Transmission dynamics of the ongoing chikungunya outbreak in Central Italy: From coastal areas to the metropolitan city of Rome, summer 2017

Abstract

A large chikungunya outbreak is ongoing in Italy, with a main cluster in the Anzio coastal municipality. With preliminary epidemiological data, and a transmission model using mosquito abundance and biting rates, we estimated the basic reproduction number R0 at 2.07 (95% credible interval: 1.47–2.59) and the first case importation between 21 May and 18 June 2017. Outbreak risk was higher in coastal/rural sites than urban ones. Novel transmission foci could occur up to mid-November.

Full text

1www.eurosurveillance.org
R 
Transmission dynamics of the ongoing chikungunya
outbreak in Central Italy: from coastal areas to the
metropolitan city of Rome, summer 2017
Mattia Manica1,2,3 , Giorgio Guzzetta3,4, Piero Poletti3,4, Federico Filipponi², Angelo Solimini², Beniamino Caputo², Alessandra
della Torre², Roberto Rosà¹, Stefano Merler
1. Dipartimento di Biodiversità ed Ecologia Molecolare/Centro Ricerca e Innovazione, Fondazione Edmund Mach, San Michele
all’Adige, Italy
2. Dipartimento di Sanitá Pubblica e Malattie Infettive, Sapienza University of Rome, Laboratory aliated to Istituto Pasteur
Italia – Fondazione Cenci Bolognetti
3. These authors contributed equally to the work
4. Center for Information Technology, Fondazione Bruno Kessler, Trento, Italy
Correspondence: Stefano Merler (merler@fbk.eu)
Citation style for this article:
Manica Mattia, Guzzet ta Giorgio, Poletti Piero, Fil ipponi Federico, Solimini A ngelo, Caputo Beniamino, della Torre Alessandr a, Rosà Roberto, Merler S tefano.
Transmission dynamics of the ong oing chikungunya outbreak in Central I taly: from coastal area s to the metropolitan cit y of Rome, summer 2017. Euro Surveill.
2017;22(44):pii=17-00685. https://doi.org/10.2807/1560-7917.ES.2017.22.44.17-00685
Article submit ted on 11 Oct 2017 / accepted on 31 Oc t 2017 / published on 02 No v 2017
A large chikungunya outbreak is ongoing in Italy, with
a main cluster in the Anzio coastal municipality. With
preliminary epidemiological data, and a transmission
model using mosquito abundance and biting rates,
we estimated the basic reproduction number R0 at
2.07 (95% credible interval: 1.47–2.59) and the first
case importation between 21 May and 18 June 2017.
Outbreak risk was higher in coastal/rural sites than
urban ones. Novel transmission foci could occur up to
mid-November.
On 7 September 2017, Italian public health authorities
reported three autochthonous cases of chikungunya
in Anzio, a coastal city 50 km south of Rome, located
in the Lazio region [1]. However, the symptom onset
for the first cases was retrospectively considered to
have occurred between 26 and 27 June. The outbreak
continued spreading in the Lazio region with the num-
ber of notified cases reaching 297 (of which 170 were
confirmed) on 13 October. Although most cases were
reported from Anzio, a distinct cluster of transmission
was also detected in the metropolitan area of Rome
[2]. The index case has not been identified, but the
mosquito vector implicated in the chikungunya virus
(CHIKV ) transmission was confirmed to beAedesalbop-
ictus, as was the case in a previous Italian CHIKV out-
break, which occurred in the region of Emilia Romagna
in 2007 [1]. In the same period than the Lazio outbreak
in 2017, a fur ther outbreak was detected in Guardavalle
Marina, a small coastal town in the Calabria region [2],
600 km south of Anzio, with 54 additional notified
cases (nine confirmed). It is still unknown whether the
Guardavalle outbreak is epidemiologically linked to the
epidemic occurring in Lazio. Here, we provide a quanti-
tative characterisation of the ongoing outbreak, using
available epidemiological data [2] and a transmission
dynamics model [3-5] informed with data on mosquito
abundance [6] and biting rate on humans [7] from pre-
vious collections in 18 sites within Lazio region.
Reproduction numbers from
epidemiological data
The instantaneous reproduction number Rt [8] was
estimated from the time series of notified cases
in Anzio, Rome and Guardavalle Marina under the
assumption of gamma distributed generation time
(shape = 4.67; scale = 3; mean= 4 days) [9] (Figure
1). By averaging Rt over the first 3 weeks of August
(initial period of exponential growth), we estimated
the basic reproduction number R0 for Anzio at 2.07
(95% credible inter val (CI):1.47–2.59), a value slightly
lower than that estimated for the 2007 outbreak in
Emilia Romagna (i.e. R0 = 3.3; 95% CI: 1.8–6.0) [3].
The decrease in Rt corresponded with the first date
of reactive vector control interventions, namely 7
September [10]. The robustness of this estimate was
confirmed by computing the basic reproduction num-
ber from the exponential growth rate [11] yielding a
very similar result (R0 = 1.88; 95% CI: 1.55–2.27). The
hypothesis of sub-exponential growth in Anzio was
subsequently ruled out [12]. For Rome and Guardavalle
Marina, the number of cases was too small to compute
a reliable estimate of R0; however, peak values of Rtfor
these two outbreaks were smaller compared with the
Anzio outbreak (Figure 1).
Mosquito abundance
We calibrated a mosquito population model [4]
toAe.albopictuscapture data obtained at several time
points throughout the period July to November 2012

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F 1
Time series of notified chikungunya cases with estimates of the instantaneous reproductive number Rt over time, averaged
over a moving window of 14 days, Anzio, Rome and Guardavalle Marina, Italy, 2017
Time (days)
Number of daily notified cases and R
t
Jun 15
Jul 1
Jul 15
Aug 1
Aug 15
Sep 1
Sep 15
Oct 1
Oct 15
0 2 4 6 8 10
Time (days)
Notified chikungunya cases Estimated instantaneous reproductive number Rt with 95% CI
Jun 15
Jul 1
Jul 15
Aug 1
Aug 15
Sep 1
Sep 15
Oct 1
Oct 15
0 2 4 6 8 10
Time (days)
Jun 15
Jul 1
Jul 15
Aug 1
Aug 15
Sep 1
Sep 15
Oct 1
Oct 15
0 2 4 6 8 10
Number of daily notified cases and R
t
Number of daily notified cases and R
t
Anzio Rome Guardavalle Marina
CI: credible interval; Rt: instantaneous reproduction number.
Rt was estimated by Markov chain Monte Carlo applied to the Poisson likelihood associated to the renewal equation Ct=Pois(Rt∑s=1tTgsC(t-s))
[8], where C(t) is the number of new cases at time t and Tg is the generation time distribution [9].
F 2
Location within the Lazio region of sites from which mosquito sampling in 2012 provided data for estimation of mosquito
abundance in 2017, Italy (n = 18 sites)
41°50′42°0′42°10′
12°10′12°20′12°30′12°40′
Site type
Coastal
Urban
Rural
Inhabitants / ha
10
100
1,000
0 10 km
Rome
Guardavalle
(Calabria)
Lazio
Stars represent locations with ongoing outbreaks in 2017 in Italy.

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F 3
Number of Aedes albopictus adult females per hectare over time, as estimated in the absence of interventions for 2017 in the
18 mosquito sampling sites, Lazio region, Italy
1 Fregene
0 2000 4000 6000
0 60 120 180
2 Focene
0 2000 4000 6000
0 60 120 180
3 Fiumicino
0 2000 4000 6000
0 60 120 180
4 Fiumicino Parco
0 2000 4000 6000
0 60 120 180
5 Ponte Galeria
0 2000 4000 6000
0 60 120 180
6 Villa Bonelli
0 2000 4000 6000
0 60 120 180
7 Trastevere
0 2000 4000 6000
0 60 120 180
8 Cornelia
0 2000 4000 6000
0 60 120 180
9 Cipro
0 2000 4000 6000
0 60 120 180
10 Flaminio
0 2000 4000 6000
0 60 120 180
11 Vittorio Emanuele
0 2000 4000 6000
0 60 120 180
12 Arco di Travertino
0 2000 4000 6000
0 60 120 180
13 Nomentano
0 2000 4000 6000
0 60 120 180
14 Nuovo Salario
0 2000 4000 6000
0 60 120 180
15 Monterotondo
0 2000 4000 6000
0 60 120 180
16 Fara Sabina
0 2000 4000 6000
0 60 120 180
17 Poggio Mirteto
0 2000 4000 6000
0 60 120 180
18 Salisano
0 2000 4000 6000
0 60 120 180
Mar 1
May 1
Jul 1
Sep 1
Nov 1
Apr 1
Jun 1
Aug 1
Oct 1
Dec 1
Mar 1
May 1
Jul 1
Sep 1
Nov 1
Apr 1
Jun 1
Aug 1
Oct 1
Dec 1
Mar 1
May 1
Jul 1
Sep 1
Nov 1
Apr 1
Jun 1
Aug 1
Oct 1
Dec 1
Time (weeks) Time (weeks) Time (weeks)
Ae. albopictus
per hectare [2017]
females
Ae. albopictus
per hectare [2017]
females
Ae. albopictus
per hectare [2017]
females
Ae. albopictus
per hectare [2017]
females
Ae. albopictus
per hectare [2017]
females
Ae. albopictus
per hectare [2017]
females
Ae. albopictus
captures [2012]
females
Ae. albopictus
captures [2012]
females
Ae. albopictus
captures [2012]
females
Ae. albopictus
captures [2012]
females
Ae. albopictus
captures [2012]
females
Ae. albopictus
captures [2012]
females
For each study site, the abundance of Aedes albopictus adult females per hectare in 2017 is presented over the March to December period (line: mean number;
shaded are a: 95% credible inter val); the grey colour is use d to depict estimate s based on recorded temperatures [13], while red is use d for estimates from
predicted temperatures based on previously observed trends (scale on the left).
In addition, for each site, the obs erved (blue dots) and estimated (boxplot s) total number of capt ure female adults during 2012, are shown from March to
December (scale on the right). Boxplots represent 2.5%, 25%, 75%, and 97.5% quantile and mean of model estimates.

4www.eurosurveillance.org
from 18 sites along a 70 km-transect from the Lazio
coast (four sites) to rural inland areas (5 sites), and
encompassing the metropolitan area of Rome (nine
sites) [6] (Figure 2). Coastal sites have a human density
(5–50 inhabitants/ha) close to that of Anzio (roughly
30 inhabitants/ha, increasing during summer months
due to touristic influx) and similar eco-climatic condi-
tions, and were therefore considered representative
for the analysis of the main outbreak; urban sites (with
human density up to 267 inhabitants/ha) were consid-
ered representative for the Rome outbreak. The model
takes as input daily temperature records obtained from
the closest weather station to each sampling site [13].
The calibrated model was re-run with 2017 tem-
peratures to estimate the mosquito abundance
during the ongoing outbreak (Figure 3). Human land-
ing capture experiments performed in 2014 within
a highly Ae. albopictus infested area in Rome [7]
were used to estimate the mosquito biting rate [14].
Remarkably, the biting rate was found to be nearly
constant over the season and its value (range: 0.08–
0.1, as shown in the Table) complies with the 0.09
(95%CI:0.05–0.16) estimate from the 2007 CHIKV out-
break [3,14].
Transmission dynamics
The probability of a CHIKV outbreak, the number of
symptomatic and asymptomatic cases and the daily
number of notified cases at different sites were com-
puted using a previously published stochastic trans-
mission model [5] (Figure 4) simulated over an area
of radius 300 m (i.e. ca 28ha), according to mosquito
abundance data [6], epidemiological data [10] and
mosquitoes flight range [15]. Potential delays between
symptom onset and notification were also accounted
for (Table). A set of 10,000 model simulations was run
for each site by sampling epidemiological parameters
from known distributions and considering a single
imported case at different times within the 1 May–15
November time window (Table). In order to predict the
time of virus introduction, the symptom onset for the
first notified case was considered to have occurred
between 23 and 29 June in coastal sites (first recorded
symptoms in Anzio: 26 June [2]) and between 12 and 18
July in urban sites (first recorded symptoms in Rome: 15
July [2]). The likely time of virus introduction was iden-
tified by selecting simulations with compliant symptom
onsets.
According to model estimates, the first CHIK V case is
likely to have been imported in the first week of June in
Anzio (range:21 May–18 June, sites 1–4 inFigure 5) and
in early July in Rome (range:28 May–16 July, sites 7–14
inFigure 5). In early June the probability of occurrence
of an outbreak is estimated to be higher in coastal
sites (11–44%) compared with urban sites (3–34%)
(Figure 6). However, in the latter sites, the probability
of outbreak increases to 22–82% at the predicted time
of arrival of the infection in Rome. The risk of large
outbreaks is estimated to be higher in coastal and
rural sites than in urban sites (Figure 6), despite the
high Ae. albopictus abundance in some urban areas
(Figure 2). This is explained by the higher human den-
sity in urban sites, which reduces the mosquito/human
ratio and thus the risk of infection. Specifically, at the
predicted time of the first case in Anzio, the number
of mosquitoes per person ranged between 1.9 and
7.3 in coastal sites and between 0.4 and 2.6 in urban
areas. The probability of observing additional trans-
mission foci in unaffected areas is estimated to remain
significant up to mid-November. This analysis was not
performed for Guardavalle Marina due to the lack of
entomological data.
Estimates of health and economic burden
Based on observed cases that occurred before the
restriction of blood donations in Lazio on 12 September
[1], the estimated time of virus introduction, the notifi-
cation rates (Table), the durations of infection (Table)
and the available estimates on the daily blood donation
rates [16], we estimated the probability that one blood
sample might have been collected from an infected
individual to be ca 0.73% (95% CI:  0.28–1.34%) in
Anzio and 0.15% (95%CI:0.05–0.29%) in Rome. Based
on average costs and Disability Adjusted Life Years
(DALY) lost per observed symptomatic CHIKV case [5],
F 4
Schematic representation of the model used to estimate
chikungunya transmission, Lazio region, Italy, 2017
ShEhIhRh
λhγh
λm
σh
SmEmIm
λm= kcmIh/Nh
λ
h
= kc
h
I
m
/N
h
γm
Estimates from observed captures
and entomological model
γh : Intrinsic incubation period
σh : Human infectious period
γm : Extrinsic incubation period
χm : Probability of human-to-vector
χh
: Probability of vector-to-human
k : Mosquito biting rate
transmission per bite
transmission per bite
E: exposed; I: infectious; λ: force of infection, i.e. the probabilit y
per unit of time for a susceptible to become infected; N: total
population; R: recovered; S: susceptibles.
Subscripts h and m refer to humans and mosquitoes respectively.
Human cases are notif ied with probabilit y pspn, which represent
the probability of developing clinical symptoms and the probability
of being detected respectively, with a delay d between symptom
onset and detection. Parameters values are reported in the Table.

5www.eurosurveillance.org
the economic burden as at 13 October is estimated at
322,000 EUR (95%CI:222,000–477,000) with a loss of
341 DALYs (95%CI:235–505). These estimates exclude
costs related to the management of blood supplies
after restrictions.
Discussion
Our modelling estimates are subjected to uncertain-
ties related to the actual mosquito abundance in Anzio
and to the provisional nature of epidemiological data
available up to now, including possible changes in
the detection rates after the outbreak identification.
Furthermore, the model is not suitable to evaluate the
potential geographical spread of the epidemic, as it
provides estimates only at the scale of 30 ha-patches,
with the assumption of homogenous mixing within the
patch. Critically, the high spatial heterogeneity in mos-
quito abundance, especially in urban areas, suggests
the need to rely on information about mosquito popu-
lations at the local scale in order to assess the impact
of current and future outbreaks. As shown by past sur-
veillance records [17,18], the number of impor ted chi-
kungunya cases in Lazio range from zero to seven per
year, therefore suggesting that multiple importations
from abroad in the city of Anzio during the summer of
2017 were unlikely; however, multiple introductions in
Rome (e.g. infected tourists coming back from Anzio)
are possible. This is a further possible limitation to the
interpretation of results related to Rome.
Despite these limitations, the model provides relevant
estimates to characterise the ongoing CHIKV outbreak
in Central Italy. First, the R0 in Anzio is shown to be
lower, but comparable to R0 associated with the 2007
CHIKV outbreak in Emilia Romagna and other outbreaks
worldwide [3]. Second, perhaps counter-intuitively, the
highest transmission potential is predicted in coastal
and rural areas (due to the higher mosquito to human
ratio compared with densely populated metropoli-
tan areas), consistently with the higher incidence of
CHIKV observed in Anzio compared with Rome [2].
Third, the model estimates the health and economic
burden related to the outbreak, which are instrumental
to evaluate cost–benefits of preventive interventions
aimed to reduce mosquito vector densities. In fact,
availability of information on insecticide treatments
carried out after CHIK V notifications would also allow
predicting their effect on mosquito population dynam-
ics. Finally, the model predicts a risk of autochthonous
transmission in Lazio region up to mid-November, as
a consequence of the expected persistence of favour-
able climatic conditions in the area [6]. Although the
F 5
Distributions of the probable time of first chikungunya virus introduction in coastal sites (sites from 1 to 4), which were
considered as representative of Anzio, and in urban sites considered as representative of Rome (sites from 7 to 14), Italy 2017
Jun 1
Jul 1
Aug 1
Sep 1
Oct 1
May 1
Time (weeks)
12 − Arco di Travertino 13 − Nomentano 14 − Nuovo Salario
9 − Cipro 10 − Flaminio 11 − Vittorio Emanuele
4 − Fiumicino Parco 7 − Trastevere 8 − Cornelia
1 − Fregene 2 − Focene 3 − Fiumicino
0.0
0.2
0.4
0.6
0.0
0.2
0.4
0.6
0.0
0.2
0.4
0.6
0.0
0.2
0.4
0.6
Probability of importation
Jun 1
Jul 1
Aug 1
Sep 1
Oct 1
May 1
Jun 1
Jul 1
Aug 1
Sep 1
Oct 1
May 1

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F 6
Model estimates of the probability of autochthonous transmission of chikungunya virus in 18 mosquito sampling sites in
Lazio region, disaggregated by potential outbreak size, in case of a single imported case at different weeks of the year from 1
May to 15 November, Italy 2017
16 − Fara Sabina 17 − Poggio Mirteto 18 − Salisano
13 − Nomentano 14 − Nuovo Salario 15 − Monterotondo
10 − Flaminio 11 − Vittorio Emanuele 12 − Arco di Travertino
7 − Trastevere 8 − Cornelia 9 − Cipro
4 − Fiumicino Parco 5 − Ponte Galeria 6 − Villa Bonelli
1 − Fregene 2 − Focene 3 − Fiumicino
Jun 1
Jul 1
Aug 1
Sep 1
Oct 1
Nov 1
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
Time (weeks)
Probability of outbreak
Outbreak size 2−10 11−100 >100
Jun 1
Jul 1
Aug 1
Sep 1
Oct 1
Nov 1
Jun 1
Jul 1
Aug 1
Sep 1
Oct 1
Nov 1
different weeks of the year from 1 May to 15 November, Italy 2017
Figure 6
Click to view
Estimates do not account for the different probabilities of importation (which depend on the absolute number of infected travellers) in urban,
rural and coastal sites.

7www.eurosurveillance.org
number of cases is declining [19], with only 23 cases
notified in October 2017, the foci of CHIKV transmission
identified in the city of Latina (22 km east of Anzio) [20]
and in Guardavalle Marina highlight the need to con-
tinue monitoring the outbreaks.
Conflict of interest
None declared.
Authors’ contributions
Conceived of the study: MM, GG, PP, RR, SM; Provided/col-
lected the data: MM, FF, AS, BC, AdT; Performed the analy-
sis: MM, GG, PP, SM; Interpreted the results: MM, GG, PP,
FF, AS, BC, AdT, RR, SM; Wrote the manuscript: GG, PP, AdT,
SM; Revised/approved the manuscript: MM, GG, PP, FF, AS,
BC, AdT, RR, SM.
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T
Epidemiological parameters used in the estimation of transmission in an outbreak of chikungunya in Central Italy, 2017
Parameter Unit Distribution
Min and max a
parameter value
Reference
Date of impor ted infection Date Uniform 1 May; 15 Nov NA
Mosquito biting rate Bites/mosquito/day Uniform 0.08; 0.10
Own estimate from
[7]
Probability of vector-to-human transmission
per bite %Uniform 14; 84 [21]
Probability of human-to-vector transmission
per bite %Uniform 75; 90 [22]
Extrinsic incubation period Days Uniform 2; 3 [23]
Intrinsic incubation period Days Uniform 1; 12 [24]
Human infectious period Days Uniform 2; 7 [24]
Probability of developing symptoms %Uniform 65; 93 [25]
Probability of being detected %Uniform 44; 80 [25]
Delay between symptom onset and detection Days Gamma Scale: 8.53; shape: 1.725 Own estimate from
[26]
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