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Abstract Discriminating between second waves of community transmission, which necessitate broad-spectrum interventions, and multiple stuttering epidemic chains from repeated importations, which require targeted controls, is crucial for outbreak preparedness. However, necessarily scarce data available in the lull between potential epidemic waves cripples standard inference engines, blurring early-warning signals. We propose a novel framework for denoising inter-wave data, revealing how timely policy in New Zealand achieved local elimination and avoided dangerous resurgence.

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