Epilepsy vs Space Weather — Completed Correlation Analysis (2015–2026)
Analysis Complete — First Results Published
This report presents the results of our first full correlation analysis between epilepsy-related Google Trends data and space-weather variables over 4,076 consecutive days (2015-03-18 → 2026-07-15).
Data: How We Built the Dataset
Google Trends Pipeline
The epilepsy search-interest series was constructed by pulling daily data from Google Trends in overlapping 90-day windows, then stitching the windows together using a normalisation procedure. Each query returns 90 days of relative interest values (0–100 scale), and consecutive windows were aligned at their overlap points to produce a continuous 4,076-day time series.
Space-Weather Variables
Matched to the same 4,076-day date range from public NASA/NOAA sources:
| Variable | Source | Resolution |
|---|---|---|
| Kp | NOAA SWPC | 3-hourly → daily mean |
| Ap | NOAA SWPC | 3-hourly → daily |
| Dst | WDC Kyoto | hourly → daily mean |
| Solar wind speed | NASA OMNI | hourly → daily mean |
| IMF B (average) | NASA OMNI | hourly → daily mean |
| F10.7 solar flux | NOAA | daily |
| Sunspot number | SILSO | daily |
Preprocessing
- Both series were detrended using STL decomposition (365-day seasonal period) to remove annual cycles and long-term drift
- Outliers were winsorised at ±3 standard deviations
- Final dataset: 4,076 matched daily observations — no missing days, no gaps
Methodology
- Cross-correlation function computed at lags τ = −14 to +14 days (Pearson and Spearman)
- Benjamini–Hochberg FDR correction at α = 0.05 across all lags × variables
- Surrogate-data validation: 1,000 random permutations per variable to establish empirical significance
- All code in Python (pandas, SciPy, statsmodels)
Key Findings
Top 10 Significant Correlations (F10.7)
All top results cluster around F10.7 solar flux at positive lags of +5 to +14 days. This means elevated solar radio flux today is associated with slightly higher epilepsy-related search activity 5–14 days later.
The table below shows the strongest Pearson correlations that survived FDR correction:
| Lag (days) | Pearson r | p-value | FDR p-value | Significant |
|---|---|---|---|---|
| +11 | +0.1417 | < 0.000001 | < 0.000001 | ✓ |
| +13 | +0.1413 | < 0.000001 | < 0.000001 | ✓ |
| +12 | +0.1407 | < 0.000001 | < 0.000001 | ✓ |
| +10 | +0.1403 | < 0.000001 | < 0.000001 | ✓ |
| +14 | +0.1391 | < 0.000001 | < 0.000001 | ✓ |
| +9 | +0.1377 | < 0.000001 | < 0.000001 | ✓ |
| +7 | +0.1366 | < 0.000001 | < 0.000001 | ✓ |
| +8 | +0.1362 | < 0.000001 | < 0.000001 | ✓ |
| +6 | +0.1362 | < 0.000001 | < 0.000001 | ✓ |
| +5 | +0.1348 | < 0.000001 | < 0.000001 | ✓ |
Bar Chart — Top 10 F10.7 Correlations
Surrogate Test — All 7 Variables
The permutation test confirms that all 7 space-weather variables produce signals stronger than random shuffles would generate. However, the effect sizes are small in absolute terms.
| Variable | Real r | Empirical p | Significant |
|---|---|---|---|
| Kp | −0.0385 | 0.0120 | ✓ |
| Ap | −0.0330 | 0.0230 | ✓ |
| Dst | +0.0331 | 0.0410 | ✓ |
| Solar wind speed | −0.0419 | 0.0050 | ✓ |
| IMF B (avg) | −0.0392 | 0.0080 | ✓ |
| F10.7 flux | +0.1333 | 0.0000 | ✓ |
| Sunspot number | +0.0741 | 0.0000 | ✓ |
Timeline Chart
90-day sliding windows stitched at overlap points · 4,076 matched daily observations · No gaps
Effect Size Table (All Variables at Optimal Lag)
| Variable | Best Lag | Pearson r | p-value | FDR Sig | Effect |
|---|---|---|---|---|---|
| F10.7 flux | +11 days | +0.1417 | < 0.000001 | ✓ | Weak positive |
| Sunspot number | +9 days | +0.0741 | 0.0000 | ✓ | Very weak positive |
| Dst | −3 days | +0.0331 | 0.0410 | ✓ | Negligible |
| Solar wind speed | −5 days | −0.0419 | 0.0050 | ✓ | Negligible negative |
| IMF B (avg) | −2 days | −0.0392 | 0.0080 | ✓ | Negligible negative |
| Kp | −1 day | −0.0385 | 0.0120 | ✓ | Negligible negative |
| Ap | −1 day | −0.0330 | 0.0230 | ✓ | Negligible negative |
Interpretation
The analysis reveals a weak but statistically significant positive association between F10.7 solar radio flux and epilepsy-related search interest, peaking at lag +11 days (r = +0.1417).
Key Points
- Not null, not strong. The signal is real (confirmed by FDR + surrogate tests), but the effect size is modest (r ≈ 0.14)
- Lag structure is smooth. Correlations at lags +5 through +14 are all positive and similarly sized — not a single spike, but a broad elevation
- Other variables show only trace signals. Kp, Ap, Dst, solar wind, and IMF-B show statistically significant but practically negligible associations (|r| < 0.05)
- Compare to literature. Earlier Chizhevsky-era work reported stronger effects in experimental settings; modern clinical EEG studies often find null results. Our finding sits between these extremes
Limitations
- Daily resolution — finer-grained (hourly) data may reveal stronger effects
- Single keyword — "epilepsy" is only one term; a broader health-term panel is needed
- No geographic breakdown — Google Trends aggregates globally; regional effects may be diluted
- Observational, not causal — correlation ≠ causation; multiple confounders possible
Next Steps (Planned)
- Expand to a panel of 20+ health-related search terms
- Acquire hourly-resolution space-weather data for fine-grained lag testing
- Test regional sub-series (US, Europe, India) if data availability permits
- Publish replication analysis on independent 2026–2027 data
- Release methodology whitepaper + code on GitHub
Analysis completed: 2026-07-17. Data period: 2015-03-18 → 2026-07-15 (4,076 days). Status: Completed.