Estimated Zika virus importations to Europe by travellers from Brazil

ORIGINAL ARTICLE

Estimated Zika virus importations to Europe by travellers from Brazil

Eduardo Massad1,2, Ser-Han Tan3, Kamran Khan4 and Annelies Wilder-Smith5,6,7*

1Department of Medicine, University of Sao Paolo, Sao Paolo, Brazil; 2London School of Hygiene and Tropical Medicine, London, UK; 3School of Computer Engineering, Nanyang Technological University, Singapore; 4Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, Canada; 5Institute of Public Health, University of Heidelberg, Germany; 6Department Public Health and Clinical Medicine, Epidemiology and Global Health, Umeå University, SE-901 85 Umeå, Sweden; 7Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore

Abstract

Background: Given the interconnectivity of Brazil with the rest of the world, Zika virus (ZIKV) infections have the potential to spread rapidly around the world via viremic travellers. The extent of spread depends on the travel volume and the endemicity in the exporting country. In the absence of reliable surveillance data, we did mathematical modelling to estimate the number of importations of ZIKV from Brazil into Europe.

Design: We applied a previously developed mathematical model on importations of dengue to estimate the number of ZIKV importations into Europe, based on the travel volume, the probability of being infected at the time of travel, the population size of Brazil, and the estimated incidence of ZIKV infections.

Results: Our model estimated between 508 and 1,778 imported infections into Europe in 2016, of which we would expect between 116 and 355 symptomatic Zika infections; with the highest number of importations being into France, Portugal and Italy.

Conclusions: Our model identified high-risk countries in Europe. Such data can assist policymakers and public health professionals in estimating the extent of importations in order to prepare for the scale up of laboratory diagnostic assays and estimate the occurrence of Guillain–Barré Syndrome, potential sexual transmission, and infants with congenital ZIKV syndrome.

Keywords: Zika virus; travel; importations; Brazil; Europe

Citation: Glob Health Action 2016, 9: 31669 - http://dx.doi.org/10.3402/gha.v9.31669

Copyright: © 2016 Eduardo Massad et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.

Received: 19 March 2016; Revised: 22 April 2016; Accepted: 22 April 2016; Published: 17 May 2016

Competing interests and funding: KK is the founder of BlueDot, a social benefit corporation that models global infectious disease threats. All other authors have no conflict to declare.

*Correspondence to: Annelies Wilder-Smith, Lee Kong Chian School of Medicine, 11 Mandalay Road, Singapore 308232, Email: anneliesws@gmail.com

Introduction

In May 2015, an outbreak of Zika virus (ZIKV) infections was first reported in Brazil, and by December 2015, 500,000–1,500,000 ZIKV infections were estimated (1). By October 2015, increasing number of microcephaly cases and other neonatal malformations were thought to be associated with ZIKV infections (2). On 1 February 2016, the clusters of microcephaly and Guillain–Barré Syndrome (GBS) cases in likely association with ZIKV infections were declared a public health emergency of international concern (3). Given the interconnectivity of Brazil with the remainder of the world, ZIKV has the potential to spread rapidly around the world via viremic travellers (4). The extent of spread depends on the travel volume to destination countries and the endemicity in the exporting country (57). Because of the mild clinical manifestations of the disease in the vast majority of cases, ZIKV infections in individual travellers are unlikely to lead to cancellation of flights or disruption of holiday/business plans. Furthermore, 80% of all infections are thought to be asymptomatic. The biggest concern is the spread to areas where suitable mosquito vectors exist and importation could trigger further outbreaks. However, given that sexual transmission of ZIKV has been reported, the spread of ZIKV via viremic travellers to areas without the Aedes mosquitoes is equally of concern (8). Sexual transmission to non-travelling contacts in Europe could propagate ZIKV infections in Europe, resulting in a potential upsurge of GBS cases as a result of imported ZIKV infections and putting pregnant women at risk. Therefore it is important to estimate the potential number of travellers returning to Europe with ZIKV infections.

ZIKV infections remain underdiagnosed and underreported because of the non-specific and mild manifestations and lack of widely available diagnostic assays. Therefore, for the time being any estimates on the epidemiological burden remain crude estimates. We based our calculations on the published estimate of 500,000–1,500,000 infections (both symptomatic and asymptomatic) for the year 2015 in Brazil (1). Reliance on reported events of importation will only underestimate the true importation risk as most imported cases will not be detected and reported, unless the clinical manifestations are more severe. In the absence of reliable surveillance data, mathematical modelling is necessary to estimate the number of importations of ZIKV from Brazil into Europe.

Methods

We applied a previously developed mathematical model on exportations to estimate the number of ZIKV importations into Europe (9). This model takes into account the travel volume, the probability of being infected at the time of travel, the population size of Brazil, and the estimated incidence of ZIKV infections (estimated numbers over population size). The model was previously developed to estimate the risk of dengue acquisition in international travellers (1012), and has also been applied to estimate polio virus importations (13).

The number of travellers departing from Brazilian airports on commercial flights to each of the European countries was obtained from the International Air Transport Association (IATA) for the year 2012. As we only had access to the year 2012 flight data, the travel pattern of outgoing flights in 2015 or 2016 was assumed to not have changed significantly.

We calculated the force of infection, λ(t) from the assumption that there had been 0.5 to 1.5 million ZIKV infections in Brazil. In addition, we assumed that the seasonal distribution of cases followed the same as for dengue, given that both viral infections share the same Aedes vectors, and initial observations have claimed that ZIKV seems to follow the path of dengue (14). As populations of Aedes aegypti and Aedes albopictus are climate sensitive and display a seasonal pattern in Brazil (1517), ZIKV infections are likely to exhibit the same seasonal pattern as dengue in Brazil.

The steps for the mathematical models are detailed in the Supplement. In brief, we first fitted a continuous function to the time distribution of notified cases from which we estimate the force of infection λ(t). The product of the force of infection by the fraction of susceptible individuals is the number of reported cases.

The individual risk of acquiring the infection from the ZIKV-infected mosquitoes, Risk (t), is given by



where, again, λ(t) is the force of infection or incidence density rate; t1 is, in the case of travellers, the moment they arrive at the endemic area; and t2 is the moment they depart. Note that the concept of risk expressed in equation (1) means the risk for travellers that remain in the ZIKV endemic area for the period between t1 and t2. For locals, t2t1 is the time interval considered for the risk calculation (e.g. the month-by-month risk calculation).

The risk varies with time. As Fig. 1 shows, this risk is highest in the months with the highest number of reported dengue cases (as a consequence of a higher density of infected mosquitoes), at its maximum by the month of April. This would also fit with the observation of the onset of excess microcephaly cases in October 2015 (6–9 months after the high season of January to April).

Fig 1

Fig. 1.   Calculated per capita risk (probability of being infected). Risk(t)=1–exp[−λ(t)t], where λ(t) is the force of infection.

As the function Risk (t) represents the individual risk of acquiring the infection, we can use it as the probability that one passenger flying from a Brazilian airport is infected with the ZIKV. By multiplying the individual probability of being infected by the number of passengers leaving Brazilian airports, we have the total number of expected infections that are flying to European countries.

Our model applies to individuals from Brazil travelling to Europe or travellers having visited Brazil and now returning to Europe.

Results

Figures 1 and 2 show the resulting curve for the individual risk of acquiring the infection and the expected number of ZIKA cases arriving in Europe by month, respectively. Table 1 and the Map show the results of the expected number of ZIKV cases exported to European countries from Brazil, based on an estimated lower bound of 500,000 and upper bound of 1.5 million ZIKV infections, respectively, assuming that these ZIKV infections exhibit the same seasonal pattern as dengue infections. In total, our models estimated between 508 and 1,778 imported cases, respectively, into all European countries, with the highest numbers being in France, Portugal, and Italy (Table 1 and Fig. 3). Of these, 80% would likely be asymptomatic; hence, we would expect between 116 and 355 symptomatic ZIKV infections.

Fig 2

Fig. 2.   Expected number of travellers with Zika virus infections arriving in Europe by month, in the year 2015.


Table 1.  Estimated imported ZIKV infections from Brazil to Europe based on the 1.5 million and 500,000 ZIKV infections scenarios in 2015
    Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total
Individual risk for each scenario 500 K 0.000146 0.000366 0.000594 0.000617 0.000414 0.00018 5.03E-05 9.06E-06 1.05E-05 9.06E-06 9.06E-06 9.06E-06  
1.5 G 0.000511 0.00128 0.00208 0.00216 0.00145 0.000631 0.000176 3.17E-05 3.67E-05 3.17E-05 3.17E-05 3.17E-05  
Albania Travellers 9 12 8 9 10 67 1 28 9 13 21 0 187
Expected cases for each scenario 500 K 0 0 0 0 0 0 0 0 0 0 0 0 0
1.5 G 0 0 0 0 0 0 0 0 0 0 0 0 0
Austria Travellers 1986 1840 2570 2203 24000 2304 2339 3235 3660 2067 2245 0 48449
Expected cases for each scenario 500 K 0 1 2 1 10 0 0 0 0 0 0 0 14
1.5 G 1 2 5 5 35 1 0 0 0 0 0 0 51
Belgium Travellers 2733 2543 2565 2647 2375 2599 3194 3513 2313 2296 1881 2449 31108
Expected cases for each scenario 500 K 0 1 2 2 1 0 0 0 0 0 0 0 6
1.5 G 1 3 5 6 3 2 1 0 0 0 0 0 22
Bulgaria Travellers 156 124 67 122 223 159 159 319 69 92 76 158 1724
Expected cases for each scenario 500 K 0 0 0 0 0 0 0 0 0 0 0 0 0
1.5 G 0 0 0 0 0 0 0 0 0 0 0 0 1
Croatia Travellers 229 271 234 354 491 926 675 597 883 551 201 200 5612
Expected cases for each scenario 500 K 0 0 0 0 0 0 0 0 0 0 0 0 1
1.5 G 0 0 0 1 1 1 0 0 0 0 0 0 3
Czech Rep Travellers 561 901 1096 1092 1945 1663 1867 1805 2438 1470 1193 1026 17057
Expected cases for each scenario 500 K 0 0 1 1 1 0 0 0 0 0 0 0 3
1.5 G 0 1 2 2 3 1 0 0 0 0 0 0 11
Denmark Travellers 3384 2151 1878 2015 2880 3274 4052 4421 1971 1506 1531 1725 30788
Expected cases for each scenario 500 K 0 1 1 1 1 1 0 0 0 0 0 0 6
1.5 G 2 3 4 4 4 2 1 0 0 0 0 0 20
Finland Travellers 1405 799 651 687 833 1026 843 660 374 537 661 0 8476
Expected cases for each scenario 500 K 0 0 0 0 0 0 0 0 0 0 0 0 2
1.5 G 1 1 1 1 1 1 0 0 0 0 0 0 7
France Travellers 29302 28118 33352 32796 35533 37932 40265 32572 37959 33476 27270 39248 407823
Expected cases for each scenario 500 K 4 10 20 20 15 7 2 0 0 0 0 0 80
1.5 G 15 36 69 71 52 24 7 1 1 1 1 1 279
Germany Travellers 21552 20305 22563 21564 21872 20226 21372 24965 29920 22872 21713 23703  
Expected cases for each scenario 500 K 3 7 13 13 9 4 1 0 0 0 0 0  
1.5 G 11 26 47 47 32 13 4 1 1 1 1 1  
Greece Travellers 712 590 1499 1141 1484 1805 2329 1836 2439 1386 646 690 16557
Expected cases for each scenario 500 K 0 0 1 1 1 0 0 0 0 0 0 0 3
1.5 G 0 1 3 2 2 1 0 0 0 0 0 0 11
Hungary Travellers 472 553 531 714 1190 1244 1437 1617 1282 788 452 465 10745
Expected cases for each scenario 500 K 0 0 0 0 0 0 0 0 0 0 0 0 2
1.5 G 0 1 1 2 2 1 0 0 0 0 0 0 7
Italy Travellers 36580 30257 34235 33521 38120 32883 35912 33365 39478 34887 28373 30136 407747
Expected cases for each scenario 500 K 5 11 20 21 16 6 2 0 0 0 0 0 83
1.5 G 19 39 71 72 55 21 6 1 1 1 1 1 289
Malta Travellers 97 67 61 65 127 86 102 96 73 51 114 108 1047
Expected cases for each scenario 500 K 0 0 0 0 0 0 0 0 0 0 0 0 0
1.5 G 0 0 0 0 0 0 0 0 0 0 0 0 1
Netherland Travellers 8055 6418 3727 7209 6997 7051 7669 7328 7006 7103 6391 6396 81350
Expected cases for each scenario 500 K 1 2 2 4 3 1 0 0 0 0 0 0 15
1.5 G 4 8 8 16 10 4 1 0 0 0 0 0 53
Norway Travellers 2423 1488 1409 2034 1728 1826 2479 1943 1677 1305 1354 2116 21782
Expected cases for each scenario 500 K 0 1 1 1 1 0 0 0 0 0 0 0 4
1.5 G 1 2 3 4 3 1 0 0 0 0 0 0 15
Poland Travellers 1016 760 884 1635 1344 1784 1828 1272 1018 1160 1025 971 14697
Expected cases for each scenario 500 K 0 0 1 1 1 0 0 0 0 0 0 0 3
1.5 G 1 1 2 4 2 1 0 0 0 0 0 0 10
Portugal Travellers 35079 29220 30250 34716 34795 35512 38984 34306 39754 30603 28926 33905 406050
Expected cases for each scenario 500 K 5 11 18 21 14 6 2 0 0 0 0 0 80
1.5 G 18 37 63 75 50 22 7 1 1 1 1 1 278
Romania Travellers 610 654 482 513 540 468 448 764 698 646 479 545 6847
Expected cases for each scenario 500 K 0 0 0 0 0 0 0 0 0 0 0 0 1
1.5 G 0 1 1 1 1 0 0 0 0 0 0 0 5
Serbia Travellers 124 69 83 64 119 115 74 140 57 140 74 75 1134
Expected cases for each scenario 500 K 0 0 0 0 0 0 0 0 0 0 0 0 0
1.5 G 0 0 0 0 0 0 0 0 0 0 0 0 1
Slovakia Travellers 11 5 8 5 13 14 28 3 20 9 11 3 130
Expected cases for each scenario 500 K 0 0 0 0 0 0 0 0 0 0 0 0 0
1.5 G 0 0 0 0 0 0 0 0 0 0 0 0 0
Slovenia Travellers 261 26 30 117 172 108 103 117 157 61 96 50 1298
Expected cases for each scenario 500 K 0 0 0 0 0 0 0 0 0 0 0 0 0
1.5 G 0 0 0 0 0 0 0 0 0 0 0 0 1
Spain Travellers 23205 22487 20259 21545 24095 25562 28821 20111 24405 21842 16803 22603 271738
Expected Cases for Each scenario 500 K 3 8 12 13 10 5 1 0 0 0 0 0 54
1.5 G 12 29 42 47 35 16 5 1 1 1 1 1 189
Switzerland Travellers 25562 11487 10804 8521 8295 9701 9461 11820 7604 9249 8803 11189 132496
Expected cases for each scenario 500 K 4 4 6 5 3 2 0 0 0 0 0 0 26
1.5 G 13 15 22 18 12 6 2 0 0 0 0 0 90
Sweden Travellers 3617 2458 2823 2082 1962 2310 2379 2015 1586 1428 1671 1576 25907
Expected cases for each scenario 500 K 1 1 2 1 1 0 0 0 0 0 0 0 6
1.5 G 2 3 6 4 3 1 0 0 0 0 0 0 20
UK Travellers 28678 22484 23246 24708 24263 25302 30344 24356 26938 22458 22068 23657 298502
Expected cases for each scenario 500 K 4 8 14 15 10 5 2 0 0 0 0 0 59
1.5 G 15 29 48 53 35 16 5 1 1 1 1 1 206
Turkey Travellers 1717 2258 2760 3890 4097 4432 4321 3641 5417 4394 2816 3691 43434
Expected cases for each scenario 500 K 0 1 2 2 2 1 0 0 0 0 0 0 8
1.5 G 1 3 6 8 6 3 1 0 0 0 0 0 28
Total number of cases for each scenario 500 K                         508
1.5 G                         1778

Fig 3

Fig. 3.   Estimated importations of Zika virus infections via travellers from Brazil to Europe in the year 2015, based on a high estimate of 1,500,000 Zika virus infections in Brazil.

Conclusions

Our estimates are consistent with those reported by the European Centre for Disease Control (ECDC). As of 3 March 2016, ECDC had recorded 209 imported cases into 16 European countries, of which 81 were into France, and 32 into Spain (18). Geographical distribution of ZIKV has steadily broadened since the virus was first detected in Brazil in 2015. By March 2016, ZIKV transmission had been reported in 28 countries and territories (19); hence, the exportation risk will be even higher than we reported. However, we were not able to calculate such a risk for the other countries as incidence data for those countries have not yet been published. Given that Brazil so far has been the country most affected with the highest absolute numbers of estimated ZIKV infections, it is justified to focus our model on Brazil as exporting country only, until more data are available from other Latin American countries.

A limitation of our study is that the underlying assumption of our model is the equal distribution of cases throughout the country, and the equal probability of travelling throughout the Brazilian population. However, in early 2015, the geographic concentration of most cases were in Northeast Brazil – but by late 2015 and early 2016, the distribution was already much wider spread with all major cities in Brazil being affected (18, 2022). Hence our modelled estimates of ZIKV exportations based on travel volume will be a more accurate reflection of the situation in 2016, assuming that the year 2016 will also see between 500,000 and 1,500,000 ZIKV infections.

According to the French Polynesian case control study on ZIKV-related GBS, one would expect 24 GBS cases out of 100,000 ZIKV infections (23). In other words, if these estimates hold true, one would need to have 5,000 imported ZIKV infections to see one case of ZIKV-associated GBS in returning travellers from ZIKV-affected countries. Given the current exportation numbers estimated to be no more than 1,800, the probability of a ZIKV-associated GBS case in Europe in 2015 or 2016 is relatively low. However, the number of ZIKV-affected countries within Latin America, the Caribbean, and beyond is rising, and hence the likelihood of substantial number of returning travellers presenting with GBS is will increase. The true risk of ZIKV-related infections that can lead to central nervous system malformations and microcephaly in pregnant women is currently unknown, especially for sexual transmission (24). However, potentially every single viremic male returning traveller could infect his pregnant or non-pregnant partner, especially in the first 2–4 weeks after ZIKV infection (2527). Hence, the Centre for Disease Control and travel medicine providers have advised for precautions (abstinence or condoms) to be taken for men returning home from ZIKV-affected countries, particularly in the first few weeks after return (28, 29).

An additional cause of concern is the risk of ZIKV establishing itself in European regions where the presence of A. albopictus is endemic, in particular for Mediterranean countries recording increasingly hotter summers (30), although the ZIKA competence for A. albopictus is not fully known at this stage.

We identified high-risk countries in Europe, and policymakers and clinicians need to be aware of such data. Furthermore, our models can be applied by individual countries or by continents alike and used as an additional tool to estimate the risk of importation based on the main contributing factors such as travel volume and the evolving ZIKA endemicity in exporting countries. Our models help policymakers estimate the extent of importations in order to prepare for the scale up of laboratory diagnostic assays and estimate the occurrence of GBS, potential sexual transmission, and infants with congenital ZIKA syndrome.

Authors’ contributions

AWS and EM had the study idea; EM developed the mathematical models and calculated the data for the tables; KK provided the air passenger data; S-H K did the map and contributed to the tables; AWS wrote the paper. All authors contributed to the final paper

Acknowledgements

This work was partially funded for AWS and EM through the European Commission 7th framework, under the Dengue Tools consortium (Grant No. 282589). The funders played no role in the study design, data collection, analysis, or preparation of the manuscript.

Paper context

The Zika virus outbreak in Brazil has gripped the world’s attention. We applied mathematical modelling to estimate the extent of Zika virus importations from Brazil to Europe for the year 2016. Our model estimated between 508 and 1,778 imported infections into Europe in 2016, with the highest number of importations being into France, Portugal, and Italy. Such data can assist policymakers to scale up preparatory measures in Europe.

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About The Authors

Eduardo Massad
Department of Medicine, University of Sao Paolo, Brazil
Brazil

Ser-Han Tan
School of Computer Engineering, Nanyang Technological University, Singapore
Singapore

Kamran Khan
Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, Canada
Canada

Annelies Wilder-Smith
Epidemiology and Global Health, Umeå University, Sweden. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
Sweden

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