covid19 – IEUREKA!

COVID-19: Pandemics and ‘Infodemics’

Posted on September 7, 2021September 6, 2021 by lindsey.pike

Drs Luisa Zuccolo and Cheryl McQuire, Department of Population Health Sciences, Bristol Medical School, University of Bristol.

The problem

Soon after the World Health Organisation (WHO) declared COVID-19 a pandemic on March 11th 2020, the UN declared the start of an infodemic, highlighting the danger posed by the fast spreading of unchecked misinformation. Defined as an overabundance of information, including deliberate efforts to disseminate incorrect information, the COVID-19 infodemic has exacerbated public mistrust and jeopardised public health.

Social media platforms remain a leading contributor to the rapid spread of COVID-19 misinformation. Despite urgent calls from the WHO to combat this, public health responses have been severely limited. In this project, we took steps to begin to understand and address this problem.

We believe that it is imperative that public health researchers evolve and develop the skills and collaborations necessary to combat misinformation in the social media landscape. For this reason, in Autumn 2020 we extended our interest in public health messaging, usually around promoting healthy behaviours during pregnancy, to study COVID-19 misinformation on social media.

We wanted to know:

What is the nature, extent and reach of misinformation about face masks on Twitter during the COVID-19 pandemic?

To answer this question we aimed to:

Upskill public health researchers in the data capture and analysis methods required for social media data research;
Work collaboratively with Research IT and Research Software Engineer colleagues to conduct a pilot study harnessing social media data to explore misinformation.

The team

Dr Cheryl McQuire got the project funded and off the ground. Dr Luisa Zuccolo led it through to completion. Dr Maria Sobczyk checked the data and analysed our preliminary data. Research IT colleagues, led by Mr Mike Jones, helped to develop the search strategy and built a data pipeline to retrieve and store Twitter data using customised application programming interfaces (APIs) accessed through an academic Twitter account. Research Software Engineering colleagues, led by Dr Christopher Woods, provided consultancy services and advised on the analysis plan and technical execution of the project.

Cheryl McQuire, Luisa Zuccolo, Maria Sobcyzk, Mike Jones, Christopher Woods. (Left to Right)

Too much information?!

Initial testing of the Twitter API showed that keywords, such as ‘mask’ and ‘masks’, returned an unmanageable amount of data, and our queries would often crash due to an overload of Twitter servers (503-type errors). To address this, we sought to reduce the number of results, while maintaining a broad coverage of the first year of the pandemic (March 2020-April 2021).

Specifically, we:

I) Searched for hashtags rather than keywords, restricting to English language.

II) Requested original tweets only, omitting replies and retweets.

III) Broke each month down into its individual days in our search queries to minimise the risk of overload.

IV) Developed Python scripts to query the Twitter API and process the results into a series of CSV files containing anonymised tweets, metadata and metrics about the tweets (no. of likes, retweets etc.), and details and metrics about the author (no. of followers etc.).

V) Merged data into a single CSV file with all the tweets for each calendar month after removing duplicates.

What did we find?

Our search strategy delivered over three million tweets. Just under half of these were filtered out by removing commercial URLs and undesired keywords, the remaining 1.7m tweets by ~700k users were analysed using standard and customized R scripts.

First, we used unsupervised methods to describe any and all Twitter activity picked up by our broad searches (whether classified as misinformation or not). The timeline of this activity revealed clear peaks around the UK-enforced mask mandates in June and September 2020.

We further described the entire corpus of tweets on face masks by mapping the network of its most common bigrams and performing sentiment analysis.

We then quantified the nature and extent of misinformation through topic modelling, and used simple counts of likes to estimate the reach of misinformation. We used semi-supervised methods including manual keyword searches to look for established types of misinformation such as face masks restricting oxygen supply. These revealed that the risk of bacterial/fungal infection was the most common type of misinformation, followed by restriction of oxygen supply, although the extent of misinformation on the risks of infection decreased as the pandemic unfolded.

Extent of misinformation (no tweets), according to its nature: 1- gas exchange/oxygen deprivation, 2- risk of bacterial/fungal infection, 3- ineffectiveness in reducing transmission, 4- poor learning outcomes in schools.

Relative to the volume of tweets including the hashtags relevant to face masks (~1.7m), our searches uncovered less than 3.5% unique tweets containing one of the four types of misinformation against mask usage.

A summary of the nature, extent and reach of misinformation on face masks on Twitter – results from manual keywords search (semi-supervised topic modelling).

A more in-depth analysis of the results attributed to the 4 main misinformation topics by the semi-supervised method revealed a number of potentially spurious topics. Refinements of these methods including iterative fine-tuning were beyond the scope of this pilot analysis.

Our initial exploration of Twitter data for public health messaging also revealed common pitfalls of mining Twitter data, including the need for a selective search strategy when using academic Twitter accounts, hashtag ‘hijacking’ meaning most tweets were irrelevant, imperfect Twitter language filters and ads often exploiting user mentions.

Next steps

We hope to secure further funding to follow-up this pilot project. By expanding our collaboration network, we aim to improve the way we tackle misinformation in the public health domain, ultimately increasing the impact of this work. If you’re interested in health messaging, misinformation and social media, we would love to hear from you – @Luisa_Zu and @cheryl_mcquire.

Note:

This blog post was original written for the Jean Golding Institute blog

Study collecting the views of young people, parents of children with long COVID, and doctors, finds that long COVID in children is poorly understood by doctors

Posted on July 21, 2021July 20, 2021 by lindsey.pike

Dr Katharine Looker

‘Enhancing the utilization of COVID-19 testing in schools’, is a study which will look at the characteristics of long COVID and COVID-19 infection in children. ‘Long COVID’ is commonly used to describe signs and symptoms that continue or develop after acute COVID‑19. The study is being funded as a result of a rapid funding call by Health Data Research UK (HDR UK), the Office for National Statistics (ONS) and UK Research and Innovation (UKRI). The study forms part of the larger Data and Connectivity National Core Study, which is led by HDR UK in partnership with ONS.

The COVID-19 testing in schools study is related to the CoMMinS (COVID-19 Mapping and Mitigation in Schools) study being undertaken by the University of Bristol in partnership with Bristol City Council, Public Health England [PHE] and Bristol schools. CoMMinS aims to give us an understanding of COVID-19 infection dynamics centred around school pupils and staff and onward transmission to family contacts, using regular testing. Our study will jointly analyse data from CoMMinS, along with information from Electronic Patient Records, and data from the COVID-19 Schools Infection Survey (SIS; jointly led by the London School of Hygiene & Tropical Medicine [LSHTM], PHE, and ONS). The SIS is a study similar to CoMMinS but carried out nationally.

To help inform research questions and methods for the study, members from the University of Bristol study team gathered views about long COVID in children between 9 March and 30 April 2021 from:

seven young people from the NIHR Bristol Biomedical Research Centre Young People’s Advisory Group (YPAG)
five families whose children have long COVID or suspected long COVID, recruited through two online UK campaign groups for long COVID, and
a survey completed by four GPs and one paediatrician, and an online meeting with two paediatricians.

It is important to note that the opinions gathered were based on small samples which may not be representative.

Through the meeting and survey with the doctors, the study team found that clinical understanding of long COVID in children is currently very limited.

The doctors said that it may be hard to distinguish between long COVID and other conditions with similar symptoms. Many of the symptoms of long COVID, like fatigue and feeling sick, aren’t very specific, and are common to many different conditions. Long COVID in children currently lacks a clinical definition, making diagnosis difficult. It isn’t yet properly understood whether long COVID is a new condition in itself, or a group of conditions like post viral fatigue, which is already recognised.

Young people, and families of children with long COVID or suspected long COVID, who were also asked for their opinion, said that feeling sick or stomach pain, extreme tiredness, and headaches were the symptoms they would rank as most ‘harmful’. For young people, this was based on them imagining having the symptoms. For the families, this was based on their first-hand experience.

The families also said that the symptoms their children were experiencing were numerous, often very severe, and more wide-ranging than those currently listed on the NHS website for long COVID. It is not yet clear what is causing the unusual symptoms.

The families said that they had struggled to get a diagnosis and treatment for their children. They also said that long COVID symptoms were having a significant impact on their children’s day-to-day lives both physically and psychologically, and that some of the children had missed school because of the symptoms. Some of the families also found fevers difficult to manage because their children had to miss school to self-isolate every time they had a fever. They wanted to know why the set of symptoms were being experienced, and why their children in particular had developed them.

It is not known how many children have or will develop long COVID. So far, studies which have tried to measure the rate of long COVID in children suggest it is rare. However, quantifying the number of cases is made difficult by a lack of clinical understanding of long COVID including the lack of an agreed clinical definition. The opinions collected suggest that relying on clinical diagnoses alone will under-estimate cases. On the other hand, there needs to be a cautious approach to estimating the number of cases based on non-specific symptoms, as other conditions which cause similar symptoms may be counted as well.

Caroline Relton, Professor of Epigenetic Epidemiology and Director of the Bristol Population Health Science Institute at the University of Bristol, joint lead for CoMMinS and one of the lead authors of the report, said: “The opinions we gathered further highlight that it is difficult to count the number of children with long COVID on the basis of diagnoses alone while long COVID in children remains poorly defined.

“There are added complications of studying long COVID in children, when it is sometimes difficult to disentangle what might be the result of experiencing infection from what might result from the wider impact of experiencing the pandemic. Isolation, school closures, disrupted education and other influences on family life could all have health consequences. Defining the extent of the problem in children and the root causes will be essential to helping provide the right treatment and to aid the recovery of young people who are suffering.”

The findings highlight that examining GP and hospital visits, and school attendance, might currently be a more useful and feasible way of assessing how COVID-19 has affected children, rather than relying only on diagnoses of long COVID. However, the study researchers also need to be aware how often healthcare is accessed according to need, and absence from school due to self-isolation, which will affect what is being measured.

Feeling sick or stomach pain, extreme tiredness, and headaches will be important symptoms to consider in the study.

Read the full report

Find the full report on the CoMMinS study news page.

Breastfeeding research improves lives and advances health, but faces conflicts

Posted on March 16, 2021 by lindsey.pike

Research shows the breast milk of women who have recovered from COVID-19 offers a source of COVID-19 antibodies.
(Shutterstock)

Meghan Azad, University of Manitoba; Katie Hinde, Arizona State University; Lars Bode, University of California San Diego; Luisa Zuccolo, University of Bristol; Merilee Brockway, University of Manitoba; Nathan C. Nickel, University of Manitoba, and Rafael Perez-Escamilla, Yale University

Breastfeeding and breast milk provide big opportunities to support maternal, infant and population health. This is especially true during the current pandemic because breastfeeding can help alleviate food insecurity, and research shows the breast milk of women who have recovered from COVID-19 offers a source of COVID-19 antibodies.

Breastfeeding saves lives and prevents illness. It is environmentally friendly and profoundly important to children’s long-term development. After all, breast milk is the only food that has evolved specifically to feed humans.

Breastfeeding matters

Beyond supplying nutrition, breast milk provides personalized immune protection and shapes the developing microbiome. Scientists have discovered enzymes, hormones, antibodies and live cells in breast milk, and these bioactive components could hold the key to developing new therapies — not only for COVID-19, but also autoimmune diseases, diabetes and cancer.

Yet, remarkably, we still don’t fully understand the composition of breast milk, or the biological basis for its many health effects. In fact, more scientific papers have been published on headaches than breastfeeding, and more federal research dollars from Canadian Institutes of Health Research and the Natural Sciences and Engineering Research Council of Canada have been invested to study corn than breast milk.

The act of breastfeeding also supports mother-infant bonding and helps to prevent breast and ovarian cancer in mothers. Unfortunately, most mothers do not even meet their own breastfeeding goals, let alone achieve recommendations of exclusive breastfeeding for six months, followed by 18 months of breastfeeding along with other foods.

This is particularly concerning during this pandemic, when mothers infected with COVID-19 may be separated from their newborns (despite World Health Organization guidance to the contrary) and breastfeeding support is often unavailable because public health visits are being cancelled and lactation services have been suspended in many places.

Tensions abound

Every parent knows that infant feeding is a complex issue, often evoking strong emotions based on personal experience. Difficult or negative breastfeeding experiences can fuel a defensive “breastfeeding denialism” attitude.

A woman with tattoos breastfeeding her infant — Breastfeeding support such as public health visits may not be available during the pandemic.
(Pexels/Anna Shvets)

Conversely, some breastfeeding advocates refuse to acknowledge that for some families, formula is necessary for medical, personal, societal or socioeconomic reasons. These extreme attitudes cause a tense and unproductive environment for researchers working to generate inclusive evidence-based guidance for infant feeding.

Industry partnerships also cause tension in this field because the infant feeding industry frequently violates the World Health Organization code for marketing of breastmilk substitutes, and transgressions have worsened during the pandemic. However, due to lack of funding for breastfeeding research, scientists are often faced with choosing between industry funding or no funding at all.

Unfortunately, these tensions often detract from the energy and resources that breastfeeding advocates, researchers, health professionals and policy-makers could be using to advance their shared goal of supporting maternal and child health.

What can be done

Of course, members of the diverse breastfeeding advocacy and research communities will not always agree — but we should aim to find common ground and work together. There are many stakeholders involved, each with a role to play:

Governments and non-profit funding organizations should acknowledge the importance of breastfeeding and breast milk and invest more resources into this field.

Researchers should build interdisciplinary teams to study breast milk as a biological system and think broadly about “breastfeeding challenges” in the context of complex social systems – including social inequities, parental leave policies, lactation difficulties and donor breast milk.

Companies, researchers and advocacy groups should co-develop a conflict of interest framework for research on breastfeeding and breast milk and reporting of results.

Messaging is key to achieving these goals. All groups need to communicate effectively with each other, and with the health-care, research and public sectors. This means providing or sharing clear resources to convey scientific evidence free of conflict of interest, targeted to each audience, such as fact sheets for policy-makers, engaging videos for the public and infographics for health-care providers.

Stakeholders also need to actively discredit unfounded claims and misinformation, such as unsubstantiated health claims made by infant nutrition companies, or rumours about the transmission of COVID-19 via breastfeeding, when there is no evidence of this occurring.

Looking forward

A woman breastfeeding a baby — COVID-19 has highlighted both the importance and fragility of breastfeeding support systems.
(Shutterstock)

Progress in breastfeeding, breast milk and lactation research is being hampered by tensions among researchers, advocates and industry.

As breast milk scientists, breastfeeding researchers and lactation specialists, we are concerned about these tensions and their potential to impede or delay discoveries in our field. Last year, we held a workshop to discuss these concerns and develop solutions.

Our workshop paper was written before the pandemic, but its recent publication is timely. The pandemic has brought researchers together in ways that seemed impossible before.

Breast milk research that would normally take years has been completed in months with unprecedented efficiency. A global network of human milk banks was established in a matter of days to share information about safe operations during the pandemic. Milk scientists and breastfeeding researchers are meeting monthly with the WHO to speed up the transition from discovery to policy.

We hope these trends will continue beyond the pandemic and become the new standard for doing and sharing research.

COVID-19 has also emphasized both the importance and fragility of breastfeeding support systems, which have suffered considerably due to current restrictions. The pandemic has also highlighted the potential of breast milk to inform new avenues of biomedical research, such as milk antibodies as potential therapeutics.

We hope this added urgency will encourage researchers, advocates, funders and policy-makers to work together to accelerate progress in supporting breastfeeding and breast milk research.

Meghan Azad, Associate Professor of Pediatrics and Child Health; Canada Research Chair in the Developmental Origins of Chronic Disease, University of Manitoba; Katie Hinde, Associate Professor, School of Human Evolution and Social Change, Arizona State University; Lars Bode, Professor of Pediatrics and Director of Mother-Milk-Infant Center of Research Excellence, University of California San Diego; Luisa Zuccolo, Senior Research Fellow, Health Sciences, University of Bristol; Merilee Brockway, Post-doctoral Fellow, Department of Pediatrics and Child Health, University of Manitoba; Nathan C. Nickel, Associate Professor of Community Health Sciences; Co-Director MILC; Associate Director, Manitoba Centre for Health Policy, University of Manitoba, and Rafael Perez-Escamilla, Professor of Public Health, Yale University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

COVID19 – should schools close early for Christmas?

Posted on December 15, 2020February 11, 2025 by lindsey.pike

Sarah Lewis, Marcus Munafo and George Davey Smith

We have previously written about the limited risk posed to pupils, teachers and the community by schools being open during the Covid19 pandemic. Schools have now been open for almost a full academic term (3 months), so it is time to take another look at the evidence. (more…)

How has the COVID-19 lockdown affected children born with a cleft lip/palate?

Posted on September 21, 2020 by lindsey.pike

Written by the Cleft Collective Team

Follow the Cleft Collective on twitter.

The COVID-19 pandemic has been difficult for many families and there is widespread concern about how the lockdown might have affected children’s health, wellbeing and education. This concern may be even greater for families of children with pediatric health conditions such as cleft lip and/or cleft palate.

The Cleft Collective cohort study, linked to the IEU, is a UK-wide research study of the causes and consequences of being born with a cleft, which is a gap in the lip or roof of the mouth. In response to the COVID-19 lockdown, the Cleft Collective team sent out a questionnaire asking parents about how the lockdown had affected their children’s surgeries and treatments, access to schooling and wellbeing.

The first results are summarised in this infographic, which highlights that many children suffered delays in their surgeries and other health care appointments due to the lockdown. They also struggled with homeschooling, worries and negative emotions.

Through links to the NHS cleft teams and the Cleft Lip and Palate Association charity (CLAPA), the Cleft Collective team are sharing their findings with healthcare professionals to help ensure that children born with a cleft are given appropriate support to help them through this time and to lead happy, healthy childhoods.

The Cleft Collective cohort study is based in the MRC Integrative Epidemiology Unit and funded by the Scar Free Foundation and the University of Bristol. The video below explains more about the study.

Can we ever achieve “zero COVID”?

Posted on July 30, 2020February 11, 2025 by relfg

Marcus Munafo and George Davey Smith

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An important ongoing debate is whether the UK’s COVID strategy should focus on suppression (maintaining various restrictions to ensure the reproduction rate of the SARS-CoV-2 virus remains at or below 1), or elimination (reducing the number of infections to a sufficiently low level that restrictions could be removed). Independent SAGE has explicitly called for a “zero COVID UK”. (more…)

Are schools in the COVID-19 era safe?

Posted on July 1, 2020February 11, 2025 by lindsey.pike

Sarah Lewis, Marcus Munafo and George Davey Smith

Follow Sarah, George and Marcus on Twitter

The COVID-19 pandemic caused by the SARS-COV2 virus in 2020 has so far resulted in a heavy death toll and caused unprecedented disruption worldwide. Many countries have opted for drastic measures and even full lockdowns of all but essential services to slow the spread of disease and to stop health care systems becoming overwhelmed. However, whilst lockdowns happened fast and were well adhered to in most countries, coming out of lockdown is proving to be more challenging. Policymakers have been trying to balance relaxing restriction measures with keeping virus transmission low. One of the most controversial aspects has been when and how to reopen schools. (more…)

We should be cautious about associations of patient characteristics with COVID-19 outcomes that are identified in hospitalised patients.

Posted on June 3, 2020 by lindsey.pike

Gareth J Griffith, Gibran Hemani, Annie Herbert, Giulia Mancano, Tim Morris, Lindsey Pike, Gemma C Sharp, Matt Tudball, Kate Tilling and Jonathan A C Sterne, together with the authors of a preprint on collider bias in COVID-19 studies.

All authors are members of the MRC Integrative Epidemiology Unit at the University of Bristol. Jonathan Sterne is Director of Health Data Research UK South West

Among successful actors, being physically attractive is inversely related to being a good actor. Among American college students, being academically gifted is inversely related to being good at sport.

Among people who have had a heart attack, smokers have better subsequent health than non-smokers. And among low birthweight infants, those whose mothers smoked during pregnancy are less likely to die than those whose mothers did not smoke.

These relationships are not likely to reflect cause and effect in the general population: smoking during pregnancy does not improve the health of low birthweight infants. Instead, they arise from a phenomenon called ‘selection bias’, or ‘collider bias’.

Understanding selection bias

Selection bias occurs when two characteristics influence whether a person is included in a group for which we analyse data. Suppose that two characteristics (for example, physical attractiveness and acting talent) are unrelated in the population but that each causes selection into the group (for example, people who have a successful Hollywood acting career). Among individuals with a successful acting career we will usually find that physical attractiveness will be negatively associated with acting talent: individuals who are more physically attractive will be less talented actors (Figure 1). Selection bias arises if we try to infer a cause-effect relationship between these two characteristics in the selected group. The term ‘collider bias’ refers to the two arrows indicating cause and effect that ‘collide’ at the effect (being a successful actor).

Figure 1: Selection effects exerted on successful Hollywood actors. Green boxes highlight characteristics that influence selection. Yellow boxes indicate the variable selected upon. Arrows indicate causal relationships: the dotted line indicates a non-causal induced relationship that arises because of selection bias.

Figure 2 below explains this phenomenon. Each point represents a hypothetical person, with their level of physical attractiveness plotted against their level of acting talent. In the general population (all data points) an individual’s attractiveness tells us nothing about their acting ability – the two characteristics are unrelated. The red data points represent successful Hollywood actors, who tend to be more physically attractive and to be more talented actors. The blue data points represent other people in the population. Among successful actors the two characteristics are strongly negatively associated (green line), solely because of the selection process. The direction of the bias (whether it is towards a positive or negative association) depends on the direction of the selection processes. If they act in the same direction (both positive or both negative) the bias will usually be towards a negative association. If they act in opposite directions the bias will usually be towards a positive association.

Figure 2: The effect of sample selection on the relationship between attractiveness and acting talent. The green line depicts the negative association seen in successful actors.

Why is selection bias important for COVID-19 research?

In health research, selection processes may be less well understood, and we are often unable to observe the unselected group. For example, many studies of COVID-19 have been restricted to hospitalised patients, because it was not possible to identify all symptomatic patients, and testing was not widely available in the early phase of the pandemic. Selection bias can seriously distort relationships of risk factors for hospitalisation with COVID-19 outcomes such as requiring invasive ventilation, or mortality.

Figure 3 shows how selection bias can distort risk factor associations in hospitalised patients. We want to know the causal effect of smoking on risk of death due to COVID-19, and the data available to us is on patients hospitalised with COVID-19. Associations between all pairs of factors that influence hospitalisation will be distorted in hospitalised patients. For example, if smoking and frailty each make an individual more likely to be hospitalised with COVID-19 (either because they influence infection with SARS-CoV-2 or because they influence COVID-19 disease severity), then their association in hospitalised patients will usually be more negative than in the whole population. Unless we control for all causes of hospitalisation, our estimate of the effect of any individual risk factor on COVID-19 mortality will be biased. For example, it would be unsurprising that within hospitalised patients with COVID-19 we observe that smokers have better health than non-smokers because they are likely to be younger and less frail, and therefore less likely to die after hospitalisation. But that finding may not reflect a protective effect of smoking on COVID-19 mortality in the whole population.

Figure 3: Selection effects on hospitalisation with COVID-19. Box colours are as in Figure 1. Blue boxes represent outcomes. Arrows indicate causal relationships, the dotted line indicates a non-causal induced relationship that arises because of selection bias.

Selection bias may also be a problem in studies based on data from participants who volunteer to download and use COVID-19 symptom reporting apps. People with COVID-19 symptoms are more likely to use the app, and so are people with other characteristics (younger people, people who own a smartphone, and those to whom the app is promoted on social media). Risk factor associations within app users may therefore not generalise to the wider population.

What can be done?

Findings from COVID-19 studies conducted in selected groups should be interpreted with great caution unless selection bias has been explicitly addressed. Two ways to do so are readily available. The preferred approach uses representative data collection for the whole population to weight the sample and adjust for the selection bias. In absence of data on the whole population, researchers should conduct sensitivity analyses that adjust their findings based on a range of assumptions about the selection effects. A series of resources providing further reading, and tools allowing researchers to investigate plausible selection effects are provided below.

For further information please contact Gareth Griffith (g.griffith@bristol.ac.uk) or Jonathan Sterne (jonathan.sterne@bristol.ac.uk).

COVID-19 and community support: Mapping unmet support needs across Wales

Posted on June 2, 2020June 2, 2020 by lindsey.pike

Dr Oliver Davis, Nina Di Cara, and the project team

Follow Oliver, Valerio, Alastair, Nina, Chris Benjamin and Public Health Wales Research & Evaluation on Twitter

Since the pandemic started, communities have been mobilising to help each other; from shopping for elderly neighbours, to offering to offering a friendly face or other support. Mutual aid networks have sprung up all over the country, and neighbours who hadn’t previously spoken have been introduced to each other via street–level WhatsApp groups. But the degree to which offers of help are matching up with the need for help has been unknown, and this poses a problem for organisations who need to make decisions about where they should target limited resources.

Screenshot from the https://covidresponsemap.wales/ site.

Ensuring support is available where needed

Community support can offer a protective factor against adverse events. Some areas are more vulnerable than others, but knowing which areas are most likely to have a mismatch between support needed and support offered is difficult. To address this issue, a collaboration between the Public Health Wales Research & Evaluation Division and the Dynamic Genetics lab, part of the MRC Integrative Epidemiology Unit at the University of Bristol and supported by the Alan Turing Institute, has mapped these support offers and needs.

Using data from Wales Council for Voluntary Action, COVID-19 Mutual Aid, Welsh Government Statistics and Research, the Office for National Statistics, and social media the project team have created a live map that highlights the areas where further support for communities may be needed. It shows data on support factors, such as number of registered volunteers and population density, against risks, such as demographics, levels of deprivation, and internet access. It aims to inform the responses of national and local government, as well as support providers in Wales.

The site also provides the links to local community groups identified helping to raise awareness of the support available locally.

This map is part of an effort to better understand which communities have better community cohesion and organisation. We are keen to find out your views on how this can be more useful, or other community mobilisation data sources which could be included. Please contact Oliver or Nina with your comments:

Dr Oliver Davis: oliver.davis@bristol.ac.uk

Nina Di Cara nina.dicara@bristol.ac.uk

Further information

If you are a community group and want to be included, please register your group with Covid-19 Mutual Aid (https://covidmutualaid.org/).
The code used to create the map has been released openly on GitHub (https://github.com/DynamicGenetics/COVID-19-Community-Response) so that it can be reproduced. Full information on the sources of data on vulnerability and community support are documented on the Open Science Framework (https://osf.io/c48hw/) and the map will be updated as new information becomes available.

Are teachers at high risk of death from Covid19?

Posted on May 26, 2020February 11, 2025 by lindsey.pike

Sarah Lewis, George Davey Smith and Marcus Munafo