Three synchronized time series scroll left-to-right: population (cyan bars), varroa mite load (red line), honey stores (amber bars). A simple anomaly detector flags any week where population drops faster than its trailing average minus two standard deviations.
dPop/dt = growth(feeding) - lossRate(varroa_load, treatment)
dVarroa/dt = spread(pressure) - clearance(treatment)
anomaly if pop[t] < mean(pop[t-8..t-1]) - 2*std(pop[t-8..t-1])
- Varroa pressure — how fast mite load grows week over week absent treatment.
- Supplemental feeding — sugar syrup / pollen patty support that boosts population growth, especially useful during dearth.
- Varroa treatment — toggles active mite control, which caps and reverses load growth.
This is exactly the workflow real beekeeping data-analytics tools follow: turn raw hive-scale/counting-board readings into time series, then flag statistically unusual drops before a colony visibly collapses.