Background: With the highest burden in northern Nigeria, malaria is a vector-borne disease that causes serious illness. Nigeria contributed 27% (61.8 million) of malaria burden worldwide and 23% (94 million) of malaria deaths globally in 2019. Despite the fact that Nigeria has made a significant step in malaria elimination, the process has remained stagnant in recent years. The global technical strategy targets of reducing malaria death to less than 50 per 1000 population at risk was unachievable for the past 5 years. As part of the national malaria strategic plan of 2021-2025 to roll back malaria, it is imperative to provide a framework that will aid in understanding the effective reproduction number R_e and the time dependent-contact rates C(t) of malaria in Nigeria which is quite missing in the literature. Methods: The data of the reported malaria cases between January 2014 and December 2017 and demography of all the northern states are used to estimate C(t) and R_e using Bayesian statistical inference. We formulated a compartmental model with seasonal-forcing term in order to account for seasonal variation of the malaria cases. In order to limit the infectiousness of the asymptomatic individuals, super-infection was also incorporated into the model. Results: The posterior mean obtained shows that Adamawa state has the highest mean R_e of 5.92 (95% CrI : 1.60-10.59) while Bauchi has the lowest 3.72 (95% CrI : 1.11-7.08). Niger state has the highest mean contact rate C(t) 0.40 (95% CrI : 0.08-0.77) and the lowest was Gombe 0.26 (95% CrI: 0.04-0.55 ). The results also confirm that there is a mosquito abundance and high reproduction number during the rainy season compared to the dry season. The results further show that over 60% of the reported cases are from the asymptomatic individuals. Conclusion: This research continues to add to our understanding of the epidemiology of malaria in Nigeria. It is strongly advised that a complete grasp of the malaria reproduction number and the contact rate between human and mosquitoes are necessary in order to develop more effective prevention and control strategies. It will support the public health practitioners strategy and effective planning for malaria eradication.

The authors have declared no competing interest.

We acknowledge funding from the Bill and Melinda Gates Foundation through WAMCAD and ICAMMDA. We also acknowledge the Malaria Consortium and NMEP for giving us the data.

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All data produced in the present study are available upon reasonable request to the authors and NMEP