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But this reliance on an inherent transmission assumption can lead to challenges in interpretation, particularly in rapidly changing epidemics that take several generations of infection to settle to equilibrium. Understanding the extent of disease transmission - and how it changes over time - is a deceptively difficult problem, and these papers highlight some key considerations in how we should estimate and interpret COVID transmission dynamics. Even if we were to be omniscient, and see every contact and every transmission event, we would still have to make conceptual decisions about whether we are looking back at transmission that has already occurred, or predicting ongoing transmission from current infectious individuals, which is subject to right truncation. [Extracted from the article]

For example, in recruiting participants randomly from those registered for healthcare in England, the REACT-1 design attempts to reduce the impact of temporal variation when making growth rate estimates (Riley et al., [4]). REACT is a program of studies separated into REACT-1 (Riley et al., [4]) that collects self-administered nose and throat swabs (Riley et al., [4]) and REACT-2 that collects self-administered lateral-flow antibody tests (Ward et al., [6]). In addition to growth rates, population surveys of infection provide estimates of prevalence at national and regional scales that can be easily understood as measures of individual risk: measured swab-positivity is easily translated into odds of infection. [Extracted from the article]

School closure is one of the most debated measures undertaken to contain the spread of the Coronavirus disease (COVID-19) pandemic. The pandemic has devastating health and socio-economic effects and must be contained, but schools play a vital role in present and future well-being, capabilities and health of children. We examine the detrimental consequences of both the closure and reopening of schools, by focusing on inequalities in the challenges affecting children and their families. This paper is grounded on Irish and Italian data from a multi-national longitudinal qualitative interview study. Research participants articulated a variety of issues and challenges that highlight inequalities in access to education during school closures, in the supportiveness of home setting, and in school preparedness to reopen, often mirroring or exacerbating pre-existing inequalities. The reported unequal lived experiences indicate that some harms are actionable, and already suggest some potential harm mitigation strategies. We conclude by advocating for enhanced public consultation to help mitigate the consequences of public dilemmas in general, and to help detect and tackle inadequacies and inequalities for school children through and beyond the pandemic, by learning from the experience of the concerned actors. [ABSTRACT FROM AUTHOR]

Motivated by the varying effectiveness of government intervention policies to contain the COVID-19 pandemic, and the potential positive relationship between ethnolinguistic diversity and social distance, this paper aims to provide empirical evidence on the relationship between ethnolinguistic diversity and the spread of COVID-19. In particular, using global data from 113 developed and developing countries during the early stages of the pandemic (from 31 December 2019 to 8 July 2020), we have found a significant negative effect of ethnolinguistic diversity on the spread of the virus. The result is robust to alternative measures of ethnolinguistic diversity and estimator that addresses endogeneity. Moreover, we also show that the impact of ethnolinguistic diversity on the spread of COVID-19 differs in economies characterized by different levels of democracy, policy stringency on addressing COVID-19 and health expenditure. [ABSTRACT FROM AUTHOR]

It is challenging to quantitatively measure the health vulnerability and risk factors of refugees and migrants residing outside of formal settlement settings. For hard-to-reach populations without available sampling frames, researchers have increasingly turned to novel sampling and statistical methods, like respondent-driven sampling (RDS). "Standard" RDS is typically conducted face-to-face at fixed sites. However, during the coronavirus disease 2019 (COVID-19) pandemic, face-to-face survey methods and recruitment approaches posed high potential risk of virus transmission and infection, making remote RDS approaches optimal. In this paper, we explore the feasibility of implementing telephone and Internet RDS strategies to assess challenges faced by Venezuelan refugees and migrants in the city of Bogotá, Colombia's capital, and the department of Norte de Santander, the main Venezuelan-Colombian border crossing site. We describe RDS assumptions, survey design, formative research, and the implementation of both strategies and present diagnostics for determining whether assumptions are met. Phone-based recruitment strategies in both locations and the Internet strategy in Bogotá achieved their calculated sample size; however, the Internet strategy in Norte de Santander did not. Most RDS assumptions were sufficiently met at sites where sample sizes were reached. These surveys provide valuable lessons for implementing innovative remote strategies with which to study hard-to-reach populations such as refugees and migrants. [ABSTRACT FROM AUTHOR]

Most COVID-19 studies commonly report figures of the overall infection at a state- or county-level. This aggregation tends to miss out on fine details of virus propagation. In this paper, we analyze a high-resolution COVID-19 dataset in Cali, Colombia, that records the precise time and location of every confirmed case. We develop a non-stationary spatio-temporal point process equipped with a neural network-based kernel to capture the heterogeneous correlations among COVID-19 cases. The kernel is carefully crafted to enhance expressiveness while maintaining model interpretability. We also incorporate some exogenous influences imposed by city landmarks. Our approach outperforms the state-of-the-art in forecasting new COVID-19 cases with the capability to offer vital insights into the spatio-temporal interaction between individuals concerning the disease spread in a metropolis. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) has attracted research interests from all fields. Phylogenetic and social network analyses based on connectivity between either COVID-19 patients or geographic regions and similarity between syndrome coronavirus 2 (SARS-CoV-2) sequences provide unique angles to answer public health and pharmaco-biological questions such as relationships between various SARS-CoV-2 mutants, the transmission pathways in a community and the effectiveness of prevention policies. This paper serves as a systematic review of current phylogenetic and social network analyses with applications in COVID-19 research. Challenges in current phylogenetic network analysis on SARS-CoV-2 such as unreliable inferences, sampling bias and batch effects are discussed as well as potential solutions. Social network analysis combined with epidemiology models helps to identify key transmission characteristics and measure the effectiveness of prevention and control strategies. Finally, future new directions of network analysis motivated by COVID-19 data are summarized. [ABSTRACT FROM AUTHOR]

Introduction Healthcare workers' work performance and mental health are associated with positive mental health outcomes and directly related to increased productivity and decreased disability costs. Methods We conducted a systematic review to identify factors associated with work performance of healthcare workers during a pandemic and conducted a meta-analysis of the prevalence of mental health outcomes in this context. Primary papers were collected and analysed using the Population/Intervention/Comparison/Outcome framework and using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. To critically appraise the studies included in the review, we used the AXIS appraisal tool to assess each cross-sectional study's quality. Results The study identified nine factors associated with the work performance and mental health of healthcare workers, including experiencing feelings of depression, anxiety, having inadequate support, experiencing occupational stress, decreased productivity, lack of workplace preparedness, financial concerns associated with changes in income and daily living, fear of transmission and burnout/fatigue. Conclusion There is a rapidly rising need to address the work performance and mental health of healthcare workers providing timely care to patients. Regular and sustained interventions, including the use of information and communication technologies such as telehealth, are warranted. [ABSTRACT FROM AUTHOR]

In this paper, we propose and analyse a compartmental model of COVID-19 to predict and control the outbreak. We first formulate a comprehensive mathematical model for the dynamical transmission of COVID-19 in the context of sub-Saharan Africa. We provide the basic properties of the model and compute the basic reproduction number |$\mathcal {R}_0$| when the parameter values are constant. After, assuming continuous measurement of the weekly number of newly COVID-19 detected cases, newly deceased individuals and newly recovered individuals, the Ensemble of Kalman filter (EnKf) approach is used to estimate the unmeasured variables and unknown parameters, which are assumed to be time-dependent using real data of COVID-19. We calibrated the proposed model to fit the weekly data in Cameroon and Gabon before, during and after the lockdown. We present the forecasts of the current pandemic in these countries using the estimated parameter values and the estimated variables as initial conditions. During the estimation period, our findings suggest that |$\mathcal {R}_0 \approx 1.8377 $| in Cameroon, while |$\mathcal {R}_0 \approx 1.0379$| in Gabon meaning that the disease will not die out without any control measures in theses countries. Also, the number of undetected cases remains high in both countries, which could be the source of the new wave of COVID-19 pandemic. Short-term predictions firstly show that one can use the EnKf to predict the COVID-19 in Sub-Saharan Africa and that the second vague of the COVID-19 pandemic will still increase in the future in Gabon and in Cameroon. A comparison between the basic reproduction number from human individuals |$\mathcal {R}_{0h}$| and from the SARS-CoV-2 in the environment |$\mathcal {R}_{0v}$| has been done in Cameroon and Gabon. A comparative study during the estimation period shows that the transmissions from the free SARS-CoV-2 in the environment is greater than that from the infected individuals in Cameroon with |$\mathcal {R}_{0h}$| = 0.05721 and |$\mathcal {R}_{0v}$| = 1.78051. This imply that Cameroonian apply distancing measures between individual more than with the free SARS-CoV-2 in the environment. But, the opposite is observed in Gabon with |$\mathcal {R}_{0h}$| = 0.63899 and |$\mathcal {R}_{0v}$| = 0.39894. So, it is important to increase the awareness campaigns to reduce contacts from individual to individual in Gabon. However, long-term predictions reveal that the COVID-19 detected cases will play an important role in the spread of the disease. Further, we found that there is a necessity to increase timely the surveillance by using an awareness program and a detection process, and the eradication of the pandemic is highly dependent on the control measures taken by each government. [ABSTRACT FROM AUTHOR]

In this paper, three stochastic mathematical models are developed for the spread of the coronavirus disease (COVID-19). These models take into account the known special characteristics of this disease such as the existence of infectious undetected cases and the different social and infectiousness conditions of infected people. In particular, they include a novel approach that considers the social structure, the fraction of detected cases over the real total infected cases, the influx of undetected infected people from outside the borders, as well as contact-tracing and quarantine period for travellers. Two of these models are discrete time–discrete state space models (one is simplified and the other is complete) while the third one is a continuous time–continuous state space stochastic integro-differential model obtained by a formal passing to the limit from the proposed simplified discrete model. From a numerical point of view, the particular case of Lebanon has been studied and its reported data have been used to estimate the complete discrete model parameters, which can be of interest in estimating the spread of COVID-19 in other countries. The obtained simulation results have shown a good agreement with the reported data. Moreover, a parameters' analysis is presented in order to better understand the role of some of the parameters. This may help policy makers in deciding on different social distancing measures. [ABSTRACT FROM AUTHOR]

This paper studies the effects of large-scale sport events with live spectators on COVID-19 infection trends at the local population level. Specifically, we compare the development of incidence rates in 41 German Nomenclature of Territorial Units for Statistics level 3 (NUTS-3) districts hosting a professional football match with at least 1,000 spectators vis-à-vis similar districts without hosting a match. Our empirical analysis builds on difference-in-difference and dynamic event study estimation for panel data. Synthetic control method is applied as a robustness check. While our findings generally do not point to significant treatment effects for the full sample of match locations, we find some noteworthy exceptions. Districts hosting first league matches with spectator attendance above the median (> 6,300 persons) and, particularly, matches without strict face mask requirements experienced a significant relative rise in incidence rates 14 days after the match. We also find that intra-district mobility increases on match days in treated districts, highlighting the significance of professional football matches as mobility-based infection transmission channel. [ABSTRACT FROM AUTHOR]

Introduction: Military forces around the world face an increased risk of the spread of communicable diseases, due to the close living quarters and congregated nature of the military camps. The Singapore Armed Forces (SAF) implemented a multi-pronged surveillance and containment strategy to reduce the risk of a coronavirus disease 2019 (COVID-19) outbreak within the local military camps. This paper details the epidemiological investigations of the COVID-19 cases in the SAF and highlights the strategies and public health measures undertaken, aligned with the national COVID-19 control strategy, to reduce the risk of COVID-19 transmission in the military camps. Materials and Methods: Medical data of our military personnel who were infected with COVID-19 during the first 180 days of the pandemic were extracted from the military electronic health records. Contact tracing and activity mapping results were obtained from unit-level epidemiological data. A review of the organization's response plans, instructions, and orders was conducted to collate the measures implemented across the same time period. Results: Prompt contact tracing and activity mapping was done for each of the 24 SAF military personnel diagnosed with COVID-19 between February 2020 and June 2020, with possible links among the cases identified and investigated. Conclusion: A combination of strategies in the formulation of public health measures based on key principles of early warning and surveillance, prompt diagnosis, and early containment were successful in preventing the formation of COVID-19 clusters within the SAF. This will provide a framework for the management of future pandemics within the military setting, driven by strong governance and leadership, to meet the military's need to maintain operational readiness in a safe manner. [ABSTRACT FROM AUTHOR]

This is an account that should be heard of an important struggle: the struggle of a large group of experts who came together at the beginning of the COVID-19 pandemic to warn the world about the risk of airborne transmission and the consequences of ignoring it. We alerted the World Health Organization about the potential significance of the airborne transmission of SARS-CoV-2 and the urgent need to control it, but our concerns were dismissed. Here we describe how this happened and the consequences. We hope that by reporting this story we can raise awareness of the importance of interdisciplinary collaboration and the need to be open to new evidence, and to prevent it from happening again. Acknowledgement of an issue, and the emergence of new evidence related to it, is the first necessary step towards finding effective mitigation solutions. [ABSTRACT FROM AUTHOR]

Understanding the contribution of routes of transmission, particularly the role of fomites in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) transmission is important in developing and implementing successful public health infection prevention and control measures. This article will look at case reports, laboratory findings, animal studies, environmental factors, the need for disinfection, and differences in settings as they relate to SARS-CoV-2 transmission. [ABSTRACT FROM AUTHOR]

We employ a new macroeconomic–epidemiological agent-based model to evaluate the "lives vs livelihoods" trade-off brought to the fore by coronavirus disease (Covid-19). An infectious disease spreads across the network of agents' social and economic contacts and feeds back on the economic dimension of the model through various channels such as employment and consumption demand. Under a lockdown (LD) scenario, the model is able to closely reproduce the epidemiological dynamics of the first wave of the Covid-19 epidemic in the Lombardy region of Italy. We find that, in the presence of non-pharmaceutical interventions, there is no trade-off between lives and macroeconomic losses as a stricter LD eventually leads to superior outcomes along both dimensions. We also evaluate the efficacy of various macroeconomic stabilization policies designed to counteract the downturn generated by the epidemic and LD measures. In an agent-based setting we gain additional insights on the way in which such policies impact not only on gross domestic product but also, for instance, on firms' defaults and relative prices. Liquidity support for firms, a short-time working scheme with compensation for workers, and direct transfer to households stand out as the most effective policy tools. [ABSTRACT FROM AUTHOR]

Estimation of age‐dependent transmissibility of COVID‐19 patients is critical for effective policymaking. Although the transmissibility of symptomatic cases has been extensively studied, asymptomatic infection is understudied due to limited data. Using a dataset with reliably distinguished symptomatic and asymptomatic statuses of COVID‐19 cases, we propose an ordinary differential equation model that considers age‐dependent transmissibility in transmission dynamics. Under a Bayesian framework, multi‐source information is synthesized in our model for identifying transmissibility. A shrinkage prior among age groups is also adopted to improve the estimation behavior of transmissibility from age‐structured data. The added values of accounting for age‐dependent transmissibility are further evaluated through simulation studies. In real‐data analysis, we compare our approach with two basic models using the deviance information criterion (DIC) and its extension. We find that the proposed model is more flexible for our epidemic data. Our results also suggest that the transmissibility of asymptomatic infections is significantly lower (on average, 76.45% with a credible interval (27.38%, 88.65%)) than that of symptomatic cases. In both symptomatic and asymptomatic patients, the transmissibility mainly increases with age. Patients older than 30 years are more likely to develop symptoms with higher transmissibility. We also find that the transmission burden of asymptomatic cases is lower than that of symptomatic patients. [ABSTRACT FROM AUTHOR]

Health rights of prisoners has long been a neglected political issue in Africa, where over one million people are detained, and almost half of whom are in pre-trial detention. African prisons constitute high-risk environments for communicable disease transmission. During the COVID-19 pandemic, the public health literature on African prison responses focused on preparedness as it related to testing capacity, quarantine practices and personal protective measures to mitigate disease spread. This article combines the right to health as narrowly defined by a prisoner's right to access non-discriminatory equivalent health care, with a broader focus on assessing normative standards of detention. A comparative legal realist assessment of prison operations in South Africa, Malawi and Zimbabwe during COVID-19 state disaster measures is presented, focusing on the environmental determinants of health (ventilation, minimum floor space, water, sanitation, hygiene and nutrition) in prisons. It reveals the inherent tensions in ensuring a balance between respecting the fundamental rights of people living and working in prisons, ensuring adequate environmental health standards and mitigating disease during public health emergencies. Despite insufficient government resourcing and inadequate coverage of COVID-19 responses, few severe outbreaks were reported. This could be due to lack of testing, reporting or other factors (asymptomatic infection, acquired immunity). Prison congestion and unrest however affected prisoners and staff fearful of hazardous living and occupational health conditions. COVID-19 as public health emergency amplifies the need to address systemic deficits in infrastructure, resourcing and efficiency of criminal justice systems. Policy level and pragmatic recommendations for enhanced human rights practice are outlined. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) vaccines are highly efficacious at preventing symptomatic infection, severe disease, and death. Most of the evidence that COVID-19 vaccines also reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is based on retrospective, observational studies. Specifically, an increasing number of studies are evaluating vaccine effectiveness against the secondary attack rate of SARS-CoV-2 using data available in existing health-care databases or contact-tracing databases. Since these types of databases were designed for clinical diagnosis or management of COVID-19, they are limited in their ability to provide accurate information on infection, infection timing, and transmission events. We highlight challenges with using existing databases to identify transmission units and confirm potential SARS-CoV-2 transmission events. We discuss the impact of common diagnostic testing strategies, including event-prompted and infrequent testing, and illustrate their potential biases in estimating vaccine effectiveness against the secondary attack rate of SARS-CoV-2. We articulate the need for prospective observational studies of vaccine effectiveness against the SARS-CoV-2 secondary attack rate, and we provide design and reporting considerations for studies using retrospective databases. [ABSTRACT FROM AUTHOR]

Mixed evidence exists of associations between mobility data and coronavirus disease 2019 (COVID-19) case rates. We aimed to evaluate the county-level impact of reducing mobility on new COVID-19 cases in summer/fall of 2020 in the United States and to demonstrate modified treatment policies to define causal effects with continuous exposures. Specifically, we investigated the impact of shifting the distribution of 10 mobility indexes on the number of newly reported cases per 100,000 residents 2 weeks ahead. Primary analyses used targeted minimum loss-based estimation with Super Learner to avoid parametric modeling assumptions during statistical estimation and flexibly adjust for a wide range of confounders, including recent case rates. We also implemented unadjusted analyses. For most weeks, unadjusted analyses suggested strong associations between mobility indexes and subsequent new case rates. However, after confounder adjustment, none of the indexes showed consistent associations under mobility reduction. Our analysis demonstrates the utility of this novel distribution-shift approach to defining and estimating causal effects with continuous exposures in epidemiology and public health. [ABSTRACT FROM AUTHOR]

The UEFA EURO 2020 football tournament was one of the largest Sporting Mega Events (SMEs) to take place during the COVID-19 pandemic. Mitigating the risk of virus transmission requires a multi-layered approach for any large event, more so in this case due to staging the tournament across eleven host countries. Yet, little is known about COVID-19 risks and mitigation from attending an event of this scale and nature. We examined the implementation of mitigation and messaging at EURO 2020 matches hosted at venues in the UK. The tournament was postponed from the summer of 2020 and played in June and July of 2021. Structured observations were conducted by 11 trained fieldwork-supporters at 10 matches played at Wembley Stadium, London, or Hampden Park, Glasgow. Fieldwork-supporters observed one-way systems and signage, and hand sanitizing stations inside the stadia, but reported significant variation in the implementation of staggered timeslots, testing upon entry, and procedures for exit. Adherence to planned measures by ticket holders and implementation by stewards waned as the tournament progressed culminating in an absence of enforced measures at the final. The non-compliance with COVID-19 mitigation measures was likely to have led to a significantly increased risk of transmission. Future events should consider how COVID-19 mitigation measures could become 'new norms' of fan behaviour, learning from what is already known about football fandom. Tournament organizers of SMEs can use these findings to promote clearer messaging on pandemic-driven changes in fan behaviour and best practices in mitigating risk at future sporting and cultural events. [ABSTRACT FROM AUTHOR]

Background Work-related exposures play an important role in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, yet few studies have compared SARS-CoV-2 expsoure risk across occupations and industries. Methods During September 2020 to May 2021, the Wisconsin Department of Health Services collected occupation and industry data as part of routine coronavirus disease 2019 (COVID-19) case investigations. Adults aged 18–64 years with confirmed or probable COVID-19 in Wisconsin were assigned standardized occupation and industry codes. Cumulative incidence rates were weighted for non-response and calculated using full-time equivalent (FTE) workforce denominators from the 2020 American Community Survey. Results An estimated 11.6% of workers (347 013 of 2.98 million) in Wisconsin, ages 18–64 years, had COVID-19 from September 2020 to May 2021. The highest incidence by occupation (per 100 FTE) occurred among personal care and services workers (22.1), healthcare practitioners and support staff (20.7), and protective services workers (20.7). High-risk sub-groups included nursing assistants and personal care aides (28.8), childcare workers (25.8), food and beverage service workers (25.3), personal appearance workers (24.4), and law enforcement workers (24.1). By industry, incidence was highest in healthcare (18.6); the highest risk sub-sectors were nursing care facilities (30.5) and warehousing (28.5). Conclusions This analysis represents one of the most complete examinations to date of COVID-19 incidence by occupation and industry. Our approach demonstrates the value of standardized occupational data collection by public health and may be a model for improved occupational surveillance elsewhere. Workers at higher risk of SARS-CoV-2 exposure may benefit from targeted workplace COVID-19 vaccination and mitigation efforts. [ABSTRACT FROM AUTHOR]

This article deals with the spread of infectious diseases from a physics perspective. It considers a population as a network of nodes representing the population members, linked by network edges representing the (social) contacts of the individual population members. Infections spread along these edges from one node (member) to another. This article presents a novel, modified version of the SIR compartmental model, able to account for typical network effects and percolation phenomena. The model is successfully tested against the results of simulations based on Monte-Carlo methods. Expressions for the (basic) reproduction numbers in terms of the model parameters are presented, and justify some mild criticisms on the widely spread interpretation of reproduction numbers as being the number of secondary infections due to a single active infection. Throughout the article, special emphasis is laid on understanding, and on the interpretation of phenomena in terms of concepts borrowed from condensed-matter and statistical physics, which reveals some interesting analogies. Percolation effects are of particular interest in this respect and they are the subject of a detailed investigation. The concept of herd immunity (its definition and nature) is intensively dealt with as well, also in the context of large-scale vaccination campaigns and waning immunity. This article elucidates how the onset of herd-immunity can be considered as a second-order phase transition in which percolation effects play a crucial role, thus corroborating, in a more pictorial/intuitive way, earlier viewpoints on this matter. An exact criterium for the most relevant form of herd-immunity to occur can be derived in terms of the model parameters. The analyses presented in this article provide insight in how various measures to prevent an epidemic spread of an infection work, how they can be optimized and what potentially deceptive issues have to be considered when such measures are either implemented or scaled down. [ABSTRACT FROM AUTHOR]

There is an ongoing debate on airborne transmission of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) as a risk factor for infection. In this study, the level of SARS-CoV-2 in air and on surfaces of SARS-CoV-2 infected nursing home residents was assessed to gain insight in potential transmission routes. During outbreaks, air samples were collected using three different active and one passive air sampling technique in rooms of infected patients. Oropharyngeal swabs (OPS) of the residents and dry surface swabs were collected. Additionally, longitudinal passive air samples were collected during a period of 4 months in common areas of the wards. Presence of SARS-CoV-2 RNA was determined using RT-qPCR, targeting the RdRp- and E-genes. OPS, samples of two active air samplers and surface swabs with Ct-value ≤35 were tested for the presence of infectious virus by cell culture. In total, 360 air and 319 surface samples from patient rooms and common areas were collected. In rooms of 10 residents with detected SARS-CoV-2 RNA in OPS, SARS-CoV-2 RNA was detected in 93 of 184 collected environmental samples (50.5%) (lowest Ct 29.5), substantially more than in the rooms of residents with negative OPS on the day of environmental sampling (n = 2) (3.6%). SARS-CoV-2 RNA was most frequently present in the larger particle size fractions [>4 μm 60% (6/10); 1–4 μm 50% (5/10); <1 μm 20% (2/10)] (Fischer exact test P = 0.076). The highest proportion of RNA-positive air samples on room level was found with a filtration-based sampler 80% (8/10) and the cyclone-based sampler 70% (7/10), and impingement-based sampler 50% (5/10). SARS-CoV-2 RNA was detected in 10 out of 12 (83%) passive air samples in patient rooms. Both high-touch and low-touch surfaces contained SARS-CoV-2 genome in rooms of residents with positive OPS [high 38% (21/55); low 50% (22/44)]. In one active air sample, infectious virus in vitro was detected. In conclusion, SARS-CoV-2 is frequently detected in air and on surfaces in the immediate surroundings of room-isolated COVID-19 patients, providing evidence of environmental contamination. The environmental contamination of SARS-CoV-2 and infectious aerosols confirm the potential for transmission via air up to several meters. [ABSTRACT FROM AUTHOR]

Background The computer simulation presented in this study aimed to investigate the effect of contact tracing on coronavirus disease 2019 (COVID-19) transmission and infection in the context of rising vaccination rates. Methods This study proposed a deterministic, compartmental model with contact tracing and vaccination components. We defined contact tracing effectiveness as the proportion of contacts of a positive case that was successfully traced and the vaccination rate as the proportion of daily doses administered per population in Malaysia. Sensitivity analyses on the untraced and infectious populations were conducted. Results At a vaccination rate of 1.4%, contact tracing with an effectiveness of 70% could delay the peak of untraced asymptomatic cases by 17 d and reduce it by 70% compared with 30% contact tracing effectiveness. A similar trend was observed for symptomatic cases when a similar experiment setting was used. We also performed sensitivity analyses by using different combinations of contact tracing effectiveness and vaccination rates. In all scenarios, the effect of contact tracing on COVID-19 incidence persisted for both asymptomatic and symptomatic cases. Conclusions While vaccines are progressively rolled out, efficient contact tracing must be rapidly implemented concurrently to reach, find, test, isolate and support the affected populations to bring COVID-19 under control. [ABSTRACT FROM AUTHOR]

The death toll in nursing homes accounted for almost 30 per cent of total Covid-19 deaths in the US during 2020. We examine the course of the pandemic in nursing homes focusing especially on whether nursing homes could have been better shielded. Across all nursing homes the key predictor of infections and deaths was community spread, i.e. a factor outside of the control of nursing homes. We find that higher-quality nursing homes, as measured by the CMS Five-Star Rating system, were not better able to protect their residents. Policy failures from the CDC and FDA, especially in the early stages of the pandemic, created extended waiting times for Covid-19 tests and slowed attempts to isolate infectious residents. But once infections were widespread, testing would have had to have been much greater to have had an appreciable effect on nursing home deaths. We find, however, that starting vaccinations just 5 weeks earlier could have saved in the order of 14,000 lives and starting them ten weeks earlier could have saved 40,000 lives. [ABSTRACT FROM AUTHOR]

The basic reproduction number, R0, and its real‐time analogue, Rt, are summary measures that reflect the ability of an infectious disease to spread through a population. Estimation methods for Rt have a long history, have been widely developed and are now enhanced by application to the COVID‐19 pandemic. While retrospective analyses of Rt have provided insight into epidemic dynamics and the effects of control strategies in prior outbreaks, misconceptions around the interpretation of Rt have arisen with broader recognition and near real‐time monitoring of this parameter alongside reported case data during the COVID‐19 pandemic. Here, we discuss some widespread misunderstandings regarding the use of Rt as a barometer for population risk and its related use as an 'on/off' switch for policy decisions regarding relaxation of non‐pharmaceutical interventions. Computation of Rt from downstream data (e.g. hospitalizations) when infection counts are unreliable exacerbates lags between when transmission happens and when events are recorded. We also discuss analyses that have shown various relationships between Rt and measures of mobility, vaccination coverage and a test–trace–isolation intervention in different settings. [ABSTRACT FROM AUTHOR]

The article explores rapid identification of infected individuals in the early stages of an outbreak in order to isolate and prevent further transmission is of utmost importance. Public health responses utilized to accomplish this goal include identification and isolation of infected individuals based on symptom presentation then monitoring of contacts for symptoms before proceeding with further isolation.

Objective Infectious diseases, such as coronavirus disease 2019 (COVID-19), are commonly transmitted by respiratory droplets and contact with contaminated surfaces. Individuals with attention-deficit/hyperactivity disorder (ADHD) are more likely to be infected with COVID-19 and experience more hospitalizations than individuals without ADHD. The current study investigated the role of ADHD symptomatology and executive functioning (EF) in germ spreading behavior frequency among young children with and without ADHD and parenting responses to these behaviors. Methods Participants included 53 children diagnosed with ADHD and 47 typically developing (TD) children between the ages of 4–5 years (76% male; Mage = 4.62; 86% Hispanic/Latinx). Parents and teachers reported on children's ADHD symptomatology and children completed three EF tasks. Germ spreading behavior frequency (direct contact of hand to face and toy in mouth) and parenting responses (verbal and nonverbal behaviors) were observed during a 5-min parent–child play situation. Results Negative binomial regression analyses indicated that both ADHD diagnostic status and poor metacognition predicted both higher rates of toy to mouth (β  = 1.94, p <.001; β  = 0.03, p =.004) and face touching frequency (β  = 0.60, p =.03; β  = 0.03, p =.004), respectively. Additionally, poor attention and worse cognitive flexibility only predicted higher rates of toy to mouth frequency (β = 0.09, p <.001; β = −0.04, p =.001), respectively. Conclusions Young children with ADHD are at high risk for spreading germs via putting toys in their mouth and touching their face. Particularly, high levels of inattention and poor EF appear to be associated with higher rates of germ spreading behaviors. [ABSTRACT FROM AUTHOR]

Phylodynamic methods reveal the spatial and temporal dynamics of viral geographic spread, and have featured prominently in studies of the COVID-19 pandemic. Virtually all such studies are based on phylodynamic models that assume—despite direct and compelling evidence to the contrary—that rates of viral geographic dispersal are constant through time. Here, we: (1) extend phylodynamic models to allow both the average and relative rates of viral dispersal to vary independently between pre-specified time intervals; (2) implement methods to infer the number and timing of viral dispersal events between areas; and (3) develop statistics to assess the absolute fit of discrete-geographic phylodynamic models to empirical datasets. We first validate our new methods using simulations, and then apply them to a SARS-CoV-2 dataset from the early phase of the COVID-19 pandemic. We show that: (1) under simulation, failure to accommodate interval-specific variation in the study data will severely bias parameter estimates; (2) in practice, our interval-specific discrete-geographic phylodynamic models can significantly improve the relative and absolute fit to empirical data; and (3) the increased realism of our interval-specific models provides qualitatively different inferences regarding key aspects of the COVID-19 pandemic—revealing significant temporal variation in global viral dispersal rates, viral dispersal routes, and the number of viral dispersal events between areas—and alters interpretations regarding the efficacy of intervention measures to mitigate the pandemic. [ABSTRACT FROM AUTHOR]

Motivation The control of the diffusion of diseases is a critical subject of a broad research area, which involves both clinical and political aspects. It makes wide use of computational tools, such as ordinary differential equations, stochastic simulation frameworks and graph theory, and interaction data, from molecular to social granularity levels, to model the ways diseases arise and spread. The coronavirus disease 2019 (COVID-19) is a perfect testbench example to show how these models may help avoid severe lockdown by suggesting, for instance, the best strategies of vaccine prioritization. Results Here, we focus on and discuss some graph-based epidemiological models and show how their use may significantly improve the disease spreading control. We offer some examples related to the recent COVID-19 pandemic and discuss how to generalize them to other diseases. [ABSTRACT FROM AUTHOR]

Background Several studies indicate that coronavirus disease 2019 (COVID-19) is primarily transmitted within indoor spaces. Therefore, environmental characterization of severe acute respiratory syndrome coronavirus 2 viral load with respect to human activity, building parameters, and environmental mitigation strategies is critical to combat disease transmission. Methods We recruited 11 participants diagnosed with COVID-19 to individually occupy a controlled chamber and conduct specified physical activities under a range of environmental conditions; we collected human and environmental samples over a period of 3 days for each participant. Results Here we show that increased viral load, measured by lower RNA cycle threshold (CT) values, in nasal samples is associated with higher viral loads in environmental aerosols and on surfaces captured in both the near field (1.2 m) and far field (3.5 m). We also found that aerosol viral load in far field is correlated with the number of particles within the range of 1–2.5 µm. Furthermore, we found that increased ventilation and filtration significantly reduced aerosol and surface viral loads, while higher relative humidity resulted in lower aerosol and higher surface viral load, consistent with an increased rate of particle deposition at higher relative humidity. Data from near field aerosol trials with high expiratory activities suggest that respiratory particles of smaller sizes (0.3–1 µm) best characterize the variance of near field aerosol viral load. Conclusions Our findings indicate that building operation practices such as ventilation, filtration, and humidification substantially reduce the environmental aerosol viral load and therefore inhalation dose, and should be prioritized to improve building health and safety. [ABSTRACT FROM AUTHOR]

Background defining features of the COVID-19 pandemic in many countries were the tragic extent to which care home residents were affected and the difficulty in preventing the introduction and subsequent spread of infection. Management of risk in care homes requires good evidence on the most important transmission pathways. One hypothesised route at the start of the pandemic, prior to widespread testing, was the transfer of patients from hospitals that were experiencing high levels of nosocomial events. Methods we tested the hypothesis that hospital discharge events increased the intensity of care home cases using a national individually linked health record cohort in Wales, UK. We monitored 186,772 hospital discharge events over the period from March to July 2020, tracking individuals to 923 care homes and recording the daily case rate in the homes populated by 15,772 residents. We estimated the risk of an increase in case rates following exposure to a hospital discharge using multi-level hierarchical logistic regression and a novel stochastic Hawkes process outbreak model. Findings in regression analysis, after adjusting for care home size, we found no significant association between hospital discharge and subsequent increases in care home case numbers (odds ratio: 0.99, 95% CI: 0.82, 1.90). Risk factors for increased cases included care home size, care home resident density and provision of nursing care. Using our outbreak model, we found a significant effect of hospital discharge on the subsequent intensity of cases. However, the effect was small and considerably less than the effect of care home size, suggesting the highest risk of introduction came from interaction with the community. We estimated that approximately 1.8% of hospital discharged patients may have been infected. Interpretation there is growing evidence in the UK that the risk of transfer of COVID-19 from the high-risk hospital setting to the high-risk care home setting during the early stages of the pandemic was relatively small. Although access to testing was limited to initial symptomatic cases in each care home at this time, our results suggest that reduced numbers of discharges, selection of patients and action taken within care homes following transfer all may have contributed to the mitigation. The precise key transmission routes from the community remain to be quantified. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has particularly adversely affected older people with frailty and functional dependency. Essential regular contact with care staff has been evidenced as an important source of infection for this group. Vaccinating care staff can reduce the incidence, duration and severity of infection, preventing onward transmission to older people and minimising the harm associated with discontinuity caused by staff absence. Voluntary vaccination programmes for staff are more likely to be effective when associated with information and education, community engagement and financial incentives, but programmes using all of these approaches have failed to establish consistently high vaccination rates among care staff during the pandemic. Mandatory vaccination, proposed as a solution in some countries, can increase vaccination rates. It is only ethical if a vaccine is effective and cost-effective, the risk associated with vaccinating care workers is proportionate to the risk reduction achieved through vaccination, and where all efforts to encourage voluntary vaccination have been exhausted. Even when these conditions have been met, careful attention is required to ensure that the penalties associated with conscientious objection are proportionate and to ensure that implementation is equitable in a way that does not disadvantage particular groups of staff. [ABSTRACT FROM AUTHOR]

The novel coronavirus (COVID‐19) was first identified in China in December 2019. Within a short period of time, the infectious disease has spread far and wide. This study focuses on the distribution of COVID‐19 confirmed cases in China—the original epicentre of the outbreak. We show that the upper tail of COVID‐19 cases in Chinese cities is well described by a power law distribution, with exponent around one in the early phases of the outbreak (when the number of cases was growing rapidly) and less than one thereafter. This finding is significant because it implies that (i) COVID‐19 cases in China is heavy tailed and disperse; (ii) a few cities account for a disproportionate share of COVID‐19 cases; and (iii) the distribution generally has no finite mean or variance. We find that a proportionate random growth model predicated by Gibrat's law offers a plausible explanation for the emergence of a power law in the distribution of COVID‐19 cases in Chinese cities in the early phases of the outbreak. [ABSTRACT FROM AUTHOR]

Background The Coronavirus Disease 2019 (COVID-19) pandemic warranted a myriad of government-ordered business closures across the USA in efforts to mitigate the spread of the virus. This study aims to discover the implications of government-enforced health policies of reopening public businesses amidst the pandemic and its effect on county-level infection rates. Methods Eighty-three US counties (n = 83) that reported at least 20 000 cases as of 4 November 2020 were selected for this study. The dates when businesses (restaurants, bars, retail, gyms, salons/barbers and public schools) partially and fully reopened, as well as infection rates on the 1st and 14th days following each businesses' reopening, were recorded. Regression analysis was conducted to deduce potential associations between the 14-day change in infection rate and mask usage frequency, median household income, population density and social distancing. Results On average, infection rates rose significantly as businesses reopened. The average 14-day change in infection rate was higher for fully reopened businesses (infection rate = +0.100) compared to partially reopened businesses (infection rate = +0.0454). The P -value of the two distributions was 0.001692, indicating statistical significance (P < 0.01). Conclusion This research provides insight into the transmission of COVID-19 and promotes evidence-driven policymaking for disease prevention and community health. [ABSTRACT FROM AUTHOR]

Background Ethnicity can influence susceptibility to infection, as COVID-19 has shown. Few countries have systematically investigated ethnic variations in infection. Methods We linked the Scotland 2001 Census, including ethnic group, to national databases of hospitalizations/deaths and serological diagnoses of bloodborne viruses for 2001–2013. We calculated age-adjusted rate ratios (RRs) in 12 ethnic groups for all infections combined, 15 infection categories, and human immunodeficiency virus (HIV), hepatitis B (HBV) and hepatitis C (HCV) viruses. Results We analysed over 1.65 million infection-related hospitalisations/deaths. Compared with White Scottish, RRs for all infections combined were 0.8 or lower for Other White British, Other White and Chinese males and females, and 1.2–1.4 for Pakistani and African males and females. Adjustment for socioeconomic status or birthplace had little effect. RRs for specific infection categories followed similar patterns with striking exceptions. For HIV, RRs were 136 in African females and 14 in males; for HBV, 125 in Chinese females and 59 in males, 55 in African females and 24 in males; and for HCV, 2.3–3.1 in Pakistanis and Africans. Conclusions Ethnic differences were found in overall rates and many infection categories, suggesting multiple causative pathways. We recommend census linkage as a powerful method for studying the disproportionate impact of COVID-19. [ABSTRACT FROM AUTHOR]

Background There is currently a lack of information regarding ocular tropism and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Globally, the cumulative number of coronavirus disease 2019 (COVID-19) cases is increasing daily. Thus the potential for ocular transmission and manifestations of SARS-CoV-2 requires more investigation. Methods A systematic search of electronic databases for ocular transmission and manifestations of SARS-CoV-2 was performed. Pooled cross-sectional studies were used for conducting a meta-analysis to estimate the prevalence of ocular transmission of SARS-CoV-2 to the respiratory system and ocular manifestations (associated symptoms) of SARS-CoV-2. Results The highest prevalence of SARS-CoV-2-positive tears using reverse transcription polymerase chain reaction was found to be 7.5%. However, the highest prevalence of ocular conjunctivitis associated with SARS-CoV-2 was 32%. Thus, SARS-CoV-2 can evidently infect the eye, as revealed in the conjunctival secretions of COVID-19 patients. Conclusion The available data reflect the influence of the ocular structure on SARS-CoV-2. The analysis showed that ocular manifestation is an indication for SARS-CoV-2, particularly conjunctivitis. Moreover, there is no evidence that the ocular structure can be an additional path of transmission for SARS-CoV-2, however, it warrants further investigation. [ABSTRACT FROM AUTHOR]

The field of molecular epidemiology responded to the SARS-CoV-2 pandemic with an unrivaled amount of whole viral genome sequencing. By the time this sentence is published we will have well surpassed 1.5 million whole genomes, more than 4 times the number of all microbial whole genomes deposited in GenBank and 35 times the total number of viral genomes. This extraordinary dataset that accrued in near real time has also given us an opportunity to chart the global and local evolution of a virus as it moves through the world population. The data itself presents challenges that have never been dealt with in molecular epidemiology, and tracking a virus that is changing so rapidly means that we are often running to catch up. Here we review what is known about the evolution of the virus, and the critical impact that whole genomes have had on our ability to trace back and track forward the spread of lineages of SARS-CoV-2. We then review what whole genomes have told us about basic biological properties of the virus such as transmissibility, virulence, and immune escape with a special emphasis on pediatric disease. We couch this discussion within the framework of systematic biology and phylogenetics, disciplines that have proven their worth again and again for identifying and deciphering the spread of epidemics, though they were largely developed in areas far removed from infectious disease and medicine. [ABSTRACT FROM AUTHOR]

Objectives Coronavirus disease (COVID-19) contagion at work is well studied for healthcare workers, however it is not enough assessed in other working settings. Very little is known, indeed, about the risk of COVID-19 transmission through occupational exposure in non-health working places. This study aims to describe a COVID-19 cluster among workers in an office in Italy. Methods This was a retrospective observational study on a cluster of COVID-19 that occurred from 20 November through 3 December 2020 in a group of six colleagues (A–F) working in the same office full time 5 days a week, 8 h a day. The workers used the following prevention measures: social distancing (desks were >1 m, 1.76–5.01 m range), plexiglas panels, hands disinfection, and use of face mask. However, they did not wear face mask when in static position sitting at their desk and they did not aerate the place frequently. Results The disease spread from one worker (subject A) to four (80%) of the five colleagues (subjects B–F). Only subject D was negative to COVID-19 on 14 days after last contact with subject A (20 November 2020) as confirmed by nasopharyngeal swab testing. Subject D, in particular, did not contact subject A in the 48 h before symptoms onset. COVID-19 positivity of subject A was promptly communicated to the colleagues, who started self-isolation from their relatives and none of their households were infected. COVID-19 transmission was observed only in households of subject A. Conclusions The rapid communication of COVID-19 positivity to the colleagues and the prompt isolation of index case's close contacts allowed to eliminate the secondary transmission to their households. The contagion of index case's colleagues occurred from second day before symptoms onset. Distancing of >1 m, use of plexiglass panels, sanitizing hand gel, and inconstant use of face mask may not be enough for infection prevention in closed places with poor ventilation and high occupancy. [ABSTRACT FROM AUTHOR]

We study the impact of socioeconomic factors on two key parameters of epidemic dynamics. Specifically, we investigate a parameter capturing the rate of deceleration at the very start of an epidemic, and a parameter that reflects the pre-peak and post-peak dynamics at the turning point of an epidemic like coronavirus disease 2019 (COVID-19). We find two important results. The policies to fight COVID-19 (such as social distancing and containment) have been effective in reducing the overall number of new infections, because they influence not only the epidemic peaks, but also the speed of spread of the disease in its early stages. The second important result of our research concerns the role of healthcare infrastructure. They are just as effective as anti-COVID policies, not only in preventing an epidemic from spreading too quickly at the outset, but also in creating the desired dynamic around peaks: slow spreading, then rapid disappearance. [ABSTRACT FROM AUTHOR]

The USA has been particularly hard hit by the COVID-19 pandemic and a wide spatial variation can be seen in its spread and mortality. This raises the question of why some regions are more resilient to the pandemic than others? We hypothesize that the individualism–collectivism cleavage explains the disparity in COVID-19 cases observed across sub-national units in the USA. Cultural disparity among different groups of people leads to differences in how they perceive health crises and thereby shapes the way they respond to pandemics. A heightened sense of obligation and responsibility increases in-group sociability and interdependence and raises the perceived vulnerability towards disease transmission among collectivistic individuals, and this leads to greater adherence to containment measures and social distancing rules. Our results provide evidence that more individualistic states tend to have more COVID-19 cases across the USA. [ABSTRACT FROM AUTHOR]