Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures

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.

Keywords: SARS-CoV-2; SEIR models; disease outbreak scenarios; social contact restrictions; spatially explicit epidemiology.

Fig. 1.

Evolution of the ratio of confirmed cases/resident population in Italy. The spatial spread over time of COVID-19 is plotted from February 25 to March 25, 2020. See also animations from day 5 to day 34 in Movies S1 and S2.

Fig. 2.

Time evolution of the COVID-19 epidemic in Italy. Time marks are as follows: a, the first patient with suspected local transmission is hospitalized in Codogno; b, first confirmed cases; and c, d, and e, main containment measures enforced by the Italian government (detailed in Materials and Methods).

Fig. 3.

Reported and simulated aggregate number of new daily hospitalized cases and deaths for COVID-19 spread in Italy (February 24 to March 25, 2020) (16, 17, 18). Computed results are obtained for the set of parameters shown in Table 2. Lines represent median model results, while shaded areas identify 95% CIs. Clockwise from lower right corner (see Insets): Italy, Marche, Liguria, Lombardia, Veneto, and Emilia-Romagna. Other regions are shown in SI Appendix, Fig. S12.

Fig. 4.

Hospitalizations (graph) and increases of hospitalization demands (maps), based on scenarios of modified transmission of COVID-19 in Italy. Data (white circles) and the lower curve (baseline scenario) show, respectively, observations and model projections of the cumulative hospitalizations as a result of the actual disease spread constrained by the enforcement of the scheduled restrictions of the Italian government (see arrows in Fig. 2). The middle curve (dashed line, scenario A) represents the expected demand of hospitalizations, had the government not imposed the further March restrictions. The map of scenario A shows the corresponding expected increase of hospitalization demand with respect to the baseline as of March 25, 2020. The uppermost curve (dotted line, scenario B) shows the expected hospitalizations, had no restrictive measure been imposed. The map of scenario B shows the corresponding increase of hospitalization demand.

Fig. 5.

Schematic representation of the spatially explicit epidemiological model. (A) Local transmission dynamics (as in Eq. 1). (B) Connections between the local communities. (C) Main routes of COVID-19 propagation in Italy as estimated via NGM (SI Appendix).