Related Experiment Video
Updated: Aug 31, 2025
Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
Published on: February 16, 2022
Test of a Homeopathic Algorithm for COVID-19: the Importance of a Broad Perspective
Anjali Miglani1, Raj Kumar Manchanda1, Amrit Kalsi1
1Directorate of AYUSH, Health and Family Welfare Department, Government of NCT of Delhi, New Delhi, India.
Insights
This study developed a COVID-19 homeopathic mini-repertory app using practitioner data. The tool, based on 1,161 cases, aids in selecting specific remedies for coronavirus disease 2019 symptoms.
Area of Science:
- Homeopathy
- Infectious Disease Epidemiology
- Computational Medicine
Background:
- Coronavirus disease 2019 (COVID-19) presents diverse symptoms, leading to numerous proposed homeopathic remedies.
- Identifying a "genus epidemicus" for COVID-19 requires a systematic approach to remedy selection.
Purpose of the Study:
- To develop and test a COVID-19 Bayesian mini-repertory and algorithm-based application (app).
- To combine clinical data for improved homeopathic remedy selection in COVID-19.
Main Methods:
- Collected data from 1,161 COVID-19 cases provided by 100 global practitioners.
- Calculated condition-confined likelihood ratios (LRs) for 59 COVID-19 symptoms across 11 selected homeopathic medicines.
- Developed a spreadsheet-based algorithm to compute combined LRs for symptom-medicine correlations.
Main Results:
- The algorithm was tested on 358 cases, achieving concordance in 288 instances.
- The homeopathic medicine *Mercurius solubilis* was excluded due to observed bias.
- The refined repertory included 10 medicines, covering 81.8% of the analyzed COVID-19 cases.
Conclusions:
- A Bayesian mini-repertory and app were created, integrating qualitative clinical experiences for COVID-19.
- The tool provides indications for specific homeopathic medicines based on common COVID-19 symptoms.
- The application is freely available for further professional testing and use.
Background:
Most of the symptoms of coronavirus disease 2019 (COVID-19) are covered by large repertory rubrics and hence many remedies have been proposed as "genus epidemicus". The aim of this study was to combine the information from various data collections to prepare a COVID-19 Bayesian mini-repertory/an algorithm-based application (app) and test it.
Methods:
In July 2021, 1,161 COVID-19 cases from 100 practitioners globally were combined. These data were used to calculate "condition-confined" likelihood ratios (LRs) for 59 symptoms of COVID-19. Out of these, 35 symptoms of the 11 medicines that had at least 20 cases each were considered. The information was entered in a spreadsheet (algorithm) to calculate combined LRs of specific combinations of symptoms. The algorithm contained the medicines Arsenicum album, Belladonna, Bryonia alba, Camphora, Gelsemium sempervirens, Hepar sulphuris, Mercurius solubilis, Nux vomica, Phosphorus, Pulsatilla and Rhus toxicodendron. To test concordance, the doctors were then invited to re-enter the symptoms of their cases into this algorithm.
Results:
The algorithm was re-tested on 358 cases, and concordance was seen in 288 cases. On analysis of the data, bias was noticed in the Merc group, which was therefore excluded from the algorithm. The remaining 10 medicines, representing 81.8% of all cases, were included in the preparation of the next version of the homeopathic mini-repertory and app.
Conclusion:
The Bayesian mini-repertory and app is based on qualitative clinical experiences of various doctors in COVID-19 and gives indications for specific medicines for common COVID-19 symptoms. It is freely available [English: https://hpra.co.uk/; Spanish: https://hpra.co.uk/es ] for further testing and utilization by the profession.
