Homeopathy 2021; 110(02): 094-101
DOI: 10.1055/s-0040-1718583
Original Research Article

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

1  Independent Researcher, Breda, the Netherlands
Tom Smedley
2  Independent Researcher, United Kingdom
Galen Ives
3  Information School, University of Sheffield, United Kingdom
Peter Gold
4  American Institute of Homeopathy (AIH), United States
Bernardo Merizalde
5  Department of Integrative Medicine and Scientific Nutrition, Thomas Jefferson University, Philadelphia; Liga Medicorum Homeopathica Internationalis (LMHI), United States
Robbert van Haselen
6  International Institute for Integrated Medicine, Kingston, United Kingdom
Raj Kumar Manchanda
7  Directorate of AYUSH, Govt. of Delhi, India
Ashley Ross
8  Durban University of Technology, Durban, South Africa
Gustavo Cataldi
9  Liga Medicorum Homeopathica Internationalis (LMHI), Argentina
Altunay Agaoglu
10  Liga Medicorum Homeopathica Internationalis (LMHI), Turkey
Tiziana di Giampietro
11  European Committee for Homeopathy (ECH), Italy
Theodore Lilas
12  Vithoulkas Compass, Greece
Frederik Schroyens
13  ZEUS-SOFT, Belgium
José E. Eizayaga
14  Department of Homeopathy, Maimonides University, Argentina
› Author Affiliations


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.

Publication History

Received: 11 August 2020

Accepted: 09 September 2020

Publication Date:
04 January 2021 (online)

© 2020. Faculty of Homeopathy. This article is published by Thieme.

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