Rapid COVID-19 Screening Based on Self-Reported Symptoms: Psychometric Assessment and Validation of the EPICOVID19 Short Diagnostic Scale

Psychometric evaluation of the Symptoms of Infection with Coronavirus-19 (SIC): results from a cross-sectional study and a phase 3 clinical trial

BACKGROUND

The Symptoms of Infection with Coronavirus-19 (SIC) is a 30-item patient-reported outcome (PRO) measure scored by body system composites to assess signs/symptoms of coronavirus disease 2019 (COVID-19). In addition to cross-sectional and longitudinal psychometric evaluations, qualitative exit interviews were conducted to support the content validity of the SIC.

METHODS

In a cross-sectional study, adults diagnosed with COVID-19 in the United States completed the web-based SIC and additional PRO measures. A subset was invited to participate in phone-based exit interviews. Longitudinal psychometric properties were assessed in ENSEMBLE2, a multinational, randomized, double-blind, placebo-controlled, phase 3 trial of the Ad26.COV2.S COVID-19 vaccine. Psychometric properties evaluated included structure, scoring, reliability, construct validity, discriminating ability, responsiveness, and meaningful change thresholds of SIC items and composite scores.

RESULTS

In the cross-sectional study, 152 participants completed the SIC (mean age, 51.0 ± 18.6 years) and 20 completed follow-up interviews. Fatigue (77.6%), feeling unwell (65.8%), and cough (60.5%) were symptoms most frequently reported. SIC inter-item correlations were all positive and mostly moderate (r ≥ 0.3) and statistically significant. SIC items and Patient-Reported Outcomes Measurement Information System-29 (PROMIS-29) scores correlated as hypothesized (all r ≥ 0.32). Internal consistency reliabilities of all SIC composite scores were satisfactory (Cronbach's alpha, 0.69-0.91). SIC composite scores correlated moderately (r = 0.30-0.49) to strongly (r ≥ 0.50) with PROMIS-29 scores and Patient Global Impression of Severity (PGIS) ratings (all P < 0.01). A variety of signs/symptoms were cited in exit interviews, and participants considered the SIC straightforward, comprehensive, and easy to use. From ENSEMBLE2, 183 participants with laboratory-confirmed moderate to severe/critical COVID-19 were included (51.5 ± 14.8 years). Strong test-retest reliabilities were observed for most SIC composite scores (intraclass correlations ≥ 0.60). Statistically significant differences across PGIS severity levels were found for all but 1 composite score, supporting known-groups validity. All SIC composite scores demonstrated responsiveness based on changes in PGIS.

CONCLUSIONS

The psychometric evaluations provided strong evidence for the reliability and validity of the SIC for measuring COVID-19 symptoms, supporting its use in vaccine and treatment trials. In exit interviews, participants described a broad range of signs/symptoms consistent with previous research, further supporting the content validity and format of the SIC.

Taste loss as a distinct symptom of COVID-19: a systematic review and meta-analysis

Chemosensory scientists have been skeptical that reports of COVID-19 taste loss are genuine, in part because before COVID-19 taste loss was rare and often confused with smell loss. Therefore, to establish the predicted prevalence rate of taste loss in COVID-19 patients, we conducted a systematic review and meta-analysis of 376 papers published in 2020-2021, with 235 meeting all inclusion criteria. Drawing on previous studies and guided by early meta-analyses, we explored how methodological differences (direct vs. self-report measures) may affect these estimates. We hypothesized that direct measures of taste are at least as sensitive as those obtained by self-report and that the preponderance of evidence confirms taste loss is a symptom of COVID-19. The meta-analysis showed that, among 138,015 COVID-19-positive patients, 36.62% reported taste dysfunction (95% confidence interval: 33.02%-40.39%), and the prevalence estimates were slightly but not significantly higher from studies using direct (n = 15) versus self-report (n = 220) methodologies (Q = 1.73, df = 1, P = 0.1889). Generally, males reported lower rates of taste loss than did females, and taste loss was highest among middle-aged adults. Thus, taste loss is likely a bona fide symptom of COVID-19, meriting further research into the most appropriate direct methods to measure it and its underlying mechanisms.

Probable COVID-19 infection is associated with subsequent poorer mental health and greater loneliness in the UK COVID-19 Mental Health and Wellbeing study

The COVID-19 pandemic has been associated with psychological distress. In addition to physical effects including fatigue and cognitive impairment, contracting COVID-19 itself may also be related to subsequent negative mental health outcomes. The present study reports data from a longitudinal, national survey of the UK adult population investigating whether contracting suspected or confirmed COVID-19 at the early stages of the pandemic (March-May 2020) was associated with poorer mental health outcomes in May/June 2020, October/November 2020 and June/July 2021. A quota survey design and a sampling frame that permitted recruitment of a national sample (n = 3077) were utilised. Experience of contracting COVID-19 during the first UK lockdown was assessed along with levels of depression, anxiety, mental wellbeing and loneliness. Around 9% of participants reported contracting COVID-19 in March/May 2020 (waves 1-3) with just under 13% of the overall sample reporting COVID-19 at any one of the first three time points. Compared to those without probable COVID-19 infection, participants with probable COVID-19 had poorer mental health outcomes at follow-up with these effects lasting up to 13 months (e.g., May/June 2020:ORdepression = 1.70, p < 0.001; ORanxiety = 1.61, p = 0.002; Oct/Nov 2020, ORdepression = 1.82, p < 0.001; ORanxiety 1.56, p = 0.013; June/July 2021, ORdepression = 2.01, p < 0.001; ORanxiety = 1.67, p = 0.008). Having a pre-existing mental health condition was also associated with greater odds of having probable COVID-19 during the study (OR = 1.31, p = 0.016). The current study demonstrates that contracting probable COVID-19 at the early stage of the pandemic was related to long-lasting associations with mental health and the relationship between mental health status and probable COVID-19 is bidirectional.

Development of a novel patient-reported outcome measure to assess signs and symptoms of COVID-19

Background

Given the urgent need for vaccines and treatments for coronavirus disease 2019 (COVID-19), the Symptoms of Infection with Coronavirus-19 (SIC), a comprehensive, patient-reported outcome (PRO) measure of signs and symptoms associated with COVID-19, was developed in full alignment with current US regulatory guidance to support evaluations of vaccines and treatments in development.

Methods

An initial version of the SIC was developed to address concepts identified through a targeted literature review and consultation with experts in infectious diseases and clinicians routinely managing COVID-19 in a hospital setting. A qualitative study was conducted in sites in the United States among 31 participants aged ≥ 18 years who were English-speaking and willing and able to provide informed consent and a self-reported history by telephone or online method. The measure was refined based on additional feedback from the clinicians and three iterative rounds of combined concept elicitation and cognitive debriefing interviews conducted with patients, caregivers, and healthy volunteers.

Results

Among 39 scientific articles identified in the literature review, 35 COVID-19 signs and symptoms were reported and confirmed during interviews with clinicians, patients, and caregivers. Patients and healthy participants suggested changes for refining the draft SIC to ensure consistent interpretation and endorsed both the 24-h recall period and use of an 11-point numeric rating scale (NRS) for capturing change in symptom severity. The final version of the SIC captures the daily presence or absence of 30 symptoms and a rating of severity for 25 of the 30 symptoms using an NRS for those symptoms reported as present.

Conclusions

The SIC comprehensively addresses observations described in the literature, by clinicians, and by patients, and captures patients’ experiences with COVID-19 in a manner that minimizes complexity and facilitates completion for both patients and healthy volunteers. This measure is thus appropriate for use in clinical trials of both therapeutics and vaccines for COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41687-022-00471-w.

Development of an Item Bank to Assess Patient-Reported Outcomes: Signs, Symptoms, and Impacts of COVID-19

BACKGROUND AND OBJECTIVE

Patients experience a wide range of signs, symptoms, and impacts related to coronavirus disease 2019 (COVID-19). A patient-reported outcome (PRO) item bank that measures the most relevant patient experiences is needed to fully evaluate treatment benefit in COVID-19 clinical trials.

METHODS

A review of the literature and social media informed a novel PRO item bank of COVID-19 signs, symptoms, and impacts and general pandemic impacts. Twenty 1:1 concept elicitation and cognitive debriefing interviews were conducted with adults in the US who had symptomatic COVID-19. A conceptual model was developed and the PRO item bank refined following interviews.

RESULTS

A heterogenous set of signs, symptoms, and impacts of COVID-19, as well as impacts associated with the pandemic overall, was identified. Fifty-five short-term and long-term signs and symptom items, 26 items assessing disease-related impacts, and seven items evaluating pandemic-related impacts are included in the item bank.

CONCLUSIONS

The novel and preliminarily content-valid IQVIA COVID-19 Daily Diary Item Bank^© and the IQVIA COVID-19 Weekly Diary Item Bank^© were developed to measure signs and symptoms, their associated severity, and disease-related and pandemic-related impacts. The items are arranged in seven groups and can be individually selected based on research needs.

Sociodemographic Differences in COVID-19 Self-Reported Symptoms by Ethnicity and Older Adults in Bolivia

The effects of COVID-19 revealed the fragility of health systems in the LAC region, with greater risk of death in older people than in younger people, as well as greater vulnerability to infection due to living with people aged 30–59 years, who have a higher prevalence of COVID-19. On the other hand, there is not much information on inequalities in the incidence of COVID-19 in indigenous people, a population with lower immunological resistance. The objectives are: 1) To determine the association between sociodemographic variables with self-reported COVID-19 symptoms. 2) To investigate whether this relationship shows inequalities by ethnicity and age. For that purpose I conducted a cross-sectional analysis using the 2020 Household Survey and investigated the association between sociodemographic variables and self -reported COVID-19 symptoms and explore the contribution of factors such as employment type, household living arrangements, years of education, age, ethnicity, gender, current status of working and residence area. I performed bivariate analysis to establish trends. Subsequently using logistic regressions to establish the risks to self-reported COVID-19 symptoms. A fully interacted model is analysed by ethnicity. I found those who were living alone were less likely than those living in a Couple with/without relatives’ household arrangement to self-reported COVID-19 symptoms (OR = 0.79, 95% CI: 0.66–0.94, p < .01). Odds of the older persons aged 45–59 (OR = 1.44, 95% CI: 1.27–1.62, p < .05) were relatively more likely than younger people (OR = 1.19, 95% CI: 1.05–1.35, p < .01). Indigenous living in a couple with/without children household arrangement were less likely than non-Indigenous (OR = 0.75, 95% CI: 0.62–0.90, p < .01). Odds of Indigenous people of age 30–44 (OR = 1.26, 95% CI: 1.04–1.53, p < .01) were more likely than non-Indigenous. Odds of Indigenous persons of age 45–59 (OR = 1.59, 95% CI: 1.32–1.91, p < .05) were more likely than non-Indigenous (OR = 1.32, 95% CI: 1.12–1.55, p < .01). As conclusions, 45–59 age group shows higher risk factors and those aged 60 + show lower risks. These are increased in people working in managerial, administrative and professional, and technical positions, those living in a household with/without relatives, men, those living in urban areas, and/or non-indigenous people.

The validity of self-reported SARS-CoV-2 results among postpartum respondents

BACKGROUND

Rapid and reliable health data on SARS-CoV-2 infection among pregnant individuals are needed to understand the influence of the virus on maternal health and child development, yet the validity of self-reported COVID-19 testing and diagnosis remains unknown.

OBJECTIVES

We assessed the validity of self-reported COVID-19 polymerase chain reaction (PCR) testing and diagnosis during delivery among postpartum respondents as well as how diagnostic accuracy varied by respondent characteristics.

METHODS

We validated receipt of a COVID-19 PCR test and test results by comparing self-reported results obtained through an electronic survey to electronic medical record data (gold standard) among a cross-sectional sample of postpartum respondents who delivered at four New York City hospitals between March 2020 and January 2021. To assess validity, we calculated each indicator's sensitivity, specificity and the area under the receiver-operating curve (AUC). We examined respondent characteristics (age, race/ethnicity, education level, health insurance, nativity, pre-pregnancy obesity and birth characteristics) as predictors of reporting accuracy using modified Poisson regression.

RESULTS

A total of 276 respondents had matched electronic record and survey data. The majority, 83.7% of respondents received a SARS-CoV-2 PCR test during their delivery stay. Of these, 12.1% had detected SARS-CoV-2. Among those tested, sensitivity (90.5%) and specificity (96.5%) were high for SARS-CoV-2 detection. The adjusted risk ratio (aRR) of accurate result reporting was somewhat lower among Hispanic women relative to white non-Hispanic women (aRR 0.90, 95% CI 0.90, 1.00) and among those who had public or no insurance vs. private (aRR 0.91, 95% CI 0.82, 1.01), controlling for recall time.

CONCLUSION(S)

High recall accuracy result reporting for COVID-19 PCR tests administered during labour and delivery suggest the potential for population-based surveys as a rapid mechanism to obtain accurate data on COVID-19 diagnostic history. Additional psychometric research is warranted to ensure accurate recall across respondent subgroups.

Cardio-metabolic risk modeling and assessment through sensor-based measurements

OBJECTIVE

Cardio-metabolic risk assessment in the general population is of paramount importance to reduce diseases burdened by high morbility and mortality. The present paper defines a strategy for out-of-hospital cardio-metabolic risk assessment, based on data acquired from contact-less sensors.

METHODS

We employ Structural Equation Modeling to identify latent clinical variables of cardio-metabolic risk, related to anthropometric, glycolipidic and vascular function factors. Then, we define a set of sensor-based measurements that correlate with the clinical latent variables.

RESULTS

Our measurements identify subjects with one or more risk factors in a population of 68 healthy volunteers from the EU-funded SEMEOTICONS project with accuracy 82.4%, sensitivity 82.5%, and specificity 82.1%.

CONCLUSIONS

Our preliminary results strengthen the role of self-monitoring systems for cardio-metabolic risk prevention.

Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19

BACKGROUND

COVID-19 illness is highly variable, ranging from infection with no symptoms through to pneumonia and life-threatening consequences. Symptoms such as fever, cough, or loss of sense of smell (anosmia) or taste (ageusia), can help flag early on if the disease is present. Such information could be used either to rule out COVID-19 disease, or to identify people who need to go for COVID-19 diagnostic tests. This is the second update of this review, which was first published in 2020.

OBJECTIVES

To assess the diagnostic accuracy of signs and symptoms to determine if a person presenting in primary care or to hospital outpatient settings, such as the emergency department or dedicated COVID-19 clinics, has COVID-19.

SEARCH METHODS

We undertook electronic searches up to 10 June 2021 in the University of Bern living search database. In addition, we checked repositories of COVID-19 publications. We used artificial intelligence text analysis to conduct an initial classification of documents. We did not apply any language restrictions.

SELECTION CRITERIA

Studies were eligible if they included people with clinically suspected COVID-19, or recruited known cases with COVID-19 and also controls without COVID-19 from a single-gate cohort. Studies were eligible when they recruited people presenting to primary care or hospital outpatient settings. Studies that included people who contracted SARS-CoV-2 infection while admitted to hospital were not eligible. The minimum eligible sample size of studies was 10 participants. All signs and symptoms were eligible for this review, including individual signs and symptoms or combinations. We accepted a range of reference standards.

DATA COLLECTION AND ANALYSIS

Pairs of review authors independently selected all studies, at both title and abstract, and full-text stage. They resolved any disagreements by discussion with a third review author. Two review authors independently extracted data and assessed risk of bias using the QUADAS-2 checklist, and resolved disagreements by discussion with a third review author. Analyses were restricted to prospective studies only. We presented sensitivity and specificity in paired forest plots, in receiver operating characteristic (ROC) space and in dumbbell plots. We estimated summary parameters using a bivariate random-effects meta-analysis whenever five or more primary prospective studies were available, and whenever heterogeneity across studies was deemed acceptable.

MAIN RESULTS

We identified 90 studies; for this update we focused on the results of 42 prospective studies with 52,608 participants. Prevalence of COVID-19 disease varied from 3.7% to 60.6% with a median of 27.4%. Thirty-five studies were set in emergency departments or outpatient test centres (46,878 participants), three in primary care settings (1230 participants), two in a mixed population of in- and outpatients in a paediatric hospital setting (493 participants), and two overlapping studies in nursing homes (4007 participants). The studies did not clearly distinguish mild COVID-19 disease from COVID-19 pneumonia, so we present the results for both conditions together. Twelve studies had a high risk of bias for selection of participants because they used a high level of preselection to decide whether reverse transcription polymerase chain reaction (RT-PCR) testing was needed, or because they enrolled a non-consecutive sample, or because they excluded individuals while they were part of the study base. We rated 36 of the 42 studies as high risk of bias for the index tests because there was little or no detail on how, by whom and when, the symptoms were measured. For most studies, eligibility for testing was dependent on the local case definition and testing criteria that were in effect at the time of the study, meaning most people who were included in studies had already been referred to health services based on the symptoms that we are evaluating in this review. The applicability of the results of this review iteration improved in comparison with the previous reviews. This version has more studies of people presenting to ambulatory settings, which is where the majority of assessments for COVID-19 take place. Only three studies presented any data on children separately, and only one focused specifically on older adults. We found data on 96 symptoms or combinations of signs and symptoms. Evidence on individual signs as diagnostic tests was rarely reported, so this review reports mainly on the diagnostic value of symptoms. Results were highly variable across studies. Most had very low sensitivity and high specificity. RT-PCR was the most often used reference standard (40/42 studies). Only cough (11 studies) had a summary sensitivity above 50% (62.4%, 95% CI 50.6% to 72.9%)); its specificity was low (45.4%, 95% CI 33.5% to 57.9%)). Presence of fever had a sensitivity of 37.6% (95% CI 23.4% to 54.3%) and a specificity of 75.2% (95% CI 56.3% to 87.8%). The summary positive likelihood ratio of cough was 1.14 (95% CI 1.04 to 1.25) and that of fever 1.52 (95% CI 1.10 to 2.10). Sore throat had a summary positive likelihood ratio of 0.814 (95% CI 0.714 to 0.929), which means that its presence increases the probability of having an infectious disease other than COVID-19. Dyspnoea (12 studies) and fatigue (8 studies) had a sensitivity of 23.3% (95% CI 16.4% to 31.9%) and 40.2% (95% CI 19.4% to 65.1%) respectively. Their specificity was 75.7% (95% CI 65.2% to 83.9%) and 73.6% (95% CI 48.4% to 89.3%). The summary positive likelihood ratio of dyspnoea was 0.96 (95% CI 0.83 to 1.11) and that of fatigue 1.52 (95% CI 1.21 to 1.91), which means that the presence of fatigue slightly increases the probability of having COVID-19. Anosmia alone (7 studies), ageusia alone (5 studies), and anosmia or ageusia (6 studies) had summary sensitivities below 50% but summary specificities over 90%. Anosmia had a summary sensitivity of 26.4% (95% CI 13.8% to 44.6%) and a specificity of 94.2% (95% CI 90.6% to 96.5%). Ageusia had a summary sensitivity of 23.2% (95% CI 10.6% to 43.3%) and a specificity of 92.6% (95% CI 83.1% to 97.0%). Anosmia or ageusia had a summary sensitivity of 39.2% (95% CI 26.5% to 53.6%) and a specificity of 92.1% (95% CI 84.5% to 96.2%). The summary positive likelihood ratios of anosmia alone and anosmia or ageusia were 4.55 (95% CI 3.46 to 5.97) and 4.99 (95% CI 3.22 to 7.75) respectively, which is just below our arbitrary definition of a 'red flag', that is, a positive likelihood ratio of at least 5. The summary positive likelihood ratio of ageusia alone was 3.14 (95% CI 1.79 to 5.51). Twenty-four studies assessed combinations of different signs and symptoms, mostly combining olfactory symptoms. By combining symptoms with other information such as contact or travel history, age, gender, and a local recent case detection rate, some multivariable prediction scores reached a sensitivity as high as 90%.

AUTHORS' CONCLUSIONS

Most individual symptoms included in this review have poor diagnostic accuracy. Neither absence nor presence of symptoms are accurate enough to rule in or rule out the disease. The presence of anosmia or ageusia may be useful as a red flag for the presence of COVID-19. The presence of cough also supports further testing. There is currently no evidence to support further testing with PCR in any individuals presenting only with upper respiratory symptoms such as sore throat, coryza or rhinorrhoea. Combinations of symptoms with other readily available information such as contact or travel history, or the local recent case detection rate may prove more useful and should be further investigated in an unselected population presenting to primary care or hospital outpatient settings. The diagnostic accuracy of symptoms for COVID-19 is moderate to low and any testing strategy using symptoms as selection mechanism will result in both large numbers of missed cases and large numbers of people requiring testing. Which one of these is minimised, is determined by the goal of COVID-19 testing strategies, that is, controlling the epidemic by isolating every possible case versus identifying those with clinically important disease so that they can be monitored or treated to optimise their prognosis. The former will require a testing strategy that uses very few symptoms as entry criterion for testing, the latter could focus on more specific symptoms such as fever and anosmia.

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

Coronavirus (COVID-19) has created an unprecedented global crisis because of its detrimental effect on the global economy and health. COVID-19 cases have been rapidly increasing, with no sign of stopping. As a result, test kits and accurate detection models are in short supply. Early identification of COVID-19 patients will help decrease the infection rate. Thus, developing an automatic algorithm that enables the early detection of COVID-19 is essential. Moreover, patient data are sensitive, and they must be protected to prevent malicious attackers from revealing information through model updates and reconstruction. In this study, we presented a higher privacy-preserving federated learning system for COVID-19 detection without sharing data among data owners. First, we constructed a federated learning system using chest X-ray images and symptom information. The purpose is to develop a decentralized model across multiple hospitals without sharing data. We found that adding the spatial pyramid pooling to a 2D convolutional neural network improves the accuracy of chest X-ray images. Second, we explored that the accuracy of federated learning for COVID-19 identification reduces significantly for non-independent and identically distributed (Non-IID) data. We then proposed a strategy to improve the model's accuracy on Non-IID data by increasing the total number of clients, parallelism (client-fraction), and computation per client. Finally, for our federated learning model, we applied a differential privacy stochastic gradient descent (DP-SGD) to improve the privacy of patient data. We also proposed a strategy to maintain the robustness of federated learning to ensure the security and accuracy of the model.

Epidemiological Algorithm for Early Detection of COVID-19 Cases in a Mexican Oncologic Center

An early detection tool for latent COVID-19 infections in oncology staff and patients is essential to prevent outbreaks in a cancer center. (1) Background: In this study, we developed and implemented two early detection tools for the radiotherapy area to identify COVID-19 cases opportunely. (2) Methods: Staff and patients answered a questionnaire (electronic and paper surveys, respectively) with clinical and epidemiological information. The data were collected through two online survey tools: Real-Time Tracking (R-Track) and Summary of Factors (S-Facts). Cut-off values were established according to the algorithm models. SARS-CoV-2 qRT-PCR tests confirmed the positive algorithms individuals. (3) Results: Oncology staff members (n = 142) were tested, and 14% (n = 20) were positives for the R-Track algorithm; 75% (n = 15) were qRT-PCR positive. The S-Facts Algorithm identified 7.75% (n = 11) positive oncology staff members, and 81.82% (n = 9) were qRT-PCR positive. Oncology patients (n = 369) were evaluated, and 1.36% (n = 5) were positive for the Algorithm used. The five patients (100%) were confirmed by qRT-PCR. (4) Conclusions: The proposed early detection tools have proved to be a low-cost and efficient tool in a country where qRT-PCR tests and vaccines are insufficient for the population.

Impact of a CE-Marked Medical Software Sensor on COVID-19 Pandemic Progression Prediction: a Register Study Using Machine Learning Methods

Background

To address the current COVID-19 and any future pandemic, we need robust, real-time, and population-scale collection and analysis of data. Rapid and comprehensive knowledge on the trends in reported symptoms in populations provides an earlier window into the progression of viral spread, and helps to predict the needs and timing of professional health care.

Objective

The objective of this study was to use a Conformité Européenne (CE)-marked medical online symptom checker service, Omaolo, and validate the data against the national demand for COVID-19–related care to predict the pandemic progression in Finland.

Methods

Our data comprised real-time Omaolo COVID-19 symptom checker responses (414,477 in total) and daily admission counts in nationwide inpatient and outpatient registers provided by the Finnish Institute for Health and Welfare from March 16 to June 15, 2020 (the first wave of the pandemic in Finland). The symptom checker responses provide self-triage information input to a medically qualified algorithm that produces a personalized probability of having COVID-19, and provides graded recommendations for further actions. We trained linear regression and extreme gradient boosting (XGBoost) models together with F-score and mutual information feature preselectors to predict the admissions once a week, 1 week in advance.

Results

Our models reached a mean absolute percentage error between 24.2% and 36.4% in predicting the national daily patient admissions. The best result was achieved by combining both Omaolo and historical patient admission counts. Our best predictor was linear regression with mutual information as the feature preselector.

Conclusions

Accurate short-term predictions of COVID-19 patient admissions can be made, and both symptom check questionnaires and daily admissions data contribute to the accuracy of the predictions. Thus, symptom checkers can be used to estimate the progression of the pandemic, which can be considered when predicting the health care burden in a future pandemic.

Epidemiology of SARS-CoV-2 Infection in Italy Using Real-World Data: Methodology and Cohort Description of the Second Phase of Web-Based EPICOVID19 Study

Digital technologies have been extensively employed in response to the SARS-CoV-2 pandemic worldwide. This study describes the methodology of the two-phase internet-based EPICOVID19 survey, and the characteristics of the adult volunteer respondents who lived in Italy during the first (April-May 2020) and the second wave (January-February 2021) of the epidemic. Validated scales and ad hoc questionnaires were used to collect socio-demographic, medical and behavioural characteristics, as well as information on COVID-19. Among those who provided email addresses during phase I (105,355), 41,473 participated in phase II (mean age 50.7 years ± 13.5 SD, 60.6% females). After a median follow-up of ten months, 52.8% had undergone nasopharyngeal swab (NPS) testing and 13.2% had a positive result. More than 40% had undergone serological test (ST) and 11.9% were positive. Out of the 2073 participants with at least one positive ST, 72.8% had only negative results from NPS or never performed it. These results indicate that a large fraction of individuals remained undiagnosed, possibly contributing to the spread of the virus in the community. Participatory online surveys offer a unique opportunity to collect relevant data at individual level from large samples during confinement.

Predictors of the Development of Mental Disorders in Hospitalized COVID-19 Patients without Previous Psychiatric History: A Single-Center Retrospective Study in South Korea

The objective of this study was to investigate the predictors for new-onset mental disorders among patients with mild to moderate COVID-19 illness during hospitalization. A retrospective cohort study was performed in patients with confirmed COVID-19 admitted to a nationally designated hospital between 1 February and 30 June 2020. Demographic, clinical, psychological assessments, and psychiatric outcomes were obtained from electronic medical record review. Multivariate logistic regression analysis was used to identify predictors of new-onset mental disorders. Among 185 patients, 130 had no history of mental disorders or cognitive impairment at the time of admission. Of 130 patients, 29 (22.3%) were newly diagnosed with mental disorders during hospitalization. The following factors were significantly associated with an increased risk of a psychiatric diagnosis: Charlson comorbidity index core ≥1 (adjusted odds ratio (aOR) = 5.115, 95% confidence interval (CI): 1.737–15.058), length of stay (aOR per 1-day increase = 1.067, 95% CI: 1.035–1.100), and self-reported depressive symptoms at the time of admission (aOR = 5.357, 95% CI: 1.745–16.444). The predictive accuracy of combining these risk factors was relatively high (area under curve = 0.851, 95% CI: 0.778–0.923). These potential risk factors could help to predict the new-onset mental disorder among hospitalized patients with COVID-19.

Taste loss as a distinct symptom of COVID-19: a systematic review and meta-analysis

Chemosensory scientists have been skeptical that reports of COVID-19 taste loss are genuine, in part because before COVID-19 taste loss was rare and often confused with smell loss. Therefore, to establish the predicted prevalence rate of taste loss in COVID-19 patients, we conducted a systematic review and meta-analysis of 376 papers published in 2020-2021, with 241 meeting all inclusion criteria. Drawing on previous studies and guided by early meta-analyses, we explored how methodological differences (direct vs. self-report measures) may affect these estimates. We hypothesized that direct measures of taste are at least as sensitive as those obtained by self-report and that the preponderance of evidence confirms taste loss is a symptom of COVID-19. The meta-analysis showed that, among 138,897 COVID-19-positive patients, 39.2% reported taste dysfunction (95% confidence interval: 35.34%-43.12%), and the prevalence estimates were slightly but not significantly higher from studies using direct (n = 18) versus self-report (n = 223) methodologies (Q = 0.57, df = 1, P = 0.45). Generally, males reported lower rates of taste loss than did females, and taste loss was highest among middle-aged adults. Thus, taste loss is likely a bona fide symptom of COVID-19, meriting further research into the most appropriate direct methods to measure it and its underlying mechanisms.

Accessibility to SARS-CoV-2 swab test during the Covid-19 pandemic: Did age make the difference?

Although COVID-19 affects older people more severely, health policies during the first wave of the pandemic often prioritized younger individuals. We investigated whether age had influenced the access to a diagnostic test for SARS-CoV-2 infection and whether clinical complexity and healthcare resources availability could have impacted such differences. This work included 126,741 Italian participants in the EPICOVID19 web-based survey, who reported having had contacts with known/suspected COVID-19 cases (epidemiological criterion) and/or COVID-19-like signs/symptoms (clinical criterion) from February to June 2020. Data on sociodemographic, medical history and access to SARS-CoV-2 nasopharyngeal swab (NPS) were collected. Logistic regressions estimated the probability of accessing NPS as a function of age and the possible modifying effect of chronic diseases' number and residential areas in such association. A total of 6136 (4.8%) participants had undergone an NPS. Older participants had lower NPS frequencies than the younger ones when reporting epidemiological (14.9% vs. 8.8%) or both epidemiological and clinical criteria (17.5% vs. 13.7%). After adjustment for potential confounders, including epidemiological and clinical criteria, the chance of NPS access decreased by 29% (OR=0.71, 95%CI:0.63-0.79) in older vs. younger individuals. Such disparity was accentuated in areas with greater healthcare resources. In conclusion, in the first wave of the pandemic, age may have affected the access to COVID-19 diagnostic testing, disadvantaging older people.

Exploring the Clinical Utility of Gustatory Dysfunction (GD) as a Triage Symptom Prior to Reverse Transcription Polymerase Chain Reaction (RT-PCR) in the Diagnosis of COVID-19: A Meta-Analysis and Systematic Review

BACKGROUND

The diagnosis of COVID-19 is made using reverse transcription polymerase chain reaction (RT-PCR) but its sensitivity varies from 20 to 100%. The presence of gustatory dysfunction (GD) in a patient with upper respiratory tract symptoms might increase the clinical suspicion of COVID-19.

AIMS

To perform a systematic review and meta-analysis to determine the pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-) and diagnostic odds ratio (DOR) of using GD as a triage symptom prior to RT-PCR.

METHODS

PubMed and Embase were searched up to 20 June 2021. Studies published in English were included if they compared the frequency of GD in COVID-19 adult patients (proven by RT-PCR) to COVID-19 negative controls in case control or cross-sectional studies. The Newcastle-Ottawa scale was used to assess the methodological quality of the included studies.

RESULTS

21,272 COVID-19 patients and 52,298 COVID-19 negative patients were included across 44 studies from 21 countries. All studies were of moderate to high risk of bias. Patients with GD were more likely to test positive for COVID-19: DOR 6.39 (4.86-8.40), LR+ 3.84 (3.04-4.84), LR- 0.67 (0.64-0.70), pooled sensitivity 0.37 (0.29-0.47) and pooled specificity 0.92 (0.89-0.94). While history/questionnaire-based assessments were predictive of RT-PCR positivity (DOR 6.62 (4.95-8.85)), gustatory testing was not (DOR 3.53 (0.98-12.7)). There was significant heterogeneity among the 44 studies (I2 = 92%, p < 0.01).

CONCLUSIONS

GD is useful as a symptom to determine if a patient should undergo further testing, especially in resource-poor regions where COVID-19 testing is scarce. Patients with GD may be advised to quarantine while repeated testing is performed if the initial RT-PCR is negative.

FUNDING

None.

Algorithm for Individual Prediction of COVID-19-Related Hospitalization Based on Symptoms: Development and Implementation Study

BACKGROUND

The COVID-19 pandemic has placed a huge strain on the health care system globally. The metropolitan area of Milan, Italy, was one of the regions most impacted by the COVID-19 pandemic worldwide. Risk prediction models developed by combining administrative databases and basic clinical data are needed to stratify individual patient risk for public health purposes.

OBJECTIVE

This study aims to develop a stratification tool aimed at improving COVID-19 patient management and health care organization.

METHODS

A predictive algorithm was developed and applied to 36,834 patients with COVID-19 in Italy between March 8 and the October 9, 2020, in order to foresee their risk of hospitalization. Exposures considered were age, sex, comorbidities, and symptoms associated with COVID-19 (eg, vomiting, cough, fever, diarrhea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnea). The outcome was hospitalizations and emergency department admissions for COVID-19. Discrimination and calibration of the model were also assessed.

RESULTS

The predictive model showed a good fit for predicting COVID-19 hospitalization (C-index 0.79) and a good overall prediction accuracy (Brier score 0.14). The model was well calibrated (intercept -0.0028, slope 0.9970). Based on these results, 118,804 patients diagnosed with COVID-19 from October 25 to December 11, 2020, were stratified into low, medium, and high risk for COVID-19 severity. Among the overall study population, 67,030 (56.42%) were classified as low-risk patients; 43,886 (36.94%), as medium-risk patients; and 7888 (6.64%), as high-risk patients. In all, 89.37% (106,179/118,804) of the overall study population was being assisted at home, 9% (10,695/118,804) was hospitalized, and 1.62% (1930/118,804) died. Among those assisted at home, most people (63,983/106,179, 60.26%) were classified as low risk, whereas only 3.63% (3858/106,179) were classified at high risk. According to ordinal logistic regression, the odds ratio (OR) of being hospitalized or dead was 5.0 (95% CI 4.6-5.4) among high-risk patients and 2.7 (95% CI 2.6-2.9) among medium-risk patients, as compared to low-risk patients.

CONCLUSIONS

A simple monitoring system, based on primary care data sets linked to COVID-19 testing results, hospital admissions data, and death records may assist in the proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic.

Taste loss as a distinct symptom of COVID-19: A systematic review and meta-analysis

Chemosensory scientists have been skeptical that reports of COVID-19 taste loss are genuine, in part because before COVID-19, taste loss was rare and often confused with smell loss. Therefore, to establish the predicted prevalence rate of taste loss in COVID-19 patients, we conducted a systematic review and meta-analysis of 376 papers published in 2020-2021, with 241 meeting all inclusion criteria. Additionally, we explored how methodological differences (direct vs. self-report measures) may affect these estimates. We hypothesized that direct prevalence measures of taste loss would be the most valid because they avoid the taste/smell confusion of self-report. The meta-analysis showed that, among 138,897 COVID-19-positive patients, 39.2% reported taste dysfunction (95% CI: 35.34-43.12%), and the prevalence estimates were slightly but not significantly higher from studies using direct (n = 18) versus self-report (n = 223) methodologies (Q = 0.57, df = 1, p = 0.45). Generally, males reported lower rates of taste loss than did females and taste loss was highest in middle-aged groups. Thus, taste loss is a bona fide symptom COVID-19, meriting further research into the most appropriate direct methods to measure it and its underlying mechanisms.

Rasch Model of the COVID-19 Symptom Checklist-A Psychometric Validation Study

While self-reported Coronavirus Disease 2019 (COVID-19) symptom checklists have been extensively used during the pandemic, they have not been sufficiently validated from a psychometric perspective. We, therefore, used advanced psychometric modelling to explore the construct validity and internal consistency of an online self-reported COVID-19 symptom checklist and suggested adaptations where necessary. Fit to the Rasch model was examined in a sample of 1638 Austrian citizens who completed the checklist on up to 20 days during a lockdown. The items' fatigue', 'headache' and 'sneezing' had the highest likelihood to be affirmed. The longitudinal application of the symptom checklist increased the fit to the Rasch model. The item 'cough' showed a significant misfit to the fundamental measurement model and an additional dependency to 'dry cough/no sputum production'. Several personal factors, such as gender, age group, educational status, COVID-19 test status, comorbidities, immunosuppressive medication, pregnancy and pollen allergy led to systematic differences in the patterns of how symptoms were affirmed. Raw scores' adjustments ranged from ±0.01 to ±0.25 on the metric scales (0 to 10). Except for some basic adaptations that increases the scale's construct validity and internal consistency, the present analysis supports the combination of items. More accurate item wordings co-created with laypersons would lead to a common understanding of what is meant by a specific symptom. Adjustments for personal factors and comorbidities would allow for better clinical interpretations of self-reported symptom data.

Early detection of COVID-19 in the UK using self-reported symptoms: a large-scale, prospective, epidemiological surveillance study

BACKGROUND

Self-reported symptoms during the COVID-19 pandemic have been used to train artificial intelligence models to identify possible infection foci. To date, these models have only considered the culmination or peak of symptoms, which is not suitable for the early detection of infection. We aimed to estimate the probability of an individual being infected with SARS-CoV-2 on the basis of early self-reported symptoms to enable timely self-isolation and urgent testing.

METHODS

In this large-scale, prospective, epidemiological surveillance study, we used prospective, observational, longitudinal, self-reported data from participants in the UK on 19 symptoms over 3 days after symptoms onset and COVID-19 PCR test results extracted from the COVID-19 Symptom Study mobile phone app. We divided the study population into a training set (those who reported symptoms between April 29, 2020, and Oct 15, 2020) and a test set (those who reported symptoms between Oct 16, 2020, and Nov 30, 2020), and used three models to analyse the self-reported symptoms: the UK's National Health Service (NHS) algorithm, logistic regression, and the hierarchical Gaussian process model we designed to account for several important variables (eg, specific COVID-19 symptoms, comorbidities, and clinical information). Model performance to predict COVID-19 positivity was compared in terms of sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) in the test set. For the hierarchical Gaussian process model, we also evaluated the relevance of symptoms in the early detection of COVID-19 in population subgroups stratified according to occupation, sex, age, and body-mass index.

FINDINGS

The training set comprised 182 991 participants and the test set comprised 15 049 participants. When trained on 3 days of self-reported symptoms, the hierarchical Gaussian process model had a higher prediction AUC (0·80 [95% CI 0·80-0·81]) than did the logistic regression model (0·74 [0·74-0·75]) and the NHS algorithm (0·67 [0·67-0·67]). AUCs for all models increased with the number of days of self-reported symptoms, but were still high for the hierarchical Gaussian process model at day 1 (0·73 [95% CI 0·73-0·74]) and day 2 (0·79 [0·78-0·79]). At day 3, the hierarchical Gaussian process model also had a significantly higher sensitivity, but a non-statistically lower specificity, than did the two other models. The hierarchical Gaussian process model also identified different sets of relevant features to detect COVID-19 between younger and older subgroups, and between health-care workers and non-health-care workers. When used during different pandemic periods, the model was robust to changes in populations.

INTERPRETATION

Early detection of SARS-CoV-2 infection is feasible with our model. Such early detection is crucial to contain the spread of COVID-19 and efficiently allocate medical resources.

FUNDING

ZOE, the UK Government Department of Health and Social Care, the Wellcome Trust, the UK Engineering and Physical Sciences Research Council, the UK National Institute for Health Research, the UK Medical Research Council, the British Heart Foundation, the Alzheimer's Society, the Chronic Disease Research Foundation, and the Massachusetts Consortium on Pathogen Readiness.

Predictors of incident viral symptoms ascertained in the era of COVID-19

Background

In the absence of universal testing, effective therapies, or vaccines, identifying risk factors for viral infection, particularly readily modifiable exposures and behaviors, is required to identify effective strategies against viral infection and transmission.

Methods

We conducted a world-wide mobile application-based prospective cohort study available to English speaking adults with a smartphone. We collected self-reported characteristics, exposures, and behaviors, as well as smartphone-based geolocation data. Our main outcome was incident symptoms of viral infection, defined as fevers and chills plus one other symptom previously shown to occur with SARS-CoV-2 infection, determined by daily surveys.

Findings

Among 14, 335 participants residing in all 50 US states and 93 different countries followed for a median 21 days (IQR 10–26 days), 424 (3%) developed incident viral symptoms. In pooled multivariable logistic regression models, female biological sex (odds ratio [OR] 1.75, 95% CI 1.39–2.20, p<0.001), anemia (OR 1.45, 95% CI 1.16–1.81, p = 0.001), hypertension (OR 1.35, 95% CI 1.08–1.68, p = 0.007), cigarette smoking in the last 30 days (OR 1.86, 95% CI 1.35–2.55, p<0.001), any viral symptoms among household members 6–12 days prior (OR 2.06, 95% CI 1.67–2.55, p<0.001), and the maximum number of individuals the participant interacted with within 6 feet in the past 6–12 days (OR 1.15, 95% CI 1.06–1.25, p<0.001) were each associated with a higher risk of developing viral symptoms. Conversely, a higher subjective social status (OR 0.87, 95% CI 0.83–0.93, p<0.001), at least weekly exercise (OR 0.57, 95% CI 0.47–0.70, p<0.001), and sanitizing one’s phone (OR 0.79, 95% CI 0.63–0.99, p = 0.037) were each associated with a lower risk of developing viral symptoms.

Interpretation

While several immutable characteristics were associated with the risk of developing viral symptoms, multiple immediately modifiable exposures and habits that influence risk were also observed, potentially identifying readily accessible strategies to mitigate risk in the COVID-19 era.

Validity and reliability of the COVID-19 symptom index, an instrument evaluating severity of general and otolaryngological symptoms

BACKGROUND

There is no clinical instrument evaluating symptoms of COVID-19.

OBJECTIVE

To develop a clinical instrument for evaluating symptoms of COVID-19 mild-to-moderate forms.

METHODS

COVID-19 patients were recruited from EpiCURA Hospital (Belgium). They completed the COVID-19 Symptom Index (CSI) twice to assess the test-retest reliability. The internal consistency was evaluated with Cronbach's alpha. CSI was completed by healthy subjects to assess the internal validity. Patients completed CSI 6 weeks after the COVID-19 resolution to evaluate the responsiveness to change.

RESULTS

Ninety-four COVID-19 patients and 55 healthy individuals completed the evaluations. Symptoms associated with the higher severity score were fatigue, headache and myalgia. The Cronbach's alpha value was 0.801, indicating high internal consistency. The test-retest reliability was adequate (r_s = 0.535, p = .001). The correlation between CSI total score and SNOT-22 was high (r_s = 0.782; p < .001), supporting a high external validity. COVID-19 patients reported significant higher CSI score than healthy individuals, suggesting an adequate internal validity. The mean CSI significantly decreased after the COVID-19 resolution, supporting a high responsiveness to change property.

CONCLUSION AND SIGNIFICANCE

The CSI is a reliable and valid patient reported outcome questionnaire for the evaluation of symptom severity of COVID-19 patients.

Association Between Smoking and SARS-CoV-2 Infection: Cross-sectional Study of the EPICOVID19 Internet-Based Survey

Background

Several studies have reported a low prevalence of current smoking among hospitalized COVID-19 cases; however, no definitive conclusions can be drawn.

Objective

We investigated the association of tobacco smoke exposure with nasopharyngeal swab (NPS) test results for SARS-CoV-2 infection and disease severity accounting for possible confounders.

Methods

The nationwide, self-administered, cross-sectional web-based Italian National Epidemiological Survey on COVID-19 (EPICOVID19) was administered to an Italian population of 198,822 adult volunteers who filled in an online questionnaire between April 13 and June 2, 2020. For this study, we analyzed 6857 individuals with known NPS test results. The associations of smoking status and the dose-response relationship with a positive NPS test result and infection severity were calculated as odds ratios (ORs) with 95% CIs by means of logistic and multinomial regression models adjusting for sociodemographic, clinical, and behavioral characteristics.

Results

Out of the 6857 individuals (mean age 47.9 years, SD 14.1; 4516/6857, 65.9% female), 63.2% (4334/6857) had never smoked, 21.3% (1463/6857) were former smokers, and 15.5% (1060/6857) were current smokers. Compared to nonsmokers, current smokers were younger, were more educated, were less affected by chronic diseases, reported COVID-19–like symptoms less frequently, were less frequently hospitalized, and less frequently tested positive for COVID-19. In multivariate analysis, current smokers had almost half the odds of a positive NPS test result (OR 0.54, 95% CI 0.45-0.65) compared to nonsmokers. We also found a dose-dependent relationship with tobacco smoke: mild smokers (adjusted OR [aOR] 0.76, 95% CI 0.55-1.05), moderate smokers (aOR 0.56, 95% CI 0.42-0.73), and heavy smokers (aOR 0.38, 95% CI 0.27-0.53). This inverse association also persisted when considering the severity of the infection. Current smokers had a statistically significantly lower probability of having asymptomatic (aOR 0.50, 95% CI 0.27-0.92), mild (aOR 0.65, 95% CI 0.53-0.81), and severe infections (aOR 0.27, 95% CI 0.17-0.42) compared to those who never smoked.

Conclusions

Current smoking was negatively associated with SARS-CoV-2 infection with a dose-dependent relationship. Ad hoc experimental studies are needed to elucidate the mechanisms underlying this association.

Trial Registration

ClinicalTrials.gov NCT04471701; https://clinicaltrials.gov/ct2/show/NCT04471701

Age-Related Changes in Clinical Presentation of Covid-19: the EPICOVID19 Web-Based Survey

BACKGROUND

The influence of aging and multimorbidity on Covid-19 clinical presentation is still unclear.

OBJECTIVES

We investigated whether the association between symptoms (or cluster of symptoms) and positive SARS-CoV-2 nasopharyngeal swab (NPS) was different according to patients' age and presence of multimorbidity.

METHODS

The study included 6680 participants in the EPICOVID19 web-based survey, who reported information about symptoms from February to June 2020 and who underwent at least one NPS. Symptom clusters were identified through hierarchical cluster analysis. The associations between symptoms (and clusters of symptoms) and positive NPS were investigated through multivariable binary logistic regression in the sample stratified by age (<65 vs ≥65 years) and number of chronic diseases (0 vs 1 vs ≥2).

RESULTS

The direct association between taste/smell disorders and positive NPS was weaker in older and multimorbid patients than in their younger and healthier counterparts. Having reported no symptoms reduced the chance of positive NPS by 86% in younger (95%CI: 0.11-0.18), and by 46% in older participants (95%CI: 0.37-0.79). Of the four symptom clusters identified (asymptomatic, generic, flu-like, and combined generic and flu-like symptoms), those associated with a higher probability of SARS-CoV-2 infection were the flu-like for older people, and the combined generic and flu-like for the younger ones.

CONCLUSIONS

Older age and pre-existing chronic diseases may influence the clinical presentation of Covid-19. Symptoms at disease onset tend to aggregate differently by age. New diagnostic algorithms considering age and chronic conditions may ease Covid-19 diagnosis and optimize health resources allocation.

TRIAL REGISTRATION

NCT04471701 (ClinicalTrials.gov).

An analysis of patient self-reported COVID-19 symptoms during the first wave of the pandemic in Ireland

BACKGROUND

Since the outbreak of COVID-19 in December 2019, there have been more than 115 million cases worldwide (1). Symptoms of COVID-19 vary widely and the spectrum of clinical presentation has yet to be fully characterised (2). Many countries have detailed their early experience with COVID-19, with a focus on the clinical characteristics of the disease. However, to our knowledge, there has been no such study detailing symptoms in the Irish population.

AIM

Our aim is to describe COVID-19 symptoms in the Irish population at the beginning of the COVID-19 pandemic and compare symptoms between those reporting positive and negative test results.

METHOD

A Web page MyCovidSymptoms.ie was created by researchers at the National University of Ireland, Galway, in April 2020 to investigate COVID-19 symptoms in Ireland. The Web page invited participants to self-report RT-PCR test outcome data (positive, negative, untested), temperature and a range of symptoms (cough, shortness of breath, fatigue, loss of taste, loss of smell).

RESULTS

One hundred and twenty-three Irish participants who had a RT-PCR test for COVID-19 logged their symptoms. Eighty-four patients reported that they tested positive for COVID-19, and 39 patients reported a negative COVID-19 test. In our cohort of respondents with a positive COVID-19 test, 49/84 (58%) respondents reported a cough. Of the 39 respondents with a negative COVID-19 test, 17 (44%) reported having a cough. The distribution of temperature was similar in both those with and without COVID-19. Levels of self-reported fatigue were high in both groups with 65/84 (77%) of COVID-19-positive patients reporting fatigue and 30/39 (77%) of those who were COVID-19-negative reporting fatigue. New symptoms emerging at the time of data collection included loss of taste and smell. We demonstrated a higher proportion of loss of smell (p = 0.02) and taste (p = 0.01) in those reporting a positive result, compared to those reporting a negative result.

CONCLUSION

These data represents an early picture of the clinical characteristics of COVID-19 in an Irish population. It also highlights the potential use of self-reported data globally as a powerful tool in helping with the pandemic.

Fear of COVID-19 for Individuals and Family Members: Indications from the National Cross-Sectional Study of the EPICOVID19 Web-Based Survey

The study analyzed the association of the fear of contagion for oneself and for family members (FMs) during the first wave of the COVID-19 pandemic, with demographic and socioeconomic status (SES) and health factors. The study was performed within the EPICOVID19 web-based Italian survey, involving adults from April-June 2020. Out of 207,341 respondents, 95.9% completed the questionnaire (60% women with an average age of 47.3 vs. 48.9 years among men). The association between fear and demographic and SES characteristics, contacts with COVID-19 cases, nasopharyngeal swab, self-perceived health, flu vaccination, chronic diseases and specific symptoms was analyzed by logistic regression model; odds ratios adjusted for sex, age, education and occupation were calculated (aORs). Fear for FMs prevailed over fear for oneself and was higher among women than men. Fear for oneself decreased with higher levels of education and in those who perceived good health. Among those vaccinated for the flu, 40.8% responded they had feelings of fear for themselves vs. 34.2% of the not vaccinated. Fear increased when diseases were declared and it was higher when associated with symptoms such as chest pain, olfactory/taste disorders, heart palpitations (aORs > 1.5), lung or kidney diseases, hypertension, depression and/or anxiety. Trends in fear by region showed the highest percentage of positive responses in the southern regions. The knowledge gained from these results should be used to produce tailored messages and shared public health decisions.

Shifting the Paradigm: The Dress-COV Telegram Bot as a Tool for Participatory Medicine

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic management is limited by great uncertainty, for both health systems and citizens. Facing this information gap requires a paradigm shift from traditional approaches to healthcare to the participatory model of improving health. This work describes the design and function of the Doing Risk sElf-assessment and Social health Support for COVID (Dress-COV) system. It aims to establish a lasting link between the user and the tool; thus, enabling modeling of the data to assess individual risk of infection, or developing complications, to improve the individual's self-empowerment. The system uses bot technology of the Telegram application. The risk assessment includes the collection of user responses and the modeling of data by machine learning models, with increasing appropriateness based on the number of users who join the system. The main results reflect: (a) the individual's compliance with the tool; (b) the security and versatility of the architecture; (c) support and promotion of self-management of behavior to accommodate surveillance system delays; (d) the potential to support territorial health providers, e.g., the daily efforts of general practitioners (during this pandemic, as well as in their routine practices). These results are unique to Dress-COV and distinguish our system from classical surveillance applications.