Maintenance of Elective Patient Care at Berlin University Children's Hospital During the COVID-19 Pandemic.

Background: In Germany, so far the COVID-19 pandemic evolved in two distinct waves, the first beginning in February and the second in July, 2020. The Berlin University Children's Hospital at Charité (BCH) had to ensure treatment for children not infected and infected with SARS-CoV-2. Prevention of nosocomial SARS-CoV-2 infection of patients and staff was a paramount goal. Pediatric hospitals worldwide discontinued elective treatments and established a centralized admission process. Methods: The response of BCH to the pandemic adapted to emerging evidence. This resulted in centralized admission via one ward exclusively dedicated to children with unclear SARS-CoV-2 status and discontinuation of elective treatment during the first wave, but maintenance of elective care and decentralized admissions during the second wave. We report numbers of patients treated and of nosocomial SARS-CoV-2 infections during the two waves of the pandemic. Results: During the first wave, weekly numbers of inpatient and outpatient cases declined by 37% (p < 0.001) and 29% (p = 0.003), respectively. During the second wave, however, inpatient case numbers were 7% higher (p = 0.06) and outpatient case numbers only 6% lower (p = 0.25), compared to the previous year. Only a minority of inpatients were tested positive for SARS-CoV-2 by RT-PCR (0.47% during the first, 0.63% during the second wave). No nosocomial infection of pediatric patients by SARS-CoV-2 occurred. Conclusion: In contrast to centralized admission via a ward exclusively dedicated to children with unclear SARS-CoV-2 status and discontinuation of elective treatments, maintenance of elective care and decentralized admission allowed the almost normal use of hospital resources, yet without increased risk of nosocomial infections with SARS-CoV-2. By this approach unwanted sequelae of withheld specialized pediatric non-emergency treatment to child and adolescent health may be avoided.

Primary headaches during the COVID-19 lockdown in Germany: analysis of data from 2325 patients using an electronic headache diary.

BACKGROUND: Lockdown measures due to the COVID-19 pandemic have led to lifestyle changes, which in turn may have an impact on the course of headache disorders. We aimed to assess changes in primary headache characteristics and lifestyle factors during the COVID-19 lockdown in Germany using digital documentation in the mobile application (app) M-sense. MAIN BODY: We analyzed data of smartphone users, who entered daily data in the app in the 28-day period before lockdown (baseline) and in the first 28 days of lockdown (observation period). This analysis included the change of monthly headache days (MHD) in the observation period compared to baseline. We also assessed changes in monthly migraine days (MMD), the use of acute medication, and pain intensity. In addition, we looked into the changes in sleep duration, sleep quality, energy level, mood, stress, and activity level. Outcomes were compared using paired t-tests. The analysis included data from 2325 app users. They reported 7.01 ± SD 5.64 MHD during baseline and 6.89 ± 5.47 MHD during lockdown without significant changes (p > 0.999). MMD, headache and migraine intensity neither showed any significant changes. Days with acute medication use were reduced from 4.50 ± 3.88 in the baseline to 4.27 ± 3.81 in the observation period (p < 0.001). The app users reported reduced stress levels, longer sleep duration, reduced activity levels, along with a better mood, and an improved energy level during the first lockdown month (p ≤ 0.001). In an extension analysis of users who continued to use M-sense every day for 3 months after initiation of lockdown, we compared the baseline and the subsequent months using repeated-measures ANOVA. In these 539 users, headache frequency did not change significantly neither (6.11 ± 5.10 MHD before lockdown vs. 6.07 ± 5.17 MHD in the third lockdown month, p = 0.688 in the ANOVA). Migraine frequency, headache and migraine intensity, and acute medication use were also not different during the entire observation period. CONCLUSION: Despite slight changes in factors that contribute to the generation of headache, COVID-19-related lockdown measures did not seem to be associated with primary headache frequency and intensity over the course of 3 months.

COVID-19 Outbreak Prediction with Machine Learning

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP;and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models.