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Over the past 2 decades, polypharmacy has become the de-facto standard of acute treatment in psychiatry where patients with psychiatric disorders receive a multiple medication regimen. There is growing evidence for a potential link between major psychiatric disorders and inflammatory processes. Combining these two aspects aims at avoiding polypharmacy attempts among patients with inflammatory activation through alternative treatment strategies. In this study, we addressed the following questions: (1) to what extent can polypharmacy be explained through the factors “diagnosis”, “previous history”, “severity at baseline”, “age”, “gender”, and “psychiatrist in charge”; (2) what are the differences between polypharmacy and monotherapy regarding efficacy and side effect profiles; and (3) what amount of between-patient variance is explainable by the natural antibody immunoglobulin M (IgM) within each diagnostic group. This naturalistic longitudinal study was comprised of 279 patients under therapy with a clinical diagnosis of depressive (ICD-10: “F3x.x”;n = 195) or schizophrenic disorders (ICD-10: “F2x.x”;n = 84). The study protocol included (1) assessment of previous history by the SADS Syndrome Check List SSCL-16 (lifetime version); (2) repeated measurements over 5 weeks assessing the time course of improvement by the Hamilton Depression Scale HAM-D and the Positive and Negative Syndrome Scale PANSS, along with medication and unwanted side effects through the Medication and Side Effects Inventory MEDIS; and (3) the collection of blood samples from which DNA and serum were extracted. The association between inflammatory response system and psychiatric disorders was detailed by fitting multi-layer Neural Net (NN) models to the observed data (“supervised learning”). The same approach was used to set up prediction models of side effects. Our data showed that polypharmacy was omnipresent. Yet the various polypharmacy regimens had no advantage over monotherapy: we even found slightly larger baseline score reductions under monotherapy, independent of primary diagnoses and for comparable baseline severities. Most patients experienced unwanted side effects. The close link between side effects and treatment regimen was revealed by a linear model in which the mere number of drugs explained a significant (p r = 0.746 (p = 0.0004) between global schizophrenia scores and IgM levels, along with a correct prediction of response of 94.4%, thus explaining 55.7% of the observed between-patient variance. For the F3 patients, our NN model identified a 19.6% subgroup exhibiting a significant correlation ofr = 0.644 (p = 0.00003) between global depression scores and IgM levels, along a correct prediction of response of 89.6%, thus explaining 41.4% of the observed between-patient variance. Polypharmacy is omnipresent in today’s acute treatment of psychiatric disorders. Given the large proportion of patients with unwanted side effects and the strong correlation between side effects and the number of drugs, polypharmacy approaches are not equally suited for every patient. In terms of efficacy, there are no advantages of polypharmacy over monotherapy. Most notably, our study appears to have cleared the way for the reliable identification of a subgroup of patients for whom the inflammatory response system is a promising target of therapeutic intervention.

Throughout the past six months, no number has dominated the public media more persistently than the reproduction number of COVID-19. This powerful but simple concept is widely used by the public media, scientists, and political decision makers to explain and justify political strategies to control the COVID-19 pandemic. Here we explore the effectiveness of political interventions using the reproduction number of COVID-19 across Europe. We propose a dynamic SEIR epidemiology model with a time-varying reproduction number, which we identify using machine learning. During the early outbreak, the basic reproduction number was 4.22±1.69, with maximum values of 6.33 and 5.88 in Germany and the Netherlands. By May 10, 2020, it dropped to 0.67±0.18, with minimum values of 0.37 and 0.28 in Hungary and Slovakia. We found a strong correlation between passenger air travel, driving, walking, and transit mobility and the effective reproduction number with a time delay of 17.24±2.00 days. Our new dynamic SEIR model provides the flexibility to simulate various outbreak control and exit strategies to inform political decision making and identify safe solutions in the benefit of global health.