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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]

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]

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]

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]

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]

Background Few pediatric cases of coronavirus disease 2019 (COVID-19) have been reported and we know little about the epidemiology in children, although more is known about other coronaviruses. We aimed to understand the infection rate, clinical presentation, clinical outcomes, and transmission dynamics for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), in order to inform clinical and public health measures. Methods We undertook a rapid systematic review and narrative synthesis of all literature relating to SARS-CoV-2 in pediatric populations. The search terms also included SARS-CoV and MERS-CoV. We searched 3 databases and the COVID-19 resource centers of 11 major journals and publishers. English abstracts of Chinese-language papers were included. Data were extracted and narrative syntheses conducted. Results Twenty-four studies relating to COVID-19 were included in the review. Children appear to be less affected by COVID-19 than adults by observed rate of cases in large epidemiological studies. Limited data on attack rate indicate that children are just as susceptible to infection. Data on clinical outcomes are scarce but include several reports of asymptomatic infection and a milder course of disease in young children, although radiological abnormalities are noted. Severe cases are not reported in detail and there are few data relating to transmission. Conclusions Children appear to have a low observed case rate of COVID-19 but may have rates similar to adults of infection with SARS-CoV-2. This discrepancy may be because children are asymptomatic or too mildly infected to draw medical attention and be tested and counted in observed cases of COVID-19. [ABSTRACT FROM AUTHOR]

Motivation The emergence of a novel strain of betacoronavirus, SARS-CoV-2, has led to a pandemic that has been associated with over 700 000 deaths as of August 5, 2020. Research is ongoing around the world to create vaccines and therapies to minimize rates of disease spread and mortality. Crucial to these efforts are molecular characterizations of neutralizing antibodies to SARS-CoV-2. Such antibodies would be valuable for measuring vaccine efficacy, diagnosing exposure and developing effective biotherapeutics. Here, we describe our new database, CoV-AbDab, which already contains data on over 1400 published/patented antibodies and nanobodies known to bind to at least one betacoronavirus. This database is the first consolidation of antibodies known to bind SARS-CoV-2 as well as other betacoronaviruses such as SARS-CoV-1 and MERS-CoV. It contains relevant metadata including evidence of cross-neutralization, antibody/nanobody origin, full variable domain sequence (where available) and germline assignments, epitope region, links to relevant PDB entries, homology models and source literature. Results On August 5, 2020, CoV-AbDab referenced sequence information on 1402 anti-coronavirus antibodies and nanobodies, spanning 66 papers and 21 patents. Of these, 1131 bind to SARS-CoV-2. Availabilityand implementation CoV-AbDab is free to access and download without registration at http://opig.stats.ox.ac.uk/webapps/coronavirus. Community submissions are encouraged. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]