P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2026-07-05 05:00:55 +02:00
Code Releases Activity

(v3.0.1.9083) website

Browse Source
This commit is contained in:
dr. M.S. (Matthijs) Berends
2026-07-03 19:41:16 +02:00
parent 65445bcfbe
commit ea996c8361
4 changed files with 13 additions and 25 deletions
Download Patch File Download Diff File Expand all files Collapse all files

26
index.md
View File

@@ -5,7 +5,7 @@
- Provides an **all-in-one solution** for antimicrobial resistance (AMR)
data analysis in a One Health approach
- **Peer-reviewed**, used in over 175 countries, cites over 100 times,
- **Peer-reviewed**, used in over 175 countries, cited over 100 times,
available in 28 languages
- Generates **antibiograms** - WISCA for empiric coverage estimates, or
traditional/syndromic for AMR surveillance
@@ -51,14 +51,10 @@ and reproducible AMR data analysis, that can therefore empower
epidemiological analyses to continuously enable surveillance and
treatment evaluation in any setting. We are a team of [many different
researchers](./authors.html) from around the globe to make this a
successful and durable project!
This work was published in the Journal of Statistical Software (Volume
104(3); [DOI
10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03)) and
formed the basis of two PhD theses ([DOI
10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and
[DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)).
successful and durable project! The `AMR` package was already cited
[over 100
times](https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=sAoHvIgAAAAJ:0EnyYjriUFMC)
in scientific research.
After installing this package, R knows [**~97 000 distinct microbial
species**](./reference/microorganisms.html) (updated mei 2026) and all
@@ -144,10 +140,6 @@ Urdu, and
Vietnamese. Antimicrobial drug (group) names and colloquial
microorganism names are provided in these languages.
The `AMR` package was cited [over 100
times](https://scholar.google.com/citations?view_op=view_citation&hl=en&citation_for_view=sAoHvIgAAAAJ:0EnyYjriUFMC)
in scientific research.
## Practical examples
### Filtering and selecting data
@@ -233,7 +225,7 @@ wisca(example_isolates,
| Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:------------------------|:-------------------------------------|:-------------------------------------|
| 69.9% (64.9-75.3%) | 93.6% (92.1-95%) | 89.9% (87-92.4%) |
| 70.1% (65.1-75.4%) | 93.6% (92.1-95%) | 89.8% (87.3-92.4%) |
WISCA supports stratification by any clinical variable, so you can
generate syndrome-specific or ward-specific coverage estimates:
@@ -248,9 +240,9 @@ wisca(example_isolates,
| Syndromic Group | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:----------------|:------------------------|:-------------------------------------|:-------------------------------------|
| Clinical | 74.5% (68.6-80.5%) | 93.7% (91.7-95.1%) | 90.4% (87.1-93.1%) |
| ICU | 57% (48.6-65.6%) | 86.7% (83.3-89.9%) | 83% (78.1-87.5%) |
| Outpatient | 57.4% (46-69.1%) | 76.7% (70.5-82.7%) | 67.7% (57.3-77.4%) |
| Clinical | 74.4% (68.2-79.9%) | 93.6% (91.9-95.1%) | 90.4% (86.9-93.3%) |
| ICU | 57% (48.6-65.9%) | 86.8% (83.4-89.8%) | 82.9% (77.5-87.1%) |
| Outpatient | 57.5% (45.9-69.3%) | 76.6% (70.6-82.3%) | 67.9% (57.6-77.2%) |
**For AMR surveillance**, traditional antibiograms remain the right tool
for tracking resistance per species over time: