INTRODUCTION:
Malaria imposes a heavy burden in Bauchi State, Nigeria, with seasonal peaks driven by rainfall and temperature. This study used an SEAIR (Susceptible-Exposed-Asymptomatic-Infected-Recovered) compartmental modeling and ARIMA forecasting to analyze transmission dynamics and evaluate interventions like insecticide-treated nets (ITNs), indoor residual spraying (IRS), and antimalarial drugs.

METHODS:
A deterministic SEAIR model coupled with vector dynamics simulated by human (N_h) and mosquito (N_v) populations, incorporating parameters from District Health Information System 2 (DHIS), National Population Commission (NPC), and literature (Table 1). Model well-posedness was confirmed via positivity and boundedness proofs: solutions remain non-negative in R_11^+, with N_h≤Λ_h/μ_h and N_v≤Λ_v/μ_v. Historical incidence data underwent ARIMA forecasting; scenarios assessed intervention impacts on R_e. Table 1: Key Parameters Parameter Description Value Source Λ_h Human recruitment rate 20,769 NPC 2020 Λ_v Mosquito recruitment rate 103,846.72 NPC 2020 μ_h Human natural death rate 0.2264 NPC 2020 β_h Human transmission rate 8.1 Estimated γ Recovery rate 0.99 Estimated Ψ ITN effectiveness 0.73 MIS 2021 Π IRS effectiveness 0.6 WHO 2021 δ_h Malaria mortality 0.0040 Adegeti 2024 N_h Total human population 7,788,504 NPC2020 (Additional parameters available upon request; full table in supplementary materials.)

RESULTS:
Seasonal peaks aligned with rainy seasons. Combined ITNs (73% effective), IRS (60%), and drugs reduced R_e below 1, cutting infections by >70%. ARIMA forecasts predict a sustained decline with interventions. Figure 1: Forecasting for Bauch State using ARIMA

CONCLUSIONS:
Integrated strategies offer cost-effective control. Prioritize annual IRS pre-rainy season, ITN scaling, drug access, surveillance, and awareness to curb transmission.