Forecasting admissions in psychiatric hospitals before and during Covid-19: a retrospective study with routine data.

The COVID-19 pandemic has strong effects on most health care systems. Forecasting of admissions can help for the efficient organisation of hospital care. We aimed to forecast the number of admissions to psychiatric hospitals before and during the COVID-19 pandemic and we compared the performance of machine learning models and time series models. This would eventually allow to support timely resource allocation for optimal treatment of patients. We used admission data from 9 psychiatric hospitals in Germany between 2017 and 2020. We compared machine learning models with time series models in weekly, monthly and yearly forecasting before and during the COVID-19 pandemic. A total of 90,686 admissions were analysed. The models explained up to 90% of variance in hospital admissions in 2019 and 75% in 2020 with the effects of the COVID-19 pandemic. The best models substantially outperformed a one-step seasonal naïve forecast (seasonal mean absolute scaled error (sMASE) 2019: 0.59, 2020: 0.76). The best model in 2019 was a machine learning model (elastic net, mean absolute error (MAE): 7.25). The best model in 2020 was a time series model (exponential smoothing state space model with Box-Cox transformation, ARMA errors and trend and seasonal components, MAE: 10.44). Models forecasting admissions one week in advance did not perform better than monthly and yearly models in 2019 but they did in 2020. The most important features for the machine learning models were calendrical variables. Model performance did not vary much between different modelling approaches before the COVID-19 pandemic and established forecasts were substantially better than one-step seasonal naïve forecasts. However, weekly time series models adjusted quicker to the COVID-19 related shock effects. In practice, multiple individual forecast horizons could be used simultaneously, such as a yearly model to achieve early forecasts for a long planning period and weekly models to adjust quicker to sudden changes.

Hydroxychloroquine as an aerosol might markedly reduce and even prevent severe clinical symptoms after SARS-CoV-2 infection.

Covid-19 is a new coronavirus disease first described in December 2019. This respiratory illness is severe and potentially fatal. Severe cases make up to 15%, lethality ranges between 1.5 and more than 10%. What is urgently needed is an efficient pharmacological treatment for the treatment of severe cases. During the infection of alveolar epithelial cells of the lung, the ACE2 receptor has a central function. The antimalarial drugs chloroquine phosphate (CQ) and hydroxychloroquine (HCQ) impair in vitro the terminal glycosylation of ACE2 without significant change of cell-surface ACE2 and, therefore, might be potent inhibitors of SARS-CoV-2 infections. Starting inhibition at 0.1 µM, CQ completely prevented in vitro infections at 10 µM, suggesting a prophylactic effect and preventing the virus spread 5 h after infection. In a first clinical trial, CQ was effective in inhibiting exacerbation of pneumonia, improving lung imaging findings, promotion of virus-negative conversion, and shortening the disease. In addition, HCQ, which is three times more potent than CQ in SARS-CoV-2 infected cells (EC50 0.72 µM), was significantly associated with viral load reduction/disappearance in COVID-19 patients compared to controls. Theoretically, CQ and HCQ could thus be effectively used in the treatment of SARS-CoV pneumonia. From a pharmacological standpoint, however, the major problems of oral treatment with these drugs are possible severe side effects and toxicity. Concretely, this relates to (a) the inconsistent individual bioavailability of these drugs at the alveolar target cells, depending on intestinal resorption, hepatic first-pass metabolism and accumulation in liver, spleen and lung, and (b) the need for a relatively high concentration of 1-5 µM at the alveolar surface. Therefore, we propose in a first dose estimation the use of HCQ as an aerosol in a dosage of 2-4 mg per inhalation in order to reach sufficient therapeutic levels at the alveolar epithelial cells. By using a low-dose non-systemic aerosol, adverse drug reactions will markedly be reduced compared with oral application. This increase in tolerability enables a broader use for prevention and after contact with an infected person, which would be an advantage especially for the high-risk, often multi-morbid and elderly patients. Empirical data on self-medication with a one-week aerosol application by two of the authors is presented. Inhalation was well tolerated without relevant side effects.