Predictions for the COVID-19 pandemic in Germany using the modified Bateman SIZ model.

AIMS OF THE STUDY: To obtain predictions for the course of the COVID-19 pandemic in Germany using the modified Bateman SIZ model and input variables based on the status quo in July 2020. To predict the effect of a change in tα on the course of the pandemic. To evaluate the robustness and sensitivity of the model in response to a change in the input parameters. MATERIALS AND METHODS: Start parameters for the modified Bateman SIZ model were obtained from observational data published by the Robert-Koch-Institute in Berlin for the period June 1 to July 13, 2020. The robustness and sensitivity of the model were determined by changing the input parameter for the doubling-time (tα) by ± 5% and ± 10%. RESULTS: The predictions show that small changes, ± 5%, in the doubling-time, tα for the rate of increase in the number of new infections, can have a major effect, both positive and negative, on the course of the pandemic. The model predicted that the number of persons infected with the virus would reach 1 million within 8 years. A 5% longer tα would reduce the number of infected persons by ~ 75%. In contrast, a 5% shorter doubling-time would increase the number of infections over 8 years to ~ 9 million when the number of infectious persons would exceed 100,000 at the end of 2022. The pandemic is predicted to have disappeared by the end of 2024. DISCUSSION: Predictions for the course of the COVID-19 pandemic in Germany based on the status quo up to July 13, 2020 have been obtained using the modified Bateman SIZ model. There are several important assumptions necessary to apply the model and thus the results must be interpreted with caution. The model, previously used to predict the course of the COVID-19 pandemic in the city of Heidelberg (pop. 166,000) gives comparable predictive data for the whole of Germany (pop. 83 million) and thus appears to be both sensitive and robust. CONCLUSION: Since a shorter doubling-time for the number of infectious persons by only 5% would result in a major clinical emergency, interventional measures such as vaccination are urgently needed. Taking into consideration that a SARS-CoV-2 vaccine is not yet available and the efficacy of the Corona-Warn-App has yet to be shown, a relaxation in the lockdown conditions in Germany in 2020 appears premature.

COVID-19 pandemic predictions using the modified Bateman SIZ model and observational data for Heidelberg, Germany: Effect of vaccination with a SARS-CoV-2 vaccine, coronavirus testing and application of the Corona-Warn-App.

AIMS OF THE STUDY: Published data show that the current progression of the COVID-19 pandemic in Heidelberg, Germany, despite the current lockdown, could continue into 2021 and become more severe. We have used the modified Bateman SIZ algorithm to predict the effects of interventional measures to control the COVID-19 pandemic. MATERIALS AND METHODS: Model parameters, e.g., doubling time and rate of decrease in the number of infectious persons were obtained from published reports. Predictions were made for the status quo on June 1, 2020, and for interventional measures obtained for 4 scenarios. These included vaccination of the whole population using a SARS-CoV-2 vaccine having an efficacy of 50% and 100%, mass-testing for COVID-19 coronavirus and application of the Corona-Warn-App. RESULTS: The principle findings were 1) without new measures to control the pandemic, the daily number of infectious persons could reach a peak of > 4,500 daily within 18 months when > 67,000 persons would have been infected. This could be prevented by using a vaccine with 50% efficacy which was almost equally effective as a vaccine with 100% efficacy. Application of the Corona-Warn-App was the most effective method and more effective than testing for COVID-19. The methodology used has been described in detail to enable other researchers to apply the modified Bateman SIZ model to obtain predictions for COVID-19 outbreaks in other regions. Application of the model has been verified by independent investigators using different commercial software packages. CONCLUSION: The modified Bateman SIZ model has been verified and used to predict the course of the COVID-19 pandemic in Heidelberg. Lockdown measures alone are insufficient to control the pandemic during 2021. Vaccination, diagnostic tests, and use of the Corona-Warn-App with quarantine could successfully control the spread of the coronavirus infection in the community. The Corona-Warn-App applied correctly may be the most effective. The model showed that vaccination with 50% efficacy is almost as effective as vaccination with 100% efficacy.

Detecting adverse drug reactions on paediatric wards: intensified surveillance versus computerised screening of laboratory values.

BACKGROUND: Adverse drug reactions (ADRs) contribute significantly to patient morbidity and mortality, as well as to costs for healthcare systems. Our aim was to evaluate the type and incidence of ADRs in a paediatric hospital population, comparatively ascertained by two different methodological approaches. METHODS: Our prospective study enrolled all patients admitted to two of the general children wards (46 beds) and the paediatric intensive care unit (6 beds) at the HELIOS Klinikum Wuppertal teaching hospital in Germany, over the study period of 3 months. We used two methods to detect ADRs. The intensified surveillance system relied on a trained physician conducting ward rounds and assessing patient charts. The computer-assisted screening of pathological laboratory parameters used values slightly below or above the age-specific normal range as a trigger signal for a potential ADR, which was subsequently assessed by trained personnel. RESULTS: By applying both methods simultaneously we observed that 14.1% of children experienced an ADR while they were hospitalised and 2.7% of children were admitted to hospital because of the ADR. Intensified surveillance resulted in the detection of 101 ADRs in 11.9% of patients, predominantly presenting with gastrointestinal symptoms, skin and CNS disorders; computer-assisted screening identified 45 ADRs in 5.7% of patients, mainly with drug-induced blood dyscrasia and liver damage. Furthermore, the ADRs detected by the intensified method were more severe, affected younger children and showed a closer causal attributability to the reaction than the ADRs observed by the computerised method. The spectra of drugs involved were similar, with the anti-infectives being suspected most frequently. The sensitivities of the intensified surveillance system and the computerised surveillance screening came to 67.2% and 44.8%, respectively, with computer-assisted screening having a specificity of 72.8%. The mean positive predictive value of the pathological laboratory values under surveillance by computer-assisted screening was 18.6%. Approximately 25% of ADR-related drugs administered were used for off-label indications. CONCLUSION: Using the published literature for comparison, we found that ADRs occur as frequently in paediatric patients as in adult patients. Intensified surveillance and computerised surveillance applied in the paediatric setting show substantial differences in their detection specificities. A higher number of and more severe ADRs can be detected by intensified surveillance than by computerised surveillance, but require higher personnel resources.