Calculator inputs
Example data table
| Scenario | Antibodies | Memory B | Memory T | Days since exposure | Booster fold | IMI (illustrative) |
|---|---|---|---|---|---|---|
| Recent booster | 420 | 22 | 260 | 45 | 2.5 | ~78 |
| Mid‑term waning | 260 | 18 | 220 | 120 | 2.0 | ~55 |
| Long interval | 160 | 16 | 200 | 240 | 1.6 | ~34 |
| High cellular bias | 180 | 14 | 360 | 150 | 1.8 | ~49 |
| Low exposure | 90 | 8 | 120 | 300 | 1.2 | ~18 |
Formula used
This calculator produces a unitless Immune Memory Index (IMI) on a 0–100 scale. It combines normalized lab markers, time-based waning, and a booster modifier.
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Normalize each marker using log scaling:
score = log10(1 + value) / log10(1 + strong_reference), capped to 0–1. -
Weighted biomarker score:
S = wAb·Ab + wB·B + wT·T, where weights are normalized to sum to 1. -
Memory decay (half-life model):
D = 0.5^(days_since_exposure / half_life). -
Booster modifier (fading exponential):
M = 1 + (booster_fold − 1)·exp(−days_since_booster / booster_duration). -
Final index:
IMI = 100 · S · D · M, capped to the 0–100 range.
Tip: Use strong references from your dataset (e.g., upper quartile responders) to keep normalized scores interpretable.
How to use this calculator
- Enter antibody, memory B, and memory T measurements in your preferred units.
- Set strong reference values that represent a strong response in your dataset.
- Provide timing since last exposure and (optionally) recent booster timing.
- Adjust half-life and booster duration to match your scenario assumptions.
- Tune weights to reflect your study emphasis, then calculate.
- Use the projection and trajectory table to compare future scenarios.
- Download CSV or PDF to share the report and inputs.
What the index measures
The Immune Memory Index (IMI) summarizes adaptive recall capacity on a 0–100 scale by combining antibody titers, memory B cells, and memory T‑cell readouts. Because the three inputs capture different arms of immunity, the score is most useful for comparing cohorts, timepoints, or study conditions using the same assay conventions.
Inputs and normalization choices
Each marker is converted to a 0–1 score using log scaling against a “strong reference” value. This reduces the impact of extreme outliers and keeps proportional changes meaningful across wide laboratory ranges. Choosing references from your own dataset (for example, upper‑quartile responders) makes the normalized scores easier to interpret. The calculator also lets you set weights for antibodies, memory B, and memory T; weights are normalized so the three contributions sum to 1.00. Use identical units and gates across samples.
Time, half-life, and waning
IMI applies a half‑life decay factor, D = 0.5^(days/half‑life), to model gradual loss of measurable memory signals. If half‑life is set to 90 days, then 90 days gives D=0.50 and 180 days gives D=0.25. A shorter half‑life increases sensitivity to time since exposure, while a longer half‑life preserves scores over longer intervals. Keep half‑life consistent across comparisons unless you have clear kinetic evidence.
Booster effects and scenario testing
A booster modifier increases the score by a fold factor that fades exponentially over a chosen duration. This enables sensitivity testing: raise the fold factor to represent stronger recall, or shorten the duration to represent brief amplification. For example, a 2.0‑fold booster with a 60‑day duration contributes more at day 15 than at day 90. A monthly projection table helps visualize expected trajectories for follow‑up planning.
Interpreting scores and limitations
Higher IMI values indicate stronger combined signals under your assumptions, not guaranteed protection. Differences in antigen, assay platform, tissue compartment, and baseline exposure history can shift the relationship between markers and outcomes. Report IMI alongside raw inputs, reference choices, and weight settings for reproducibility. If measurements are near a lower detection limit, consider replicates or robust summaries before scoring.
FAQs
What does the Immune Memory Index represent?
IMI is a 0–100 summary score that combines normalized antibody, memory B‑cell, and memory T‑cell signals, adjusted for time since exposure and optional booster effects. It helps compare scenarios and cohorts under consistent assumptions.
How should I choose “strong reference” values?
Use values that represent strong responders in your own dataset, such as the 75th percentile or a well‑characterized positive control. References anchor the 0–1 normalization, so keep them stable when comparing timepoints or groups.
Do the weights need to add up to 100%?
No. You can enter any non‑negative weights. The calculator automatically normalizes them so the antibody, memory B, and memory T contributions sum to 100% internally, keeping the combined biomarker score comparable across runs.
What does the half-life parameter control?
It determines how quickly the index decays with time since last exposure. A smaller half‑life produces faster decline, while a larger half‑life preserves scores longer. Use one half‑life for comparisons unless you have evidence for different kinetics.
How do booster settings affect the score?
Booster fold increases the index above baseline, and booster duration controls how quickly that boost fades. A larger fold or longer duration yields higher IMI soon after boosting. Setting days since booster very large makes the modifier approach 1.
Is this a medical diagnostic tool?
No. IMI is for education and research workflows, not diagnosis or treatment decisions. Immune protection depends on many factors beyond these markers, including variants, mucosal immunity, and individual health context. Consult qualified clinicians for medical guidance.