Although testing is widely regarded as critical to fighting the COVID-19 pandemic, what measure and level of testing best reflects successful infection control remains unresolved. Our aim was to compare the sensitivity of two testing metrics – population testing number and testing coverage – to population mortality outcomes and identify a benchmark for testing adequacy. We aggregated publicly available data through 12 April on testing and outcomes related to COVID-19 across 36 OECD (Organization for Economic Development) countries and Taiwan. Spearman correlation coefficients were calculated between the aforementioned metrics and following outcome measures: deaths per 1 million people, case fatality rate and case proportion of critical illness. Fractional polynomials were used to generate scatter plots to model the relationship between the testing metrics and outcomes. We found that testing coverage, but not population testing number, was highly correlated with population mortality (rs = −0.79, P = 5.975 × 10−9 vs. rs = −0.3, P = 0.05) and case fatality rate (rs = −0.67, P = 9.067 × 10−6 vs. rs = −0.21, P = 0.20). A testing coverage threshold of 15–45 signified adequate testing: below 15, testing coverage was associated with exponentially increasing population mortality; above 45, increased testing did not yield significant incremental mortality benefit. Taken together, testing coverage was better than population testing number in explaining country performance and can serve as an early and sensitive indicator of testing adequacy and disease burden.

This paper demonstrates how the combustion of fossil fuels for transport purpose might cause health implications. Based on an original case study [i.e. the Hubei province in China, the epicentre of the coronavirus disease-2019 (COVID-19) pandemic], we collected data on atmospheric pollutants (PM2.5, PM10 and CO2) and economic growth (GDP), along with daily series on COVID-19 indicators (cases, resuscitations and deaths). Then, we adopted an innovative Machine Learning approach, applying a new image Neural Networks model to investigate the causal relationships among economic, atmospheric and COVID-19 indicators. Empirical findings emphasise that any change in economic activity is found to substantially affect the dynamic levels of PM2.5, PM10 and CO2 which, in turn, generates significant variations in the spread of the COVID-19 epidemic and its associated lethality. As a robustness check, the conduction of an optimisation algorithm further corroborates previous results.

Hand hygiene (HH) performance on entering intensive care units (ICUs) is commonly accepted but often inadequately performed. We developed a simple, inexpensive module that connects touchless dispensers of alcohol sanitiser (TDAS) to the automatic doors of a paediatric ICU, and assessed the impact of this intervention on HH compliance of hospital staff and visitors. A prospective observational study was conducted over a 3-week period prior to the intervention, followed by a 4-week period post intervention. HH performance was monitored by a research assistant whose office location enabled direct and video-assisted observation of the ICU entrance. A total of 609 entries to the ICU was recorded. Overall HH performance was 46.9% (92/196) before and 98.5% (406/413) after the intervention. Our findings suggest that HH performance on entering an ICU can be improved via a mechanism that makes operation of an automatic door dependent on use of a TDAS system, and thus contribute to infection control.

Hypertension represents one of the most common pre-existing conditions and comorbidities in Coronavirus disease 2019 (COVID-19) patients. To explore whether hypertension serves as a risk factor for disease severity, a multi-centre, retrospective study was conducted in COVID-19 patients. A total of 498 consecutively hospitalised patients with lab-confirmed COVID-19 in China were enrolled in this cohort. Using logistic regression, we assessed the association between hypertension and the likelihood of severe illness with adjustment for confounders. We observed that more than 16% of the enrolled patients exhibited pre-existing hypertension on admission. More severe COVID-19 cases occurred in individuals with hypertension than those without hypertension (21% vs. 10%, P = 0.007). Hypertension associated with the increased risk of severe illness, which was not modified by other demographic factors, such as age, sex, hospital geological location and blood pressure levels on admission. More attention and treatment should be offered to patients with underlying hypertension, who usually are older, have more comorbidities and more susceptible to cardiac complications.

At present, there is scarce evidence about the burden associated with the isolation of COVID-19 patients. We aimed to assess the differences between COVID-19 and other influenza-like illnesses (ILIs) in disease burden brought by isolation. We conducted an online survey of 302 respondents who had COVID-19 or other ILIs and compared the burden of isolation due to sickness with one-to-one propensity score matching. The primary outcomes are the duration and productivity losses associated with isolation, the secondary outcome is the health-related quality of life (HRQoL) valuation on the day of the survey. Acute symptoms of outpatient COVID-19 and other ILIs lasted 17 (interquartile range (IQR) 9–32) and 7 (IQR 4–10) days, respectively. The length of isolation due to COVID-19 was 18 (IQR 10–33) days and that due to other ILIs was 7 (IQR 4–11) days, respectively. The monetary productivity loss of isolation due to COVID-19 was 1424.3 (IQR 825.6–2545.5) USD and that due to other ILIs was 606.1 (IQR 297.0–1090.9) USD, respectively. HRQoL at the time of the survey was lower in the COVID-19 group than in the ‘other ILIs’ group (0.89 and 0.96, P = 0.001). COVID-19 infection imposes a substantial disease burden, even in patients with non-severe disease. This burden is larger for COVID-19 than other ILIs, mainly because the required isolation period is longer.

Recent epidemiological studies analysing sex-disaggregated patient data of coronavirus disease 2019 (COVID-19) across the world revealed a distinct sex bias in the disease morbidity as well as the mortality – both being higher for the men. Similar antecedents have been known for the previous viral infections, including from coronaviruses, such as severe acute respiratory syndrome (SARS) and middle-east respiratory syndrome (MERS). A sound understanding of molecular mechanisms leading to the biological sex bias in the survival outcomes of the patients in relation to COVID-19 will act as an essential requisite for developing a sex-differentiated approach for therapeutic management of this disease. Recent studies which have explored molecular mechanism(s) behind sex-based differences in COVID-19 pathogenesis are scarce; however, existing evidence, for other respiratory viral infections, viz. SARS, MERS and influenza, provides important clues in this regard. In attempt to consolidate the available knowledge on this issue, we conducted a systematic review of the existing empirical knowledge and recent experimental studies following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The qualitative analysis of the collected data unravelled multiple molecular mechanisms, such as evolutionary and genetic/epigenetic factors, sex-linkage of viral host cell entry receptor and immune response genes, sex hormone and gut microbiome-mediated immune-modulation, as the possible key reasons for the sex-based differences in patient outcomes in COVID-19.

Identification of societal activities associated with SARS-CoV-2 infection may provide an evidence base for implementing preventive measures. Here, we investigated potential determinants for infection in Denmark in a situation where society was only partially open. We conducted a national matched case-control study. Cases were recent RT-PCR test-positives, while controls, individually matched on age, sex and residence, had not previously tested positive for SARS-CoV-2. Questions concerned person contact and community exposures. Telephone interviews were performed over a 7-day period in December 2020. We included 300 cases and 317 controls and determined odds ratios (ORs) and 95% confidence intervals (95% CI) by conditional logistical regression with adjustment for household size and country of origin. Contact (OR 4.9, 95% CI 2.4–10) and close contact (OR 13, 95% CI 6.7–25) with a person with a known SARS-CoV-2 infection were main determinants. Contact most often took place in the household or work place. Community determinants included events with singing (OR 2.1, 95% CI 1.1–4.1), attending fitness centres (OR 1.8, 95% CI 1.1–2.8) and consumption of alcohol in a bar (OR 10, 95% CI 1.5–65). Other community exposures appeared not to be associated with infection, these included shopping at supermarkets, travel by public transport, dining at restaurants and private social events with few participants. Overall, the restrictions in place at the time of the study appeared to be sufficient to reduce transmission of disease in the public space, which instead largely took place following direct exposures to people with known SARS-CoV-2 infections.

Amplifying the testing capacity and making better use of testing resources is a crucial measure when fighting any pandemic. A pooled testing strategy for SARS-CoV-2 has theoretically been shown to increase the testing capacity of a country, especially when applied in low prevalence settings. Experimental studies have shown that the sensitivity of reverse transcription-polymerase chain reaction is not affected when implemented in small groups. Previous models estimated the optimum group size as a function of the historical prevalence; however, this implies a homogeneous distribution of the disease within the population. This study aimed to explore whether separating individuals by age groups when pooling samples results in any further savings on test kits or affects the optimum group size estimation compared to Dorfman's pooling, based on historical prevalence. For this evaluation, age groups of interest were defined as 0–19 years, 20–59 years and over 60 years old. Generalisation of Dorfman's pooling was performed by adding statistical weight to the age groups based on the number of confirmed cases and tests performed in the segment. The findings showed that when the pooling samples are based on age groups, there is a decrease in the number of tests per subject needed to diagnose one subject. Although this decrease is minuscule, it might account for considerable savings when applied on a large scale. In addition, the savings are considerably higher in settings where there is a high standard deviation among the positivity rate of the age segments of the general population.

This study aims to locate the knots of cumulative coronavirus disease 2019 (COVID-19) case number during the first-level response to public health emergency in the provinces of China except Hubei. The provinces were grouped into three regions, namely eastern, central and western provinces, and the trends between adjacent knots were compared among the three regions. COVID-19 case number, migration scale index, Baidu index, demographic, economic and public health resource data were collected from 22 Chinese provinces from 19 January 2020 to 12 March 2020. Spline regression was applied to the data of all included, eastern, central and western provinces. The research period was divided into three stages by two knots. The first stage (from 19 January to around 25 January) was similar among three regions. However, in the second stage, growth of COVID-19 case number was flatter and lasted longer in western provinces (from 25 January to 18 February) than in eastern and central provinces (from 26 February to around 11 February). In the third stage, the growth of COVID-19 case number slowed down in all the three regions. Included covariates were different among the three regions. Overall, spline regression with covariates showed the different change patterns in eastern, central and western provinces, which provided a better insight into regional characteristics of COVID-19 pandemic.

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.

This paper proposes and analyses a stochastic model for the spread of an infectious disease transmitted between clients and care workers in the UK domiciliary (home) care setting. Interactions between clients and care workers are modelled using specially generated networks, with network parameters reflecting realistic patterns of care needs and visit allocation. These networks are then used to simulate a susceptible-exposed-infected-recovered/dead (SEIR/D)-type epidemic dynamics with different numbers of infectious and recovery stages. The results indicate that with the same overall capacity provided by care workers, the minimum peak proportion of infection and the smallest overall size of infection are achieved for the highest proportion of overlap between visit allocation, i.e. when care workers have the highest chances of being allocated a visit to the same client they have visited before. An intuitive explanation of this is that while providing the required care coverage, maximising overlap in visit allocation reduces the possibility of an infectious care worker inadvertently spreading the infection to other clients. The model is generic and can be adapted to any directly transmitted infectious disease, such as, more recently, corona virus disease 2019, provided accurate estimates of disease parameters can be obtained from real data.

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.

A pooled sample analysis strategy for novel coronavirus (severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2)) is proposed for a large population in this paper. The population to be tested is divided into divisions based on earlier observed detection rate of SARS-CoV-2 first. Samples collected are then grouped in appropriate pooled size. The number of tests per person in that population is expressed as a function of two variables: the observed detection rate and the pooled size or number of samples grouped. The minimum number of tests per person can be further shown to be a function of only one of these two variables, because these two parameters are found to be related at this minimum. A management scheme on grouping the samples is proposed in order to reduce the number of tests, to save time, which is of utmost importance in fighting an epidemic. The proposed testing scheme will be useful for supporting the government in making decisions to handle regular routine detection tests for identifying asymptomatic patients and implementing health code system in large population of millions of citizens. Another important point is to use smaller number of test kits, allowing more resources to speed up the mass screening tests, particularly in places not so rich.

It is unclear if – after symptom onset of a primary case of coronavirus disease-2019 (COVID-19) in a household – ensuing chains of transmissions among household members occur and if household epidemiology of COVID-19 is modified by the different circulating variants. We analysed data of 52 774 household clusters to investigate the day of symptom onset of ensuing cases in households relative to the symptom onset of the primary case within the household. Irrespective of cluster size or age of the primary case, 95% of all secondary household cases had symptom onset within 14 days after the symptom onset of the primary case. Stratification by variant showed that the mean interval from symptom onset of the primary case to the symptom onset of secondary cases decreased significantly from 4.8 days (wildtype) to 4.5 days (alpha) and 4.0 days (delta). Similarly, the cumulative proportion of 95% of secondary cases occurred within 14 days (wild type), 12 days (alpha) and 10 days (delta). Our findings suggest that during dominant delta circulation – apart from rare individual constellations – a 10-day household quarantine after symptom onset of the primary case is sufficient for household contacts who remain COVID-free.

It is important to understand the temporal trend of the paediatric severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load to estimate the transmission potential of children in schools and communities. We determined the differences in SARS-CoV-2 viral load dynamics between nasopharyngeal samples of infected asymptomatic and symptomatic children. Serial cycle threshold values of SARS-CoV-2 from the nasopharynx of a cohort of infected children were collected for analysis. Among 17 infected children, 10 (58.8%) were symptomatic. Symptomatic children, when compared to asymptomatic children, had higher viral loads (mean cycle threshold on day 7 of illness 28.6 vs. 36.7, P = 0.02). Peak SARS-CoV-2 viral loads occurred around day 2 of illness in infected children. Although we were unable to directly demonstrate infectivity, the detection of significant amount of virus in the upper airway of asymptomatic children suggest that they have the potential to shed and transmit SARS-CoV-2. Our study highlights the importance of contact tracing and screening for SARS-CoV-2 in children with epidemiological risk factors regardless of their symptom status, in order to improve containment of the virus in the community, including educational settings.

Nosocomial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks among health care workers have been scarcely reported so far. This report presents the results of an epidemiologic and molecular investigation of a SARS-CoV-2 outbreak among laundromat facility workers in a large tertiary centre in Israel. Following the first three reported cases of SARS-CoV-2 among laundromat workers, all 49 laundromat personnel were screened by qRT-PCR tests using naso- and oropharingeal swabs. Epidemiologic investigations included questionnaires, interviews and observations of the laundromat facility. Eleven viral RNA samples were then sequenced, and a phylogenetic analysis was performed using MEGAX.

The integrated investigation defined three genetic clusters and helped identify the index cases and the assumed routes of transmission. It was then deduced that shared commute and public showers played a role in SARS-CoV-2 transmission in this outbreak, in addition to improper PPE use and social gatherings (such as social eating and drinking). In this study, we present an integrated epidemiologic and molecular investigation may help detect the routes of SARS-CoV-2 transmission, emphasising such routes that are less frequently discussed. Our work reinforces the notion that person-to-person transmission is more likely to cause infections than environmental contamination (e.g. from handling dirty laundry).

This study investigated the characteristics of transmission routes of COVID-19 cluster infections (⩾10 linked cases within a short period) in Gangwon Province between 22 February 2020 and 31 May 2021. Transmission routes were divided into five major categories and 35 sub-categories according to the relationship between the infector and the infectee and the location of transmission. A total of 61 clusters occurred during the study period, including 1741 confirmed cases (55.7% of all confirmed cases (n = 3125)). The the five major routes of transmission were as follows: ‘using (staying in) the same facility (50.7%), ‘cohabiting family members’ (23.3%), ‘social gatherings with acquaintances’ (10.8%), ‘other transmission routes’ (7.0%), and ‘social gatherings with non-cohabiting family members/relatives’ (5.5%). For transmission caused by using (staying in) the same facility, the highest number of confirmed cases was associated with churches, followed by medical institutions (inpatient), sports facilities, military bases, offices, nightlife businesses, schools, restaurants, day-care centres and kindergarten, and service businesses. Our analysis highlights specific locations with frequent transmission of infections, and transmission routes that should be targeted in situations where adherence to disease control rules is difficult.

Serosurveillance is an important epidemiologic tool for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), used to estimate infection rates and the degree of population immunity. There is no general agreement on which antibody biomarker(s) should be used, especially with the rollout of vaccines globally. Here, we used random forest models to demonstrate that a single spike or receptor-binding domain (RBD) antibody was adequate for classifying prior infection, while a combination of two antibody biomarkers performed better than any single marker for estimating time-since-infection. Nucleocapsid antibodies performed worse than spike or RBD antibodies for classification, but can be useful for estimating time-since-infection, and in distinguishing infection-induced from vaccine-induced responses. Our analysis has the potential to inform the design of serosurveys for SARS-CoV-2, including decisions regarding a number of antibody biomarkers measured.

Since the start of the coronavirus disease-2019 (COVID-19) pandemic, there has been interest in using wastewater monitoring as an approach for disease surveillance. A significant uncertainty that would improve the interpretation of wastewater monitoring data is the intensity and timing with which individuals shed RNA from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into wastewater. By combining wastewater and case surveillance data sets from a university campus during a period of heightened surveillance, we inferred that individual shedding of RNA into wastewater peaks on average 6 days (50% uncertainty interval (UI): 6–7; 95% UI: 4–8) following infection, and that wastewater measurements are highly overdispersed [negative binomial dispersion parameter, k = 0.39 (95% credible interval: 0.32–0.48)]. This limits the utility of wastewater surveillance as a leading indicator of secular trends in SARS-CoV-2 transmission during an epidemic, and implies that it could be most useful as an early warning of rising transmission in areas where transmission is low or clinical testing is delayed or of limited capacity.