Abstract
The spread of coronavirus disease 2019 (COVID-19) in Italy prompted drastic measures for transmission containment. We examine the effects of these interventions, based on modeling of the unfolding epidemic. We test modeling options of the spatially explicit type, suggested by the wave of infections spreading from the initial foci to the rest of Italy. We estimate parameters of a metacommunity Susceptible-Exposed-Infected-Recovered (SEIR)-like transmission model that includes a network of 107 provinces connected by mobility at high resolution, and the critical contribution of presymptomatic and asymptomatic transmission. We estimate a generalized reproduction number ([Formula: see text] = 3.60 [3.49 to 3.84]), the spectral radius of a suitable next-generation matrix that measures the potential spread in the absence of containment interventions. The model includes the implementation of progressive restrictions after the first case confirmed in Italy (February 21, 2020) and runs until March 25, 2020. We account for uncertainty in epidemiological reporting, and time dependence of human mobility matrices and awareness-dependent exposure probabilities. We draw scenarios of different containment measures and their impact. Results suggest that the sequence of restrictions posed to mobility and human-to-human interactions have reduced transmission by 45% (42 to 49%). Averted hospitalizations are measured by running scenarios obtained by selectively relaxing the imposed restrictions and total about 200,000 individuals (as of March 25, 2020). Although a number of assumptions need to be reexamined, like age structure in social mixing patterns and in the distribution of mobility, hospitalization, and fatality, we conclude that verifiable evidence exists to support the planning of emergency measures.
Highlights
The spread of coronavirus disease 2019 (COVID-19) in Italy prompted drastic measures for transmission containment
The pathogen causing the acute pneumonia among affected individuals is the new coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [8, 9, 13, 14]
We model in space and time the countrywide spread of the COVID-19 epidemic in Italy (Materials and Methods), for which detailed epidemiological data are continuously updated and made public [16, 18, 30]
Summary
The spread of coronavirus disease 2019 (COVID-19) in Italy prompted drastic measures for transmission containment. SARS-CoV-2 | spatially explicit epidemiology | disease outbreak scenarios | SEIR models | social contact restrictions. We model in space and time the countrywide spread of the COVID-19 epidemic in Italy (Materials and Methods), for which detailed epidemiological data are continuously updated and made public [16, 18, 30]. Scientific and technological cross-fertilization yields broad availability of georeferenced epidemiological data and of modeling tools that aid decisions on emergency management To this end, spatially explicit models of the COVID-19 epidemic that include e.g. regional individual mobilities, the progression of social distancing, and local capacity of medical infrastructure provide significant information. It is possible to quantify the effect of local containment measures (like diachronic spatial maps of averted hospitalizations) and the assessment of the spatial and temporal planning of the needs of emergency measures and medical infrastructure as a major contingency planning aid
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