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This article discusses the use of optimal control in both therapeutics and epidemiology. Optimal control is a valuable tool for optimizing drug infusion protocols in cancer therapy and has gained attention in epidemiology due to the Covid-19 pandemic. The article highlights the similarities between these two contexts, such as the use of models and the need for ad-hoc techniques to implement optimal control strategies. The special issue discussed in the article includes papers on both epidemiology and therapeutics, with controls related to social distancing measures and vaccination in the former, and doses of drugs in the latter. The papers propose various approaches to dealing with model uncertainty and implement optimal controls using different techniques. Overall, this article provides an overview of the applications of optimal control in therapeutics and epidemiology. [Extracted from the article]

Objective: Alcohol‐related seizures (ARS) are one of the most important consequences of alcohol withdrawal syndrome (AWS). However, demographic and clinical characteristics, and furthermore, the relationship of ARS with delirium tremens (DT), have not yet been evaluated in detail. Therefore, the aim of the present study was to reveal the correlates of ARS and examine the interaction of ARS with the occurrence of DT and with the severity of AWS. Methods: In the retrospective study (Study 1) 2851 medical charts of inpatient admissions characterized by AWS and DT were listed. Demographic and clinical variables of ARS were assessed. In the follow‐up study (Study 2), patients admitted with AWS without (N = 28) and with (N = 18) ARS were enrolled. Study 1 was performed between 2008 and 2023, and Study 2 was performed in 2019 in Hungary. To determine the severity of AWS, the Clinical Institute Withdrawal Assessment Scale for Alcohol, Revised (CIWA‐Ar) was used. ARS is a provoked, occasional seizure; therefore, patients with epilepsy syndrome were excluded from the two studies. Statistical analyses were performed by the means of chi‐square tests, multinomial logistic regressions, mixed ANOVA, and derivation. Results: The occurrence of DT, the history of ARS, and somatic co‐morbidities were found to be risk factors for the appearance of ARS. ARS was proved to be a risk factor for the development of DT. In the follow‐up study, there was no difference in the decrease of CIWA‐Ar scores between the groups. Significance: Our present findings support the likelihood of kindling, which is one of the most important mechanisms underlying the development of ARS, but do not directly prove its presence. Additionally, our results revealed that the severity of AWS is not influenced by the presence of ARS. Plain Language Summary: Provoked, occasional seizures during AWS are defined as ARS. In the present study, predictors and interactions of these seizures with DT—the most severe form of withdrawal—and with the severity of withdrawal were examined in retrospective and follow‐up studies. The present study shows that a history of withdrawal seizures, the occurrence of DT, and somatic comorbidities are predictors of the development of seizures. Furthermore, our findings suggest that the presence of seizures does not influence the severity of withdrawal. [ABSTRACT FROM AUTHOR]