ABSTRACT
Objectives: To describe the development and usage of coronabambini.ch as an example of a pediatric electronic public health application and to explore its potential and limitations in providing information on disease epidemiology and public health policy implementation. Design: We developed and maintained a non-commercial online decision support tool, coronabambini.ch, to translate the Swiss FOPH pediatric (age 0-18 years) COVID-19 guidelines around testing and school/daycare attendance for caregivers, teachers, and healthcare personnel. We analyzed the online decision tool as well as a voluntary follow-up survey from October 2020 to September 2021 to explore its potential as a surveillance tool for public health policy and epidemiology. Participants 68′269 users accessed and 52′726 filled out the complete online decision tool. 3% (1′399/52′726) filled out a voluntary follow-up. 92% (18′797/20′330) of users were parents. Results: Certain dynamics of the pandemic and changes in testing strategies were reflected in the data captured by coronabambini.ch: e.g. in terms of disease epidemiology, gastrointestinal symptoms were reported more frequently in younger age-groups (13% (3′308/26′180) in children 0-5 years versus 9% (3′934/42′089) in children ≥6 years, X 2 =184, P =<.001). As a reflection of public health policy, the proportion of users consulting the tool for a positive contact without symptoms in children 6-12 years increased from 4% (1′415/32′215) to 6% (636/9′872) after the FOPH loosened testing criteria in this age-group, X 2 =69, P =<.001. Adherence to the recommendation was generally high (84% (1′131/1′352)) but differed by the type of recommendation: 89% (344/385) for ″stay at home and observe″, 75% (232/310) for ″school attendance″. Conclusions: Usage of coronabambini.ch was generally high in areas where it was developed and promoted. Certain patterns in epidemiology and adherence to public health policy could be depicted but selection bias was difficult to measure showing the potential and challenges of digital decision support as public health tools.
Subject(s)
COVID-19 , Signs and Symptoms, DigestiveABSTRACT
The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no effective antiviral drug except treatments for symptomatic therapy. Flux balance analysis is an efficient method to analyze metabolic networks. It allows optimizing for a metabolic function and thus e.g., predicting the growth rate of a specific cell or the production rate of a metabolite of interest. Here flux balance analysis was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the replication of the SARS-CoV-2 virus within the host tissue. Making use of expression data sets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then host-specific essential genes and gene-pairs were determined through in-silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, as well as ferroptosis, sphingolipid metabolism, cysteine metabolism, and fat digestion. By in-silico screening of FDA approved drugs on the putative disease-specific essential genes and gene-pairs, 45 drugs and 99 drug combinations were predicted as promising candidates for COVID-19 focused drug repositioning (https://github.com/sysbiolux/DCcov). Among the 45 drug candidates, six antiviral drugs were found and seven drugs that are being tested in clinical trials against COVID-19. Other drugs like gemcitabine, rosuvastatin and acetylcysteine, and drug combinations like azathioprine-pemetrexed might offer new chances for treating COVID-19.
Subject(s)
Coronavirus Infections , Severe Acute Respiratory Syndrome , COVID-19 , Lung DiseasesABSTRACT
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.