Swiss Medical Weekly

We describe a fast, noninvasive, low-cost survey method designed to understand the mode of transmission of an emerging pathogen. It is inspired from the standard household prevalence survey consisting in sampling households and counting the total number of people infected in each household, but refines it with the aim of improving diagnosis and estimating more parameters of the model of intra-household transmission. The survey was carried out in May-June 2020, during part of the first national French lockdown and received responses from more than 6,000 households involving a total of 20,000 people. We explain how we conceived the questionnaire, how we disseminated it, to the public through an open website hosted by CNRS, marketed through media and social media, and to a socially representative panel hosted by two survey institutes (BVA, Bilendi). We used the data obtained from the representative panel to correct for sampling biases in the CNRS survey using a classical raking procedure. Our results indicate that raking correctly canceled statistical biases between the two populations. We obtain the empirical distribution in households of the number and nature of symptoms. The main factors affecting the presence of symptoms are age, gender, body mass index (BMI), household size, but not necessarily in the expected direction. Our study shows that combining self-reporting and representative surveys allows investigators to obtain information on prevalence and household transmission mechanisms on emerging diseases at low cost.

Predictions about the further development of the Corona pandemic are widely diverging. Here, a simple yet powerful algorithm is introduced for extrapolating infection rate and number of total infections from available data. The calculation predicts that under present conditions the infection rate in Germany will culminate in a few weeks and decrease to low values by mid-June 2020. Total number of infections will reach several 100000 though. A refinement of the calculation is presented in the supplemental material and shows that the lock down in Germany has reduced the total number of infections from a target value of 338 000 to 184 000, corresponding to a decrease of about 45%.

ObjectiveTo characterize the clinical features of children and adolescents hospitalized with SARS-CoV-2 infections and to explore predictors for disease severity. DesignNationwide prospective observational cohort study. SettingData collected from 169 out of 351 childrens hospitals in Germany between March 18, 2020 and April 30, 2021 and comparison with the Statutory Notification System. Participants1,501 children and adolescents up to 19 years of age with laboratory confirmed SARS-CoV-2 infections who were admitted to childrens hospitals and subsequently reported to the COVID-19 registry of the German Pediatric Infectious Disease Society (DGPI). Main outcome measuresAdmission to intensive care, in-hospital. ResultsAs compared to the information in the statutory notification system, up to 30% of all children and adolescents hospitalized in Germany during the study period were reported to the DGPI registry. Median age was three years (IQR, 0-12), with 36% of reported cases being infants. Although roughly half of patients in the registry were not admitted to the hospital due to their SARS-CoV-2 infection, 72% showed infection-related symptoms during hospitalization. Preexisting comorbidities were present in 28%, most commonly respiratory disorders, followed by neurological, neuromuscular, and cardiovascular diseases. Median length of hospitalization was five days (IQR 3-10). Only 20% of patients received a SARS-CoV-2-related therapy. Infants were less likely to require therapy as compared to older children. Overall, 111 children and adolescents were admitted to intensive care units (ICU). In a fully adjusted model, patient age, trisomy 21, coinfections and primary immunodeficiencies (PID) were significantly associated with intensive care treatment. In a bivariate analysis, pulmonary hypertension, cyanotic heart disease, status post (s/p) cardiac surgery, fatty liver disease, epilepsy and neuromuscular impairment were statistically significant risk factors for ICU admission. ConclusionOverall, a small proportion of children and adolescents was hospitalized in Germany during the first year of the pandemic. The majority of patients within our registry was not admitted due to COVID-19 suggesting an overestimation of the disease burden even in hospitalized children. Nevertheless, a large proportion of children and adolescents with confirmed COVID-19 reported in Germany could be captured. This allowed for detailed assessment of overall disease severity and underlying risk factors in our cohort. The main risk factors for COVID-19 disease associated intensive care treatment were older patient age, trisomy 21, PIDs and coinfection at the time of hospitalization. Trial registrationRegistry of hospitalized pediatric patients with SARS-CoV-2 infection (COVID-19), DRKS00021506

The officially reported daily Covid-19 fatality rate is modelled with a trend line based on a nominal day-to-day reproduction rate and a cosine to take account of weekly fluctuations. Although the time trajectories of officially reported infections and fatalities are pronouncedly different, the reproduction rates obtained therefrom are similar. The long-term effective reproduction rate is around 0.835 and the administrative measures to contain the pandemic seem not to have an immediate reducing effect but well the ease of restrictions an increasing one. The fatality trajectory represented by its trend line can be projected from the number of daily infections by assuming a time lapse between symptom onset and death between 17 and 19 days and a time-dependent nominal lethality. The time trajectory of this lethality increases from 2.5% at March 16 when public life was restricted to 6% within 20 days indicating relatively more infections of vulnerable people. After stipulating face mask wearing at April 27, the nominal lethality decreases down to 1% later in summer. A detailed analysis shows that mask wearing really reduces the number of fatal infections and the officially reported daily infections in May and June are less lethal than before.

The case detection ratio of COVID-19 infections varies over time due to changing testing capacities, modified testing strategies and also, apparently, due to the dynamics in the number of infected itself. In this paper we investigate these dynamics by jointly looking at the reported number of detected COVID-19 infections with non-fatal and fatal outcomes in different age groups in Germany. We propose a statistical approach that allows us to spotlight the case detection ratio and quantify its changes over time. With this we can adjust the case counts reported at different time points so that they become comparable. Moreover we can explore the temporal development of the real number of infections, shedding light on the dark number. The results show that the case detection ratio has increased and, depending on the age group, is four to six times higher at the beginning of the second wave compared to what it was at the peak of the first wave. The true number of infection in Germany in October was considerably lower as during the peak of the first wave, where only a small fraction of COVID-19 infections were detected. Our modelling approach also allows quantifying the effects of different testing strategies on the case detection ratio. The analysis of the dynamics in the case detection rate and in the true infection figures enables a clearer picture of the course of the COVID-19 pandemic.

BackgroundIn Germany, diagnosis-based hospital surveillance of severe acute respiratory infections (SARI) incidence is operated weekly. However, diagnosis data from the most recent weeks is provisional and may be subject to systematic changes that we wish to account for to achieve a timely assessment of SARI trends. MethodsWe developed a novel nowcasting method based on (i) the already available data for the current week and (ii) historic changes in the data in weeks following initial reporting. The available weekly data were rescaled using multiplicative factors based on the historic patterns, to obtain the expected final incidence. We fit a parametric Beta distribution for observed upward changes in reported incidence, and a mixture distribution in case of the existence of downward changes, e.g., due to later corrections in diagnoses. We evaluated the model performance for the respiratory winter season 2024/2025. ResultsThe average weekly SARI incidence upon initial estimation in the first week after hospitalisation was 79.4% (median) of the incidence five weeks after hospitalisation, which was considered final. It increased to 95.1% in the second week after hospitalisation. The nowcast predictions matched the final incidences well and were able to indicate trends in real-time. Prediction intervals were well-calibrated and nowcasting can be performed in subgroups given sufficient case numbers. ConclusionsThe SARI surveillance in Germany achieved a high completeness already in the initial weeks after hospitalisation. The newly-developed nowcasting method yielded robust results with reliable uncertainty quantification. In combination, this facilitates a close to real-time assessment of SARI hospitalisation incidence in Germany.

As many countries plan to lift lockdown measures aimed at suppression of COVID-19, data from early regional epidemics in Italy were analysed to ascertain the effectiveness of the timing of preventative measures. The cumulative caseload data were extracted from regional epidemics in Italy. Epidemic features in regions where lockdown was applied early were compared to those where lockdown was applied later for statistical differences. There were statistically significant differences in the timing of the peak, the cumulative incidence at peak and the case/km2 at peak between regions where the lockdown had been applied early and those where it was applied late. The peak occurred 7 days earlier with four times less cases/km2 in regions where the lockdown was applied within 10 days of the start of the epidemic. Cumulative caseloads, cases/km2 and/or the number of days into an epidemic can be used to plan future localised suppression measures as part of a national post-lockdown policy. There were 350 (95% confidence interval (CI) 203) cumulative cases and 2.4 (CI 1.1) cases/km2 on day 8 of the regional epidemics.

The novel coronavirus (SARS-CoV-2), identified in China at the end of December 2019 and causing the disease COVID-19, has meanwhile led to outbreaks all over the globe with about 2.2 million confirmed cases and more than 150,000 deaths as of April 17, 2020 [37]. In view of most recent information on testing activity [32], we present here an update of our initial work [4]. In this work, mathematical models have been developed to study the spread of COVID-19 among the population in Germany and to asses the impact of non-pharmaceutical interventions. Systems of differential equations of SEIR type are extended here to account for undetected infections, as well as for stages of infections and age groups. The models are calibrated on data until April 5, data from April 6 to 14 are used for model validation. We simulate different possible strategies for the mitigation of the current outbreak, slowing down the spread of the virus and thus reducing the peak in daily diagnosed cases, the demand for hospitalization or intensive care units admissions, and eventually the number of fatalities. Our results suggest that a partial (and gradual) lifting of introduced control measures could soon be possible if accompanied by further increased testing activity, strict isolation of detected cases and reduced contact to risk groups.

AIMIn late February and early March 2020, Switzerland experienced rapid growth of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections with 30,243 confirmed cases and 1,860 deaths as of 10 May 2020. The sequential introduction of non-pharmaceutical interventions (NPIs) resulted in successful containment of the epidemic. A better understanding of how the timing of implementing NPIs influences the dynamics and outcome of SARS-CoV-2 epidemics will be crucial for the management of a potential resurgence in Switzerland. METHODSWe developed a dynamic transmission model that describes infection, hospitalization, recovery and death due to SARS-CoV-2 in Switzerland. Using a maximum likelihood framework, we fitted the model to aggregated daily numbers of hospitalized patients, ICU occupancy and death from 25 February to 10 May 2020. We estimated critical parameters of SARS-CoV-2 transmission in Switzerland and explored counterfactual scenarios of an earlier and later implementation of NPIs. RESULTSWe estimated the basic reproduction number R0 = 2.61 (95% compatibility interval, CI: 2.51-2.71) during the early exponential phase of the SARS-CoV-2 epidemic in Switzerland. After the implementation of NPIs, the effective reproduction number approached Re = 0.64 (95% CI: 0.61-0.66). Based on the observed doubling times of the epidemic before and after the implementation of NPIs, we estimated that one week of early exponential spread required 3.1 weeks (95% CI: 2.8-3.3 weeks) of lockdown to reduce the number of infections to the same level. Introducing the same sequence of NPIs one week earlier or later would have resulted in substantially lower (399, 95% prediction interval, PI: 347-458) and higher (8,683, 95% PI: 8,038-9,453) numbers of deaths, respectively. CONCLUSIONSThe introduction of NPIs in March 2020 prevented thousands of SARS-CoV-2-related deaths in Switzerland. Early implementation of NPIs during SARS-CoV-2 outbreaks can reduce the number of deaths and the necessary duration of strict control measures considerably.

We develop a two strain, age-structured, compartmental model to assess the spreading potential of the B.1.1.7 variant across several European metropolitan areas and countries. The model accounts for B.1.1.7 introductions from the UK and different locations, as well as local mitigation policies in the time period 2020/09 - 2021/02. In the case of an increase of transmissibility of 50%, the B.1.1.7 variant has the potential to become dominant in all investigated areas by the end of March 2021.

The interpretation of the number of COVID-19 cases and deaths in a country or region is strongly dependent on the number of performed tests. We developed a novel SIR based epidemiological model (SIVRT) which allows the country-specific integration of testing information and other available data. The model thereby enables a dynamic inspection of the pandemic and allows estimating key figures, like the number of overall detected and undetected COVID-19 cases and the infection fatality rate. As proof of concept, the novel SIVRT model was used to simulate the first phase of the pandemic in Luxembourg. An overall number of infections of 13.000 and an infection fatality rate of 1,3% was estimated, which is in concordance with data from population-wide testing. Furthermore based on the data as of end of May 2020 and assuming a partial deconfinement, an increase of cases is predicted from mid of July 2020 on. This is consistent with the current observed rise and shows the predictive potential of the novel SIVRT model.

The novel coronavirus (SARS-CoV-2), identified in China at the end of December 2019 and causing the disease COVID-19, has meanwhile led to outbreaks all over the globe, with about 571,700 confirmed cases and about 26,500 deaths as of March 28th, 2020. We present here the preliminary results of a mathematical study directed at informing on the possible application or lifting of control measures in Germany. The developed mathematical models allow to study the spread of COVID-19 among the population in Germany and to asses the impact of non-pharmaceutical interventions.

We investigate six scenarios spanning main parts of the decision space of non-medical interventions against the CoV-2 epidemic in Germany. Based on the notion of interventions-lifting we classify and evaluate the scenarios by five attributes (indicators): amount of interventions-lifting, death numbers, Public Health Care capacity, population immunity, peak dates of infections. For quantitative reasoning we use a simulated modified SEIR-model calibrated with actual data. We identify margins for intervention-liftings wrt. 13.05.2020 and discuss the relation to the effective reproduction number with a 6d-generation time. We show that, in order to constrain death numbers comparable to a strong Influenza epidemic, there is only a small corridor of 16% of possible liftings, with an additional 4% margin contributed by automated contact tracing. We show also that there is a much broader corridor of 50%+18%, though not overloading critical Public Health Care capacity, implying high death numbers.

ObjectivesTo longitudinally assess severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence and clustering of seropositive children within school classes in March-April 2021 compared to June-July and October-November 2020. To examine the evolution of symptoms and the extent of under-detection of SARS-CoV-2 in children. DesignProspective cohort study of randomly selected schools and classes. SettingSchools remained open for physical attendance in Switzerland from May 2020 to the end of 2020/2021 school year. Lower school level (age range 7-10 years) and middle school level (8-13 years) children in primary schools, and upper school level (12-17 years) children in secondary schools were invited for SARS-CoV-2 serological testing in the Ciao Corona study in the canton of Zurich, Switzerland. Three testing rounds were completed in June-July 2020 (T1; after the first wave of SARS-CoV-2 infections), October-November 2020 (T2; during the peak of the second wave), and March-April 2021 (T3; after the second wave and with SARS-CoV-2 variants of concern becoming dominant). Parents completed questionnaires on sociodemographic information and symptoms. Participants2487 children (median age 12 years, age range 7-17 years) recruited from 275 classes in 55 schools participated in the testing in March-April 2021; total of 2974 children participated in at least one of the 3 testing rounds. Main outcome measuresSARS-CoV-2 serology results; clustering of seropositive children within classes; reported symptoms. ResultsThe proportion of children who were SARS-CoV-2 seropositive increased from 1.5% (95% credible interval (CrI) 0.6% to 2.6%) in June-July 2020, to 6.6% (95% CrI 4.0% to 8.9%) in October-November, and to 16.4% (95% CrI 12.1% to 19.5%) in March-April 2021. By March-April 2021, children in upper school level (12.4%; 95% CrI 7.3% to 16.7%) were less likely to be seropositive than those in middle (19.5%; 95% CrI 14.2% to 24.4%) or lower school levels (16.0%; 95% CrI 11.0% to 20.4%). Children in the upper school level had a 5.1% (95% CI -9.4% to -0.7%) lower than expected seroprevalence by March-April 2021 than those in middle school level, based on difference-in-differences analysis. The ratio of PCR-diagnosed to all seropositive children changed from 1 to 21.7 (by June-July 2020) to 1 to 3.5 (by March-April 2021). Symptoms were reported by 37% of newly seropositive and 16% seronegative children. Potential clusters of 3 or more newly seropositive children were detected in 24 of 119 (20%) classes with a high participation rate, from which a median of 17 clusters could be expected due to random distribution of seropositive children within the classes. Clustering was lowest in middle and upper school levels. Retention rate in the cohort was high (84% of T1 participants attended T3). Among participants, supporting society and research were reported more commonly for participation than personal reasons. Fear of blood sampling was the most frequently reported reason for non-participation, reported for 64% of children. ConclusionsBy March-April 2021, 16.4% of children and adolescents were seropositive in the canton of Zurich, Switzerland. The majority of clusters of SARS-CoV-2 seropositive children in school classes could be explained by community rather than intra-class transmission of infections. Seroprevalence and clustering was lowest in upper school levels during all timepoints. Trial registrationClinicalTrials.gov NCT04448717. What is already known on the topicO_LITransmission of SARS-CoV-2 in school setting largely followed community transmission in 2020. C_LIO_LIWith implemented preventive measures, secondary attack rates were low and clustering of SARS-CoV-2 infections within classes and schools (outbreaks) were observed rarely. C_LI What this study addsO_LIWith high community incidence and new variants of SARS-CoV-2, seroprevalence increased in school children between October 2020 - March 2021 in the canton of Zurich in Switzerland, and was higher in lower school levels. C_LIO_LIMost of the potential clusters of children who tested seropositive within classes could be explained by community rather than intra-class transmission of SARS-CoV-2, especially in middle and upper school levels. C_LIO_LIMore children who tested seropositive in March-April 2021 were diagnosed and reported symptoms potentially related to SARS-CoV-2 infection more frequently than those who tested seropositive in June-July or October-November 2020. C_LIO_LIThe most frequent reason for non-participation was fear of blood sampling (62% of children). C_LI

With a mathematical method based on linear algebra, from open access data (data.gouv.fr, google, apple) we produce forecasts for the number of patients in intensive care in France with an average error of 4% at 7 days, 7% at 14 days, 8% at 21 days, 10% at one month, 17% at 2 months, and 31% at 3 months. For the other epidemic indicators, the error is on average 6% at 7 days and 25% at 2 months.

Several countries have implemented lockdowns to control their COVID-19 epidemic. However, questions like "where" and "when" still require answers. We assessed the impact of national and regional lockdowns considering the French first epidemic wave of COVID-19 as a case study. In a regional lockdown scenario aimed at preventing intensive care units (ICU) saturation, almost all French regions would have had to implement a lockdown within 10 days and 96% of ICU capacities would have been used. For slowly growing epidemics, with a lower reproduction number, the expected delays between regional lockdowns increases. However, the public health costs associated with these delays tend to grow exponentially with time. In a quickly growing pandemic wave, defining the timing of lockdowns at a regional rather than national level delays by a few days the implementation of a nationwide lockdown but leads to substantially higher morbidity, mortality and stress on the healthcare system.

Predictive epidemic modeling can enhance situational awareness during emerging and seasonal outbreaks and has received increasing interest in recent years. A common distinction is between nowcasting, which corrects recent incidence data for reporting delays, and forecasting, which predicts future trends. This paper presents an integrated system for nowcasting and multi-model short-term forecasting of hospitalizations from severe acute respiratory infections (SARI) in Germany (November 2023-September 2024). We propose a modular approach combining a statistical nowcasting model with various data-driven forecasting methods, including a time series model, a gradient boosting approach, and a neural network. These are combined into an ensemble approach, which achieves the best average performance. The resulting forecasts are overall well-calibrated up to four weeks ahead, but struggled to capture the unusual double peak which occurred during the test season. While the presented analysis is retrospective, it serves as a blueprint for a collaborative real-time forecasting platform for respiratory diseases in Germany (the RESPINOW Hub). We conclude with an outlook on this system, which was launched in the fall of 2024 and covers a broader range of data sources and modeling approaches.

Congenital heart defects are a leading cause of neonatal morbidity and mortality worldwide. Early diagnosis is essential to enable timely treatment and improve patient outcomes; however, access to early detection and specialized care is unevenly distributed across regions in Argentina. This study describes the prevalence and timing of diagnosis of congenital heart defects and critical congenital heart defects among live births covered by the public health system nationwide. We performed a cross-sectional, descriptive, ecological study including all live births between 2014 and 2019 with a diagnosis of congenital heart defects reported up to five years of age in the National Registry of Congenital Heart Diseases. We calculated prevalence rates and median age at case notification for each province to assess geographic disparities. Out of 2,473,720 live births, 16,150 cases of congenital heart defects (prevalence 65.3 per 10,000 live births) and 3,700 cases of critical congenital heart defects (15.0 per 10,000) were identified. Provincial prevalence ranged widely, from 34.9 to 212.2 per 10,000 for congenital heart defects and from 10.7 to 27.3 per 10,000 for critical cases. The national median age at notification was 75 days for all congenital heart defects and 29 days for critical cases, with notable provincial differences. These findings demonstrate significant provincial variability in both the prevalence of congenital heart defects and the age at which cases are reported to the national health registry. Strengthening early detection efforts and ensuring equitable access to specialized care are crucial to reduce morbidity and mortality associated with these conditions in Argentina and similar settings.

IntroductionCOVID-19 pandemic has modified the epidemiology of lower respiratory tract infections (LRTI), particularly in patients presenting a chronic respiratory disease (CRD). LRTI incidence substantially decreased at the start of the COVID-19 pandemic. However, studies focusing on the post-pandemic period are missing. We aimed to evaluate the impact of the pandemic and post-pandemic periods on hospital admissions for LRTI, with a focus on patients with CRD. MethodsFrom July 2013 to June 2023, monthly numbers of adult hospitalisations for LRTI (excluding SARS-CoV-2) were extracted from the anonymized French National Hospital Discharge Database. They were modelled by regressions with autocorrelated errors. Three periods were defined: (1) early pandemic and successive lockdowns (April 2020 to May 2021); (2) gradual lifting of restrictions and widespread SARS-CoV-2 vaccination (June 2021 to June 2022); (3) withdrawal of restriction measures (July 2022 to July 2023). Analyses were computed for the entire series, by gender, age, severity, and pre-existing CRD ResultsBefore the pandemic, LRTI hospitalisations showed a winter seasonal pattern with a rising trend. Pre-pandemic incidence was 96 (90.5 to 101.5) per 100,000 population. Compared with the pre-pandemic period, seasonality was no longer present and significant reductions were estimated in the first two periods: -43.64% (-50.11 to -37.17) and -32.97% (-39.88 to -26.05), respectively. A rebound with a positive trend and a seasonal pattern was observed in period 3. Similar results were observed for CRD patients with no significant difference with pre-pandemic levels in the last period (-9.21%; -20.9% to 1.67%), albeit with differential changes according to the type of CRD. ConclusionsCOVID-19 pandemic containment measures contributed to significant changes in LRTI incidence, with a rapid increase and return to a seasonal pattern after their gradual lifting, particularly in patients with CRD.

The implementation of Non Pharmaceutical Interventions (NPI), triggered by the emergence of covid-19, decrease the RSV circulation. Data, from our ongoing surveillance; show a late introduction of RSV at the end of December and a 4 month delayed epidemic start without significant change in our NPI policy. This data indicates that RSV still have the potential to give a late season outbreak in northern hemisphere. RSV surveillance should be reinforced and RSV Pharmaceutical Interventions maintained for at risk neonate