Coronavirus disease (COVID-19)

Given its high economic and societal cost, policymakers might be reluctant to implement a large-scale lockdown in case of coronavirus disease (COVID-19) epidemic rebound. They may consider it as a last resort option if alternative control measures fail to reduce transmission.

We developed a modelling framework to ascertain the use of lockdown to ensure intensive care unit (ICU) capacity does not exceed a peak target defined by policymakers.

We used a deterministic compartmental model describing transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the trajectories of COVID-19 patients in healthcare settings, accounting for age-specific mixing patterns and an increasing probability of severe outcomes with age. The framework is illustrated in the context of metropolitan France.

The daily incidence of ICU admissions and the number of occupied ICU beds are the most robust indicators to decide when a lockdown should be triggered. When the doubling time of hospitalisations estimated before lockdown is between 8 and 20 days, lockdown should be enforced when ICU admissions reach 3.0–3.7 and 7.8–9.5 per million for peak targets of 62 and 154 ICU beds per million (4,000 and 10,000 beds for metropolitan France), respectively. When implemented earlier, the lockdown duration required to get back below a desired level is also shorter.

We provide simple indicators and triggers to decide if and when a last-resort lockdown should be implemented to avoid saturation of ICU. These metrics can support the planning and real-time management of successive COVID-19 pandemic waves.

Population-based studies characterising outcomes of COVID-19 in European settings are limited, and effects of socio-economic status (SES) on outcomes have not been widely investigated.

We describe the epidemiological characteristics of COVID-19 cases, highlighting incidence and mortality rate differences across SES during the first wave in Barcelona, Catalonia, Spain.

This population-based study reports individual-level data of laboratory-confirmed COVID-19 cases diagnosed from 24 February to 4 May 2020, notified to the Public Health Agency of Barcelona and followed until 15 June 2020. We analysed end-of-study vital status and the effects of chronic conditions on mortality using logistic regression. Geocoded addresses were linked to basic health area SES data, estimated using the composed socio-economic index. We estimated age-standardised incidence, hospitalisation, and mortality rates by SES.

Of 15,554 COVID-19-confirmed cases, the majority were women (n = 9,028; 58%), median age was 63 years (interquartile range: 46–83), 8,046 (54%) required hospitalisation, and 2,287 (15%) cases died. Prevalence of chronic conditions varied across SES, and multiple chronic conditions increased risk of death (≥ 3, adjusted odds ratio: 2.3). Age-standardised rates (incidence, hospitalisation, mortality) were highest in the most deprived SES quartile (incidence: 1,011 (95% confidence interval (CI): 975–1,047); hospitalisation: 619 (95% CI: 591–648); mortality: 150 (95% CI: 136–165)) and lowest in the most affluent (incidence: 784 (95% CI: 759–809); hospitalisation: 400 (95% CI: 382–418); mortality: 121 (95% CI: 112–131)).

COVID-19 outcomes varied markedly across SES, underscoring the need to implement effective preventive strategies for vulnerable populations.

Very few studies describe factors associated with COVID-19 diagnosis in children.

We here describe characteristics and risk factors for COVID-19 diagnosis in children tested in 20 paediatric centres across Italy.

We included cases aged 0–18 years tested between 23 February and 24 May 2020. Our primary analysis focused on children tested because of symptoms/signs suggestive of COVID-19.

Among 2,494 children tested, 2,148 (86.1%) had symptoms suggestive of COVID-19. Clinical presentation of confirmed COVID-19 cases included besides fever (82.4%) and respiratory signs or symptoms (60.4%) also gastrointestinal (18.2%), neurological (18.9%), cutaneous (3.8%) and other unspecific influenza-like presentations (17.8%). In multivariate analysis, factors significantly associated with SARS-CoV-2 positivity were: exposure history (adjusted odds ratio (AOR): 39.83; 95% confidence interval (CI): 17.52–90.55; p < 0.0001), cardiac disease (AOR: 3.10; 95% CI: 1.19–5.02; p < 0.0001), fever (AOR: 3.05%; 95% CI: 1.67–5.58; p = 0.0003) and anosmia/ageusia (AOR: 4.08; 95% CI: 1.69–9.84; p = 0.002). Among 190 (7.6%) children positive for SARS-CoV-2, only four (2.1%) required respiratory support and two (1.1%) were admitted to intensive care; all recovered.

Recommendations for SARS-CoV-2 testing in children should consider the evidence of broader clinical features. Exposure history, fever and anosmia/ageusia are strong risk factors in children for positive SARS-CoV-2 testing, while other symptoms did not help discriminate positive from negative individuals. This study confirms that COVID-19 was a mild disease in the general paediatric population in Italy. Further studies are needed to understand risk, clinical spectrum and outcomes of COVID-19 in children with pre-existing conditions.