Maplore® Pratirodhप्रतिरोधResistance, mapped Resistome India
What the drains of four cities carry, month by month: 447 samples of open-drain wastewater, read for every known antibiotic-resistance gene.
In short
- 447 samples
- Open drains in Delhi (100), Mumbai (189), Kolkata (49) and Chennai (109), collected every month from March 2022 to March 2024.CSIR-CCMB, Nature Communications 2026
- 3,354 genes
- Distinct resistance genes found at least once, named in the CARD database as the authors annotated them.
- 1 sample, 1 month
- Each is a single grab sample from one drain on one day. A month's reading is the median of the samples that month, often four or fewer.
- 0 hospitals
- None is named or located, here or ever. These are open city drains, published at city level.
The data
CSIR-Centre for Cellular and Molecular Biology sequenced everything in each sample (shotgun metagenomics) and matched the reads against the Comprehensive Antibiotic Resistance Database. They published the processed tables openly, which is what made this atlas possible.
| Paper | Singh, Garg et al. Metagenomic profiling of antimicrobial resistance in wastewater from metropolitan cities of India. Nature Communications, 17 March 2026. Open access |
|---|---|
| Processed data | Zenodo 17590646, licence CC BY 4.0. Checked against the record's checksum before use. |
| Raw reads | NCBI BioProject PRJNA1415085 |
| Their code | SowpatiLab/Metro_metagenomics (MIT) |
The authors recorded no temperature, pH, pollutants or antibiotic levels with the samples. Maplore adds each city's monthly rain and temperature from the Copernicus ERA5 record, over the districts that make up the city.
What we compute
| Measure | How |
|---|---|
| All resistance genes | Reads matching any resistance gene, per million bacterial reads in the sample; the month's median sample. |
| Last-resort genes | The same, for carbapenemases (KPC, NDM, VIM, IMP, and the OXA-48, -23, -24, -58 and -143 families) and mobile colistin resistance (MCR). Named families, because the database's broad "carbapenem" label also covers enzymes that don't break carbapenems. |
| By drug class | The share of a month's resistance-gene reads from genes against each class. A gene can act on several classes, so the shares add up to more than 100%. |
| Rain, temperature | ERA5 daily values over the city's districts, summed or averaged by calendar month. A month with a missing day is left missing. |
Units are this dataset's own. Another study's numbers, from a different method or normalisation, are never put on the same axis.
Files
Free to use with credit to CSIR-CCMB (the data) and Maplore (the monthly summary and the weather). Every number carries the run that produced it in Maplore's records.
The registered analysis
Chennai's rains come with the northeast monsoon, October to December. Delhi, Mumbai and Kolkata get theirs June to September. If resistance in the drains follows the rain, Chennai's pattern should be shifted against the other three. We wrote down how we would test that, and what would count as a yes, before loading the data.
Registered 27 September 2026 at 16:01 IST, commit c9027ab, before the data was downloaded. Not changed since. Not run yet: the map shows rain and resistance side by side, and makes no claim about them until it is.
Read the plan in full
Pratirodh — pre-registered seasonal analysis
Registered 27 Sep 2026, before the CCMB data were downloaded. The commit that adds this file is the timestamp. Any change after that is a new, dated commit to this file, with the reason, and the results page lists every change.
Question
Does monthly antibiotic-resistance-gene load in open-drain wastewater track each city’s rainfall?
Hypothesis
H1. In each city, monthly total resistance-gene load (pratirodh_arg_load, the dataset’s own
normalised unit) is associated with that city’s monthly rainfall at a lag of 0, 1 or 2 months.
H2. If H1 holds, Chennai’s seasonal peak in load is offset from Delhi’s, Mumbai’s and Kolkata’s by roughly the gap between the northeast monsoon (Oct–Dec) and the southwest monsoon (Jun–Sep).
The direction is not assumed: heavy rain may dilute drains (load down) or wash resistance in from land and sewers (load up). The sign is estimated, and reported whatever it is.
Data
- Outcome: CCMB wastewater metagenomes (Singh, Garg et al., Nat Commun 2026; Zenodo 17590646, CC BY 4.0). 447 samples, 19 sites in 4 cities, monthly, Mar 2022 to Mar 2024. City-month load is the dataset’s own city-month aggregate where it provides one; otherwise the median over that city’s sites that month. Samples failing the authors’ QC are excluded as the authors excluded them.
- Rainfall (primary): ERA5 daily precipitation summed to calendar months, averaged over each city’s
area (coverage-weighted over the LGD districts that make up the city: Delhi’s 13 districts, Mumbai
City + Mumbai Suburban, Kolkata, Chennai). Computed by
pipeline/covariates.pyand stored invaluebefore the outcome is loaded. - Rainfall (check): IMD gridded rainfall, where its licence allows, as a sensitivity analysis only.
- Temperature: ERA5 monthly mean 2 m temperature over the same area, as a covariate.
Analysis
- Per city, Spearman correlation between monthly load and rainfall at lags 0, 1 and 2 months (rainfall leading). Three lags × four cities = 12 tests; p-values are Holm-adjusted across all 12. Point estimates with 95% intervals by block bootstrap (3-month blocks, 2,000 resamples).
- Across cities: a linear mixed model, log(load) ~ rainfall(lag k) + temperature + (1 | city), fitted for k = 0, 1, 2; the lag reported is the one with the lowest AIC, and all three are shown. A second model replaces rainfall with monsoon phase (in-season / out-of-season for that city’s own monsoon) as the fixed effect of interest.
- H2: for each city, the month of peak load in a harmonic fit (one annual sine + cosine) and its bootstrap interval; the Chennai offset is the difference between its peak month and the mean of the other three cities’ peak months.
What counts as support
- H1 is supported only if the same-sign lagged association holds in at least 3 of the 4 cities at one common lag, with the Holm-adjusted p < 0.05 in at least 2 of them, and the mixed-model rainfall coefficient’s 95% interval excludes zero.
- H2 is supported only if Chennai’s peak-month interval does not overlap the pooled peak-month interval of the other three cities, and the offset is in the direction of the northeast monsoon.
Confounders, stated in advance
- Dilution: heavy rain lowers concentration in open drains without changing what is shed.
- Seasonal antibiotic use: fever and diarrhoea seasons overlap the monsoon, so rain and prescribing move together. This analysis cannot separate them; a positive result is an association, not a mechanism.
- Two years is about two monsoon cycles per city. Power is low. All results are exploratory and are labelled that way wherever they appear.
Kill rule
If H1 is not supported, the null is published as part of the data descriptor, with the full per-city table, and the third paper (the seasonal analysis) is dropped. The atlas keeps showing the city series beside rainfall, without any claim about a relationship.
Not in this analysis
No site-level model (19 sites, one grab sample per site-month, is too thin). No gene-by-gene tests (they would be a new registration). No data from any other dataset.
Limits
- A single grab sample is a snapshot of one drain on one day. Months with few samples jump around.
- The public record gives each sample's city and month, not its drain, so each city is one point on the map at the centre of its districts.
- Genes in wastewater are not infections. They say what is circulating in the city's microbes, not who is ill.
- Two years is about two monsoons per city: anything seen here is a pattern to test, not a finding.
For labs
Pratirodh is built to take more data: any lab can add samples in one open format, with its method and an evidence tier on every result, so a field test never looks as strong as a sequenced one. Contributing labs are credited on every result they supply. Write to us.