COVERSE is Australia’s national charity dedicated to supporting Australians who have suffered harms from the COVID-19 vaccines. It was founded in 2022, and its mission and activities can be found on its website at coverse.org.au.
One of the most important aspects to COVERSE’s work has been numerous submissions to public inquiries in Australia, especially federal Australian Government and Parliament of Australia inquiries relevant to the interests of COVERSE’s constituency. These submissions can all be found at coverse.org.au/submissions.
In order to generate data to assist with understanding the issues and needs of the COVID-19 vaccine-injured and bereaved community in Australia, the organisation established an online Vaccine Injury Profile. This Profile allowed users registered on the COVERSE website to provide details of their personal experiences as people who had suffered a serious adverse event following COVID-19 vaccination. This captured information about vaccine doses, symptoms, medical diagnoses, treatments, financial and social issues, and a range of other aspects. All of this helped inform COVERSE’s national advocacy efforts and the preparation of its public submissions and other content.
This document containts summary statistics and analysis of COVERSE’s Vaccine Injury Profile data.
It is not a scientific paper, it has not been peer-reviewed, nor has the Vaccine Injury Profile been granted ethical approval for use in research. Responses are not available for public inspection, as they were collected under strict privacy conditions that disallowed this kind of disclosure of individuals’ information.
The public material is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license. In short, this license allows you to use and share this material as long as the following are adhered to:
Last edited: 7th May 2026.
The Vaccine Injury Profile asked more than 40 questions related to Australian individual’s experience with COVID-19 vaccine injury, including health as well as social and financial. The entire list of questions can be viewed online.
The list of questions was developed based on several considerations, including the experiences of international counterparts in conductioning their own surveys, information gleaned from social media support groups, and COVERSE’s need for particular data to guide its advocacy efforts.
Not all individuals who started the Profile completed all required questions, and not all of those individuals completed all of the non-required questions. In order to make the Profile as easy as possible to complete, COVERSE designed the Profile with many of the questions (especially the qualitative ones) not being required to be completed.
Note that the underlying demoninator, N, we used for statistical analysis varied from question to question, depending on whether the question was compulsory, how many people answered the question, whether the people who answered the question had completed all other required questions, and demographic specificities (e.g. biological sex). This allowed us to utilise larger or more appropriate data in the analysis of each question. Throughout the analysis the relevant N used for each calculation will be provided as well as a basic descriptor to understand the choice of N. Where we utilised all Profile responses we depict the denominator as Nall. Where we incorporate only those responses from individuals who answered a particular question, we depict, for example, N138 to indicate we only used 138 responses. We appreciate that this is not a usual methodology for statistical analysis of this type, however our aim was to extract as much useful information as we could from the data we have, within reason, to guide our advocacy.
Most summary data is presented as a percentage of the given N, and where appropriate we also provide calculated values (using standard Microsoft Excel formulae) such as median, average, standard deviation (σ), confidence interval (CI), etc.
For the open-ended text questions we utilised the Microsoft 365 Copilot chatbot to analyse responses. These responses were manually cleaned (fixed typographic errors, ensured consistent disease naming, removed any and all personal identifiable content, etc.) before being entered into Copilot.
A total of 594 individuals begun answering the Vaccine Injury Profile (Nall), with 525 having completed all required questions. Within Nall responses, 65.0% indicated being female, and 35.0% being male.
Age values were calculated by subtracting the year of birth from the date the respondant begun completing the Profile. The average age of N535 respondents was 50.4±14.0 (95% CI 49.2-51.6). The average age of females was 51.4±13.4 (95% CI 50.3-52.5), and average age of males was 48.3±15.0 (95% CI 47.0-49.6). The age distribution is presented in Figure age.
All of Australia’s State and Territories were representated amongst the respondants, with the proportion from each region displayed in Figure states.
| state | % respondents |
|---|
When asked if they would be interested in participating in research involving COVID-19 vaccine-injured people, 83.7% indicated that they would.
Respondents were asked about the COVID-19 vaccine doses they received, including whether they experienced an adverse reaction to each. A total of 1,530 doses were recorded in the Profile, of which 62.3% were followed by an adverse reaction. Results for each dose are summarised in Figure doses.
| dose n | N (received dose n) | % (reacted to dose n) | N (could recall exact date of their dose n) | median date of receiving dose n |
|---|---|---|---|---|
| 1 | 594 | 62.0% | 468 | 23rd August 2021 |
| 2 | 505 | 71.5% | 381 | 19th September 2021 |
| 3 | 273 | 65.2% | 185 | 19th January 2022 |
| 4 | 117 | 35.0% | 53 | 15th July 2022 |
| 5+ | 41 | 12% | 9 | 26th April 2023 |
Figure dates depicts the date distribution of doses reported by respondents, and Figure brands provides a percentage breakdown of the brands of COVID-19 vaccines that respondents received. The percentages of both figures are out of Nall respondents.
Time to symptom onset varied, however the most commonly reported periods were within 1-12 hours and 1-7 days. Figure onset shows the percentage of Nall respondents who reacted within each depicted time period.
Of N318 respondents who specifically indicated that they got subsequent doses after reacting to an earlier dose, 78.3% indicated that their health worsened, 2.52% felt better, and 19.2% had no change in their symptoms.
N263 indicated whether or not they were subject to a workplace COVID-19 vaccine mandate for the dose that they became injured by, of which 51.0% reported that they were formally mandated to recive that does while 8.37% report that while they were not formalled mandated they were bullied or coerced by their workplace to receive the dose. Note that this particular question was added to the Profile some time after the Profile was initially opened for submissions, hence why we could not capture a greater proportion of all respondents.
For a collection of 71 symptoms, we asked respondents to indicate if they had experienced each as part of their post-vaccination symptoms, and how severe these symptoms were at their worst (minimal, mild, moderate, moderately severe, or severe). Figure symptoms presents an overview of these results, from the N577 people who completed this question. Note that N/A indicates that the respondent specifically indicated that the particular symptom was not applicable to them, however it is assumed that no choice of N/A or symptom severity also indicates that the symptom was not applicable.
In addition to these pre-defined symptoms, we also asked about any other symptoms that people experienced. There was a wide range of free-text responses from N300 people, which included diagnoses, test results and functional capacity as well as symptom descriptions. We combined these other response with the pre-defined symptom responses and summarised the combined N565 records with assitance from Copilot:
Most respondents reported symptoms spanning multiple organ systems simultaneously. Symptom burden was frequently high, with many respondents describing moderate to severe impairment. The responses describe a highly heterogeneous but recognisable pattern of multi‑organ symptomatology, dominated by fatigue, neurological dysfunction, cardiovascular involvement, musculoskeletal pain, immune/allergic manifestations, and autonomic instability.
The top 10 most commonly reported symptoms, that appear across the majority of responses, encapsulate:
Recurring multi-symptom constellations that stand out across many of the reponses are:
The dominant theme is not any single organ related symptom, but a systemic illness characterised by profound fatigue with exercise intolerance and widespread neurological-autonomic dysfunction, around which cardiovascular, musculoskeletal, immune, and gastrointestinal symptoms cluster. The symptom patterns most strongly resemble established multi‑system neuro‑immune illnesses such as ME/CFS, dysautonomia, and small‑fiber neuropathy, often with overlapping inflammatory or hypersensitivity features, rather than any single well‑bounded disease entity.
Conversely, Copilot pointed out that patterns do not resemble: isolated psychiatric illness, single‑organ diseases, acute self‑limited inflammatory reactions, or traditional degenerative neurological disorders alone.
While data are all self-reported, Copilot also notes that the dataset shows high internal coherence, cross‑participant convergence, symptom‑cluster stability, and severity‑graded continuity, which together indicate a strong and robust syndrome‑level signal.
Figure side provides the breakdown of which side of the body people felt their symptoms were more dominant.
| side | % respondents (Nall) |
|---|---|
| left | 21.5% |
| right | 9.26% |
| varies | 16.8% |
| no difference | 29.6% |
| N/A | 22.7% |
Of N564 respondents, only 7.62% indicated that their health was definitely improving, with a further 21.3% indicating that they were slowly improving. Contrasting these numbers, 26.8% say they were not improving, 17.6% were actually getting worse, and 26.8% were experiencing ongoing fluctuation in their health.
Of those who were improving, slowly improving, or fluctuating, N192 indicated that the median time it took before they begun to experience symptom improvement was 8 months. The distribution of responses is shown in Figure improvement.
58.4% of N546 respondents indicated that their usual GP recognised that they had experienced an adverse event following their COVID-19 vaccinations.
Of N546 respondents, 51.5% were able to confirm that their adverse reaction had been reported to Australia’s Therapeutic Goods Administration (TGA, the national drug regulator responsible for vaccine approvals and adverse event monitoring). Further breakdown of this reporting is shown in Figure reported.
| reported | % respondents |
|---|---|
| yes (self-reported) | 37.7% |
| yes (reported by GP / specialist) | 10.4% |
| yes (reported by hospital) | 3.30% |
| no | 31.3% |
| unsure | 17.2% |
Out of those who confirmed that their AEFI had been reported, 12.4% of N275 indicated that the TGA had been in contact with them about their reaction. Note, however, that the wording of this question in its original form was ambiguous, and was only later revised to ask specifically if the TGA had made contact to seek further information or arrange additional medical investigations. We suspect that the actual number of instances where the TGA ever made contact with reporters for further information is closer to 0%, based on our many interactions with members of our community. However, we are unable to separate responses to the earlier wording of our question from the later more specific version to verify this.
83.9% of N546 respondents had been referred to a medical specialist for further investigation (respondents also included allied health specialists such as psychologist, dietitians, respiratory therapists, etc.). Of the N455 who indicated which specialists they had seen, the median number of distinct specialists seen was 2. The distribution of specialities are summarised in Figure specialists.
Of N542 responses, 53.7% indicate they had received a formal medical diagnosis. Of these diagnoses, N268 provided further details. We utilised Copilot to assist with analysing these responses:
These responses coherently cluster around several clear areas:
We note that pericarditis is the single most common of these diagnoses.
The data also includes functional and psychiatric diagnoses. However, these are usually coexisting with structural or inflammatory diagnoses, suggesting they are secondary diseases rather than the cause of such multisytem pathologies.
Copilot notes that the dataset describes a concentration of inflammatory, immune-mediated, autonomic, cardiac, and neurological diagnoses, frequently coexisting within the same individuals. Rather than isolated single‑organ diseases, many responses suggest multisystem illness patterns involving immune dysregulation, neuroinflammation or neuropathy, cardiovascular inflammation or instability, and autonomic nervous system dysfunction.
Moreover, when combined with the above symptom responses, Copilot notes the consistency between symptom phenomenology and diagnostic frameworks strongly suggests that respondents are describing recognisable medical syndromes affecting multiple interacting systems, rather than isolated or purely subjective symptom reporting.
N449 indicated that the median time it took to recieve these diagnoses was 1-3 months. The distribution of time to diagnosis is presented in Figure diagnosis times.
26.3% of N520 respondents received a diagnosis that later proved to be incorrect.
We asked respondents to indicate test results, diagnosis or symptoms that seemed of particular significance. We used Copilot to assist with analyse of these N320 responses.
The free-text responses varied widely, with different people choosing to articulate different aspects of their medical discovery. However, the pattern of cardiovascular, neurological, immunological, and autonomic disease featured heavily. Most notably, there was a recurrent disconnect between severe lived symptoms and either inconclusive or delayed diagnostic findings.
Within the variety of responses, the diagnostic tests most commonly noted as significant for the patient were cardiac MRIs, inflammatory/coagulation markers (CRP, D‑dimer), and MRI‑based neurological testing, and the notable diagnoses reflect what has already been articulated above.
A number of questions were asked about treatment approaches (prescription, alternative, self-medicated, etc.), and their perceived effectiveness towards improving symptoms. Responses were unstructured and highly diverse, so we analysed N493 with assistance from Copilot:
The most common prescribed medication was colchicine, which aligns with pericarditis being the most common diagnoses. Other medications that many have tried include prednisone/prednisolone, non-steroidal anti-inflammatory drugs (NSAIDs), low-dose naltrexone (LDN), beta-blockers, amitriptyline/endep, proton pump inhibitors, and intravenous immunoglobulin (IVIG).
Most people also attempted non-pharmaceutical, allied health, and alternative health treatment approaches alongside conventional care, of which the most common were acupuncture, naturopathy, physiotherapy, myotherapy, chiropractic / osteopathy, hyperbaric oxygen therapy (HBOT), intermittent fasting, dietary changes, and Chinese medicine.
Overall, the interventions that were found most helpful where: magnesium, acupuncture, vitamin C and D, LDN, colchicine, ibuprofen and N‑acetylcysteine (NAC). However, many people were either unable to articulate which treatments assisted them, or did not experience any notable improvement from any of them.
The least helpful treatments were more often the convential pharmaceutical products, especially where respondent reported side-effects from them. This includes most of the products also identified as most helpful for some people, highlighting highly individualsed responses to these medications amidst a heterogeneous patient cohort.
When matched to the official diagnoses that respondents reported, the following patterns were observed:
Overall, specifically targetted therapiesg towards clear diagnoses were helpful. However, it is important to note that in the vast majority of respondents who experienced improvement, helpful treatments led to symptom relief rather than cure.
At the peak of symptom severity, 69.9% of N525 respondents were unable to undertake any work, including domestic chores. In the month prior to respondents completing the Vaccine Injury Profile, this number had reduced to 22.7%. Figure capacity depicts estimation of work functional capacity at symptom peak at in the month prior to completing the Vaccine Injury Profile.
Figure workload presents the distribution of how long it look N492 respondents to be able to resume their normal activities and workload, with almost 40% of respondents still unable to undertake their normal tasks at the time of survey completion.
Most respondents, N484, indicated that they continue to require additional assistance, primary regarding medical care. Overall results regarding respondent needs are displayed in Figure needs.
More than half, 54.9%, of N525 respondents were not able to access any financial assistance, and only 7.24% did not require any. Others were able to access a variety of financial supports, including workplace entitlements, loans, and government programs. Figure financial presents the range of financial assistance options that people have been able to access.
Note that at the time of survey completion, a mere 1.71% report successful claims with the Australian Government’s COVID-19 Vaccine Claims Scheme.
Of those (N81) who were entitled to workers’ compensation (i.e. they were instructed to receive COVID-19 vaccination by their workplace), exactly two thirds (66.7%) had issues with their workers’ compensation claim. The majority of these issues were denial of claim (55%) or the workplace discouraging or not adequately informing or supporting claimants of their entitlements (11%). The majority of the remainder (30%) deal with delayed processing of claims or inadequate reimbursement of medical expenses.
When asked about their COVID-19 infection status, 61.0% of N515 respondents reported that they had experienced a COVID-19 infection. However, only 2.91% had contracted COVID-19 prior to their vaccinations, and only 6.99% contracted it in-between their COVID-19 vaccination doses.
Those who had not had COVID-19 were asked whether this was confirmed by SARS-CoV-2 antibody serology testing. 26.1% of N184 report a negative serology test, and the remainder had not done this test.
51.5% of N513 respondents report having pre-existing medical conditions at the time of their COVID-19 vaccinations, of which 75.4% report a worsening of those conditions following COVID-19 vaccination.
A wide range of pre-existing conditions were reported. The most prevalent were: asthma (12.9%), hypertension (10.6%), depression (8.71%), anxiety (7.20%), arthritis (5.30%), fibromyalgia (4.92%), ME/CFS (4.92%), migraines (4.55%), PTSD (4.55%), and diabetes (4.17%).
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