A data-driven model of the COVID-19 spread among interconnected populations: epidemiological and mobility aspects following the lockdown in Italy

https://doi.org/10.1007/s11071-021-06840-2 O R I G I NA L PA P E R

A data-driven model of the COVID-19 spread among interconnected populations: epidemiological and mobility aspects following the lockdown in Italy

Paolo Di Giamberardino · Daniela Iacoviello · Federico Papa · Carmela Sinisgalli

Received: 17 June 2021 / Accepted: 11 August 2021 / Published online: 3 September 2021

© The Author(s) 2021

Abstract An epidemic multi-group model formed by interconnected SEIR-like structures is formulated and used for data fitting to gain insight into the COVID-19 dynamics and into the role of non-pharmaceutical con- trol actions implemented to limit the infection spread since its outbreak in Italy. The single submodels pro- vide a rather accurate description of the COVID-19 evolution in each subpopulation by an extended SEIR model including the class of asymptomatic infectives, which is recognized as a determinant for disease diffu- sion. The multi-group structure is specifically designed to investigate the effects of the inter-regional mobil- ity restored at the end of the first strong lockdown in Italy (June 3, 2020). In its time-invariant version, the model is shown to enjoy some analytical stability prop- erties which provide significant insights on the efficacy of the implemented control measurements. In order to P. D. Giamberardino (

B

Department of Computer, Control and Management Engineering A. Ruberti, Sapienza University of Rome, Rome, Italy

e-mail: paolo.digiamberardino@uniroma1.it D. Iacoviello

e-mail: daniela.iacoviello@uniroma1.it F. Papa·C. Sinisgalli

Institute for Systems Analysis and Computer Science “A.

Ruberti” - CNR, Rome, Italy F. Papa

e-mail: federico.papa@iasi.cnr.it C. Sinisgalli

e-mail: carmela.sinisgalli@iasi.cnr.it

highlight the impact of human mobility on the disease evolution in Italy between the first and second wave onset, the model is applied to fit real epidemiological data of three geographical macro-areas in the period March–October 2020, including the mass departure for summer holidays. The simulation results are in good agreement with the data, so that the model can repre- sent a useful tool for predicting the effects of the com- bination of containment measures in triggering future pandemic scenarios. Particularly, the simulation shows that, although the unrestricted mobility alone appears to be insufficient to trigger the second wave, the human transfers were crucial to make uniform the spatial dis- tribution of the infection throughout the country and, combined with the restart of the production, trade, and education activities, determined a time advance of the contagion increase since September 2020.

Keywords COVID-19·Multi-group epidemic ODE model·COVID-19 spread in Italy·System control and identification

1 Introduction

The “coronavirus disease 2019” (COVID-19), caused by SARS-CoV-2, posed novel challenges to all world countries, often evidencing their vulnerability in the practical management of emergency states, particu- larly concerning the health effects, but also the impli- cations for economic growth and social development.

Understanding and quantifying the dominant variables that govern the current pandemic evolution, espe- cially in order to limit the impact of future outbreaks, imposes the need of framing the determinants of the epidemic dynamics [48]. Many literature studies take into account several types of possible interventions, mainly identifying two domains of variables: pathogen- associated variables and society-based variables. This latter variable domain appears to be particularly rele- vant for COVID-19, since individuals are actually the vectors of SARS-CoV-2 virus.

Thus, although vaccines are currently available, social distancing and personal protective measures are still important mechanisms for controlling the COVID- 19 spread. The effectiveness of social distancing is studied by many authors, whose papers propose pro- jections where the impact of containment measures in reducing the infection spread is shown [8,11,16,27, 44].

In the early stage of pandemic, only qualitative data analysis was performed, as in [30] where data regard- ing how the human mobility changed in the USA at the beginning of the pandemic course are studied; in that paper, it is also stressed the importance of quantifying the social distancing practices, emphasizing the oppor- tunity of determining relationships between confirmed cases and the social distancing plateau. The concept of

“social distancing” can also be associated with travel restrictions, that is to the attempt of limiting the virus transmission by reducing the amount of travels among regions. One of the first studies about the effects of human mobility on COVID-19 spread is proposed in [28]. Through the analysis of time mobility data from Wuhan, the study shows the importance of applying travel restrictions in the early stage of the outbreak, evidencing their lack of effectiveness in late stages.

Among the growing number of literature papers on the topic, we focus on the quantitative studies con- cerning the effects of limiting social distancing and human mobility. These studies are generally based on mathematical modelling, still exploiting rather differ- ent approaches, and most often they use public or volunteered datasets to assess the impact of differ- ent non-pharmaceutical countermeasures, [12,13,23, 39,40]. As an example, we mention the works of correlational analysis based on (generalized) regres- sion models of city clusters; in particular, applications are mainly proposed for single countries, e.g., China [53], USA countries [2], and some European countries

(namely France, Spain, and Italy) [25], or for cross-city analysis in many worldwide countries [47]. In [7], a framework that employs an epidemic Renormalization Group (eRG) approach to model the effects of inter- and extra-European border control and of social distancing for single countries is proposed; the model describes the time-evolution of the infected cases in a specific isolated region, while including the interactions among multiple regions of the World.

In many recent works, both deterministic/stochastic and discrete/continuous models have been applied for the description, forecast and control of the COVID- 19 epidemic spread. In the framework of deterministic compartmental models, the classes of Susceptible (S), Exposed (E), Infected (I), and Removed (R) subjects are generally introduced, yielding SEIR models. For the COVID-19 pandemic, because of the specificity of the disease, other categories are generally introduced referring to the condition of infected patients.

In this respect, notable modeling setups take into account the symptomatology level, thus distinguish- ing presymptomatic and asymptomatic infected indi- viduals [41], with the addition of the quarantined class [16,17] or acutely and mildly infection level along with the conditions of hospitalization and home-isolation [21,24].

Different modeling approaches can include dis- tributed time delays[50] or the computational model using probabilistic cellular automata [22,32].

Moreover, among the most recent (and actually huge) literature on the COVID-19 modeling, we also mention some papers proposing interesting variants of the classical SIR model and using different approaches:

the age-structured SIR model [36], the stochastic, discrete, age-structured compartmental model [1] in which distributed time delays related to the periods of incubation, infection, and quarantine are considered as in [35]. Another aspect common to these latter works that matters in relation to our study is that they ana- lyze the first epidemic period (until October 2020) in order to highlight how different strategies implemented in that peculiar time interval can affect the future epi- demic trajectory.

It is important to stress that a particularly critical epi- demiological characteristic has been recognized in the fraction of infectious cases remaining undocumented owing to mild or very limited symptoms; indeed, esti- mating the extent of undiagnosed infections is crucial for evaluating the overall prevalence and contagious-

ness and then the pandemic potential of the disease, [17,31].

Some variants of the original SIR/SEIR framework, modeling peculiar aspects of epidemic transmission dynamics, have been proposed in the past to gener- ate insights into the evolution of specific infectious diseases and assess the potential impact of different intervention strategies; so, these studies can usefully support the research on the present COVID-19 pan- demic. Among numerous modeling works, we men- tion the book [9] and the paper [46], presenting in- depth overviews of theoretical and applicative results on measles, ebola, and other viral infections. Examples of more specific works are [15,43] on the measles dis- ease, and [14,37,38] on HIV/AIDS. Also, it is worth mentioning that very recent works deal with the impact of the COVID-19 co-infection in patient with pre- existent morbidity [11]; for example, [4] reports a sta- tistical population-based study to estimate risk and pos- sible outcome of the association between HIV infection and COVID-19.

The epidemic models of the kind described con- cern the transmission of the viruswithin population.

Concerning aspects of the virus transmissionbetween populations, multi-group epidemic models, which are suitable extensions of SIR/SEIR frameworks, can be used to represent the COVID-19 spread among differ- ent (heterogeneous) populations, and to study the effect of interactions and travel restrictions on the pandemic evolution, [3,6,10,20,33,34,42]. The heterogeneity of the subpopulations is intended with respect to the epi- demic properties and can depend on their different geo- graphical allocation or other structural variables (e.g., economy, age, mobility).

The model presented in this paper is based on a previ- ous epidemic model reported in [17], which represented the COVID-19 evolution by a SEIR-type model includ- ing two subpopulations of infected subjects, undiag- nosed and diagnosed, and explicitly accounting for a fraction of asymptomatic infective subjects. The present model structure, depicted in Sect.2, incorpo- ratesN interconnected epidemic models of that kind, particularly with the aim of representing the effects of individual interactions and geographical exchanges among groups. In Sect. 3, analytical results on the dynamics of both the isolated subsystem and the inter- connected model are given. In Sect. 4, the general model is specialized for N = 3 and it is applied to simulate the disease evolution in Italy, and precisely

in three macroareas, in order to evidence some inter- esting aspects related to the increased human mobil- ity following the first pandemic wave. The numeri- cal results show that the model is apt in describing the summer period 2020 of the COVID-19 epidemic in Italy and the effect of the holiday exodus from North to Center-South, by explicitly accounting for different scenarios characterizing geographically dis- tinct macro-areas of the Italian territory (northern, cen- tral, and southern). The detailed analysis reported in Sect.4includes two periods: (i) the first one character- ized by the strong lockdown implemented in our coun- try and (ii) the following reopening period going from the control relaxation (beginning of June 2020) until new social, economic and mobility restrictions were implemented to contain the second contagion wave (mid-October 2020). As explained in the following, the time period selected for the analysis, as well as the number and the geographical localization of the sub- groups considered, are actually motivated by the main goal of this paper, that is the in-depth analysis of the human mobility (satisfactorily captured in this period by the assumed model structure) after the removal of the containment measures following the first severe lock- down. However, in Sect.5, the same model structure is exploited to evaluate the mobility impact after the period of interest, extending the analysis to the present days and confirming some epidemiological and mobil- ity aspects already highlighted in Sect.4.

2 A multi-group epidemic model for the spread of COVID-19 amongNgroups

The model proposed here has a multi-group structure that incorporates different subunits, each one describ- ing the dynamic evolution of the COVID-19 within a homogeneous population, whose epidemic evolution differs from that of all other units. For instance, differ- ent groups can represent different geographical areas or structurally different populations. The N groups (namely subunits) are interconnected by a mobility net- work that accounts for the transfers of individuals who are allowed to travel from a group to another. Most typically, the model can describe a geographical sys- tem composed byNregions with people of each region moving for work, study or simply personal/holiday rea- sons.

Fig. 1 Block diagram of the epidemic model representing a sin- gle subpopulation

Each of the N submodels is a simplified version of the model previously proposed in [17] for the description of the first phase of the epidemic spread in our country (thereby modeled as a whole homo- geneously mixing group). In particular, the compart- ment of subjects isolated while waiting for the results of the swab tests, included in [17], has been removed due to the increasing capability of doing fast tests along the considered period, especially at the end of lockdown, which provide almost immediate responses of positiveness (so producing a new diagnosed and isolated infected) or negativeness (leaving the tested subject in the class of susceptible people). In addi- tion to a compartment of exposed individuals, which is proper of SEIR models, our model explicitly dis- tinguishes between diagnosed and undiagnosed infec- tive patients. As shown in [17], the proposed struc- ture appears appropriate to mimic the Italian case, by incorporating also control actions reproducing govern- ment restrictions and emergency actions implemented to detect the infected cases, especially asymptomatic or mildly symptomatic ones. The block diagram reported in Fig.1shows the general structure and the state vari- ables of one regional submodel.

Precisely, each submodel takes into account the fol- lowing five state variables:

Si(t), number of susceptible individuals;

Ei(t), number of exposed (infected but not yet infec- tive) individuals;

Iu_i(t), number of undiagnosed infective patients, account- ing for two subpopulations: (i) asymptomatic or developing mild or limited symptoms dur- ing their whole infection period; (ii) develop- ing, at a certain point, recognizable symptoms, still remaining undocumented;

Id_i(t), number of diagnosed infective patients, receiv- ing medical treatments (to cure the infection or its complications). It is assumed that they can- not transmit the virus since they are isolated (at home or at hospital);

Ri(t), number of healed patients (spontaneously or after therapy).

The complete model formulation includesN struc- turally identical groups or subsystems, with each group i,{i =1,2, . . . ,N}, described by a SEIR-type model with undiagnosed and diagnosed infected subjects, as reported above. For sake of compactness, when the whole system is considered, a vectorial notation is introduced for the state space variables, defining the vector

S(t)=

S1(t)· · · SN(t)T

(1) and, with the same procedure, E(t), Iu(t), Id(t)and R(t). The N subsystems are connected by means of a mobility network allowing people to move among groups. In the following, we refer to a specific epidemic group by its identifieri, also using the same subscript to denote the related state variables and parameters. In general, however, we assume that the only individu- als allowed to move are the ones having no evidence and/or diagnosis of infection, i.e., the ones belonging to compartmentsSi,Ei,Iui,Ri,i=1,2, . . . ,N. Fig.2 shows how the mobility network works forN =3 sub- systems, also illustrating the epidemic core model of each subsystem.

So, the spread of COVID-19 among N epidemio- logically distinct groups can be formally described by means of N systems of time-varying ODE models of the following kind:

˙

S_i =B_i−βi(1−u_i)S_iI_ui−μiS_i

− N j=1,j=i

c_i,j(1−z_i,j)S_i+ N j=1,j=i

c_j,i(1−z_j,i)S_j, (2)

˙

E_i =βi(1−u_i)S_iI_ui−viE_i−k_iE_i−μiE_i

− N j=1,j=i

c_i,j(1−z_i,j)E_i+ N j=1,j=i

c_j,i(1−z_j,i)E_j+ΛEi,

(3)

I˙ui =kiEi−viIui−hiφiIui−γI_ui(1−φi)Iui−μiIui

− N j=1,j=i

c_i,j(1−z_i,j)Iui+ N j=1,j=i

c_j,i(1−z_j,i)Iuj+ΛI_ui,

(4)

˙

I_di =h_iφiI_ui+vi(E_i+I_ui)−γI_di(1+f_i)I_di

−μI_di(1−wi)Idi, (5)

Fig. 2 Block diagram of a mobility scheme among N=3 interconnected groups of epidemic diffusion. Controls for groupi:u_i∈ [0,1], social contact limitations;

vi∈ [0,1], test campaign intensity;wi∈ [0,1], efficacy of therapies against COVID-19 complications;

f_i≥0, efficacy of therapies against COVID-19;

z_i,j∈ [0,1], limitation of movements between groups iandj. Coefficientsc_i,j: transition probability of a subject from compartmenti to compartmentj

˙

R_i =γI_ui(1−φi)I_ui+γI_di(1+f_i)I_di−μiR_i

− N j=1,j=i

c_i,j(1−z_i,j)R_i+ N j=1,j=i

c_j,i(1−z_j,i)R_j, (6) wherei = 1,2, . . . ,N. Now, we briefly explain the meaning of all the quantities included in the ODE sys- tem (2)-(6).Biis the net input rate in compartmentSi, which accounts for both the newborn (susceptible) indi- viduals and the balance between immigration and emi- gration;μi is the per capita death rate owing to causes not related to the infection (natural death of the popu- lation) and it represents the loss rate from any compart- ment of the model except forId_i;μI_di is the per capita death rate of diagnosed patients Idi;βi is the relative contagiousness of individuals in compartmentIu_i and it accounts for two main factors, which are the conta- gion probability from one infected-susceptible contact (related to the aggressiveness of the virus) and the fre- quency of contacts;φi represents the fraction of the infective population Iui that will show recognizable symptoms and that will consequently be diagnosed and isolated (possibly receiving therapies);kidescribes the transition from Ei to Iui, and it is set to ki = 1/τi, whereτi is the mean length of the incubation period (see Fig.3);hi refers to the transition from Iu_i toId_i, taken ashi =1/τsi, whereτsi is the average time from infection until the occurrence of the first recognizable symptoms;γI_ui models the outflow from the infective compartmentIui associated to recovery from infection and, then, it is assumedγI_ui =1/τri, withτri the mean recovery period without any medical assistance; simi- larlyγI_di models the outflow from the infective com- partmentsIdi due to recovery from the infection and,

Fig. 3 Schematic picture of the disease progression

then, it is γI_di = 1/τ˜ri, with τ˜ri denoting the mean recovery period of monitored patients;ci j is a weight accounting for the transition probability of a subject moving from thei-th compartmentSiorEiorIuito the correspondingj-th compartment. Note that the coeffi- cientsci jcan also be time-varying in order to represent the variation of transfer probabilities possibly occur- ring over time. This variability is especially required for long-term analysis when “ordinary” mobility regi- mens alternate with highly “intense” transfer periods, like summer or Christmas or Easter seasons. Note also that, for the sake of generality, the recovery rates from compartmentsIui andIdi are taken into account by the separate rate constantsγI_ui andγI_di respectively, even though to a first approximation, and in the absence of experimental evidences, they are assumed equal in the following simulation.

As far as the control actions are concerned, the time- varying quantitiesui(t),vi(t),wi(t),zi,j(t), taking val- ues in[0,1], and fi(t) ≥ 0 are introduced to repre-

sent the intervention measures adopted by the author- ities to contain the disease outbreak. More precisely, ui(t)quantifies possible actions locally implemented by authorities to reduce the contact rate, and then the relative infectivityβi, of populationi. It accounts for all the government decrees introduced to limit the phys- ical interactions among people, but also for the infor- mative campaign about hygienic measures, TV/radio announcements, and so on. The quantityvi(t)repre- sents the intensity of the swab test campaign performed on subpopulationi, which changes daily depending on the number of swab tests actually performed. For the sake of simplicity, and in the absence of other indica- tions, we assume that the amount of performed tests is equally distributed among people of compartments Si,Ei andIu_i, so that the same per capita test rate can be assumed for all these compartments. This implies that the exit fluxes of tested (positive) individuals leav- ingEi andIu_i are proportional to the number of indi- viduals within the same compartment, i.e.,vi(t)Ei(t) andvi(t)Iui(t), respectively. We notice that the flux vi(t)Si(t)of (negative) test results exitingSi does not explicitly appear in the model equation (see also Fig.1) since it does not contribute to the dynamical evolution (actually an identical flux amount comes back to com- partmentSi). The control actionswi(t)and fi(t)refer to the efficacy of the therapies adopted by theith health system, either to reduce side effects of COVID-19 and, respectively, to cure the infection. Furthermore, the time-varying controlszi,j(t),i,j =1,2, . . . ,N, rep- resent the interventions and mobility restrictions imple- mented by the central government or local authorities to limit people transfers between groupsiand j.

Finally, the pair of input fluxesΛE_i(t)andΛI_ui(t) is introduced (see Eqs. (3), (4)) to model the cumu- lative entry of infected people coming from outer groups/areas whose epidemic dynamics is not incorpo- rated in theN group model. Since documented infec- tive people are not allowed to travel, it is reasonable to account for such cumulative outer inputs only in the equations ofEiandIu_i,i =1, . . . ,N.

We notice that the proposed model does not incorpo- rate the possibility of re-infection. Indeed in our model, once recovered, a patient (R) is no longer susceptible of infection and cannot re-enter the S class. This simplify- ing hypothesis, which is actually the object of clinical studies and debates on the persistence and actual length of the immunity period, can be a valid assumption if a

short-term analysis, like the one presented in the next section, is performed.

As a remark on the asymptomatic undiagnosed sub- jects, we observe that a susceptible subject (of any group i) can become infected if a non-safe contact with an infected undiagnosed subject occurs. During the infectious period that follows incubation, the newly infected subject can at some time develop recognizable symptoms being easily diagnosed, and possibly recov- ering after medication and assistance without serious consequences. However, in a number of cases, the infectious individual can be asymptomatic or mildly symptomatic until full recovery, remaining hidden and undocumented as a positive case inIui. So, in fact, the class of asymptomatic undiagnosed subjects represents the most dangerous class responsible for the possible epidemic spread since the individuals are allowed to move, thus transmitting the contagion and increasing the number of infections also to other groups. Exhaus- tive swab test campaigns performed on the entire pop- ulation can improve the capability of diagnosis con- tributing to mitigate the infection diffusion as reported in [29].

3 Equilibria and stability analysis

The first step of the analysis, in Subsection3.1, takes into account eachith subsystem as an isolated dynam- ical model, neglecting all the mobility contributions, i.e., setting zi,j = 1, i,j = 1, . . . ,N. In order to take into account the actual situation in which control actions are always present, the other controls are not set to zero but they are considered as constants. So, the following setting is introduced in all the analysis

ui(t)=u^c_i, vi(t)=v_i^c, wi(t)=w_i^c, fi(t)= f_i^c, zi,j(t)=z^c_i,j,i,j =1,2, . . . ,N. (7) The resulting study can be also associated to the condi- tions over a limited time interval in which the controls do not vary sensibly. In addition, as usual for equilibria and stability analysis, the external inputs are set to zero, so thatΛEi(t)=Λ^c_Ei =0 andΛI_ui(t) =Λ^c_I

ui = 0.

Introducing the compact expressions

˜

ci,j=ci,j(1−z_i,^c_j),μ˜i=μi+ N j=1,j=i

˜

ci,j,i=1,2, . . . ,N,

(8) the system addressed in the present section is

S˙i =Bi−βi(1−u^c_i)SiIui− ˜μiSi+ N j=1,j=i

˜ c_j,iSj,

E˙i =βi(1−u^c_i)SiIui−

vi^c+ki+ ˜μi Ei+

N j=1,j=i

˜ c_j,iEj,

I˙ui =kiEi−

v^ci +hiφi+γI_ui(1−φi)+ ˜μi

Iui+

N j=1,j=i

˜ c_j,iIuj,

˙

I_di =h_iφiI_ui+v^ci(E_i+I_ui)−

γI_di(1+f_i^c)+μI_di(1−wi^c) I_di,

˙

R_i =γI_ui(1−φi)I_ui+γI_di(1+f_i^c)I_di− ˜μiR_i+ N j=1,j=i

˜ c_j,iR_j.

(9) 3.1 Analysis of theith isolated subsystem

Equilibrium points for system (9), and their stability analysis are here addressed from the point of view of eachith submodel, assuming the absence of incoming and outgoing fluxes (zi,j =1,i,j =1, . . . ,N). Under these positions, the equilibrium points can be computed solving the nonlinear system

Bi−βi(1−u^c_i)I_u^e_iS_i^e−μiS_i^e=0, (10) βi(1−u^c_i)S_i^eI_u^e_i −

v_i^c+ki+μi

E_i^e=0, (11) kiE_i^e−

v^c_i +hiφi+γI_ui(1−φi)+μi

I_u^e_i =0, (12) hiφiI_u^e_i +vi^cE^e_i +vi^cI_u^e_i

−

γI_di(1+ f_i^c)+μI_di(1−w^c_i)

I_d^e_i =0, (13) γI_ui(1−φi)Iu^e_i +γI_di(1+ f_i^c)I_d^e_i −μiR^e_i =0.

(14) From (12) the expression forE_i^e,

E_i^e=

v_i^c+hiφi+γI_ui(1−φi)+μi

ki

I_u^e_i, (15) is obtained and can be used in (11), giving

βi(1−u^c_i)S_i^eI_u^e_i

−(^vi^c+k_i+μi)^vi^c+h_iφi+γ_Iui(1−φi)+μi

ki I_u^e_i =0, (16)

from which the two solutions

I_u^e_i =0 (17)

and S_i^e=

v_i^c+k_i+μi v^c_i +h_iφi+γI_ui(1−φi)+μi

k_iβi(1−u^c_i)

(18) can be computed. Denoting by the superscript 1 the equilibrium point associated to (17) and by 2 the one

associated to (18), using (17) in (10)–(14), the solu- tion

P_i^e1=

B_i

μi 0 0 0 0 T

(19) is obtained. Equilibrium points like (19), characterized by the absence of any kind of infected people, are usu- ally addressed asepidemic free.

The second equilibrium can be obtained starting from (18); settingS_i^e2=S_i^e, the other components of P_i^e2=

S_i^e2 E_i^e2 I_u^e2_i I_d^e2

i R_i^e2T

(20) can be computed as follows

I_u^e2

i = B_i−μiS^e2_i

βi(1−u^c_i)S_i^e2, E_i^e2= B_i−μiS_i^e2

v_i^c+k_i+μi ,

I_d^e2

i =

h_iφi+v_i^c

(B_i−μiS_i^e2) βi(1−u^c_i)

γI_di(1+ f_i^c)+μI_di(1−w^c_i) S_i^e2 + v_i^c(B_i−μiS_i^e2)

γI_di(1+ f_i^c)+μI_di(1−w_i^c) v^c_i+ki+μi,

R^e2_i =

⎛

⎝γI_ui(1−φi)+ γI_di(1+f_i^c) hiφi+v^ci

γI_di(1+ f_i^c)+μI_di(1−w^c_i)

⎞

⎠

× Bi−μiS_i^e2 μiβi(1−u^c_i)S_i^e2 +

⎛

⎝ γI_di(1+ f_i^c)v_i^c(B_i−μiS_i^e2) μi

γI_di(1+ f_i^c)+μI_di(1−wi^c) v^ci+ki+μi

⎞

⎠.

An important note is that equilibrium (20) is admis- sible if and only if all its components are nonnegative.

Since forBi−μiS_i^e2=0 the equilibrium (20) coincides with the epidemic-free solution (19), the admissibility of an independent solution is assured only for

Bi−μiS_i^e2>0. (21)

In this case the equilibrium defines a condition in which a certain number of infected individuals is always present and then the epidemic is active. This kind of equilibrium define the so-calledendemic conditions.

It can be noted that in the epidemic-free condition, the equilibrium point is not dependent on the control actions, since no epidemic is present. On the contrary, the endemic equilibrium point is strongly dependent on the controls, if present.

Once the equilibrium points are known, it is possible to study their stability characteristics. Local conditions

can be easily given on the basis of the linear approxi- mation in a neighborhood of each of them.

The first step is the computation of the Jacobian matrix for the system (9) in the isolated conditions con- sidered above, without incoming and outgoing people fluxes. The result is a matrix of the form

J =

J1,1 0 J2,1 J2,2

, (22)

with

J1,1 = _−β

i(1−u^ci)Iui−μi 0 −βi(1−u^ci)Si

βi(1−u^ci)Iui −(vi^c+ki+μi) βi(1−u^ci)Si 0 ki −(v^ci+hiφi+γ_Iui(1−φi)+μi)

,

(23)

J_2,1 =

0v_i^c v_i^c+hiφi

0 0 γI_ui(1−φi)

, (24)

J2,2 =

−(γI_di(1+f_i^c)+μI_di(1−w^c_i)) 0 γI_di(1+ f_i^c) −μi

. (25)

Being the eigenvalues of J2,2 always real negative (λ1 = −(γI_di(1 + f_i^c)+μI_di(1 −w^c_i)) < 0 and λ2 = −μi < 0), stability of local approximations depends on the different forms assumed by J1,1once computed inP_i^e1andP_i^e2.

Setting, for sake of compactness, mi =(v_i^c+ki+μi), ni

=(v_i^c+hiφi+γI_ui(1−φi)+μi), (26) the matrix obtained forP_i^e1can be written as

J1,1(P^e1)=

⎛

⎜⎝

−μi 0 −βi(1−u_i^c)_μ^Bi_i 0 −mi βi(1−u^c_i)_μ^Bi_i

0 ki −ni

⎞

⎟⎠. (27)

along with

From the triangular structure of the matrix and being

−μi <0, stability is assured once the matrix −mi βi(1−u_i^c)_μ^Bi

i

ki −ni

(28) has eigenvalues, i.e., the roots of the characteristic poly- nomial equation

λ²+(mi+ni)λ+mini−kiβi(1−u^c_i)Bi

μi =0, with negative real part. Thanks to the positiveness of the model parameters and making use of the Descartes’

rule of signs, the required condition holds if and only if

mini −kiβi(1−u_i^c)Bi

μi >0. (29)

Easy computations, along with positions in (26), lead to

Bi

μi =S_i^e1< mini

kiβi(1−u^c_i)=S_i^e2. (30) When the stability of the second equilibrium point P_i^e2is investigated, the same procedure as above brings to the computation of the matrix

J1,1=

⎛

⎜⎜

⎝

−_S^Be2ⁱ i

0 −βi(1−u_i^c)S_i^e2

B_i

S_i^e2 −μi −mi βi(1−u^c_i)S_i^e2

0 ki −ni

⎞

⎟⎟

⎠. (31)

Its eigenvalues are given by the roots of the character- istic polynomial

pJ1,1(λ)=λ³+

B_i

S^e2_i +mi+ni

λ² +

Bi

S_i^e2mi+ _S^Be2ⁱ i

ni +mini−kiβi(1−u^c_i)S_i^e2

λ +_S^Be2ⁱ

i

mini−μikiβi(1−u^c_i)S_i^e2

=λ³+

Bi

S_i^e2 +mi +ni

λ²+ _S^Be2ⁱ i

(mi+ni)λ +

Bi

S_i^e2 −μi

mini.

(32)

For the Routh–Hurwitz stability criterion, necessary and sufficient conditions to be fulfilled are

Bi

S_i^e2+m_i+n_i Bi

S_i^e2(m_i+n_i)− Bi

S^e2_i −μi

m_in_i>0, (33)

B_i S_i^e2−μi

m_in_i>0. (34)

It is possible to verify that condition (33) is always satisfied since, after some manipulations, the equiva- lence with

Bi S_i^e2

Bi

S^e2_i (m_i+n_i)+m²_i +n²_i +m_in_i

+μim_in_i>0 (35) can be proved. Condition (34) is equivalent to

Bi

S_i^e2−μi >0 ⇔ S_i^e1

S_i^e2 >1. (36) A preliminary observation is that condition (36) is the same as condition (21) and then when the endemic equi- librium exists, it is also locally asymptotically stable.

At the same time, being (30) not satisfied, the epidemic- free equilibrium is unstable. The opposite situation arises when (36), and then (21), are not satisfied: the