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In the context of COVID-19 pandemic in Catalonia (Spain), the present study analyses respiratory samples collected by the primary care network using Acute Respiratory Infections Sentinel Surveillance System (PIDIRAC) during the 2019-2020 season to complement the pandemic surveillance system in place to detect SARS-CoV-2. The aim of the study is to describe whether SARS-CoV-2 was circulating before the first confirmed case was detected in Catalonia, on February 25.sup.th, 2020. The study sample was made up of all samples collected by the PIDIRAC primary care network as part of the Influenza and Acute Respiratory Infections (ARI) surveillance system activities. The study on respiratory virus included coronavirus using multiple RT-PCR assays. All positive samples for human coronavirus were subsequently typed for HKU1, OC43, NL63, 229E. Every respiratory sample was frozen at-80°C and retrospectively studied for SARS-CoV-2 detection. A descriptive study was performed, analysing significant differences among variables related to SARS-CoV- 2 cases comparing with rest of coronaviruses cases through a bivariate study with Chi-squared test and statistical significance at 95%. Between October 2019 and April 2020, 878 respiratory samples from patients with acute respiratory infection or influenza syndrome obtained by PIDIRAC were analysed. 51.9% tested positive for influenza virus, 48.1% for other respiratory viruses. SARS-CoV-2 was present in 6 samples. The first positive SARS-CoV-2 case had symptom onset on 2 March 2020. These 6 cases were 3 men and 3 women, aged between 25 and 50 years old. 67% had risk factors, none had previous travel history nor presented viral coinfection. All of them recovered favourably. Sentinel Surveillance PIDIRAC enhances global epidemiological surveillance by allowing confirmation of viral circulation and describes the epidemiology of generalized community respiratory viruses' transmission in Catalonia. The system can provide an alert signal when identification of a virus is not achieved in order to take adequate preparedness measures.

Since COVID-19 has brought great challenges to global public health governance, developing methods that track the evolution of the virus over the course of an epidemic or pandemic is useful for public health. This paper uses anomaly detection models to analyze SARS-CoV-2 virus genome k-mers to predict possible new critical variants in the collected samples. We used the sample data from Argentina, China and Portugal obtained from the Global Initiative on Sharing All Influenza Data (GISAID) to conduct multiple rounds of evaluation on several anomaly detection models, to verify the feasibility of this virus early warning and surveillance idea and find appropriate anomaly detection models for actual epidemic surveillance. Through multiple rounds of model testing, we found that the LUNAR (learnable unified neighborhood-based anomaly ranking) and LUNAR+LUNAR stacking model performed well in new critical variants detection. The results of simulated dynamic detection validate the feasibility of this approach, which can help efficiently monitor samples in local areas.

Assembly and publication of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome in January 2020 enabled the immediate development of tests to detect the new virus. This began the largest global testing programme in history, in which hundreds of millions of individuals have been tested to date. The unprecedented scale of testing has driven innovation in the strategies, technologies and concepts that govern testing in public health. This Review describes the changing role of testing during the COVID-19 pandemic, including the use of genomic surveillance to track SARS-CoV-2 transmission around the world, the use of contact tracing to contain disease outbreaks and testing for the presence of the virus circulating in the environment. Despite these efforts, widespread community transmission has become entrenched in many countries and has required the testing of populations to identify and isolate infected individuals, many of whom are asymptomatic. The diagnostic and epidemiological principles that underpin such population-scale testing are also considered, as are the high-throughput and point-of-care technologies that make testing feasible on a massive scale.Population-scale testing is an essential component of responses to the COVID-19 pandemic and is likely to become increasingly important in public health. Here, Mercer and Salit describe the roles of testing during the COVID-19 pandemic, including in genomic surveillance, contact tracing and environmental testing.

Immune-checkpoint inhibitors (ICIs), including anti-cytotoxic T lymphocyte antigen 4 (CTLA-4), anti-programmed cell death 1 (PD-1) and anti-programmed cell death 1 ligand 1 (PD-L1) antibodies, are arguably the most important development in cancer therapy over the past decade. The indications for these agents continue to expand across malignancies and disease settings, thus reshaping many of the previous standard-of-care approaches and bringing new hope to patients. One of the costs of these advances is the emergence of a new spectrum of immune-related adverse events (irAEs), which are often distinctly different from the classical chemotherapy-related toxicities. Owing to the growing use of ICIs in oncology, clinicians will increasingly be confronted with common but also rare irAEs; hence, awareness needs to be raised regarding the clinical presentation, diagnosis and management of these toxicities. In this Review, we provide an overview of the various types of irAEs that have emerged to date. We discuss the epidemiology of these events and their kinetics, risk factors, subtypes and pathophysiology, as well as new insights regarding screening and surveillance strategies. We also highlight the most important aspects of the management of irAEs.Immune-checkpoint inhibitors (ICIs) have dramatically improved the survival of patients with certain forms of cancer; however, these agents also have adverse effects that are often quite different to those of more traditional cancer therapies. In this Review, the authors describe the epidemiology, treatment and management of the various immune-related adverse events that can occur in patients receiving ICIs.

Influenza-like Illness (ILI) caused by the influenza virus, causes morbidity in Ghana. Records of ILI outbreaks in recent times and the COVID-19 pandemic disrupted surveillance activities, raises the quest to evaluate the ILI surveillance system. We evaluated the ILI surveillance system of Okai Koi North District to assess the system performance. We adapted the CDC Updated guidelines for evaluating public health surveillance systems for this evaluation at Okai Koi North District. We extracted and reviewed ILI 2018-2021 morbidity data from the district's sentinel site and the National Influenza Center (NIC). We observed surveillance activities and interviewed key informants using an observational checklist and semi-structured questionnaire. Data were analyzed for frequencies and proportions, and results presented in charts and tables, Of the 525 suspected samples reported from the district's sentinel site, 58 (11%) of 525 were Influenza positive with PVP, 11%. The system detected outbreaks over the evaluation period and has a year-round case detection. The system requires PCR for the detection of influenza virus. Nine (70%) of 13 staff indicated ILI surveillance system served as the backbone for case identification during the COVID-19 pandemic period. There is (89%) data completeness among sampled forms and data from the district and National Influenza Center. The system relies mostly on international donors. Nine (64%) of 13 staff confirmed no budget allocation for system operation. About 58% (306/525) of the ILI samples were transported to NIC for confirmatory test within the set 48 hours timeline. The system is useful as its meeting most of its objectives and it is sensitive. The system though complex, is flexible, representative, timely in most activities and has good data quality. We urge national stakeholders to establish thresholds for system.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly become a major global health problem, and public health surveillance is crucial to monitor and prevent virus spread. Wastewater-based epidemiology has been proposed as an addition to disease-based surveillance because virus is shed in the feces of ≈40% of infected persons. We used next-generation sequencing of sewage samples to evaluate the diversity of SARS-CoV-2 at the community level in the Netherlands and Belgium. Phylogenetic analysis revealed the presence of the most prevalent clades (19A, 20A, and 20B) and clustering of sewage samples with clinical samples from the same region. We distinguished multiple clades within a single sewage sample by using low-frequency variant analysis. In addition, several novel mutations in the SARS-CoV-2 genome were detected. Our results illustrate how wastewater can be used to investigate the diversity of SARS-CoV-2 viruses circulating in a community and identify new outbreaks.

Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer-related death worldwide. Risk factors for HCC include chronic hepatitis B and hepatitis C, alcohol addiction, metabolic liver disease (particularly nonalcoholic fatty liver disease) and exposure to dietary toxins such as aflatoxins and aristolochic acid. All these risk factors are potentially preventable, highlighting the considerable potential of risk prevention for decreasing the global burden of HCC. HCC surveillance and early detection increase the chance of potentially curative treatment; however, HCC surveillance is substantially underutilized, even in countries with sufficient medical resources. Early-stage HCC can be treated curatively by local ablation, surgical resection or liver transplantation. Treatment selection depends on tumour characteristics, the severity of underlying liver dysfunction, age, other medical comorbidities, and available medical resources and local expertise. Catheter-based locoregional treatment is used in patients with intermediate-stage cancer. Kinase and immune checkpoint inhibitors have been shown to be effective treatment options in patients with advanced-stage HCC. Together, rational deployment of prevention, attainment of global goals for viral hepatitis eradication, and improvements in HCC surveillance and therapy hold promise for achieving a substantial reduction in the worldwide HCC burden within the next few decades.

Sudden, large-scale and diffuse human migration can amplify localized outbreaks of disease into widespread epidemics 1 – 4 . Rapid and accurate tracking of aggregate population flows may therefore be epidemiologically informative. Here we use 11,478,484 counts of mobile phone data from individuals leaving or transiting through the prefecture of Wuhan between 1 January and 24 January 2020 as they moved to 296 prefectures throughout mainland China. First, we document the efficacy of quarantine in ceasing movement. Second, we show that the distribution of population outflow from Wuhan accurately predicts the relative frequency and geographical distribution of infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) until 19 February 2020, across mainland China. Third, we develop a spatio-temporal ‘risk source’ model that leverages population flow data (which operationalize the risk that emanates from epidemic epicentres) not only to forecast the distribution of confirmed cases, but also to identify regions that have a high risk of transmission at an early stage. Fourth, we use this risk source model to statistically derive the geographical spread of COVID-19 and the growth pattern based on the population outflow from Wuhan; the model yields a benchmark trend and an index for assessing the risk of community transmission of COVID-19 over time for different locations. This approach can be used by policy-makers in any nation with available data to make rapid and accurate risk assessments and to plan the allocation of limited resources ahead of ongoing outbreaks. Modelling of population flows in China enables the forecasting of the distribution of confirmed cases of COVID-19 and the identification of areas at high risk of SARS-CoV-2 transmission at an early stage.

The coronavirus disease 2019 (COVID-19) pandemic exposed critical limitations in the availability of timely mortality data to inform situational assessments and guide evidence-based public health responses at local, national and global levels. Less than half of the Member States of the World Health Organization (WHO) (73 out of 194) generated the required mortality data. Member States able to meet the sudden demand for real-time data did so through strong public health leadership and strategies for coordinated data acquisition, analysis and dissemination. In most other countries, attempts were made to conduct mortality surveillance but yielded only partial data with limited utility. This experience highlighted the need for a series of strategic shifts to strengthen mortality surveillance programmes in all countries, towards complete recording of deaths and their causes with timely data dissemination. These shifts include modifying systems to enable streamlining of the compilation and use of death records from all sources while meeting the requirements of official registration processes; using electronic protocols for data management and release; and ensuring effective leadership, coordination and data use for public health action. Recently, the Africa Centres for Disease Control and Prevention developed a conceptual framework for strengthening national mortality surveillance and operational guidance for implementation. These activities and resources for improving national mortality surveillance can inform global initiatives to support pandemic preparedness and response programmes. Such initiatives will enable global readiness for early epidemic detection and disease control measure prioritization, while also building routine mortality statistics programmes for population health assessment, health policy and research.

We develop a new method for estimating the effective reproduction number of an infectious disease ( R ) and apply it to track the dynamics of COVID-19. The method is based on the fact that in the SIR model, R is linearly related to the growth rate of the number of infected individuals. This time-varying growth rate is estimated using the Kalman filter from data on new cases. The method is easy to implement in standard statistical software, and it performs well even when the number of infected individuals is imperfectly measured, or the infection does not follow the SIR model. Our estimates of R for COVID-19 for 124 countries across the world are provided in an interactive online dashboard , and they are used to assess the effectiveness of non-pharmaceutical interventions in a sample of 14 European countries.