Mild and moderate COVID-19 during Alpha, Delta and Omicron pandemic waves in urban Maputo, Mozambique, December 2020-March 2022: A population-based surveillance study.

In sub-Saharan Africa, reported COVID-19 numbers have been lower than anticipated, even when considering populations' younger age. The extent to which risk factors, established in industrialised countries, impact the risk of infection and of disease in populations in sub-Saharan Africa, remains unclear. We estimated the incidence of mild and moderate COVID-19 in urban Mozambique and analysed factors associated with infection and disease in a population-based surveillance study. During December 2020-March 2022, 1,561 households (6,049 participants, median 21 years, 54.8% female, 7.3% disclosed HIV positive) of Polana Caniço, Maputo, Mozambique, were visited biweekly to report respiratory symptoms, anosmia, or ageusia, and self-administer a nasal swab for SARS-CoV-2 testing. Every three months, dried blood spots of a subset of participants (1,412) were collected for detection of antibodies against SARS-CoV-2 spike glycoprotein and nucleocapsid protein. Per 1000 person-years, 364.5 (95%CI 352.8-376.1) respiratory illness episodes were reported, of which 72.2 (95%CI 60.6-83.9) were COVID-19. SARS-CoV-2 seroprevalence rose from 4.8% (95%CI 1.1-8.6%) in December 2020 to 34.7% (95%CI 20.2-49.3%) in June 2021, when 3.0% were vaccinated. Increasing age, chronic lung disease, hypertension, and overweight increased risk of COVID-19. Older age increased the risk of SARS-CoV-2 seroconversion. We observed no association between socio-economic status, behaviour and COVID-19 or SARS-CoV-2 seroconversion. Active surveillance in an urban population confirmed frequent COVID-19 underreporting, yet indicated that the large majority of cases were mild and non-febrile. In contrast to reports from industrialised countries, social deprivation did not increase the risk of infection nor disease.

Epidemic intelligence in Europe: a user needs perspective to foster innovation in digital health surveillance.

BACKGROUND: European epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens. METHODS: We conducted a qualitative study to identify the need for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Centre for Disease Prevention and Control (ECDC). We adopted a stepwise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, and to validate and further define with practitioners the problems to address and the most adapted solutions to their work conditions. We characterized their EI activities, professional logics, and desired changes in their activities using NvivoⓇ software. RESULTS: Our analysis highlights that EI practitioners wished to collectively review their EI strategy to enhance their preparedness for emerging infectious diseases, adapt their routines to manage an increasing amount of data and have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariate sets for epidemiological assessments of national EI. The set of solutions identified to facilitate risk detection and risk assessment included visualization, text mining, and predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for partial rather than full automation of analyses to maintain control over the data and inputs and to adapt parameters to versatile objectives and characteristics. CONCLUSIONS: The study showed that the set of solutions needed by practitioners had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on harmonization between agencies and sectors while maintaining flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.