# Map

## Column

### Map

Data last updated on Sat Sep 19 16:30 BST 2020. We define an area to be a hotspot if weekly reported cases per 100,000 population exceed 50. For past weeks we compare the reported cases to the threshold. For future weeks, we give probabilities based on our model, which assumes a situation in which no change in interventions (e.g. local lockdowns) occur. To define weeks we use specimen dates, ie the day on which tests are taken. We consider an area to have increasing new infections if our model estimates that the reproduction number R is greater than 1 with probability of at least 90%. “Likely increasing” indicates a probability between 75% and 90%. Decreasing and likely decreasing are defined analogously, but consider R less than 1.

# Table

Data last updated on Sat Sep 19 16:30 BST 2020. We define an area to be a hotspot if weekly reported cases per 100,000 population exceed 50. To define weeks we use specimen dates, ie the day on which tests are taken. Median and 90% credible interval for weekly cases and Rt can be viewed via the "Column Visibility" button. Colour code for P(hotspot): 0.00 – 0.05 0.05 – 0.15 0.15 – 0.25 0.25 – 0.5 0.50 – 0.75 0.75 – 1.00. Colour code for P(R>1): 0.00 – 0.1 0.1 – 0.25 0.25 – 0.75 0.75 – 0.90 0.90 – 1.00.

# Details

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### Model description

The results on this page have been computed using epidemia 0.6.0. Epidemia extends the Bayesian semi-mechanistic model proposed in Flaxman, S., Mishra, S., Gandy, A. et al. Nature 2020.

The model is based on a self-renewal equation which uses time-varying reproduction number $$R_{t}$$ to calculate the infections. However, due to a lot of uncertainty around reported cases in early part of epidemics, we use reported deaths to back-calculate the infections as a latent variable. Then the model utilizes these latent infections together with probabilistic lags related to SARS-CoV-2 to calibrate against the reported deaths and the reported cases since the beginning of June 2020. A detailed mathematical description of the original model can be found here. The model used in this website is an evolution of this original model, incorporating randomness in the infections to account for areas with small number of infections.

$$R_{t}$$ for each local authority is parameterized as a linear function of the $$R_t$$ for its region/nation as a whole (which we fit too), and a random effect specific to the local authority for each week over the course of the epidemic. The weekly random effects are encoded as a random walk, where at each successive step the random effect has an equal chance of moving upward or downward. For regions/nations, only a region-specific weekly random walk is used.

### Limitations

• Predictions on this page assume no change in current interventions (lockdowns, school closures, and others) in the local area beyond those already taken about a week before the end of observations.
• An increase in cases in an area can be due to an increase in testing. The model currently does not account for this.
• Each area (local authority) is treated independently apart from the overall Rt estimate for its region. Thus the epidemic in a region is neither affected by nor affects any other region. It also does not include importations from other countries.
• The population within an area is considered to be homogeneous, i.e., all individuals are considered equally likely to be affected by the disease progression.

## Column

### Explanation of terms

• Dates for cases are specimen dates, ie the date on which tests were taken.
• An area is defined as hotspot if weekly cases per 100,000 population exceed 50.
• Change in new infections gives the probability (chance) of new infections increasing or decreasing in the local authority.
• Columns with column name as Weekly Cases per 100k [Date], in the Table tab, show the reported number of cases per 100,000 population for the week specified by the Date.
• Columns with column name as P(hotspot) [Date] shows the estimated probability of an area being a hotspot for the week specified by the Date.
• Column P(R>1) [Date] gives the probability of time varying reproduction number being greater than 1 for the week specified by the Date.

### Data sources

• Daily cases data for all local authorities in England is taken from UK gov site
• Daily cases data for all local authorities in Wales is taken from Public Health Wales COVID-19 dashboard
• Weekly deaths data for all Local authorities in England and Wales comes from ONS
• Daily cases and death data for Scotland is taken from Public Health Scotland
• Daily cases and death data for Northern Ireland is taken from Department of Health Northern Ireland
• Lower tier local authority boundaries: Office for National Statistics licensed under the Open Government Licence v.3.0

Data last updated on Sat Sep 19 16:30 BST 2020.

# FAQs

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### How often do you update the website?

We aim to update the website every evening after the release of the public data. Updates are usually available by the morning.

### What do you mean by "Hotspot"?

Hotspots are areas that have more than 50 reported cases per 100,000 population.

### What do you show on the map?

In the map we show the probability of an area being a hotspot in the next one, two and three weeks. The projections for hotspot assume no change in interventions and human behaviour since a week before the last observed data.

### What does "P(hotspot)" mean for a given week in the Table?

This is the probability of an area getting more than 50 cases per 100,000 population for the defined week.

### What does "Change in new infections" show in the Map?

Change in new infections shows how confident our model is about the time varying reproduction number ($$R_t$$) being greater than or less than one. We consider an area to have increasing new infections if our model estimates that the reproduction number R is greater than 1 with probability of at least 90%. Likely increasing indicates a probability between 75% and 90%. Decreasing and likely decreasing are defined analogously, but consider R less than 1.

### What does "P(R>1)" mean for a given week in the Table?

This corresponds to the 'change in new infections' option on the Map. This is the probability of the reproduction number for the given week exceeding 1, which is the threshold of determining whether the number of cases will continue to grow (R>1) or not (R<1). For example, P(R>1)=50% means that there is an equal chance of the rate of new infections increasing as there is of it decreasing.

### Which is the most significant figure - the probability of Rt exceeding one, or the percentage risk of the borough becoming a hotspot?

Becoming a hotspot is arguably more important, as it combines both the current state of the epidemic as well as it's future direction.

### Area X has a high probability of becoming a hotspot. Should a (local) lockdown be imposed?

Whatever is on this website should be considered in conjunction with other more detailed evidence before making decisions. At best the website should be used to inform which areas to look at in more detail.

### Area Y has a high probability of R being above 1. Should I be concerned?

Generally, we think that the probability of becoming a hotspot is a more useful measure. R only gives an indication whether or not the number of new infections is increasing (R>1) or decreasing (R<1). If R is indeed above 1, and case numbers are low, then this could come down through effective track and trace and/or medical support, robust testing and behaviour change. It is more of a concern if case numbers are already higher.

## Publisher

Authors: Swapnil Mishra1, Jamie Scott2, Harrison Zhu2, Neil Ferguson1, Samir Bhatt1, Seth Flaxman2, Axel Gandy2

1MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London

2Department of Mathematics, Imperial College London

Website Development: Aided by Fabian Valka

This research was partly funded by the The Imperial College COVID19 Research Fund.

## Licence

The results, maps and figures shown on this website are licenced under a Attribution 4.0 International (CC BY 4.0) Licence.

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