Only a combination of social distancing and massive testing can effectively stop COVID-19 progression in densely populated urban areas

Alvarez, M. M.; Trujillo-de Santiago, G.

We present a simple epidemiological model that includes demographic density, social distancing, and efficacy of massive testing and quarantine as main parameters to model the progression of COVID-19 pandemics in densely populated urban areas (i.e., above 10,000 hab km2). Our model demonstrates that effective containment of pandemic progression in densely populated cities is achieved only by combining social distancing, widespread testing, and quarantining of infected subjects. This finding has profound epidemiological significance, and sheds light on the controversy regarding the relative effectiveness of widespread testing and social distancing. Our simple epidemiological simulator can also be used to assess the efficacy of a governmental/societal response to an outbreak. This study has also relevant implications on the concept of smart cities; densely populated areas are hot spots highly vulnerable to epidemic crisis.