Estimating the Impact of Human Mobility Restrictions on COVID-19 Transmission in Lagos, Nigeria: A Hybrid Simulation Framework Integrating Mobility and Epidemiological Data

Abstract In the absence of pharmaceutical countermeasures at the onset of the COVID-19 pandemic, many countries restricted population movement in an attempt to control viral spread. Debate continues regarding the effectiveness of this approach, particularly in sub-Saharan Africa, where mobility and epidemiological data streams are often fragmented, and the timing of mobility restrictions coincided with a period of substantial under-ascertainment of infections. To evaluate the impact of these restrictions on COVID-19 spread in Lagos, Nigeria, we developed a hybrid epidemic simulation framework that integrates population-representative tour templates derived from location-based services data, cross-resolution mobility reconstruction, and a serology-supervised compartmental model that jointly infers transmission dynamics. We then simulated policy-relevant counterfactual scenarios, including early, delayed, and no-lockdown scenarios, as well as targeted contact reductions at the most populous locations. We estimate that mobility restrictions in Lagos reduced cumulative COVID-19 infections by 33.8\% (1,532,869 cases) relative to a no-lockdown counterfactual. Our findings inform future pandemic response by quantifying the impact of this commonly deployed non-pharmaceutical intervention in a Nigerian city. Additionally, this framework can be adapted to evaluate transmission dynamics of other infectious diseases under surveillance limitations, such as future pandemics, providing an evidence base for policy decisions even in data-scarce urban environments.