INTRODUCTION:
Early detection and rapid response are central to pandemic prevention, yet persistent delays in surveillance and response continue to undermine preparedness in many low- and middle-income countries (LMICs). These delays allow initially containable outbreaks to escalate into large-scale epidemics or pandemics. This study develops a delay-structured mathematical modeling framework to quantify the impact of surveillance gaps on epidemic outcomes and preparedness capacity, using nigeria as a representative LMIC case study.

METHODS:
We developed an extended compartmental Susceptible–Exposed–Infectious–Recovered (SEIR) type model that explicitly distinguishes undetected and detected infectious states. Time delays representing case detection, reporting, and response activation were incorporated into the transmission dynamics. A health system preparedness module was included to capture staged response capacity and system constraints. Model simulations explored scenarios reflecting realistic surveillance efficiencies, reporting timelines, and response capacities observed in nigeria and comparable LMIC settings. Threshold analyses were conducted to identify delay levels beyond which outbreak containment becomes unlikely.

RESULTS:
The model demonstrates the existence of a critical delay threshold beyond which marginal increases in detection and response time result in disproportionately larger epidemic peaks, prolonged transmission, and rapid health system overload. Scenarios with earlier detection and faster reporting show substantially improved outbreak containment, even without major increases in healthcare capacity. In contrast, prolonged surveillance and response delays generate reinforcing feedback loops of undetected transmission and system saturation, significantly increasing epidemic and pandemic risk.

CONCLUSIONS:
These findings provide quantitative evidence that reducing surveillance and response delays is a highly effective and underutilized strategy for pandemic preparedness. The proposed modeling framework offers a transferable tool to support evidence-based policy decisions and prioritize surveillance investments in nigeria and other low- and middle-income countries, strengthening preparedness before the next outbreak emerges.