A subset of events within the UK Government Events Research Programme (ERP), developed to examine the risk of transmission of COVID-19 from attendance at events, was examined to explore the public health impact of holding mass sporting events. We used contact tracing data routinely collected through telephone interviews and online questionnaires, to describe the potential public health impact of the large sporting and cultural events on potential transmission and incidence of COVID-19. Data from the EURO 2020 matches hosted at Wembley identified very high numbers of individuals who tested positive for COVID-19 and were traced through NHS Test & Trace. This included both individuals who were potentially infectious (3036) and those who acquired their infection during the time of the Final (6376). This is in contrast with the All England Lawn Tennis Championships at Wimbledon, where there were similar number of spectators and venue capacity but there were lower total numbers of potentially infectious cases (299) and potentially acquired cases (582). While the infections associated with the EURO 2020 event may be attributed to a set of socio-cultural circumstances which are unlikely to be replicated for the forthcoming sporting season, other aspects may be important to consider including mitigations for spectators to consider such as face coverings when travelling to and from events, minimising crowding in poorly ventilated indoor spaces such as bars and pubs where people may congregate to watch events, and reducing the risk of aerosol exposure through requesting that individuals avoid shouting and chanting in large groups in enclosed spaces.

During a disease outbreak, healthcare workers (HCWs) are essential to treat infected individuals. However, these HCWs are themselves susceptible to contracting the disease. As more HCWs get infected, fewer are available to provide care for others, and the overall quality of care available to infected individuals declines. This depletion of HCWs may contribute to the epidemic's severity. To examine this issue, we explicitly model declining quality of care in four differential equation-based susceptible, infected and recovered-type models with vaccination. We assume that vaccination, recovery and survival rates are affected by quality of care delivered. We show that explicitly modelling HCWs and accounting for declining quality of care significantly alters model-predicted disease outcomes, specifically case counts and mortality. Models neglecting the decline of quality of care resulting from infection of HCWs may significantly under-estimate cases and mortality. These models may be useful to inform health policy that may differ for HCWs and the general population. Models accounting for declining quality of care may therefore improve the management interventions considered to mitigate the effects of a future outbreak.

Epidemic forecasting provides an opportunity to predict geographic disease spread and counts when an outbreak occurs and plays a key role in preventing or controlling their adverse impact. However, conventional prediction models based on complex mathematical modelling rely on the estimation of model parameters, which yields unreliable and unsustainable results. Herein, we proposed a simple model for predicting the epidemic transmission dynamics based on nonlinear regression of the epidemic growth rate and iterative methods, which is applicable to the progression of the COVID-19 outbreak under the strict control measures of the Chinese government. Our model yields reliable and accurate results as confirmed by the available data: we predicted that the total number of infections in mainland China would be 91 253, and the maximum number of beds required for hospitalised patients would be 62 794. We inferred that the inflection point (when the growth rate turns from positive to negative) of the epidemic across China would be mid-February, and the end of the epidemic would be in late March. This model is expected to contribute to resource allocation and planning in the health sector while providing a theoretical basis for governments to respond to future global health crises or epidemics.

Much of our current understanding about novel coronavirus disease 2019 (COVID-19) comes from hospitalised patients. However, the spectrum of mild and subclinical disease has implications for population-level screening and control. Forty-nine participants were recruited from a group of 99 adults repatriated from a cruise ship with a high incidence of COVID-19. Respiratory and rectal swabs were tested by polymerase chain reaction (PCR) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Sera were tested for anti-SARS-CoV-2 antibodies by enzyme-linked immunosorbent assay (ELISA) and microneutralisation assay. Symptoms, viral shedding and antibody response were examined. Forty-five participants (92%) were considered cases based on either positive PCR or positive ELISA for immunoglobulin G. Forty-two percent of cases were asymptomatic. Only 15% of symptomatic cases reported fever. Serial respiratory and rectal swabs were positive for 10% and 5% of participants respectively about 3 weeks after median symptom onset. Cycle threshold values were high (range 31–45). Attempts to isolate live virus were unsuccessful. The presence of symptoms was not associated with demographics, comorbidities or antibody response. In closed settings, incidence of COVID-19 could be almost double that suggested by symptom-based screening. Serology may be useful in diagnosis of mild disease and in aiding public health investigations.

Healthcare staff have been at the centre of the fight against the COVID-19 pandemic, facing diverse work-related stressors. Building upon studies from various countries, we aimed to investigate (1) the prevalence of various work-related stressors among healthcare professionals in Germany specific to the COVID-19 pandemic, (2) the psychological effects of these stressors in terms of clinical symptoms, and (3) the healthcare professionals' help-seeking behaviour. To this end, N = 300 healthcare professionals completed an online survey including the ICD-10 Symptom Rating checklist (ISR), event-sampling questions on pandemic-related stressors and self-formulated questions on help-seeking behaviour. Participants were recruited between 22 May and 22 July 2020. Findings were analysed using t tests, regressions and comparisons to large clinical and non-clinical samples assessed before and during the pandemic. Results show that healthcare professionals were most affected by protective measures at their workplace and changes in work procedures. Psychological symptoms, particularly anxiety and depression, were significantly more severe than in a non-clinical pre-pandemic sample and in the general population during the pandemic. At the same time, most professionals indicated that they would not seek help for psychological concerns. These findings indicate that healthcare employers need to pay greater attention to the mental health of their staff.

As a result of the COVID-19 pandemic, whether and when the world can reach herd immunity and return to normal life and a strategy for accelerating vaccination programmes constitute major concerns. We employed Metropolis–Hastings sampling and an epidemic model to design experiments based on the current vaccinations administered and a more equitable vaccine allocation scenario. The results show that most high-income countries can reach herd immunity in less than 1 year, whereas low-income countries should reach this state after more than 3 years. With a more equitable vaccine allocation strategy, global herd immunity can be reached in 2021. However, the spread of SARS-CoV-2 variants means that an additional 83 days will be needed to reach global herd immunity and that the number of cumulative cases will increase by 113.37% in 2021. With the more equitable vaccine allocation scenario, the number of cumulative cases will increase by only 5.70% without additional vaccine doses. As SARS-CoV-2 variants arise, herd immunity could be delayed to the point that a return to normal life is theoretically impossible in 2021. Nevertheless, a more equitable global vaccine allocation strategy, such as providing rapid vaccine assistance to low-income countries/regions, can improve the prevention of COVID-19 infection even though the virus could mutate.

The occupational risk of acquiring minor respiratory infections for paediatric student nurses was estimated by performing serological examinations with influenza A, B, C, parainfluenza, mumps, respiratory syncytial virus, adenovirus and Mycoplasma pneumoniae at 6-month intervals over a period of 4 years in paediatric student nurses at two schools of nursing and students at one school of medical technology, litre increases against all tested agents occurred 1·86 times more often in the student nurses than in the medical technology students, the most frequent agents in both groups being influenza A and B. No difference in the relative distribution of the agents could be verified in the two occupational groups. Data on the protective value of pre-infectious antibody levels for influenza A, B, and coronavirus OC43 and on the importance of the spread of single agents among classmates are presented.

SARS-CoV-2 serological tests are used to assess the infection seroprevalence within a population. This study aims at assessing potential biases in estimating infection prevalence amongst healthcare workers (HCWs) when different diagnostic criteria are considered. A multi-site cross-sectional study was carried out in April–September 2020 amongst 1.367 Italian HCWs. SARS-CoV-2 prevalence was assessed using three diagnostic criteria: RT-PCR on nasopharyngeal swab, point-of-care fingerprick serological test (POCT) result and COVID-19 clinical pathognomonic presentation. A logistic regression model was used to estimate the probability of POCT-positive result in relation to the time since infection (RT-PCR positivity). Among 1.367 HCWs, 69.2% were working in COVID-19 units. Statistically significant differences in age, role and gender were observed between COVID-19/non-COVID-19 units. Prevalence of SARS-CoV-2 infection varied according to the criterion considered: 6.7% for POCT, 8.1% for RT-PCR, 10.0% for either POCT or RT-PCR, 9.6% for infection pathognomonic clinical presentation and 17.6% when at least one of the previous criteria was present. The probability of POCT-positive result decreased by 1.1% every 10 days from the infection. This study highlights potential biases in estimating SARS-CoV-2 point-prevalence data according to the criteria used. Although informative on infection susceptibility and herd immunity level, POCT serological tests are not the best predictors of previous COVID-19 infections for public health monitoring programmes.

To understand the characteristics and influencing factors related to cluster infections in Jiangsu Province, China, we investigated case reports to explore transmission dynamics and influencing factors of scales of cluster infection. The effectiveness of interventions was assessed by changes in the time-dependent reproductive number (Rt). From 25th January to 29th February, Jiangsu Province reported a total of 134 clusters involving 617 cases. Household clusters accounted for 79.85% of the total. The time interval from onset to report of index cases was 8 days, which was longer than that of secondary cases (4 days) (χ2 = 22.763, P < 0.001) and had a relationship with the number of secondary cases (the correlation coefficient (r) = 0.193, P = 0.040). The average interval from onset to report was different between family cluster cases (4 days) and community cluster cases (7 days) (χ2 = 28.072, P < 0.001). The average time interval from onset to isolation of patients with secondary infection (5 days) was longer than that of patients without secondary infection (3 days) (F = 9.761, P = 0.002). Asymptomatic patients and non-familial clusters had impacts on the size of the clusters. The average reduction in the Rt value in family clusters (26.00%, 0.26 ± 0.22) was lower than that in other clusters (37.00%, 0.37 ± 0.26) (F = 4.400, P = 0.039). Early detection of asymptomatic patients and early reports of non-family clusters can effectively weaken cluster infections.

Ever since the World Health Organization (WHO) declared the new coronavirus disease 2019 (COVID-19) as a pandemic, there has been a public health debate concerning medical resources and supplies including hospital beds, intensive care units (ICU), ventilators and protective personal equipment (PPE). Forecasting COVID-19 dissemination has played a key role in informing healthcare professionals and governments on how to manage overburdened healthcare systems. However, forecasting during the pandemic remained challenging and sometimes highly controversial. Here, we highlight this challenge by performing a comparative evaluation for the estimations obtained from three COVID-19 surge calculators under different social distancing approaches, taking Lebanon as a case study. Despite discrepancies in estimations, the three surge calculators used herein agree that there will be a relative shortage in the capacity of medical resources and a significant surge in PPE demand if the social distancing policy is removed. Our results underscore the importance of implementing containment interventions including social distancing in alleviating the demand for medical care during the COVID-19 pandemic in the absence of any medication or vaccine. The paper also highlights the value of employing several models in surge planning.

We report the development of a regression model to predict the prevalence of severe acute respiratory syndrome coronavirus (SARS-CoV-2) antibodies on a population level based on self-reported symptoms. We assessed participant-reported symptoms in the past 12 weeks, as well as the presence of SARS-CoV-2 antibodies during a study conducted in April 2020 in Ischgl, Austria. We conducted multivariate binary logistic regression to predict seroprevalence in the sample. Participants (n = 451) were on average 47.4 years old (s.d. 16.8) and 52.5% female. SARS-CoV-2 antibodies were found in n = 197 (43.7%) participants. In the multivariate analysis, three significant predictors were included and the odds ratios (OR) for the most predictive categories were cough (OR 3.34, CI 1.70–6.58), gustatory/olfactory alterations (OR 13.78, CI 5.90–32.17) and limb pain (OR 2.55, CI 1.20–6.50). The area under the receiver operating characteristic curve was 0.773 (95% CI 0.727–0.820). Our regression model may be used to estimate the seroprevalence on a population level and a web application is being developed to facilitate the use of the model.

Previous studies have reported the basic reproduction number (R0) of coronavirus disease from publicly reported data that lack information such as onset of symptoms, presence of importations or known super-spreading events. Using data from the Republic of Korea, we illustrated how estimates of R0 can be biased and provided improved estimates with more detailed data. We used COVID-19 contact trace system in Korea, which can provide symptom onset date and also serial intervals between contacted people. The total R0 was estimated as 2.10 (95% confidence interval (CI) 1.84–2.42). Also, early transmission of COVID-19 differed by regional or social behaviours of the population. Regions affected by a specific church cluster, which showed a rapid and silent transmission under non-official religious meetings, had a higher R0 of 2.40 (95% CI 2.08–2.77).

The motivations that govern the adoption of digital contact tracing (DCT) tools are complex and not well understood. Hence, we assessed the factors influencing the acceptance and adoption of Singapore's national DCT tool – TraceTogether – during the COVID-19 pandemic. We surveyed 3943 visitors of Tan Tock Seng Hospital from July 2020 to February 2021 and stratified the analyses into three cohorts. Each cohort was stratified based on the time when significant policy interventions were introduced to increase the adoption of TraceTogether. Binary logistic regression was preceded by principal components analysis to reduce the Likert items. Respondents who ‘perceived TraceTogether as useful and necessary’ had higher likelihood of accepting it but those with ‘Concerns about personal data collected by TraceTogether’ had lower likelihood of accepting and adopting the tool. The injunctive and descriptive social norms were also positively associated with both the acceptance and adoption of the tool. Liberal individualism was mixed in the population and negatively associated with the acceptance and adoption of TraceTogether. Policy measures to increase the uptake of a national DCT bridged the digital divide and accelerated its adoption. However, good public communications are crucial to address the barriers of acceptance to improve voluntary uptake widespread adoption.

COVID-19, although a respiratory illness, has been clinically associated with non-respiratory symptoms. We conducted a negative case–control study to identify the symptoms associated with SARS-CoV-2-positive results in Portugal. Twelve symptoms and signs included in the clinical notification of COVID-19 were selected as predictors, and the dependent variable was the RT-PCR test result. The χ2 tests were used to compare notified cases on sex, age group, health region and presence of comorbidities. The best-fit prediction model was selected using a backward stepwise method with an unconditional logistic regression. General and gastrointestinal symptoms were strongly associated with a positive test (P < 0.001). In this sense, the inclusion of general symptoms such as myalgia, headache and fatigue, as well as diarrhoea, together with actual clinical criteria for suspected cases, already updated and included in COVID-19 case definition, can lead to increased identification of cases and represent an effective strength for transmission control.

COVID-19 serosurvey provides a better estimation of people who have developed antibody against the infection. But limited information on such serosurveys in rural areas poses many hurdles to understand the epidemiology of the virus and to implement proper control strategies. This study was carried out in the rural catchment area of Model Rural Health Research Unit in Odisha, India during March–April 2021, the initial phase of COVID vaccination. A total of 60 village clusters from four study blocks were identified using probability proportionate to size sampling. From each cluster, 60 households and one eligible participant from each household (60 per cluster) were selected for the collection of blood sample and socio-demographic data. The presence of SARS-CoV-2 antibody was tested using the Elecsys Anti-SARS-CoV-2 immunoassay. The overall seroprevalence after adjusting for test performance was 54.21% with an infection to case ratio of 96.89 along with 4.25% partial and 6.79% full immunisation coverage. Highest seroprevalence was observed in the age group of 19–44 years and females had both higher seroprevalence as well as vaccine coverage. People of other backward caste also had higher seropositivity than other caste categories. The study emphasises on continuing surveillance for COVID-19 cases and prioritizing COVID-19 vaccination for susceptible groups for better disease management.

The extensive register infrastructure available for coronavirus disease 2019 surveillance in Scania county, Sweden, makes it possible to classify individual cases with respect to hospitalisation and disease severity, stratify on time since last dose and demographic factors, account for prior infection and extract data for population controls automatically. In the present study, we developed a case–control sampling design to surveil vaccine effectiveness (VE) in this ethnically and socioeconomically diverse population with more than 1.3 million inhabitants. The first surveillance results show that estimated VE against hospitalisation and severe disease 0–3 months after the last dose remained stable during the study period, but waned markedly 6 months after the last dose in persons aged 65 years or over.

In general, mass gatherings might pose a risk to the public health (PH). The UEFA EURO 2020 tournament (EURO 2020) was one of the first mass gathering events since the start of the coronavirus disease 2019 (COVID-19) pandemic in Germany. To allow early detection and response to any EURO 2020-associated impact on the COVID-19-related epidemiological situation, we initiated enhanced surveillance activities using the routine surveillance system in collaboration with the regional PH authority of Bavaria. Several preventive measures regarding the attendance of football matches and public viewing were implemented according to state regulations. We describe the results from the enhanced surveillance during the EURO 2020. In total, five cases who had attended a football match in the stadium of Munich, nine cases, who attended a football match in a stadium outside of Germany, and 123 cases in association with public viewing events were identified by enhanced surveillance. Concluding, the EURO 2020 seems to not have had a major impact on the COVID-19 pandemic development in Germany. Health measures for stadium visitors and the restriction of large public viewing events may have potentially contributed to the low case numbers detected, emphasising the need of appropriate PH surveillance and regulations to limit the potential risk to PH during mass gathering events.

The objective of this study was to analyse the dynamics of spatial dispersion of the coronavirus disease 2019 (COVID-19) in Brazil by correlating them to socioeconomic indicators. This is an ecological study of COVID-19 cases and deaths between 26 February and 31 July 2020. All Brazilian counties were used as units of analysis. The incidence, mortality, Bayesian incidence and mortality rates, global and local Moran indices were calculated. A geographic weighted regression analysis was conducted to assess the relationship between incidence and mortality due to COVID-19 and socioeconomic indicators (independent variables). There were confirmed 2 662 485 cases of COVID-19 reported in Brazil from February to July 2020 with higher rates of incidence in the north and northeast. The Moran global index of incidence rate (0.50, P = 0.01) and mortality (0.45 with P = 0.01) indicate a positive spatial autocorrelation with high standards in the north, northeast and in the largest urban centres between cities in the southeast region. In the same period, there were 92 475 deaths from COVID-19, with higher mortality rates in the northern states of Brazil, mainly Amazonas, Pará and Amapá. The results show that there is a geospatial correlation of COVID-19 in large urban centres and regions with the lowest human development index in the country. In the geographic weighted regression, it was possible to identify that the percentage of people living in residences with density higher than 2 per dormitory, the municipality human development index (MHDI) and the social vulnerability index were the indicators that most contributed to explaining incidence, social development index and the municipality human development index contributed the most to the mortality model. We hope that the findings will contribute to reorienting public health responses to combat COVID-19 in Brazil, the new epicentre of the disease in South America, as well as in other countries that have similar epidemiological and health characteristics to those in Brazil.

Family feasting during the Spring Festival is a Chinese tradition. However, close contact during this period is likely to promote the spread of coronavirus disease 2019 (COVID-19). This study developed a dynamic infectious disease model in which the feast gatherings of families were considered the sole mode of transmission. The model simulates COVID-19 transmission via family feast gatherings through a social contact network. First, a kinship-based, virtual social contact network was constructed, with nodes representing families and connections representing kinships. Families in kinship with each other comprised of the largest globally coupled network, also known as a clique, in which a feast gathering was generated by randomly selecting two or more families willing to gather. The social contact network in the model comprised of 215 cliques formed among 608 families with 1517 family members. The modelling results indicated that when there is only one patient on day 0, the number of new infections will reach a peak on day 29, and almost all families and their members in the social contact network will be infected by day 60. This study demonstrated that COVID-19 can spread rapidly through continuous feast gatherings through social contact networks and that the disease will run rampant throughout the network.

A fever clinic within a hospital plays a vital role in pandemic control because it serves as an outpost for pandemic discovery, monitoring and handling. As the outbreak of coronavirus disease 2019 (COVID-19) in Wuhan was gradually brought under control, the fever clinic in the West Campus of Wuhan Union Hospital introduced a new model for construction and management of temporary mobile isolation wards. A traditional battlefield hospital model was combined with pandemic control regulations, to build a complex of mobile isolation wards that used adaptive design and construction for medical operational, medical waste management and water drainage systems. The mobile isolation wards allowed for the sharing of medical resources with the fever clinic. This increased the capacity and efficiency of receiving, screening, triaging and isolation and observation of patients with fever. The innovative mobile isolation wards also controlled new sudden outbreaks of COVID-19. We document the adaptive design and construction model of the novel complex of mobile isolation wards and explain its characteristics, functions and use.