Suffolk's CoronaWatch is the home of data and information produced under the Suffolk Joint Strategic Needs Assessment (JSNA). 

Please note: there are continual updates and improvement to the national COVID-19 data page. Occasionally, this will affect the timeliness of the data dashboard and other CoronaWatch products. We are updating when data is available.  The date that the data is refreshed is always available within the CoronaWatch dashboard. 

This page will be frequently updated as we are able to analyse and publish more information. 

Unfortunately, we are unable to respond to individual requests for COVID-19 data at the moment. This is due to exceptionally large volume of requests we are receiving.  However, we will log and monitor all queries, and aim to answer them within our FAQs section as soon as we possibly can. You can submit a query via the Knowledge and Intelligence Team: 

January 2022

National information

The latest Office for National Statistics (ONS) data (updated December 2021) indicates that COVID-19 was the second-highest leading cause of death in care home residents in England and Wales in 2020.  

  • In England, there were 155,376 deaths of care home residents in 2020 (wherever the death occurred); an increase of 18.5% compared with 2019, and 18.3% compared with the five-year average between 2015 and 2019. 

  • Taking into account the population size and age structure, age-standardised mortality rates (ASMRs) in England, for male and female care home residents, were statistically significantly higher in 2020 compared with the five previous years. ASMRs were statistically significantly higher for male compared with female care home residents in 2020. 

  • Dementia and Alzheimer disease was the leading cause of death in both male and female care home residents in England (accounting for 26.0% and 34.3% of deaths respectively) in 2020. COVID-19 was the second highest leading cause of death in both male and female care home residents in England (17.8% and 14.2% respectively). 

  • The majority of deaths of care home residents occurred within the care home in England (86.6%). 

Care home deaths

Despite care homes in Suffolk continuing to care for their residents with the utmost kindness and professionalism, data indicates that during the first ‘wave’ of the pandemic in Suffolk, a higher rate of COVID-19 related deaths within care homes in Suffolk compared to some other areas. The reasons for this are complex, but factors which we think are contributing to this include:

  • More people choosing to, and then being supported to, die in their usual place of residence
  • The earlier and more extensive COVID-19 testing which took place in Suffolk compared to some areas, which enabled earlier, more accurate attribution of subsequent deaths to COVID-19.

In Suffolk there are very good practices for providing end of life care. Dying within a care home setting is often reflective of a person’s wish to remain in their usual place of residence, in comfortable surroundings with people around them who are familiar, as opposed to being within a hospital setting. To help inform our understanding of this issue, which is complex and multi-factorial, SCC are currently working with CCG and primary care colleagues to audit GP records. These records relate to care home residents who have died from COVID-19, to determine whether they had end of life care plans (known locally as “yellow folders”) recording their wishes at the end of life, and whether those wishes were met. A summary of findings from this audit is provided below.

Care homes audit 2020

Approximately 5,500 people live in care homes in Suffolk, across around 200 residential and nursing homes. Care home residents are potentially frail and often suffer from two or more long-term conditions at the same time, placing them at greater risk of diseases including COVID-19. They are also the recipients of advance care planning (ACP), which aims to reduce distress to patients with life-limiting and incurable conditions by putting in place “do not attempt cardiopulmonary resuscitation” orders (DNACPRs). Patients with ACPs in place often die in their usual place of residence (DiUPR) rather than being transferred to unfamiliar and frightening environments (e.g. hospitals) and being subjected to potentially distressing and invasive investigations at the end of life.

We identified individuals with ACPs using routinely-collected general practice (GP) data and death registration data. 47 of 51 Suffolk GP practices approached during this study responded; 61 residents across 29 care homes were included in the final analysis. 46 of 61 COVID-related care home deaths in this study (75%) had a DNACPR order in place at the time of death, and individuals with higher frailty scores tended to have a DNACPR order in place. This may suggest that residents were having their treatment approach adapted to suit their individual needs. 56 individuals were identified in both datasets based on provided information. Of these, 36 (64%) died in their usual residence, 19 (34%) in hospital, and 1 in hospice. This suggests that end-of-life planning may have played a part in the increased care home deaths observed in Suffolk county for some individuals. However, this study was relatively small, and repeating this with wider coverage (including Waveney CCG and GP systems using other software) might be helpful.

Overall, this study identified a high proportion of DNACPRs and DiUPR in the Suffolk care home population who died from COVID-19 between March and June 2020. More frail individuals comprised a higher proportion of DNACPRs, in keeping with end-of-life planning.

Discharging from hospital

  • Since March 2020, the whole health care system in Suffolk has worked together more closely than ever before in response to the challenges presented by coronavirus.
  • One of its actions was to secure additional bed capacity at local care homes. The aim is to enable the discharge of all hospital patients who are medically fit to leave, leaving staff to focus on treating those patients who really need to be there.
  •  An extra 182 beds in care homes were made available for use, which is an increase of just under 9% of the beds that the council commissions. This has greatly strengthened the ability to cope with the extra demand. Fortunately, although demand has increased it has not been overwhelming, meaning not all the extra beds have been needed.
  • As part of these beds, we have commissioned some specialist care provision for people who need admission to a care home but cannot be admitted. These step-down beds will be used where care homes cannot take in a person who is COVID-19 positive, but these individuals still need care and a period of isolation.  60 beds in three locations across Suffolk have been commissioned.


  • You can find data about deaths in care homes via the data dashboard.
  • You may also find the Local Government Association (LGA) report on care home deaths useful. A range of geographies and comparators can be chosen. 
  • When comparing data, it can also be helpful to compare to local authorities that are statistically similar to us: CIPFA nearest neighbours. Compared to CIPFA statistical neighbours, Suffolk is in the lower half of authorities in relation to the percentage of COVID-19 deaths in care homes for the year to date. 

Local data dashboard 

View the data dashboard for Suffolk in a new tab, or by looking at the visualisation below.

The data is refreshed on a daily basis. 

National data dashboard information 

These links are nationally published dashboards that contain local information, and may be of use: 

Can you tell me what households in my street have coronavirus?

  • We are continuing to monitor data on a daily basis.
  • Internally, we have access to household level data, that helps us to monitor cases, and enables early detection of potential increases in cases of coronavirus. This data is sensitive, and we cannot share it on the website.
  • We can share information at neighbourhood (middle super output area) level through the CoronaWatch Dashboard, and there is also mapping on the website.
  • The links above are the most detailed publicly available data we can provide at this time.

Can you tell me if a specific care home has reported a coronavirus outbreak, or how many deaths there have been?

  • Internally, we have access to individual care home data, that helps us to monitor cases, and enables early detection of potential increases in cases of coronavirus. This data is sensitive, and we cannot share it on the website.

What is the lowest geography I can get coronavirus case information at?

  • The website has a publicly available COVID-19 map that looks at neighbourhood level data on positive cases diagnosed in the last week (7 day rolling average).    
  • These neighbourhoods are technically known as Middle Super Output Areas (MSOAs).  MSOAs are a type of Census geography, and tend to have a population of between 5,000-15,000 people.  You can find more information out about MSOAs on the Office for National Statistics website. 
  • For data confidentiality reasons, cases are not published when they number 0,1 or 2 in a neighbourhood. 
  • The data is updated on a daily basis, and you can look at data for the whole of England. 
  • You can also view vaccination uptake for the same geographies.

What is the Suffolk mortality rate from COVID-19, can we divide the number of deaths directly into the number of cases?

  • This question is simple, but difficult to answer.
  • The case fatality rate is the number of confirmed deaths divided by the number of confirmed cases.
  • We cannot simply divide the number of deaths in Suffolk by the number of confirmed cases.
  • This is not accurate because it relies on every case of COVID-19 being identified through testing. Evidence suggests that many cases of COVID-19 are not identified as at the start of the pandemic the availability of testing was limited, and quite a high proportion of people with COVID-19 are asymptomatic, and may therefore not know they need a test.
  • When there are people who have the disease but are not diagnosed, the case fatality rate will overestimate the true risk of death. With COVID-19, we think there are many undiagnosed people.

What data do you have on ethnicity and COVID-19?

  • While some ethnicity data is shared with us, ethnicity data can sometimes be incomplete. 
  • We are exploring ways of obtaining better quality data in this regard, working with local health partners, which we hope will help to inform our work in the future, but are still working through both the analytical and information governance issues relating to this.

Below is a list produced by the Office for National Statistics that identifies common terms used as part of the COVID-19 response:


As part of Suffolk's continuing support to the ongoing emergency response and recovery phase of the COVID-19 pandemic, the following dashboard is designed to highlight key economic and social indicators. 

This dashboard is currently a work in progress, with additional data being added as and when it becomes available (publicly and internally).

View the data dashboard here in a new tab, or view it below.

This report includes data on:

  • Employment and Unemployment Rates
  • Alternative Claimant Counts
  • Universal Credit Statistics - People & Households
  • Business Counts (Births and Failures)
  • Poverty Statistics
  • Crime Statistics
  • Mobility Changes

Latest ONS data from December 2021 indicates: 

  • An estimated 1.3 million people (2%) in private households in the UK reported having long COVID in the four weeks to 6 December 2021.
  • Of people with self-reported long COVID, 21% (270,000 people) first had (or suspected they had) coronavirus (COVID-19) less than 12 weeks previously.
  • Those who first had (or suspected they had) COVID-19 at least 12 weeks previously made up 70% (892,000 people), and 40% (506,000 people) first had (or suspected they had) COVID-19 at least one year previously.
  • Of the proportion of people with self-reported long COVID whose symptoms adversely affected their day-to-day activities, 20% reported their ability to undertake day-to-day activities had been “limited a lot”.

Applying the above metric to Suffolk, it is estimated that 15,200 people living in Suffolk (2.0% of the Suffolk population) are experiencing self-reported long COVID.

The ONS regularly update their latest insights page with data, research and insights about COVID-19.  You can sign up to their email alerts for daily updates in your inbox.

You can view the latest data below: 

Research Action Digest And Review

Last RADAR  - This is the final edition of the RADAR report from April 2021. 

Public Health Suffolk's weekly RADAR:

  • summarises COVID-19 related research evidence, published in the previous week, that is relevant to the Suffolk system,
  • makes recommendations for action, and
  • aims to ensure research evidence is rapidly disseminated and turned into local action.

Our approach will be to include "need to know" research and not "nice to know". We will not be including research specific to NHS healthcare as this is covered elsewhere, including nationally.

Key sources of information include (but is not limited to) the following peer reviewed journals and websites:

Each article we include has also been given a grading level based on GRADE. This is an internationally recognised grading system used by the American Medical Association/ Cochrane for consistency in grading evidence.  Articles are graded for Quality of Evidence and Strength of Recommendation. produce a publicly available COVID-19 map that looks at neighbourhood level data on positive cases diagnosed in the last week (7 day rolling average).    

  • These neighbourhoods are technically known as Middle Super Output Areas (MSOAs).  MSOAs are a type of Census geography, and tend to have a population of between 5,000-15,000 people.  You can find more information out about MSOAs on the Office for National Statistics website. 
  • Enter your postcode to find your area, double click on the map, use the + and - buttons to zoom, or "drag and drop" to move around the map.
  • Select an MSOA (click on the area) to see the number of cases for the period
  • For areas such as Suffolk, the numbers of cases are low.  For data confidentiality reasons, cases are not published when they number 0,1 or 2 in a neighbourhood. This means a lot of the data on the map is shaded in white.   

We are working on integrating this data with our data dashboard.

A COVID Symptom Study (CSS) app has been developed. Some estimates are published on maps on their website (to local authority level), and at a lower level to people who have signed up to share data on symptoms through the app. 

On 12 August 2020, a new UK-wide methodology to record COVID-19 deaths was announced and the deaths data reported on the Government's Coronavirus (COVID-19) in the UK website changed as a result. This may affect reported figures and ability to compare areas or change over time. Suffolk CoronaWatch uses deaths data reported from the ONS, so the figures our local website are not affected by this change.

Office for National Statistics (ONS) data

Coronavirus (Covid-19) has spread across the vast majority of neighbourhoods in England and Wales. The ONS have produced an interactive map that allows you to see the number of deaths, where COVID-19 was mentioned as a cause on the death certificate.  

Public Health Suffolk continue to undertake local analysis of this data.


All viruses including the virus that causes COVID-19 evolve over time. When a virus makes copies of itself, it can change slightly, these changes are called “mutations”. A virus with one or more new mutations is referred to as a “variant” of the original virus.

When a virus is widely circulating in a population and causing lots of infections, the likelihood of the virus mutating increases. The more opportunities a virus has to spread, the more it replicates – and the more opportunities it has to undergo changes.

Most viral mutations have little to no impact on the virus’s ability to cause infections and disease. Depending on where the changes are located in the virus’s genetic material, they may affect a virus’s properties, such as how easily the virus can spread, or virus severity.  

This information was taken from the World Health Organization website. 

Names of variants

Previously, variants were named after the places they were first identified (for example the 'Kent' variant).  However, the World Health Organization has published new names for variants using Greek letters.  So the variant previously commonly known as the Kent variant is now commonly known as the Alpha variant.  For a full list of names have a look on the World Health Organization website.

Data on variants

Public Health Suffolk are collecting data on variants of concern (VOC) and variants under investigation (VUI) internally, we receive this information from the UK Health Security Agency (UKHSA). You can find out more about VOCs and VUIs in the regularly updated UKHSA Technical Briefings.

Work in Suffolk to identify vulnerable populations

Following the outbreak of Covid-19, the Suffolk Office of Data and Analytics (SODA) set out to identify which individuals and households in Suffolk may be particularly vulnerable to the impact of the Coronavirus.

A list of the clinically extremely vulnerable or ‘shielded’ residents was shared by central government. Emergency food parcels were sent out to those who indicated that they were struggling to get food supplies and support was offered through the Home But Not Alone service to those with care needs. 

In addition to residents on the shielding list, a team of analytical, data, information governance, and IT specialists was pulled together to identify other potentially vulnerable groups. A data-led exercise was conducted to identify people and households who may be particularly clinically, financially or socially vulnerable. Data from Suffolk County Council, the District and Borough Councils, and other organisations such as DWP was brought together to produce a list of individuals falling into these groups across Suffolk.

A list of the most vulnerable was produced and shared with the relevant local authorities. Each person on this list was contacted by the Home But Not Alone service in order to check their welfare and signpost to any relevant support services they may need.

The outputs of this work are sensitive and cannot be shared on the Healthy Suffolk website, however the tools below may also be of interest, and are in the public domain.

Tools that can help identify potentially vulnerable populations

In response to the COVID-19 (coronavirus) pandemic, many tools were produced nationally to help identify potentially vulnerable or high risk populations.

This information is taken directly from the GOV.UK website. 
You can find a more detailed briefing on R as well as the latest data via the GOV.UK briefing on the R number and growth rate in the UK.

What is R? 

The reproduction number (R) is the average number of secondary infections produced by 1 infected person.

  • An R number of 1 means that on average every person who is infected will infect 1 other person, meaning the total number of new infections is stable.
  • If R is 2, on average, each infected person infects 2 more people.
  • If R is 0.5 then on average for each 2 infected people, there will be only 1 new infection.
  • If R is greater than 1 the epidemic is growing, if R is less than 1 the epidemic is shrinking.

R can change over time. For example, it falls when there is a reduction in the number of contacts between people, which reduces transmission.

What is a growth rate?

The growth rate reflects how quickly the number of infections are changing day by day It is an approximation of the change of number infections each day. If the growth rate is greater than zero (+ positive), then the disease will grow. If the growth rate is less than zero (- negative) then the disease will shrink.

The size of the growth rate indicates the speed of change. A growth rate of +5% will grow faster than one with a growth rate of +1%. Likewise, a disease with a growth rate of -4% will be shrinking faster than a disease with growth rate of -1%. Further technical information on growth rate can be found on Plus magazine.

How are growth rates different to R estimates?

R does not tell us how quickly an epidemic is changing. Different diseases with the same R can give epidemics that grow at very different speeds. For instance, a disease with R=2 with infection lasting years will grow much more slowly than a disease with R=2 with infection lasting days.

The growth rate provides us with information on the size and speed of change, whereas the R value only gives us information on the direction of change.

The Wider Impacts of COVID-19 on Health (WICH) monitoring tool is an interactive webtool. This tool presents weekly and monthly data on a range of indicators including those on the wider determinants, hospital activity, mortality and life expectancy. Many of the indicators are available by region and data can be viewed by inequalities, such as ethnicity, age and sex. The tool uses near real time data to monitor indicators during the COVID-19 pandemic. The tool can be accessed here

The local Fingertips profile brings together selected indicators that are thought to reflect the diversity of potential impacts on health as a result of the COVID-19 pandemic. It includes indicators from across Fingertips profiles and is available below: