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The outbreak of coronavirus infectious disease-2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has rapidly spread throughout the world. Several studies have shown that detecting SARS-CoV-2 in untreated wastewater can be a useful tool to identify new outbreaks, establish outbreak trends, and assess the prevalence of infections. On 06 May 2021, over a year into the pandemic, we conducted a scoping review aiming to summarize research data on SARS-CoV-2 in sewage. Papers dealing with raw sewage collected at wastewater treatment plants, sewer networks, septic tanks, and sludge treatment facilities were included in this review. We also reviewed studies on sewage collected in community settings such as private or municipal hospitals, healthcare facilities, nursing homes, dormitories, campuses, airports, aircraft, and cruise ships. The literature search was conducted using the electronic databases PubMed, EMBASE, and Web Science Core Collection. This comprehensive research yielded 1090 results, 66 of which met the inclusion criteria and are discussed in this review. Studies from 26 countries worldwide have investigated the occurrence of SARS-CoV-2 in sewage of different origin. The percentage of positive samples in sewage ranged from 11.6 to 100%, with viral concentrations ranging from ˂LOD to 4.6 × 108 genome copies/L. This review outlines the evidence currently available on wastewater surveillance: (i) as an early warning system capable of predicting COVID-19 outbreaks days or weeks before clinical cases; (ii) as a tool capable of establishing trends in current outbreaks; (iii) estimating the prevalence of infections; and (iv) studying SARS-CoV-2 genetic diversity. In conclusion, as a cost-effective, rapid, and reliable source of information on the spread of SARS-CoV-2 and its variants in the population, wastewater surveillance can enhance genomic and epidemiological surveillance with independent and complementary data to inform public health decision-making during the ongoing pandemic. Graphic Abstract Supplementary Information The online version contains supplementary material available at 10.1007/s12560-021-09498-6.

To investigate the characteristic findings between laboratory-confirmed and clinically suspected patients with COVID-19. In this retrospective study, we included patients admitted to the Xiangya Hospital from Jan 24 to Feb 10, 2020. Two researchers separately collected and sorted out the patients' epidemiological, demographic, clinical, laboratory, and radiologic findings. SPSS was performed to analyze the collected data. 241 patients were admitted, including 28 (45.5; IQR, 34.0-52.5) confirmed and 213 (42.0; IQR, 30.0-57.0) suspected patients. The prevalence of COVID-19 disease in males was significantly higher than in females (64.3% vs. 35.7%, P = 0.033). Before admission of the confirmed and suspected undiagnosed cases, the onset of symptoms is often manifested as respiratory symptoms such as fever (35.7% vs. 27.7%) and cough (30.7% vs. 32.1%). Twenty patients (71.4%) had an exposure history to high-risk areas, and 14 patients (50.0%) traveled or lived in a high-risk area in the confirmed group, which was significantly different from the suspected group. The pulmonary imaging of the patients in the confirmed group was primarily manifested as ground-glass opacity (89.3%). A total of 499 nucleic acid testing (NAT) was performed to determine the 28 COVID-19 positive throat swabs among the 241 patients. Whether there is a history of high-risk area exposure in the epidemiological investigation is essential in distinguishing the suspected patients from the confirmed patients. Multiple nucleic acid tests were used as the basis for the diagnosis of COVID-19, and during CT examination, ground-glass opacity was used as a COVID-19 indicator. Trail registration Trail registration number. 202012195, Date of registration: 2020.12.22 "retrospectively registered""."

Associations between social determinants of health (SDOH), demographic factors including preferred language, and SARS-CoV-2 detection are not clear. We conducted a retrospective cohort study among those seeking testing for SARS-CoV-2 at a multi-site, urban community health center. Logistic regression and exact matching methods were used to identify independent predictors of SARS-CoV-2 detection among demographic, SDOH, and neighborhood-level variables. Of 1,361 included individuals, SARS-CoV-2 was detected among 266 (19.5%). Logistic regression demonstrated that SARS-CoV-2 detection was less likely in White participants relative to Hispanic participants (adjusted odds ratio [aOR] 0.18, 95% confidence interval [CI] 0.05-0.46). and more likely in patients who prefer Spanish relative to those that prefer English (aOR 2.04, 95% CI 1.43-2.96). No observed SDOH predicted SARS-CoV-2 detection in adjusted models. A robustness analysis using a matched subset of the study sample produced findings similar to those in the main analysis. Preferring to receive care in Spanish is an independent predictor of SARS-CoV-2 detection in a community health center cohort.

Little is known about the relationship of COVID-19 outcomes with onychomycosis. We investigated the relationship of onychomycosis with COVID-19 outcomes. A retrospective cohort study was performed on SARS-CoV-2 positive adult outpatients or inpatients who had onychomycosis and other skin diseases. Overall, 430 adults were identified with SARS-CoV-2 and a skin disease, including 98 with diagnosed onychomycosis. In bivariable logistic regression models, onychomycosis was associated with increased hospitalization {odds ratio(OR) [95% confidence interval (CI)]: 3.56 [2.18-5.80]}, initial inpatient vs. outpatient visits (OR [95% CI]: 2.24 [1.35-3.74]), use of oxygen therapy (OR [95% CI]: 2.77 [1.60-4.79]), severe-critical vs. asymptomatic-mild severity (OR [95% CI]: 2.28 [1.32-3.94]), and death (OR [95% CI]: 7.48 [1.83-30.47]) from COVID-19, but not prolonged hospitalization (OR [95% CI]: 1.03 [0.47-2.25]). In multivariable models adjusting for socio-demographics, comorbidities, and immunosuppressant medication use, the associations with onychomycosis remained significant for hospitalization, inpatient visits, oxygen therapy, severe-critical COVID-19. Onychomycosis was a significant independent risk factor for COVID-19 severity, hospitalization, and receiving supplemental oxygen therapy.

Agricultural workers, designated as "essential" at the start of the COVID-19 pandemic, work in harsh labor conditions, and now have the added challenge of continuing to work during the COVID-19 pandemic. The aim of this study was to assess agricultural workers' COVID-19 related history, employer-based safety measures, individual preventive practices, and COVID-19 vaccination uptake. A questionnaire study was conducted among agricultural workers in Central Florida about COVID-19 during the month of June 2020 and again in July 2021. Among 92 agricultural workers in June 2020, 47% were obese; 11% had had a COVID-19 nasal test; 87% were able to social distance at work and 34% reported employer provided face masks; 15% reported not willing to get the COVID-19 vaccine and 25% were unsure. 40% could self-isolate if they contracted COVID-19. In a follow-up visit in July 2021, 53% of participants reported receiving a COVID-19 vaccine. Agricultural workers are particularly vulnerable to COVID-19 due to existing health risk factors and lack of essential protective resources. Occupational health protections social safety net programs are urgently needed to prevent infections in vulnerable workers, and reduce community spread, and increase COVID-19 vaccination rates.

Asian Americans have experienced compounding stressors during the pandemic as a result of racial discrimination. We aim of to investigate the prevalence of depression symptoms among Asian Americans before and during the COVID-19 pandemic and examine differences based on socio-demographic factors. Data are from a cross-sectional study (N = 636) among Chinese and South Asian adults in Chicago collected between February and May 2020. One cohort of participants were surveyed from each ethnic group before the pandemic and a second cohort of participants were surveyed during the pandemic. Depression symptoms increased more than two-fold, from 9% pre-pandemic to 21% during the COVID-19 pandemic. We found an increase in depression symptoms during the pandemic for South Asians, men and adults older than 30 years. These findings call for public health education that effectively addresses anti-Asian harassment and violence and ensure that culturally competent mental health services are provided to Asian Americans from diverse ethnic backgrounds.

The presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater has been reported as a result of fecal shedding of infected individuals. In this study, the occurrence of SARS-CoV-2 RNA was explored in primary-treated wastewater from two municipal wastewater treatment plants in Quintana Roo, Mexico, along with groundwater from sinkholes, a household well, and submarine groundwater discharges. Physicochemical variables were obtained in situ, and coliphage densities were determined. Three virus concentration methods based on adsorption-elution and sequential filtration were used followed by RNA isolation. Quantification of SARS-CoV-2 was done by RT-qPCR using the CDC 2020 assay, 2019-nCoV_N1 and 2019-nCoV_N2. The Pepper mild mottle virus, one of the most abundant RNA viruses in wastewater was quantified by RT-qPCR and compared to SARS-CoV-2 concentrations. The use of three combined virus concentration methods together with two qPCR assays allowed the detection of SARS-CoV-2 RNA in 58% of the wastewater samples analyzed, whereas none of the groundwater samples were positive for SARS-CoV-2 RNA. Concentrations of SARS-CoV-2 in wastewater were from 1.8 × 103 to 7.5 × 103 genome copies per liter (GC l-1), using the N1 RT-qPCR assay, and from 2.4 × 102 to 5.9 × 103 GC l-1 using the N2 RT-qPCR assay. Based on PMMoV prevalence detected in all wastewater and groundwater samples tested, the three viral concentration methods used could be successfully applied for SARS-CoV-2 RNA detection in further studies. This study represents the first detection of SARS-CoV-2 RNA in wastewater in southeast Mexico and provides a baseline for developing a wastewater-based epidemiology approach in the area.

Drug overdose remains a leading cause of death in the US, with growing rates attributable to illicit fentanyl use. Recent HIV outbreaks among people who inject drugs (PWID) and service disruptions from COVID-19 have renewed concerns on HIV resurgence. We examined the relationship between fentanyl use and three injection-related HIV risk behaviors among PWID in Baltimore City (BC) and Anne Arundel Country (AAC), Maryland. PWID (N = 283) were recruited to the study through targeted sampling at street-based locations in BC and AAC from July 2018 to March 2020. Receptive syringe sharing (RSS) [adjusted odds ratio (AOR): 2.8, 95% confidence interval (CI): 1.2-6.3] and daily injecting (AOR: 1.9, 95% CI: 1.0-3.6) were associated with injecting fentanyl and cocaine together. Fentanyl availability and COVID-19 bring new HIV prevention challenges, particularly among those who inject fentanyl with cocaine, highlighting the importance to expand and sustain harm reduction, prevention, and treatment services for PWID to reduce HIV and overdose burden.

Purpose The COVID-19 pandemic has affected the way many individuals go about their daily lives. This study attempted to model the complexity of change in lifestyle quality as a result of the COVID-19 pandemic and its context within the UK adult population. Methods Data from the COVID-19 Psychological Research Consortium Study (Wave 3, July 2020; N = 1166) were utilised. A measure of COVID-19-related lifestyle change captured how individuals’ lifestyle quality had been altered as a consequence of the pandemic. Exploratory factor analysis and latent profile analysis were used to identify distinct lifestyle quality change subgroups, while multinomial logistic regression analysis was employed to describe class membership. Results Five lifestyle dimensions, reflecting partner relationships, health, family and friend relations, personal and social activities, and work life, were identified by the EFA, and seven classes characterised by distinct patterns of change across these dimensions emerged from the LPA: (1) better overall (3.3%), (2) worse except partner relations (6.0%), (3) worse overall (2.5%), (4) better relationships (9.5%), (5) better except partner relations (4.3%), (6) no different (67.9%), and (7) worse partner relations only (6.5%). Predictor variables differentiated membership of classes. Notably, classes 3 and 7 were associated with poorer mental health (COVID-19 related PTSD and suicidal ideation). Conclusions Four months into the pandemic, most individuals’ lifestyle quality remained largely unaffected by the crisis. Concerningly however, a substantial minority (15%) experienced worsened lifestyles compared to before the pandemic. In particular, a pronounced deterioration in partner relations seemed to constitute the more severe pandemic-related lifestyle change.

Epidemiological models have played a central role in the COVID-19 pandemic, particularly when urgent decisions were required and available evidence was sparse. They have been used to predict the evolution of the disease and to inform policy-making. In this paper, we address two kinds of epidemiological models widely used in the pandemic, namely, compartmental models and agent-based models. After describing their essentials—some real examples are invoked—we discuss their main strengths and weaknesses. Then, on the basis of this analysis, we make a comparison between their respective merits concerning three different goals: prediction, explanation, and intervention. We argue that there are general considerations which could favour any of those sorts of models for obtaining the aforementioned goals. We conclude, however, that preference for particular models must be grounded case-by-case since additional contextual factors, as the peculiarities of the target population and the aims and expectations of policy-makers, cannot be overlooked.

Background Health care workers (HCWs) are on the frontline, playing a crucial role in the prevention of infection and treatment of patients. Aims This study was aimed to evaluate the prevalence of hospital-acquired coronavirus disease 2019 (COVID-19) infection at work and related factors at the University Hospital of Trieste workers exposed to COVID-19 patients. Methods From March 1 to May 31, of 4216 employees, 963 were in contact with COVID-19 patients or colleagues and were followed up. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in nasopharyngeal swabs was determined every 3 days, by RT-PCR. Results During the follow-up period, 193 workers were positive for COVID-19 (5%), and 165 of these (86%) were symptomatic. We identified five major cluster outbreaks of COVID-19 infection in Trieste Hospitals, four of which occurred before the implementation of universal masking for HCWs and patients (1–14 March 2020). COVID-19 infection was significantly higher in high-risk ward workers (Infectious Diseases, and Geriatric and Emergency Medicine, odds ratio [OR] 13.4; 95% confidence interval [CI] 5.8–31), in subjects with symptoms (OR 5.4; 95% CI 2.9–10) and in those with contacts with COVID-19 patients and colleagues (OR 2.23; 95% CI 1.01–4.9). Conclusions Hospital workers were commonly infected due to contact with COVID-19 patients and colleagues, mainly in the first 15 days of the pandemic, before the implementation of universal mask wearing of HCWs and patients. Repetitive testing and follow-up permitted the identification of COVID-19 cases before symptom onset, obtaining better infection prevention and control.

The highly contagious nature of SARS-CoV-2 has led to several studies on the transmission of the virus. A little studied potential fomite of great concern in the community is currency, which has been shown to harbor microbial pathogens in several studies. Since the onset of the COVID-19 pandemic, many businesses in the United States have limited the use of banknotes in favor of credit cards. However, SARS-CoV-2 has shown greater stability on plastic in several studies. Herein, the stability of SARS-CoV-2 at room temperature on banknotes, money cards and coins was investigated. In vitro studies with live virus suggested SARS-CoV-2 was highly unstable on banknotes, showing an initial rapid reduction in viable virus and no viral detection by 24 hours. In contrast, SARS-CoV-2 displayed increased stability on money cards with live virus detected after 48 hours. Environmental swabbing of currency and money cards on and near the campus of Brigham Young University supported these results, with no detection of SARS-CoV-2 RNA on banknotes, and a low level on money cards. However, no viable virus was detected on either. These preliminary results suggest that the use of money cards over banknotes in order to slow the spread of this virus may be ill-advised. These findings should be investigated further through larger environmental studies involving more locations.

Background A description of individuals with SARS-CoV-2 infection comparing the first and second waves could help adapt health services to manage this highly transmissible infection. Objective We aimed to describe the epidemiology of individuals with suspected SARS-CoV-2 infection, and the characteristics of patients with a positive test comparing the first and second waves in Catalonia, Spain. Methods This study had 2 stages. First, we analyzed daily updated data on SARS-CoV-2 infection in individuals from Girona (Catalonia). Second, we compared 2 retrospective cohorts of patients with a positive reverse-transcription polymerase chain reaction or rapid antigen test for SARS-CoV-2. The severity of patients with a positive test was defined by their admission to hospital, admission to intermediate respiratory care, admission to the intensive care unit, or death. The first wave was from March 1, 2020, to June 24, 2020, and the second wave was from June 25, 2020, to December 8, 2020. Results The numbers of tests and cases were lower in the first wave than in the second wave (26,096 tests and 3140 cases in the first wave versus 140,332 tests and 11,800 cases in the second wave), but the percentage of positive results was higher in the first wave than in the second wave (12.0% versus 8.4%). Among individuals with a positive diagnostic test, 818 needed hospitalization in the first wave and 680 in the second; however, the percentage of hospitalized individuals was higher in the first wave than in the second wave (26.1% versus 5.8%). The group that was not admitted to hospital included older people and those with a higher percentage of comorbidities in the first wave, whereas the characteristics of the groups admitted to hospital were more alike. Conclusions Screening systems for SARS-CoV-2 infection were scarce during the first wave, but were more adequate during the second wave, reflecting the usefulness of surveillance systems to detect a high number of asymptomatic infected individuals and their contacts, to help control this pandemic. The characteristics of individuals with SARS-CoV-2 infection in the first and second waves differed substantially; individuals in the first wave were older and had a worse health condition.

Aims Cardiovascular diseases (CVDs) increase mortality risk from coronavirus infection (COVID-19). There are also concerns that the pandemic has affected supply and demand of acute cardiovascular care. We estimated excess mortality in specific CVDs, both ‘direct’, through infection, and ‘indirect’, through changes in healthcare. Methods and results We used (i) national mortality data for England and Wales to investigate trends in non-COVID-19 and CVD excess deaths; (ii) routine data from hospitals in England (n = 2), Italy (n = 1), and China (n = 5) to assess indirect pandemic effects on referral, diagnosis, and treatment services for CVD; and (iii) population-based electronic health records from 3 862 012 individuals in England to investigate pre- and post-COVID-19 mortality for people with incident and prevalent CVD. We incorporated pre-COVID-19 risk (by age, sex, and comorbidities), estimated population COVID-19 prevalence, and estimated relative risk (RR) of mortality in those with CVD and COVID-19 compared with CVD and non-infected (RR: 1.2, 1.5, 2.0, and 3.0). Mortality data suggest indirect effects on CVD will be delayed rather than contemporaneous (peak RR 1.14). CVD service activity decreased by 60–100% compared with pre-pandemic levels in eight hospitals across China, Italy, and England. In China, activity remained below pre-COVID-19 levels for 2–3 months even after easing lockdown and is still reduced in Italy and England. For total CVD (incident and prevalent), at 10% COVID-19 prevalence, we estimated direct impact of 31 205 and 62 410 excess deaths in England (RR 1.5 and 2.0, respectively), and indirect effect of 49 932 to 99 865 deaths. Conclusion Supply and demand for CVD services have dramatically reduced across countries with potential for substantial, but avoidable, excess mortality during and after the pandemic., Graphical Abstract Graphical Abstract

Phenomenological models are popular for describing the epidemic curve. We present how they can be used at different phases in the epidemic, by modelling the daily number of new hospitalisations (or cases). As real-time prediction of the hospital capacity is important, a joint model of the new hospitalisations, number of patients in hospital and in intensive care unit (ICU) is proposed. This model allows estimation of the length of stay in hospital and ICU, even if no (or limited) individual level information on length of stay is available. Estimation is done in a Bayesian framework. In this framework, real-time alarms, defined as the probability of exceeding hospital capacity, can be easily derived. The methods are illustrated using data from the COVID-19 pandemic in March–June 2020 in Belgium, but are widely applicable.

On 13 May 2020, a COVID-19 cluster was detected in a French processing plant. Infected workers were described. The associations between the SARS-CoV-2 infection and the socio-demographic and occupational characteristics were assessed in order to implement risk management measures targeting workers at increased risk of contamination. Workers were tested by RT-PCR from samples taken during screening campaigns. Workers who tested positive were isolated and their contacts were quarantined. Workers were described and associations with the SARS-CoV-2 infection were assessed through risk ratios using multivariable Poisson regression. Of the 1347 workers, 87.5% were tested: 140 cases were identified; 4 were hospitalised, including 2 admitted to intensive care. In the company, the cluster remained limited to deboning and cutting activities. The attack rate was 11.9% in the company, reaching 16.6% in the cutting department. Being an employee of a subcontractor significantly increased the risk of infection by 2.98 [1.81–4.99]. In the cutting department, an association with virus infection was found for a group of non-French speaking workers from the same Eastern European country (RR = 2.67 [1.76–4.05]). They shared accommodation or carpooled more frequently than the other cases. The outbreak investigation revealed a significantly increased risk of SARS-CoV-2 infection for workers of subcontractors and some foreign-born workers. There are many such populations in meat processing plants; the observed associations and the ways in which these workers are contaminated need to be confirmed by further work. Prevention campaigns should now target these workers. Environmental risk factors in the workplace setting remain to be clarified.

Background Clinical and virologic characteristics of COVID-19 infections in veterans in New England have not been described. The average US veteran is a male older than the general US population. SARS-CoV-2 infection is known to cause poorer outcomes among men and older adults, making the veteran population an especially vulnerable group for COVID-19. Objective This study aims to evaluate clinical and virologic factors impacting COVID-19 outcomes. Methods This retrospective chart review included 476 veterans in six New England states with confirmed SARS-CoV-2 infection between April and September 2020. Whole genome sequencing was performed on SARS-CoV-2 RNA isolated from these veterans, and the correlation of genomic data to clinical outcomes was evaluated. Clinical and demographic variables were collected by manual chart review and were correlated to the end points of peak disease severity (based on oxygenation requirements), hospitalization, and mortality using multivariate regression analyses. Results Of 476 veterans, 274 had complete and accessible charts. Of the 274 veterans, 92.7% (n=254) were men and 83.2% (n=228) were White, and the mean age was 63 years. In the multivariate regression, significant predictors of hospitalization (C statistic 0.75) were age (odds ratio [OR] 1.05, 95% CI 1.03-1.08) and non-White race (OR 2.39, 95% CI 1.13-5.01). Peak severity (C statistic 0.70) also varied by age (OR 1.07, 95% CI 1.03-1.11) and O2 requirement on admission (OR 45.7, 95% CI 18.79-111). Mortality (C statistic 0.87) was predicted by age (OR 1.06, 95% CI 1.01-1.11), dementia (OR 3.44, 95% CI 1.07-11.1), and O2 requirement on admission (OR 6.74, 95% CI 1.74-26.1). Most (291/299, 97.3%) of our samples were dominated by the spike protein D614G substitution and were from SARS-CoV-2 B.1 lineage or one of 37 different B.1 sublineages, with none representing more than 8.7% (26/299) of the cases. Conclusions In a cohort of veterans from the six New England states with a mean age of 63 years and a high comorbidity burden, age was the largest predictor of hospitalization, peak disease severity, and mortality. Non-White veterans were more likely to be hospitalized, and patients who required oxygen on admission were more likely to have severe disease and higher rates of mortality. Multiple SARS-CoV-2 lineages were distributed in patients in New England early in the COVID-19 era, mostly related to viruses from New York State with D614G mutation.

Coronavirus disease-2019 (COVID-19) elicits a range of different responses in patients and can manifest into mild to very severe cases in different individuals, depending on many factors. We aimed to establish a prediction model of severe risk in COVID-19 patients, to help clinicians achieve early prevention, intervention and aid them in choosing effective therapeutic strategy. We selected confirmed COVID-19 patients who were admitted to First Hospital of Changsha city between 29 January and 15 February 2020 and collected their clinical data. Multivariate logical regression was used to identify the factors associated with severe risk. These factors were incorporated into the nomogram to establish the model. The ROC curve, calibration plot and decision curve were used to assess the performance of the model. A total of 228 patients were enrolled and 33 (14.47%) patients developed severe pneumonia. Univariate and multivariate analysis showed that shortness of breath, fatigue, creatine kinase, lymphocytes and h CRP were independent factors for severe risk in COVID-19 patients. Incorporating age, chronic obstructive pulmonary disease (COPD) and these factors, the nomogram achieved good concordance indexes of 0.89 [95% confidence interval (CI) 0.832–0.949] and well-fitted calibration plot curves (Hosmer–Lemeshow test: P = 0.97). The model provided superior net benefit when clinical decision thresholds were between 15% and 85% predicted risk. Using the model, clinicians can intervene early, improve therapeutic effects and reduce the severity of COVID-19, thus ensuring more targeted and efficient use of medical resources.

Summary Objectives Risk of reinfection with SARS-CoV-2 among health-care workers (HCWs) is unknown. We assessed the incidence rate of SARS-CoV-2 reinfection in the real-life setting of a longitudinal observational cohort of HCWs from the Hopital Europeen Georges Pompidou, Assistance Publique-Hopitaux de Paris, France, during the first and second waves of COVID-19 epidemic. Methods From March to December 2020, HCWs were subjected to molecular and serology testing of SARS-CoV-2. Reinfection was defined as a positive test result during the first wave, either by serology or PCR, followed by a positive PCR during the second wave. Evolution of COVID-19 status of HWCs was assessed by a Sankey diagram. Results A total of 7765 tests (4579 PCR and 3186 serology) were carried out and 4168 HCWs had at least one test result during the follow-up period with a positivity rate of 15.9%. No case of reinfection during the second wave could be observed among 102 positive HCWs of the first wave, nor among 175 HCWs found positive by PCR during the second wave who were negative during the first wave. Conclusions SARS-CoV-2 reinfection was not observed among HCWs, suggesting a protective immunity against reinfection that lasts at least 8 months post infection.

Vulnerability to coronavirus disease (COVID)-19 varies due to differences in interferon gamma (IFNγ) immunity. We investigated whether a key modifiable interferon precursor, interleukin-18, was related to COVID-19, overall and by severity, using Mendelian randomisation. We used four established genome-wide significant genetic predictors of interleukin-18 applied to the most recent genome-wide association study of COVID-19 (June 2021) to obtain Mendelian randomisation inverse variance weighted estimates by severity, i.e. any (cases = 112 612, non-cases = 2 474 079), hospitalised (cases = 24 274, non-cases = 2 061 529) and very severe (cases = 8779, non-cases = 1 001 875) COVID-19. To be comprehensive, we also conducted an exploratory analysis for IFNγ and two related cytokines with less well-established genetic predictors, i.e. interleukin-12 and interleukin-23. Genetically predicted interleukin-18 was associated with lower risk of any COVID-19 (odds ratio (OR) 0.96 per standard deviation, 95% confidence interval (0.94–0.99, P-value 0.004)) and of very severe COVID-19 (OR 0.88, 95% CI 0.78–0.999, P-value 0.048). Sensitivity analysis and a more liberal genetic instrument selection gave largely similar results. Few genome-wide significant genetic predictors were available for IFNγ, interleukin-12 or interleukin-23, and no associations with COVID-19 were evident. Interleukin-18 could be a modifiable target to prevent COVID-19 and should be further explored in an experimental design.

As of 03 January 2021, the WHO African region is the least affected by the coronavirus disease-2019 (COVID-19) pandemic, accounting for only 2.4% of cases and deaths reported globally. However, concerns abound about whether the number of cases and deaths reported from the region reflect the true burden of the disease and how the monitoring of the pandemic trajectory can inform response measures. We retrospectively estimated four key epidemiological parameters (the total number of cases, the number of missed cases, the detection rate and the cumulative incidence) using the COVID-19 prevalence calculator tool developed by Resolve to Save Lives. We used cumulative cases and deaths reported during the period 25 February to 31 December 2020 for each WHO Member State in the region as well as population data to estimate the four parameters of interest. The estimated number of confirmed cases in 42 countries out of 47 of the WHO African region included in this study was 13 947 631 [95% confidence interval (CI): 13 334 620–14 635 502] against 1 889 512 cases reported, representing 13.5% of overall detection rate (range: 4.2% in Chad, 43.9% in Guinea). The cumulative incidence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was estimated at 1.38% (95% CI: 1.31%–1.44%), with South Africa the highest [14.5% (95% CI: 13.9%–15.2%)] and Mauritius [0.1% (95% CI: 0.099%–0.11%)] the lowest. The low detection rate found in most countries of the WHO African region suggests the need to strengthen SARS-CoV-2 testing capacities and adjusting testing strategies.

This study was endeavoured to contribute in furthering our understanding of the molecular epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by sequencing and analysing the first full-length genome sequences obtained from 48 coronavirus disease-2019 (COVID-19) patients in five districts in Western Serbia in the period April 2020–July 2020. SARS-CoV-2 sequences in Western Serbia distinguished from the Wuhan sequence in 128 SNPs in total. The phylogenetic structure of local SARS-CoV-2 isolates suggested the existence of at least four distinct groups of SARS-CoV-2 strains in Western Serbia. The first group is the most similar to the strain from Italy. These isolates included two 20A sequences and 15−30 20B sequences that displayed a newly occurring set of four conjoined mutations. The second group is the most similar to the strain from France, carrying two mutations and belonged to 20A clade. The third group is the most similar to the strain from Switzerland carrying four co-occurring mutations and belonging to 20B clade. The fourth group is the most similar to another strain from France, displaying one mutation that gave rise to a single local isolate that belonged to 20A clade.

This study describes the development of a pilot sentinel school absence syndromic surveillance system. Using data from a sample of schools in England the capability of this system to monitor the impact of disease on school absences in school-aged children is shown, using the coronavirus disease 2019 (COVID-19) pandemic period as an example. Data were obtained from an online app service used by schools and parents to report their children absent, including reasons/symptoms relating to absence. For 2019 and 2020, data were aggregated into daily counts of ‘total’ and ‘cough’ absence reports. There was a large increase in the number of absence reports in March 2020 compared to March 2019, corresponding to the first wave of the COVID-19 pandemic in England. Absence numbers then fell rapidly and remained low from late March 2020 until August 2020, while lockdown was in place in England. Compared to 2019, there was a large increase in the number of absence reports in September 2020 when schools re-opened in England, although the peak number of absences was smaller than in March 2020. This information can help provide context around the absence levels in schools associated with COVID-19. Also, the system has the potential for further development to monitor the impact of other conditions on school absence, e.g. gastrointestinal infections.

As acute infectious pneumonia, the coronavirus disease-2019 (COVID-19) has created unique challenges for each nation and region. Both India and the United States (US) have experienced a second outbreak, resulting in a severe disease burden. The study aimed to develop optimal models to predict the daily new cases, in order to help to develop public health strategies. The autoregressive integrated moving average (ARIMA) models, generalised regression neural network (GRNN) models, ARIMA–GRNN hybrid model and exponential smoothing (ES) model were used to fit the daily new cases. The performances were evaluated by minimum mean absolute per cent error (MAPE). The predictive value with ARIMA (3, 1, 3) (1, 1, 1)14 model was closest to the actual value in India, while the ARIMA–GRNN presented a better performance in the US. According to the models, the number of daily new COVID-19 cases in India continued to decrease after 27 May 2021. In conclusion, the ARIMA model presented to be the best-fit model in forecasting daily COVID-19 new cases in India, and the ARIMA–GRNN hybrid model had the best prediction performance in the US. The appropriate model should be selected for different regions in predicting daily new cases. The results can shed light on understanding the trends of the outbreak and giving ideas of the epidemiological stage of these regions.

We quantified the potential impact of different social distancing and self-isolation scenarios on the coronavirus disease 2019 (COVID-19) pandemic trajectory in Saudi Arabia and compared the modelling results to the confirmed epidemic trajectory. Using the susceptible, exposed, infected, quarantined and self-isolated, requiring hospitalisation, recovered/immune individuals, fatalities model, we assessed the impact of a non-pharmacological interventions’ subset. An unmitigated scenario (baseline), mitigation scenarios (25% reduction in social contact/twofold increase in self-isolation) and enhanced mitigation scenarios (50% reduction in social contact/twofold increase in self-isolation) were assessed and compared to the actual epidemic trajectory. For the unmitigated scenario, mitigation scenarios, enhanced mitigation scenarios and actual observed epidemic, the peak daily incidence rates (per 10 000 population) were 77.00, 16.00, 9.00 and 1.14 on days 71, 54, 35 and 136, respectively. The peak fatality rates were 35.00, 13.00, 5.00 and 0.016 on days 150, 125, 60 and 155, respectively. The R0 was 1.15, 1.14, 1.22 and 2.50, respectively. Aggressive implementation of social distancing and self-isolation contributed to the downward trend of the disease. We recommend using extensive models that comprehensively consider the natural history of COVID-19, social and behavioural patterns, age-specific data, actual network topology and population to elucidate the epidemic's magnitude and trajectory.

Background The COVID-19 pandemic has radically shifted living practices, thereby influencing changes in the health status and behaviors of every person. Objective The aim of this study was to investigate the impact of COVID-19 on the self-reported health status and health behaviors along with any associated factors in adults in the Republic of Korea wherein no stringent lockdown measures were implemented during the pandemic. Methods We conducted a web-based self-reported survey from November 2020 to December 2020. The study participants (N=2097) were identified through quota sampling by age, sex, and geographical regions among residents aged 19 years or older in Korea. The survey collected information on basic demographics, changes in self-reported health status, and health behaviors during the COVID-19 pandemic. Self-reported health status and health behaviors were categorized into 3 groups: unchanged, improved, or worsened. A chi-square test and logistic regression analyses were conducted. Results With regard to changes in the self-reported health status, the majority (1478/2097, 70.5%) of the participants reported that their health was unchanged, while 20% (420/2097) of the participants reported having worser health after the COVID-19 outbreak. With regard to changes in health behaviors, the proportion of participants who increased tobacco consumption was similar to that of those who decreased tobacco consumption (110/545, 20.2% vs 106/545, 19.5%, respectively), while the proportion of those who decreased their drinking frequency was more than twice as many as those who increased their drinking frequency (578/1603, 36.1% vs 270/1603, 16.8%, respectively). Further, those who decreased their exercising frequency were more than those who increased their exercising frequency (333/823, 15.9% vs 211/823, 10%, respectively). The factor that had the greatest influence on lifestyle was age. In the subgroup analysis, the group aged 20-29 years had the highest number of individuals with both a worsened (100/377, 26.5%) and an improved (218/377, 15.7%) health status. Further, individuals aged 20-29 years had greater odds of increased smoking (6.44, 95% CI 2.15-19.32), increased alcohol use (4.64, 95% CI 2.60-8.28), and decreased moderate or higher intensity aerobic exercise (3.39, 95% CI 1.82-6.33) compared to individuals aged 60 years and older. Younger adults showed deteriorated health behaviors, while older adults showed improved health behaviors. Conclusions The health status and the behavior of the majority of the Koreans were not found to be heavily affected by the COVID-19 outbreak. However, in some cases, changes in health status or health behavior were identified. This study highlighted that some groups were overwhelmingly affected by COVID-19 compared to others. Certain groups reported experiencing both worsening and improving health, while other groups reported unchanged health status. Age was the most influential factor for behavior change; in particular, the younger generation’s negative health behaviors need more attention in terms of public health. As COVID-19 prolongs, public health interventions for vulnerable groups may be needed.

Background The COVID-19 pandemic has highlighted the inability of health systems to leverage existing system infrastructure in order to rapidly develop and apply broad analytical tools that could inform state- and national-level policymaking, as well as patient care delivery in hospital settings. The COVID-19 pandemic has also led to highlighted systemic disparities in health outcomes and access to care based on race or ethnicity, gender, income-level, and urban-rural divide. Although the United States seems to be recovering from the COVID-19 pandemic owing to widespread vaccination efforts and increased public awareness, there is an urgent need to address the aforementioned challenges. Objective This study aims to inform the feasibility of leveraging broad, statewide datasets for population health–driven decision-making by developing robust analytical models that predict COVID-19–related health care resource utilization across patients served by Indiana’s statewide Health Information Exchange. Methods We leveraged comprehensive datasets obtained from the Indiana Network for Patient Care to train decision forest-based models that can predict patient-level need of health care resource utilization. To assess these models for potential biases, we tested model performance against subpopulations stratified by age, race or ethnicity, gender, and residence (urban vs rural). Results For model development, we identified a cohort of 96,026 patients from across 957 zip codes in Indiana, United States. We trained the decision models that predicted health care resource utilization by using approximately 100 of the most impactful features from a total of 1172 features created. Each model and stratified subpopulation under test reported precision scores >70%, accuracy and area under the receiver operating curve scores >80%, and sensitivity scores approximately >90%. We noted statistically significant variations in model performance across stratified subpopulations identified by age, race or ethnicity, gender, and residence (urban vs rural). Conclusions This study presents the possibility of developing decision models capable of predicting patient-level health care resource utilization across a broad, statewide region with considerable predictive performance. However, our models present statistically significant variations in performance across stratified subpopulations of interest. Further efforts are necessary to identify root causes of these biases and to rectify them.

Background Contact tracing and intensive testing programs are essential for controlling the spread of COVID-19. However, conventional contact tracing is resource intensive and may not result in the tracing of all cases due to recall bias and cases not knowing the identity of some close contacts. Few studies have reported the epidemiological features of cases not identified by contact tracing (“unlinked cases”) or described their potential roles in seeding community outbreaks. Objective For this study, we characterized the role of unlinked cases in the epidemic by comparing their epidemiological profile with the linked cases; we also estimated their transmission potential across different settings. Methods We obtained rapid surveillance data from the government, which contained the line listing of COVID-19 confirmed cases during the first three waves in Hong Kong. We compared the demographics, history of chronic illnesses, epidemiological characteristics, clinical characteristics, and outcomes of linked and unlinked cases. Transmission potentials in different settings were assessed by fitting a negative binomial distribution to the observed offspring distribution. Results Time interval from illness onset to hospital admission was longer among unlinked cases than linked cases (median 5.00 days versus 3.78 days; P Conclusions The unlinked cases were positively associated with time to hospital admission, severity of infection, and epidemic size—implying a need to design and implement digital tracing methods to complement current conventional testing and tracing. To minimize the risk of cluster transmissions from unlinked cases, digital tracing approaches should be effectively applied in high-risk socioeconomic settings, and risk assessments should be conducted to review and adjust the policies.

We conducted a retrospective observational study in patients with laboratory-confirmed coronavirus disease (COVID-19) who received medical care in 688 COVID-19 ambulatory units and hospitals in Mexico City between 24 February 2020 and 24 December 2020, to study if the elderly seek medical care later than younger patients and their severity of symptoms at initial medical evaluation. Patients were categorised into eight groups (s.d.: 16.8 years; 50.4% were women). Mean time from symptom onset to medical care was 4.04 (s.d.: 3.6) days and increased with older age categories (P < 0.0001). Mortality risk increased by 6.4% for each day of delay in medical care from symptom onset. The risk of presenting with the symptoms of severity was greater with increasing age categories. In conclusion, COVID-19 patients with increasing ages tend to seek medical care later, with higher rates of symptoms of severity at initial presentation in both ambulatory units and hospitals.

In a Nicaraguan population-based cohort, SARS-CoV-2 seroprevalence reached 28% in the first 6 months of the country's epidemic and reached 35% 6 months later. Immune waning was uncommon. Individuals with a seropositive household member were over three times as likely to be seropositive themselves, suggesting the importance of household transmission.

Effectiveness of corona virus disease-19 (COVID-19) vaccines used in India is unexplored and need to be substantiated. The present case-control study was planned to elicit the effectiveness of COVID-19 vaccines in preventing infection and disease severity in the general population of Bihar, India. This case-control study was conducted among people aged ≥45 years during April to June 2021. The cases were the COVID-19 patients admitted or visited All India Institute of Medical Sciences (AIIMS), Patna, Bihar, India, and were contacted directly. The controls were the individuals tested negative for severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) at the Virology laboratory, AIIMS-Patna and contacted telephonically for collection of relevant information. The vaccine effectiveness (VE) was calculated by using the formula (VE = 1 – odds ratio). The adjusted VE for partial and full vaccination were estimated to be 52.0% (95% confidence interval (CI) 39.0–63.0%) and 83.0% (95% CI 73.0–89.0%) respectively for preventing SARS CoV-2 infection. The sub-group analyses of the cases have shown that the length of hospital stays (LOS) (partially vaccinated: 9 days vs. unvaccinated: 12 days; P = 0.028) and the severity of the disease (fully vaccinated: 30.3% vs. partially vaccinated: 51.3% and unvaccinated: 54.1%; P = 0.035) were significantly low among vaccinated compared to unvaccinated individuals. To conclude, four out of every five fully vaccinated individuals are estimated to be protected from contracting SARS CoV-2 infection. Vaccination lowered LOS and chances of development of severe disease.

Nearly 1 year into the coronavirus disease 2019 pandemic, the first severe acute respiratory syndrome coronavirus 2 vaccines received emergency use authorisation and vaccination campaigns began. A number of factors can reduce the averted burden of cases and deaths due to vaccination. Here, we use a dynamic model, parametrised with Bayesian inference methods, to assess the effects of non-pharmaceutical interventions (NPIs) (such as social distancing, mask mandates, school and workplace closure), and vaccine administration and uptake rates on infections and deaths averted in the United States. We show that scenarios depicting higher compliance with NPIs avert more than 60% of infections and 70% of deaths during the period of vaccine administration, and that increasing the vaccination rate from 5 to 11 million people per week could increase the averted burden by more than one-third. These findings underscore the importance of maintaining NPIs and increasing vaccine administration rates.

Introduction: Olfactory and taste disorders (OTDs) have been reported in COVID-19 caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the mechanisms of which remain unclear. We conducted a detailed analysis of OTDs as part of 2 seroepidemiological investigations of COVID-19 outbreaks. Methods: Two retrospective cohort studies were conducted in a high school and primary schools of Northern France following a COVID-19 epidemic in February-March 2020. Students, their relatives, and school staff were included. Anti-SARS-CoV-2 antibodies were identified using a flow-cytometry-based assay detecting anti-S IgG. Results: Among 2,004 participants (median [IQR] age: 31 [11–43] years), 303 (15.2%) tested positive for SARS-CoV-2 antibodies. OTDs were present in 91 (30.0%) and 92 (30.3%) of them, respectively, and had 85.1 and 78.0% positive predictive values for SARS-CoV-2 infection, respectively. In seropositive participants, OTDs were independently associated with an age above 18 years, female gender, fatigue, and headache. Conclusion: This study confirms the higher frequency of OTDs in females than males and adults than children. Their high predictive value for the diagnosis of COVID-19 suggests that they should be systematically searched for in patients with respiratory symptoms, fever, or headache. The association of OTDs with headache, not previously reported, suggests that they share a common mechanism, which deserves further investigation.

This study aims at providing estimates on the transmission risk of SARS-CoV-2 in schools and day-care centres. We calculated secondary attack rates (SARs) using individual-level data from state-wide mandatory notification of index cases in educational institutions, followed by contact tracing and PCR-testing of high-risk contacts. From August to December 2020, every sixth of overall 784 independent index cases was associated with secondary cases in educational institutions. Monitoring of 14 594 institutional high-risk contacts (89% PCR-tested) of 441 index cases during quarantine revealed 196 secondary cases (SAR 1.34%, 0.99–1.78). SARS-CoV-2 infection among high-risk contacts was more likely around teacher-indexes compared to student-/child-indexes (incidence rate ratio (IRR) 3.17, 1.79–5.59), and in day-care centres compared to secondary schools (IRR 3.23, 1.76–5.91), mainly due to clusters around teacher-indexes in day-care containing a higher mean number of secondary cases per index case (142/113 = 1.26) than clusters around student-indexes in schools (82/474 = 0.17). In 2020, SARS-CoV-2 transmission risk in educational settings was low overall, but varied strongly between setting and role of the index case, indicating the chance for targeted intervention. Surveillance of SARS-CoV-2 transmission in educational institutions can powerfully inform public health policy and improve educational justice during the pandemic.

Background The effectiveness of public health measures depends upon a community’s compliance as well as on its positive or negative emotions. Objective The purpose of this study was to perform an analysis of the expressed emotions in English tweets by Greek Twitter users during the first phase of the COVID-19 pandemic in Greece. Methods The period of this study was from January 25, 2020 to June 30, 2020. Data collection was performed by using appropriate search words with the filter-streaming application programming interface of Twitter. The emotional analysis of the tweets that satisfied the inclusion criteria was achieved using a deep learning approach that performs better by utilizing recurrent neural networks on sequences of characters. Emotional epidemiology tools such as the 6 basic emotions, that is, joy, sadness, disgust, fear, surprise, and anger based on the Paul Ekman classification were adopted. Results The most frequent emotion that was detected in the tweets was “surprise” at the emerging contagion, while the imposed isolation resulted mostly in “anger” (odds ratio 2.108, 95% CI 0.986-4.506). Although the Greeks felt rather safe during the first phase of the COVID-19 pandemic, their positive and negative emotions reflected a masked “flight or fight” or “fear versus anger” response to the contagion. Conclusions The findings of our study show that emotional analysis emerges as a valid tool for epidemiology evaluations, design, and public health strategy and surveillance.

Background The Italian Lombardy region has been the epicenter of COVID-19 since February 2020. This study analyses the epidemiology of pediatric type 1 diabetes (T1D) onset during the first two pandemic waves and three previous years. Methods All the 13 pediatric diabetes centers in Lombardy prospectively evaluated charts of children at T1D onset (0–17 years), during year 2020. After calculating the annual incidence, the data were compared with those of the 3 previous years, using generalized linear models, adjusted for age and sex. Monthly T1D new onsets and diabetic ketoacidosis (DKA) were investigated yearly from 2017 to 2020. Data were extracted from outpatients charts of the pediatric diabetes centers and from the database of the national institute of statistics. Findings The estimated incidence proportion of T1D was 16/100·000 in 2020, compared to 14, 11 and 12 in 2019, 2018 and 2017, respectively. When adjusting for age and gender, the incidence was significantly lower in 2018 and 2017 compared to 2020 (adjusted incidence ratio: 0.73 and 0.77 respectively, with 95% CI: 0.63 to 0.84, and 0.67 to 0.83; p = 0·002 and p = 0·01), but no difference was found between the years 2020 and 2019. A reduction trend in the percentage of T1D diagnosis during the first wave (March-April) over the total year diagnoses was observed compared to previous years (11·7% in 2020, 17·7% in 2019, 14·1% in 2018 and 14·4% 2017). No difference was observed during the second wave (October-December) (32·8% in 2020, 33·8% in 2019, 34% in 2018, 30·7% in 2017). The proportion of DKA over the total T1D diagnoses during the second wave had higher trend than the first one (41·7% vs 33·3%), while severe DKA over the total DKA appeared higher during the first wave (60% vs 37·1%). Interpretation The study suggests an increase in the incidence of pediatric T1D in Lombardy throughout the past five years. Pandemic waves may have affected the clinical presentation at onset. Funding None.

Background: We describe the epidemiology of COVID-19 in South Africa following importation and during implementation of stringent lockdown measures. Methods: Using national surveillance data including demographics, laboratory test data, clinical presentation, risk exposures (travel history, contacts and occupation) and outcomes of persons undergoing COVID-19 testing or hospitalised with COVID-19 at sentinel surveillance sites, we generated and interpreted descriptive statistics, epidemic curves, and initial reproductive numbers (Rt). Findings: From 4 March to 30 April 2020, 271,670 SARS-CoV-2 PCR tests were performed (462 tests/100,000 persons). Of these, 7,892 (2.9%) persons tested positive (median age 37 years (interquartile range 28-49 years), 4,568 (58%) male, cumulative incidence of 13.4 cases/100,000 persons). Hospitalization records were found for 1,271 patients (692 females (54%)) of whom 186 (14.6%) died. Amongst 2,819 cases with data, 489/2819 (17.3%) travelled internationally within 14 days prior to diagnosis, mostly during March 2020 (466 (95%)). Cases diagnosed in April compared with March were younger (median age, 37 vs. 40 years), less likely female (38% vs. 53%) and resident in a more populous province (98% vs. 91%). The national initial Rt was 2.08 (95% confidence interval (CI): 1.71-2.51). Interpretation: The first eight weeks following COVID-19 importation were characterised by early predominance of imported cases and relatively low mortality and transmission rates. Despite stringent lockdown measures, the second month following importation was characterised by community transmission and increasing disease burden in more populous provinces. Funding Statement: South African National and provincial health departments. Declaration of Interests: We declare no competing interests. Ethics Approval Statement: Surveillance activities for NMC including SARS-COV-2 infection are conducted by the NICD according to National Health Act Regulations. Publication of surveillance data in the peer-reviewed literature was approved by the University of the Witwatersrand Human Research Ethics Committee (Medical) protocol M160667.