Keywords COVID-19 - repertory - Bayes' theorem - data collection - prognostic factor
Introduction
Since the end of 2019, the whole world has become affected by an epidemic of the coronavirus
disease 2019 (COVID-19), caused by the SARS-CoV-2 virus. By the end of May 2020, more
than six million confirmed cases had been reported and about 370,000 deaths.[1 ] At that time, no vaccination was available and no effective therapy had been reported.
COVID-19 patients display a wide range of symptoms, but the most frequent are: fever
or chills; cough; shortness of breath or difficulty breathing; fatigue; muscle or
body aches; headache; recent loss of taste or smell; sore throat; congestion or runny
nose; nausea or vomiting and diarrhoea.[2 ] This range of symptoms makes it difficult to gain a clear perspective on how homeopathy
can be applied in this epidemic. Former epidemics have shown that a limited number
of homeopathic medicines can be used in the treatment of the disease because the symptoms
produced by the microorganism predominate over constitutional symptoms. However, with
the range of symptoms caused by the infection in different patients we may expect
a variety of eligible homeopathic medicines, because the totality of those symptoms
does not clearly indicate one single homeopathic medicine.
An initial selection of homeopathic medicines could be based on the symptomatology
of the epidemic by repertorisation (the repertory is an index to the symptoms of all
medicines in the homeopathic materia medica). This, however, results in a large number
of possible medicines because symptoms such as ‘fever’, ‘weakness’, ‘dry cough’, ‘loss
of smell’ and ‘dyspnoea’ are very common, and this results in large repertory rubrics.
Alternatively, the choice of possible medicines could be based on previous epidemics.[3 ] Other possible criteria could derive from fundamental research, such as the finding
that Bryonia alba contains flavonoids which have an inhibitory effect on peroxidase-catalysed oxidation,[4 ] relating this to the possible role of the angiotensin-converting enzyme 2 receptor
and protease inhibitors in this disease.[5 ]
The most important question, however, is this: What works in daily practice? All over
the world, patients have been treated with homeopathy, and case descriptions can be
a valuable source of information, particularly if they show us which medicines have
been used successfully and which symptoms indicate specific medicines. In this article,
we report how a working group of the Liga Medicorum Homeopathica Internationalis (LMHI)
collected and analysed cases of COVID-19-like illness. This was not only a process
of learning from homeopathic experience but also of learning how to collect data and
evaluate a new disease. There was no completely worked-out protocol at the outset;
this was adapted as data came in. The protocol was, therefore, also partly a product
of learning from experience. We applied modern statistical techniques to assess the
data, which were translated into a Bayesian mini-repertory, presented in an app containing
a Bayesian algorithm for combining symptoms to differentiate between medicines.
Methods
The primary objective of our evaluation was to discover the relationship between specific
symptoms and specific medicines, especially of symptoms occurring frequently in this
disease. Such relationships enable a more precise differentiation between medicines
by applying Bayes' theorem.
We collected, and are still collecting, data from cases with confirmed COVID-19 (by
PCR, etc.) and where COVID-19 is suspected because of a combination of symptomatology
and COVID-19 contact. Clinicians were invited to submit data in any available format
(such as text, spreadsheet or database) through every network we could find. Such
networks were: LMHI, European Council for Homeopathy (ECH), American Institute of
Homeopathy (AIH), Ärztegesellshaft für Klassische Homöopathie (ÄKH), as well as published[6 ] and unpublished case series. Most cases were submitted by the AIH, which use a web-based
questionnaire with pre-formatted questions such as ‘dry or productive cough’, ‘fever’,
as well as free text fields for full case descriptions. Such symptoms were standardised
for quantitative analysis.
We first undertook a qualitative analysis of each case: Was the illness and reaction
to therapy well described? Were homeopathic symptoms well described? Were other (homeopathic)
therapies prescribed that could have influenced outcome? If it appeared likely that
a specific homeopathic medicine was related to the improvement of the patient, the
case was entered into an Excel spreadsheet. The qualitative analysis was done by author
LR; in unclear cases consensus was sought with author PG. The spreadsheet and discussion
points were shared with all authors after standardisation of symptoms. Contributors
of cases were instructed to anonymise cases and follow ethical guidelines of their
countries. Anonymity was also guaranteed by the standardisation of symptoms. In some
countries, such as India, ethical clearance was obtained. Generally, ethical clearance
and informed consent were waived because the data analysis was based on registration
of normal daily practice. The use of anonymised data is in agreement with the European
Data Protection Law (EU) (2016/679, article 89).[7 ] The spreadsheet contained some pre-defined variables to categorise each anonymised
case (patient code, gender, age, country, practitioner), the prescribed medicine,
the severity of the case, laboratory confirmation, prescribed medicine, time to onset
of improvement and presence or absence of fever and result of the medicine. Symptoms
not corresponding to any of those pre-formatted were initially entered in cells as
free text.
Many of the unformatted symptoms were complex. A symptom such as ‘Headache, worse
during the night’ had to be split into the symptom ‘Headache’ and the sub-symptom
‘Headache, worse during the night’, to allow all cases with headache to be counted
separately. The next step was to standardise symptoms with synonymous descriptors:
for example, of the symptom ‘fatigue’. The ‘fatigue’ in COVID-19 cases is more than
straightforward tiredness, and several different expressions such as ‘prostration’,
‘heaviness’ and ‘weakness’ were used by different practitioners. We chose the word
‘fatigue’ because it was the most frequently used, for example, by the World Health
Organization.
The project began in March 2020, at which time there were few data and the contributors
generally had no experience of data collection. A newsletter was sent frequently to
the contributors, giving instructions, showing results and providing feedback. The
basic principles of a Bayesian repertory, applying likelihood ratio (LR), were explained
step-by-step using data from the case collection. The collected data were also used
to demonstrate sources of bias. A growing list of symptoms that appeared in many cases
was added to the pre-defined variables of the spreadsheet and contributors were asked
to check these symptoms in every patient. The outcome of qualitative and quantitative
analysis of the spreadsheet was disseminated among LMHI members.
Cases were collected and analysed on a weekly basis and new synonyms were merged.
These data sufficed to calculate the constituents of the LR; LR = (a/(a + c))/(b/(b + d)):
a = The number of patients in the population responding well to the medicine with
the symptom.
b = The number of patients in the remainder of the population with the symptom.
c = The number of patients in the population responding well to the medicine without
the symptom.
d = The number of patients in the remainder of the population without the symptom.
The 95% confidence interval (CI) of some LRs was calculated with Confidence Interval
Analyser (BMJ).
To facilitate the necessary calculations for a combination of symptoms, an app was
developed, calculating the combined LRs. The app was built using Webflow, which allows
the programmer to build the app visually using its editor. The part of the app that
combines LRs to recommend medicines uses JavaScript, as this allows the calculations
to take place immediately on the user's device rather than the distant server. This
also allows us to use Netlify, a cloud computing company which distributes the website
globally, providing the fastest response wherever the user is in the world. Additionally,
hosting on Netlify is free of charge. The combination of Webflow + JavaScript + Netlify
allowed us to produce the website quickly and ensure that it operates with the greatest
global speed. The app was also designed to be easy to update with further symptoms,
medicines and data.
The app was tested with a published case series of 18 cases.[6 ] Out of these 18 cases, 17 had been prescribed medicines that were included in the
database underlying the app, whilst one had received another medicine. The question
was: Does the use of only the most frequently occurring symptoms in an initial screening
give results that are consistent with the outcome of the standard homeopathic method
using all available symptoms, common and particular? We also calculated a correlation
matrix between each of the symptoms of the app using Pearson's contingency coefficient.
Patients
By the end of May 2020, 161 patients (91 females, 58 males and 12 unknown gender)
were included from Argentina, Austria, Belgium, Brazil, China, France, Germany, India,
Iran, Italy, the Netherlands, Turkey and the United States. Age range: 0 to 84 years.
Results
By the end of May, 161 cases had been collected, for whom 31 homeopathic medicines
were prescribed. The most prescribed medicines were Bryonia alba (Bry) (n = 45), Gelsemium sempervirens (Gels ) (n = 25), Arsenicum album (Ars ) (n = 21), Phosphorus (Phos) (n = 12) and Camphora (Camph) (n = 12). Bry , Gels and Ars represented 91 cases (56.5% of the total), Phos and Camph another 14.9%. Six medicines had 3 to 6 cases, four medicines had two cases and 16
medicines one single case each. The total number of symptoms recorded was 1404.
Symptoms from various sources were first entered in free text cells, as shown in [Fig. 1 ]. If necessary, symptoms with modalities were split into symptom and sub-symptom.
Fig. 1 Part of the actual database. Symptoms deriving from case descriptions are partly
separated from each other.
The semantic clean-up was the most laborious task, reducing over a thousand different
symptoms to a few hundred. There was a risk of missing nuances in this process, but
our main goal was to locate common symptoms. After the clean-up process, a pivot table
indicated the frequency of each symptom and the number of medicines related to the
symptom. From this, the LRs of each symptom for the respective medicines could be
calculated, resulting in a ‘mini-repertory’ for three homeopathic medicines, see [Table 1 ].
Table 1
Mini repertory for Arsenicum , Bryonia and Gelsemium regarding COVID-19-like illness. Symptoms are sub-rubrics for corresponding repertory
rubrics, like ‘HEAD – PAIN – COVID-19-like disease, in’
Symptoms
Total
Ars
LR Ars
Bry
LR Bry
Gels
LR Gels
161
21
45
25
Fatigue
87
11
0.96
20
0.77
20
1.62
Dry cough
73
8
0.82
26
1.43
10
0.86
Dyspnoea
51
5
0.72
18
1.41
4
0.46
Headache
48
6
0.95
17
1.41
9
1.26
Slow onset
46
6
1.00
14
1.13
9
1.32
Fever
46
3
0.47
15
1.25
9
1.32
Chill
42
5
0.90
13
1.16
14
2.72
Diarrhoea
35
8
1.98
10
1.03
6
1.13
Oppression chest
32
4
0.95
7
0.72
5
1.01
Throat pain
31
4
0.99
11
1.42
7
1.59
Muscle/bone pain
27
4
1.16
9
1.29
4
0.95
Chest pain
24
3
0.95
10
1.84
3
0.78
Anxiety
23
8
3.56
5
0.72
3
0.82
Loss of taste and/or smell
23
3
1.00
6
0.91
Dry mouth
17
4
2.05
9
2.90
1
0.34
Thirst
16
2
0.95
9
3.31
2
0.78
Thirstless
15
1
0.48
4
0.94
5
2.72
Nausea
15
4
2.42
2
0.40
3
1.36
Back pain
13
1
0.56
8
4.12
1
0.45
Chest pain < cough
12
1
0.61
7
3.61
3
1.81
> Open air
11
1
0.67
4
1.47
1
0.54
Cough < talking
11
2
1.48
4
1.47
Desire cold drinks
11
4
1.47
1
0.54
Cough < deep respiration
10
2
1.67
7
6.01
Abbreviation: COVID-19, coronavirus disease 2019.
The 95% CI of some LRs is shown in [Table 2 ]. Statistical significance at a 95% level, however, is only partly related to clinical
relevance.[8 ] In the present context, the fact that some LRs are statistically significant indicates
that a sample containing at least 20 subjects who respond to a particular medicine
gives reasonably accurate estimates.
Table 2
95% confidence interval (95% CI) of some likelihood ratio (LR) values
Symptom
Medicine
LR
95% CI
Fatigue
Gels
1.62
1.252–2.106
Dry cough
Bry
1.43
1.022–1.990
Dyspnoea
Bry
1.41
0.888–2.227
Headache
Bry
1.41
0.874–2.287
Chill
Gels
2.72
1.684–4.392
Diarrhoea
Ars
1.98
1.040–3.753
Anxiety
Ars
3.56
1.722–7.343
Dry mouth
Bry
2.90
1.193–7.048
Abbreviations: Ars, Arsenicum ; Bry , Bryonia ; Gels , Gelsemium .
There are significant differences between this mini-repertory and the well-known rubrics
of the existing homeopathic repertory. Firstly, the entries are not based on absolute
occurrence. This would have resulted in boldface typeface for most entries because
of the repeated clinical confirmation. Secondly, the data for the three medicines
are compared with each other and are only valid for COVID-19-like disease; the LRs
are ‘condition dependent’.[9 ] This can result in LRs of less than 1, indicating a relative contraindication. For
example, the LRs below 1 linking Ars and Bry with ‘fatigue’ do not indicate that these medicines are not indicated for COVID-19 by this symptom, but rather that fatigue indicates Gels (LR = 1.62) more than average, whilst Ars (LR = 0.96) and Bry (LR = 0.77) are indicated less than average when the three prescribed medicines are
compared. This mutual comparison of those medicines indicated by a disease is the
third departure from the existing repertory. The result is that rather than simply
giving boldface entries for all three medicines, this repertory is able to show a
distinctive ordering of the three.
This ordering of individual symptoms can result in even larger differences between
medicines when symptoms are combined by multiplying the respective LRs. [Table 3 ] shows composite LRs for three combinations of three symptoms. The composite LRs
demonstrate that repertory entries based on LR values can differentiate medicines
much better than the same based on absolute occurrence, thus making common symptoms
more useful.
Table 3
Combined likelihood ratio (LR) of combinations of three symptoms
Combinations of symptoms
LR Ars
LR Bry
LR Gels
Diarrhoea + chill + anxiety
6.33
0.85
2.50
Dry cough + headache + back pain
0.43
8.31
0.49
Fatigue + chill + thirstless
0.41
0.83
12.01
Abbreviations: Ars, Arsenicum ; Bry , Bryonia ; Gels , Gelsemium .
Let us assume that the (unknown) prior chance that any of the three medicines would
work is 10%. A combination of three common symptoms could then raise this chance for
a specific medicine to between 40 and 60%, further highlighting the contrast with
the other medicines (see [Table 4 ]).
Table 4
Effect of combined likelihood ratio (LR) values of [Table 3 ] on posterior chance, assuming a prior chance of 10%
Posterior chance with 10% prior chance
Ars
Bry
Gels
Diarrhoea + chill + anxiety
41%
9%
22%
Dry cough + headache + back pain
5%
48%
5%
Fatigue + chill + thirstless
4%
8%
57%
Abbreviations: Ars, Arsenicum ; Bry , Bryonia ; Gels , Gelsemium .
The systematic collection of treatment data and application of Bayes' theorem to calculate
LRs allows relatively common symptoms to differentiate better between medicines, and
this is enhanced by the combination of these symptoms. However, combining LRs requires
a calculator, and it will be some time before computer-based repertories are able
to perform the necessary calculations. To overcome this problem, authors TS, GI and
LR have created an app that performs these calculations based on actual clinical data.
The app presents 20 symptoms for which LRs have been measured in COVID-19 cases, giving
suggestions for eligible medicines from those symptoms chosen; with the currently
available data, the medicines are currently confined to Arsenicum , Bryonia and Gelsemium . Symptoms were selected to be included in the app, which differentiated between these
three medicines even if the LRs were not statistically significant. If data were not
available, the LR was set at 1, giving neither an indication nor a contraindication.
The app can be found with the following link: https://hpra.co.uk/ . A print of the screen for symptom selection is shown in [Fig. 2 ].
Fig. 2 Screenshot of the COVID-19 mini-repertory app. Explanation of symptoms could be obtained
by clicking on the question mark to the right of the symptom.
The app appears as a ‘black box’ with simply input and output, but that is only because
it would be too complicated to show the full underlying algorithm and repertory rubrics.
The app is based on the data shown in [Table 1 ], published in the LMHI newsletter, and it shows a positive indication if the combined
LR ≥ 3. As a rule of thumb, LR = 3 corresponds to a rise of posterior chance of about
20%. If the combined LR lies between 3 and 6, the medicine will appear in plain type;
if combined LR is between 6 and 10, the medicine appears in italics; if combined LR
≥ 10 the medicine appears in boldface type.
Testing the App with Real Cases
A collection of case studies from Hong Kong (see reference 6) involving COVID-19 was
published with 18 cases; these responded well to Ars (one case), Bry (four cases), Gels (12 cases) and Eupatorium perfoliatum (Eup-p) (one case).[6 ] The cases were presented with clear descriptions of background, symptomatology and
outcome. This, as well as the fact that all cases except one responded to the medicines
present in the app, offered an opportunity to test the app with the symptoms seen
in these cases.
The comparison of the standard case taking and the app can be demonstrated by case
HK1.1. The observed symptoms in the case taking were:
Slow onset and progression of symptoms.
Feeling irritable from the cough; does not want to talk to anyone. Prefers to be alone.
Obvious increase in thirst with desire to drink warm water in large quantity.
Generally ameliorated after perspiration.
Mainly dry cough, with very occasional greenish sputum.
Extremely bad pulsating headache in the temple, and middle chest pain aggravated from
coughing.
Cough aggravated by talking and lying down, and after waking up in the morning.
Cough associated with tickling feeling in the throat, ameliorated by warm drinks.
Out of these eight complex symptom descriptions, consisting of 13 different single
repertory symptoms, five single symptoms could be found in the app:
Thirst
Dry cough
Headache
Chest pain < cough
Cough < talking.
The output of the app was ‘Strong indication for Bryonia’ and this was indeed the
medicine that was prescribed by the clinician. An experienced homeopathic practitioner
would probably recognise the medicine at first sight because of symptoms such as ‘irritable
from cough’, ‘aversion to company’, ‘thirst for large quantities’ and ‘headache from
cough’, which are not included in the app. However, with the combination of three
symptoms, ‘thirst’, ‘dry cough’ and ‘headache’, the app would already have returned
a ‘Moderate indication for Bryonia’. This results from the following LRs for Bry : LR = 3.31 for thirst; LR = 1.43 for dry cough; and LR = 1.41 for headache. The combined
LR for these three symptoms is 3.31 × 1.43 × 1.41 = 6.67, high enough for a ‘moderate
indication’. ‘Chest pain < cough’ adds LR = 3.61 and ‘Cough < talking’ LR = 1.47,
rendering a combined LR = 35.4, representing a strong indication.
The outcome of testing all cases is shown in [Table 5 ]. If the combined LR of the selected symptoms was between 3 and 6, the indication
was ‘slight’; if the combined LR was between 6 and 10, the indication was ‘moderate’;
and if combined LR > 10, the indication was ‘strong’.
Table 5
Recommendations of the app after entering the symptoms available in 18 cases[6 ]
Case
Prescribed medicine
No. repertory symptoms
No. app symptoms
App advised medicine
HK1.1
Bry
13
5
Bry (strong)
HK2.1
Bry
22
5
Bry (slight)
HK3.1
Bry
26
6
Bry (strong)
HK3.2
Gels
16
5
Gels (strong), Bry (slight)
HK3.3
Gels
11
4
Gels (moderate)
HK3.4
Gels
14
3
Gels (strong)
HK3.5
Gels
15
6
Gels (strong), Bry (slight)
HK4.1
Gels
9
4
Gels (strong)
HK4.2
Gels
12
4
Gels (slight), Bry (slight)
HK4.3
Ars
9
4
Ars (slight), Gels (slight)
HK4.4
Gels
11
4
Gels (moderate)
HK4.5
Gels
11
4
Gels (strong)
HK5.1
Gels
9
4
Gels (strong)
HK5.2
Bry
13
6
Bry (moderate), Gels (moderate)
HK5.3
Gels
9
3
Bry (slight)
HK5.4
Gels
9
3
Bry (slight)
HK5.5
Eup-p
7
4
Bry (slight), Gels (slight)
HK6.1
Bry
25
8
Bry (strong)
Abbreviations: Ars, Arsenicum ; Bry , Bryonia ; Gels , Gelsemium .
Note: The ‘No. repertory symptoms’ is the number of symptoms described for each case.
‘No. app symptoms’ is the number of these symptoms available in the app. ‘App advised
medicine’ represents the recommendation of the app (together with the strength of
the recommendation).
This set of cases contained only one case (HK5.5) that could not be handled by the
app, because it contained no data for that particular medicine (Eup-p ). In this case, the app gave only slight indications, but did not contradict the
choice based on a full homeopathic evaluation by offering a moderate or strong indication
for one of its own medicines.
In the remaining 17 cases, for 11 (65%) the recommendation of the app was entirely
consistent with the full homeopathic evaluation, giving a moderate or strong indication
for the prescribed medicine. In one case (HK5.2), a second medicine came up to the
same degree; in this case, a specific symptom, ‘coldness up and down the back’ (not
available in the app), clarified the choice of Gels . In three cases (HK2.1, HK4.2, HK4.3), the recommendation was consistent but with
only a slight indication; it did not, though, contradict the definitive choice. In
these cases, the full evaluation clarified the choice because of specific symptoms.
In one case (HK5.4), the recommendation of the app slightly contraindicated the choice
after standard case taking which was made on the basis of specific symptoms, such
as ‘Heaviness of the eyelids’, which clearly indicated the prescribed medicine. In
one other case (HK5.3) both the app and the repertorisation placed Bry first, but Gels was chosen on the whole picture. To summarise: in 16 out of 17 cases, the app made
the same recommendation as the repertorisation, but using fewer symptoms and only
common ones.
Discussion
The LMHI coordinated case collection from various sources in different formats, partly
formatted as repertorisations, partly as pre-defined symptoms, and partly in free
text format. In the course of this process, more information became available about
common symptoms of the disease and contributors were guided by more pre-defined symptoms.
Such a database can be an invaluable source of both qualitative and quantitative information.
For this article, we focused on quantitative aspects, but the selection of suitable
cases also required a qualitative analysis to identify those cases where a curative
effect appeared likely. Amongst other factors, cases could not be used in this analysis
where more than one homeopathic medicine had worked, as we sought to establish relationships
between specific medicines and specific symptoms. Nevertheless, such cases can be
useful in a more qualitative context. Cases where a causal relationship between improvement
and the medicine was unclear were also excluded, but again these can be useful for
other analyses, as can cases where there is no improvement.
Limitations of the Study
This data collection should be regarded as a learning project; it began with little
knowledge about the disease-specific symptoms and with largely inexperienced contributors.
There was no worked-out protocol and most symptoms were collected retrospectively.
As the number of cases increased, directions for data collection were adjusted and
communicated, and more symptoms were identified prospectively.
This analysis of observational data cannot be used as proof for the effectiveness
of homeopathy. Whilst we made some effort to discard cases where a causal relationship
between the medicine and improvement was unlikely, context effects and spontaneous
recovery cannot be ruled out. False positive cases, where the improvement is caused
by spontaneous recovery or context effects, result in an under-estimation of the LR.[10 ] On the other hand, the retrospective assessment of most symptoms could involve confirmation
bias: some symptoms are well-known indicators for specific medicines, such as ‘heaviness
of eyelids’ for Gels . Heaviness of eyelids was recorded in seven out of 25 Gels cases and not in the remainder of the population; it is possible that the symptom
was present but remained unnoticed if Gels had not been considered. The risk of confirmation bias is greater in the case of
keynote symptoms and this could cause over-estimation of LRs.[11 ]
A further potential bias arises from the fact that the multiplication of LRs assumes
that these are independent, when in actuality some symptoms are correlated, and this
could potentially give rise to artificially inflated LR products. An examination of
the correlation matrix of the 20 symptoms using Pearson's contingency coefficient,
C, revealed that of the 190 possible pair-wise correlations, most (158, 83.2%) were
statistically non-significant, and of those that were significant, all were relatively
small (C ≤ 0.26) except one. This latter was between the symptoms ‘Chest pain’ and
‘Chest pain aggravated by cough’ (C = 0.51); as a result, ‘Chest pain’ was subsequently
removed from the app, leaving 19 symptoms. Implementing a multivariate procedure in
the app to eliminate this potential bias was beyond the scope of the current project,
but it is intended to include this in future versions.
It is worth noting that this correlation between symptoms is also a problem in the
existing repertory[12 ] and homeopathic practitioners have to handle this intuitively: e.g. they will be
careful in combining symptoms where correlation can be expected, such as ‘dry mouth’
and ‘thirst’. (In our data, these two symptoms showed a significant but relatively
small correlation, C = 0.20.)
The urgency of this project prevented proper preparation and probably introduced some
bias. Nevertheless, counting the prevalence of symptoms and estimating LR resulted
in better differentiation between medicines with common symptoms, a differentiation
that would be absent with the existing typology in repertory rubrics. The data collection
showed that a very limited number of medicines appeared repeatedly in improved cases—Ars , Bry and Gels in 57.5% of all cases. These three medicines have a considerably higher prior chance
of being curative than other medicines. Adding two or three further medicines might
suffice for about 70% of all cases.
The reliability of the Bayesian mini-repertory with 20 common symptoms and three medicines,
based on our data, has been tested with 18 real cases and showed results similar to
conventional case-taking that relied on considerably more symptoms, both common and
specific. We expect therefore that a Bayesian repertorisation using a limited number
of common COVID-19 symptoms could improve the effectiveness of homeopathic COVID-19
treatment, especially if there are no specific symptoms to indicate particular medicines.
This effectiveness could be further improved by adding two or three more medicines
to the existing app after acquiring additional data.
The database underlying the app is derived from mostly mild cases. It is uncertain
whether severe cases, for instance those with pneumonia, would respond to the same
medicines. If it transpires that more severe cases are successfully treated with other
homeopathic medicines the app must be adapted, or a separate app for severe (pneumonia)
cases must be developed.
Conclusion
The pandemic outbreak of a new infectious disease offered the opportunity to develop
new repertory rubrics of homeopathic symptoms de novo , using up-to-date statistical methods and worldwide data collection. Despite certain
biases arising from the urgency of the project and the dynamic nature of adjustments
to the protocol, important differences between LRs for common symptoms for different
medicines became apparent. For a reference set of cases, a computer algorithm based
on LRs of 20 common symptoms and three medicines gave the same results as a full homeopathic
evaluation, despite being based upon considerably fewer, and common, symptoms. This
tool offers valuable support and more precise selection of medicines to the homeopathic
practitioner.
