Rainfall Incidence Lag
Find the nonnegative rainfall-to-incidence lag with greatest paired Pearson correlation; not a causal estimate.
Description
Lagged correlation compares earlier rainfall with later incidence.
Lagged correlation compares earlier rainfall with later incidence. The lag aligns two observed series; it does not itself explain why disease incidence changed.
The tool returns bestLagWeeks (best lag weeks); maximumCorrelation (maximum correlation). Input measurements and model parameters are supplied by the caller; the tool computes the stated summary rather than acquiring those measurements.
When to use Rainfall Incidence Lag
Use Rainfall Incidence Lag when you already have rainfallSeries, incidenceSeries, maximumLagWeeks and want bestLagWeeks, maximumCorrelation. This is useful for checking a supplied calculation, documenting a measurement convention, or comparing observations prepared on the same basis.
How to use Rainfall Incidence Lag and understand its results
- Enter rainfallSeries, incidenceSeries, maximumLagWeeks using the field table below. Match the units, ordering and data boundaries described in the calculation.
- Run the tool after checking the stated bounds and matching paired observations or categories. A malformed request gives a tool error rather than a usable measurement.
- Read bestLagWeeks, maximumCorrelation. Use the calculation and interpretation below to distinguish amounts, ratios, signs and decision flags where applicable.
- Review the supplied parameters and assumptions before using the result in a report or comparing it with another observation.
| Input field | Meaning | Value type and bounds | Requirement |
|---|---|---|---|
| rainfallSeries | Rainfall series | list of number | Required |
| incidenceSeries | Incidence series | list of number | Required |
| maximumLagWeeks | Maximum lag weeks | integer | Required |
Try integer lags from zero through a requested maximum of at most 52, requiring at least three paired observations. Skip constant pairs and select the largest signed correlation; ties keep the smallest lag.
r(lag) = Pearson correlation(rainfall[0:N−lag], incidence[lag:N]) The symbols refer to the supplied quantities or generated values described above; unit conversions and comparison boundaries follow the stated implementation.
{
"input": {
"rainfallSeries": [
0,
1,
0,
2,
0
],
"incidenceSeries": [
0,
0,
1,
0,
2
],
"maximumLagWeeks": 1
},
"output": {
"bestLagWeeks": 1,
"maximumCorrelation": 1
}
}Limitations and assumptions
The result is a descriptive scan without correction for multiple lags, seasonality or serial dependence. A large negative correlation loses to a smaller positive one.
Numerical inputs must be finite. The tool checks its declared input shape and calculation constraints; those checks do not verify the quality, calibration or representativeness of the supplied observations.
Alternative or Complementary methods
Incidence Getis Ord Hotspots: Compute local Getis–Ord Gi* standardized scores from supplied incidence and self-inclusive spatial weights; caller cutoff is not a multiple-testing correction. Climate Envelope Screen: Screen climate observations against a caller-defined suitability envelope, not a transmission prediction.
Methodological background: Measures of scale: sample variance and standard deviation.1
References
-
Measures of scale: sample variance and standard deviation — NIST/SEMATECH
Similar or alternative tools
- Incidence Getis Ord Hotspots
Compute local Getis–Ord Gi* standardized scores from supplied incidence and self-inclusive spatial weights; caller cutoff is not a multiple-testing correction.
- Climate Envelope Screen
Screen climate observations against a caller-defined suitability envelope, not a transmission prediction.