics, University of Liverpool’s Department of Electrical Engineering and Institute of Population Health Sciences. Evergreen Life is working in partnership with the NHS on Project OASIS, to help the NHS respond to the COVID-19 pandemic.
Visualisation of COVID in the months after lockdown
The animations below show the % of Evergreen Life users reporting COVID symptoms and those following guidance on each day since the 24th March.
Users reporting COVID-19 symptoms
Users reporting that they are following guidance
National trends
The graphs below demonstrate the % of users who are symptomatic and the % of those that are following guidance over the past 8 weeks.
COVID-19 heatmaps
The 4 maps below show the % of users who are reporting no symptoms, symptoms, following guidance, and not following guidance respectively. Where the colour of an area is darker it represents a higher percentage of users. Areas with no colour indicate that there are not enough responses. It's vitally important that we gather as much data on the spread of the disease as possible - so please share our app with your friends and family and help us learn more.

Our data is being used to directly assist the NHS and the government in battling COVID-19 through Project OASIS, the more data you help us gather the more effective we can be.
*Statistically significant at the 99% confidence level; †Statistically significant at the 95% confidence level; ‡Statistically significant at the 90% confidence level
Insights disclaimer: Using information from over 45,000 Evergreen Life users, these figures are derived from univariate Linear Probability Models (LPMs) (https://bit.ly/3aKaCeG) and multivariate LPMs, where we control for users’ gender and age (including a 2nd degree polynomial for age). Results are robust (in terms of sign, magnitude and statistical significance) to using logistic regression (https://bit.ly/3aHN0HI). LPM results are preferred for ease of interpretation.
Population-weighted heatmaps: These maps use information provided by over 40,000 Evergreen Life users. Each data point uses the latest response for each user that has responded within the 2 weeks prior to each day.
In order to display maps that account for potential age and gender bias resulting from a non-random sample of respondents, the counts of users reporting that they are symptomatic/following guidance are weighted by age and gender (x), within each Local Resilience Force (LRF). These weights are calculated as p(x)/q(x).
p(x) is calculated using mid-year population estimate proportions, by age and gender, aggregated to the LRF level. There are 8 age categories for each gender (0-15 ; 16-25; 26-35; 36-45; 46-55; 56-65; 66-75; 76+), and the 16 proportions sum to one for each LRF. q(x) is calculated analogously to p(x), but instead using the population of users who have responded to the Evergreen Life COVID-19 questionnaire.
The pseudo counts for each LRF are then calculated as the sum of weighted counts for each of the 16 bins. The resulting count gives greater weight to users who are underrepresented in terms of their gender and age, and vice versa.
The reported percentage is taken as the expected value of the Beta Distribution corresponding to the pseudo counts (plus one), and  CIs taken as the frequencies that correspond to the regularised incomplete beta function taking a value of 2.5% and 97.5%.
We define areas with insufficient data as those with an estimated size of the (beta distributed) error exceeding 10%.
National trend line graphs: These graphs use information provided by over 40,000 Evergreen Life users. Each data point uses the latest response for each user that has responded within the 2 weeks prior to each day. The graphs use exactly the same weighting methodology as the maps, and simply aggregate up to the national level, from the LRFs. The reported percentage is taken as the expected value of the Beta Distribution corresponding to the pseudo counts (plus one), and  CIs taken as the frequencies that correspond to the regularised incomplete beta function taking a value of 2.5% and 97.5%.

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