Searching for the Genus Epidemicus in Chinese Patients: Findings from the Clificol COVID-19 Clinical Case Registry.

BACKGROUND: The Clificol COVID-19 Support Project is an innovative international data collection project aimed at tackling some of the core questions in homeopathy. This paper reports on the further investigation of the genus epidemicus concept during the first wave of the pandemic in the Chinese population. METHODS: The design is an observational clinical case registry study of Chinese patients with confirmed or suspected coronavirus disease 2019 (COVID-19). The symptoms were prospectively collected via a 150-item questionnaire. The concept of genus epidemicus, including the role of treatment individualization, was investigated by analyzing whether presenting symptoms clustered into distinct groups. Two standard statistical analysis techniques were utilized: principal component analysis for extracting the most meaningful symptoms of the dataset; the k-means clustering algorithm for automatically assigning groups based on similarity between presenting symptoms. RESULTS: 20 Chinese practitioners collected 359 cases in the first half of 2020 (766 consultations, 363 prescriptions). The cluster analysis found two to be the optimum number of clusters. These two symptomatic clusters had a high overlap with the two most commonly prescribed remedies in these sub-populations: in cluster 1 there were 297 prescriptions, 95.6% of which were Gelsemium sempervirens; in cluster 2 there were 61 prescriptions, 95.1% of which were Bryonia alba. CONCLUSION: This is the first study to investigate the notion of genus epidemicus by using modern statistical techniques. These analyses identified at least two distinct symptom pictures. The notion of a single COVID-19 genus epidemicus did not apply in the studied population.

Data Collection during the COVID-19 Pandemic: Learning from Experience, Resulting in a Bayesian Repertory.

BACKGROUND: A novel pandemic disease offered the opportunity to create new, disease-specific, symptom rubrics for the homeopathic repertory. OBJECTIVE: The aim of this study was to discover the relationship between specific symptoms and specific medicines, especially of symptoms occurring frequently in this disease. MATERIALS AND METHODS: Worldwide collection of data in all possible formats by various parties was coordinated by the Liga Medicorum Homeopathica Internationalis. As the data came in, more symptoms were assessed prospectively. Frequent analysis and feedback by electronic newsletters were used to improve the quality of the data. Likelihood ratios (LRs) of symptoms were calculated. An algorithm for combining symptom LRs was programmed and published in the form of an app. The app was tested against 18 well-described successful cases from Hong Kong. RESULTS: LRs of common symptoms such as 'Fatigue' and 'Headache' provided better differentiation between medicines than did existing repertory entries, which are based only on the narrow presence or absence of symptoms. A mini-repertory for COVID-19 symptoms was published and supported by a web-based algorithm. With a choice of 20 common symptoms, this algorithm produced the same outcome as a full homeopathic analysis based upon a larger number of symptoms, including some that are traditionally considered more specific to particular medicines. CONCLUSION: A repertory based on clinical data and LRs can differentiate between homeopathic medicines using a limited number of frequently occurring epidemic symptoms. A Bayesian computer algorithm to combine symptoms can complement a full homeopathic analysis of cases.