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The lack of medical treatments and vaccines upon the arrival of the SARS-CoV-2 virus has made non-pharmaceutical interventions the best allies in safeguarding human lives in the face of the COVID-19 pandemic. Here we propose a self-organized epidemic model with multi-scale control policies that are relaxed or strengthened depending on the extent of the epidemic outbreak. We show that optimizing the balance between the effects of epidemic control and the associated socio-economic cost is strongly linked to the stringency of control measures. We also show that non-pharmaceutical interventions acting at different spatial scales, from creating social bubbles at the household level to constraining mobility between different cities, are strongly interrelated. We find that policy functionality changes for better or worse depending on network connectivity, meaning that some populations may allow for less restrictive measures than others if both have the same resources to respond to the evolving epidemic. • A higher stringency of local measures facilitates epidemic control, resulting in a train of epidemic waves manageable from a health point of view. • More stringent measures provide less disruption to the socio-economic fabric than in the case of the implementation of lax measures. • Focusing on the different autonomous communities of Spain, we show that their different architectures drive the outcome of control measures. • Those most connected regions demand more stringent measures at the local level to achieve the desired epidemic control. [ABSTRACT FROM AUTHOR]

• A novel multi-scale model of COVID-19 transmission dynamics • Simulation of COVID-19 dynamics in China and Italy • Panic situations increase the risk of disease transmission in crowds despite social distancing • Presymptomatic transmission accelerates the onset of exponential growth • Limited movement of the population flattens the epidemic curve Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that emerged in Wuhan, China in December 2019. It has caused a global outbreak which represents a major threat to global health. Public health resorted to non-pharmaceutical interventions such as social distancing and lockdown to slow down the spread of the pandemic. However, the effect of each of these measures remains hard to quantify. We design a multi-scale model that simulates the transmission dynamics of COVID-19. We describe the motion of individual agents using a social force model. Each agent can be either susceptible, infected, quarantined, immunized or deceased. The model considers both mechanisms of direct and indirect transmission. We parameterize the model to reproduce the early dynamics of disease spread in Italy. We show that panic situations increase the risk of infection transmission in crowds despite social distancing measures. Next, we reveal that pre-symptomatic transmission accelerates the onset of the exponential growth of cases. After that, we demonstrate that the persistence of SARS-CoV-2 on hard surfaces determines the number of cases reached during the peak of the epidemic. Then, we show that the restricted movement of the individuals flattens the epidemic curve. Finally, model predictions suggest that measures stricter than social distancing and lockdown were used to control the epidemic in Wuhan, China. [ABSTRACT FROM AUTHOR]