Development of an active risk-based surveillance strategy for avian influenza in Cuba Academic research paper on "Veterinary science"

Accepted Manuscript

Title: Development of an active risk-based surveillance strategy for avian influenza in Cuba

Author: E. Ferrer P. Alfonso C. Ippoliti M. Abeledo P. Calistri P. Blanco A. Conte B. Sanchez O. Fonseca M. Percedo A. Perez O. Fernandez A. Giovannini

PII: DOI:

Reference:

S0167-5877(14)00198-6

http://dx.doi.org/doi:10.1016/j.prevetmed.2014.05.012 PREVET 3590

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PREVET

Received date: Revised date: Accepted date:

20-5-2013

9-5-2014

25-5-2014

Please cite this article as: Ferrer, E., Alfonso, P., Ippoliti, C., Abeledo, M., Calistri, P., Blanco, P., Conte, A., Sanchez, B., Fonseca, O., Percedo, M., Perez, A., Fernandez, O., Giovannini, A.,Development of an active risk-based surveillance strategy for avian influenza in Cuba, Preventive Veterinary Medicine (2014), http://dx.doi.org/10.1016/j.prevetmed.2014.05.012

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Title: Development of an active risk-based surveillance strategy for avian influenza in Cuba

1 1 ..2 1..2 3 2,

Ferrer E. , Alfonso P. , Ippoliti C. , Abeledo M. , Calistri P. , Blanco P. , Conte A. , Sánchez

3 1 1,4, 1 . . 2

B. , Fonseca O. , Percedo M. , Pérez A. , Fernández O. , Giovannini A.

(1) Centro Nacional de Sanidad Agropecuaria (CENSA). San José de Las Lajas 32700, Mayabeque, Cuba.

(2) Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise "G. Caporale", Via Campo Boario, 64100 Teramo, Italy.

(3) Instituto de Ecología y Sistemática (IES), Carretera Varona, Km 6, La Habana, Cuba.

(4) Instituto de Medicina Veterinaria. Calle 12 No. 355, e/ 15 y 17 Vedado. 10400, La Habana, Cuba.

Corresponding author: Giovannini A. a.giovannini@izs.it Abstract

The authors designed a risk-based approach to the selection of poultry flocks to be sampled in order to further improve the sensitivity of avian influenza (AI) active surveillance program in Cuba. The study focused on the western region of Cuba, which harbours nearly 70% of national poultry holdings and comprise several wetlands where migratory waterfowl settle (migratory waterfowl settlements - MWS). The model took into account the potential risk of commercial poultry farms in western Cuba contracting from migratory waterfowl of the orders Anseriformes and Charadriiformes through dispersion for pasturing of migratory birds around the MWS. We computed spatial risk index by geographical analysis with Python scripts in ESRI® ArcGIS 10 on data projected in the reference system NAD 1927— UTM. Farms located closer to MWS had the highest values for the risk indicator p. and in total 31 farms were chosen for targeted surveillance during the risk period. The authors proposed to start active surveillance in the study area 3 weeks after the onset of Anseriformes migration, with additional, sampling repeated twice in the same selected poultry farms at 15 days interval (Comin etal, 2012; EFSA, 2008) to cover the whole migration season. In this way, the antibody detectability would be favoured in case of either a posterior AI introduction or enhancement of a previous seroprevalence under the sensitivity level. The model identified the areas with higher risk for AIV introduction from MW, aiming at selecting poultry premises for the application of risk-based surveillance. Given the infrequency of HPAI introduction into domestic poultry populations and the relative paucity of occurrences of LPAI epidemics, the evaluation of the effectiveness of this approach would require its application for several migration seasons to allow the collection of sufficient reliable data.

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Keywords: avian influenza, risk factors, Cuba, modelling diseases, spatial analysis Introduction

Until 1996, highly pathogenic avian influenza (HPAI) viruses belonging to serotypes H5 and H7 viruses were successfully eradicated or failed to persist in nature (Salomon and Webster, 2009). However, avian influenza (AI) has greatly enhanced its significance in the last years. It has been calculated that the impact of AI on the world-wide poultry industry from 1999 to 2004 (Capua and Alexander, 2004) involved more than 200 million animals. Today, it is unknown whether the ecology of these viruses has changed and whether highly pathogenic H5N1 viruses continue to be propagated in domestic or wild bird reservoirs.

Furthermore, some avian influenza virus (AIV) can infect humans with serious public health implications (WHO, 2013; WHO, 2014). Changes in agricultural practices, enhanced animal health surveillance, and/or virus evolution may have contributed to the apparent increase in animal influenza outbreaks reported in recent times; that turns AI into an increasing concern for veterinarians worldwide (Ducatez et al, 2008).

The surveillance and control of AI have historically focused on the detection and eradication of infections due to HPAI viruses in poultry populations. However, reports of low pathogenicity avian influenza (LPAI) viruses in poultry are recurrent, with outbreaks annually affecting several countries (WAHIS, 2014). Most of AI infections in poultry are caused by LPAI virus strains, which may belong to any serotype, including H5 and H7. These H5 or H7 LPAI viruses may circulate causing unnoticeable clinical signs, unless they mutate into HPAI viruses (Alexander and Brown, 2009). Therefore the recurrence of LPAI virus circulation is a constant risk to poultry industries throughout the world.

Various approaches have been applied for the diagnosis of AI (OIE, 2008), including techniques for the detection of the virus, its genome, antigens or antibodies. However, the antibodies to AIV, as evidence of infection, often persist for the entire production life of the infected poultry (Fouchier etal, 2003), allowing a high opportunity for long-term diagnosis. The detection of a significant increase of antibody titer allows an opportunity for early warning. Consequently, antibody detection to LPAI viruses is compulsory for several countries, e.g. in the EU countries (European Commission, 2007; European Commission, 2009).

The control and eradication of AI are based on passive and active surveillance, disease notification, prevention of possible contacts, biosecurity measures, and movement restriction of live birds, poultry products, by-products and potentially infective material, and depopulation of

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infected farms (OIE, 2013). However, when timely disease detection fails, the stamping out as a key control measure could become ineffective because the virus could be already seriously disseminated into vast poultry populations. In such cases, the economical consequences of the outbreak could be devastating.

Wild waterfowl (particularly ducks, geese, swans, gulls and shorebirds) are considered the original source of all AIV known subtypes (Munster et al, 2007). Hence the active surveillance, aimed at early detection of the disease, in several countries or regions, has included the sampling of wild bird (Burns et al., 2012; Iglesias et al, 2010). However, wild birds are not well suited for active surveillance for a number of reasons. AI virus in wild waterfowl shows a seasonal prevalence, a very variable pattern, which can vary over time and between locations within a species (Olsen et al, 2006; Figuerola et al, 2007; Hill et al, 2012). It is, therefore, difficult to make an initial assessment of the most important species to target on the basis of virus detection alone, which demands high sampling intensity for detecting viruses.

Furthermore, sampling of wild birds is a labour-intensive, costly, and time-consuming task that has not been exempted from discussion at the decision-making level in the European Union and other regions affected by the disease (Martinez et al, 2011). The detection of viruses in migrating birds does not necessarily mean that these viruses have been, or will be, successfully introduced into a new geographic area (Martin et al, 2009) and, consequently, resident waterfowl could be a best target to assess the establishment of AI in a geographical area. However, sampling and testing of wild birds is not required by the Terrestrial Animal Health Code to declare a country, zone or compartment free from AI (OIE, 2013).

The design of surveillance programs has to be carefully planned, taking into consideration the local epidemiological and ecological conditions, the areas where migrating waterfowl transit and settle (Miller et al., 2009; OIE, 2013; U.S., 2007, 2008), and social and economic conditions (Alfonso et al., 2008; Fiebig, 2011; Martin et al, 2011; Stärk et al, 2006).

The geographical location of Cuba makes this island an important site along bird migration routes for resting or wintering (Blanco, 2006) (Figure 1).

The Cuban poultry population susceptible to AIV comprises 14 million of poultry, predominantly reared for egg production, which constitute an important source of proteins of animal origin for residents.

People living in rural areas of Cuba raise poultry, mainly for own consumption rather than for commercial purposes. Details of the structure of Cuban poultry farming are reported in the Supplementary Document 1. In summary, 88% of Cuban poultry belong to commercial farms while the rest of the poultry rearing has a low average density (around 17,4 birds/Km2).

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Cuban AI surveillance program focuses on determining either the evidence or the presence of infections by subtypes H5 and H7, as those of main concern for poultry due to its ability to become highly pathogenic after transmission to alternative hosts (Martin et al.., 2009). This approach is in agreement with the chapter on AI of the Terrestrial Animal Health Code (OIE, 2013). Currently, the AI surveillance in Cuba is based on a passive and an active component. The passive surveillance is usually the most effective for early detection of exotic diseases with severe clinical forms, such as the HPAI. It is, however, less effective in detecting the LPAI strains and it requires laboratory confirmation and typing of the virus strain responsible for the outbreak. The active component is based on serology by inhibition of hemagglutination assay (IHA), which is designed to be able to detect at least a prevalence of 5% AI infected holdings, with 30% infected animals within an infected holding (IMV, 2006). These values of target prevalence can lead to missing the presence of infection or delay in its detection, depending on the dynamics of the infection in the population (Gonzalez etal., 2010). However, the sensitivity of active surveillance can be improved using a risk-based surveillance (Cameron, 2012; Salman, 2003; Thrusfield, 2005). This, in the case of AI can be implemented through a risk-based selection of poultry farms to be surveyed, and through the concentration of surveillance activities during the period at risk for AI introduction, i.e. during waterfowl migration. Aim of this paper is to describe the design of the risk-based surveillance and the criteria used to choose which poultry flocks have to be surveyed.

MATERIALS AND METHODS

1. Study area

The study area is the western region of Cuba. This region includes five provinces named Pinar del Río, Artemisa, Mayabeque, La Habana, Matanzas, and Isla de la Juventud (Figure 1). It covers nearly 29% of the country surface and harbors approximately 70% of the poultry commercial holdings of Cuba. In this area, the consequences of a possible AI introduction would be more devastating than elsewhere in Cuba, as mentioned by Rutten et al. (2012) for zones of high poultry concentration.

This geographical area contains several wetland areas (Figure 1), including the largest one of the Caribbean region, Ciénaga de Lanier y Sur de la Isla de la Juventud (RAMSAR http://ramsar.wetlands.org/), which harbours several waterfowl species implicated as AIV reservoir (Acosta and Mugica, 2006; Blanco, 2006; Munster et al, 2007; Olsen et al, 2006).

2. Source of data

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i. Poultry data

"Location" (geographical coordinates) and "census" data from each of the 300 poultry farms registered in the western region of Cuba were obtained from the National Poultry Register and National Veterinary Service. Backyard poultry are not included. For each farm, information on biosecurity were also collected, focusing on inadequate anti-bird netting or drinking water supply accessible by wild birds.

The 300 commercial poultry farms in the census used in this study (dark and light dots in Figure 2 and Figure s3 in supplementary material) are located in the central part of the regions of Pinar del Rio, Artemisa, Mayabeque, Matanzas, and widely distributed in La Habana province facing the northern coast. In the island 'Isla de la Juventud', poultry farms are located in the northern part, being the southern part of a national park.

ii. Migratory waterfowl data

Data on the abundance of wild birds belonging to the orders of Anseriformes and Charadriiformes in the western part of Cuba, have been described in previous ornithological studies (Acosta and Mugica, 2006; Blanco, 2006). Data used in this study have been obtained from these previous studies and summarized in Table 1.

3. Statistical analysis

i. Descriptive statistics

For each wetland, the mean of the number of migratory waterfowl transiting during migration was associated to the coordinates of the centroid of the wetland. Data from Guberti (2006) on the frequency distribution of displacement distances have been used to model the displacement of migratory waterfowl around the wetlands for pasturing. An inverse distance function was regressed on Guberti (2006) raw data and then rescaled to 0—10 Km to adapt the European data to Cuban situation. Ten kilometres have been considered the maximum range of daily movement of wild birds for pasturing around the arrival settlement in the Cuban ecological system (Blanco, 2006). The probability of pasturing at larger distances was considered very low or negligible.

ii. Spatial model

For each migratory waterfowl settlement, the daily pasturing dispersal in function of the distance is:

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0.34063702

0.34063782 is a conversion factor to rescale the inverse distance function regressed on Guberti (2006) raw data to the 0-10 km range relevant for the Cuban environment.

A raster of 1 Km x 1 Km spatial resolution has been created in which for each pixel, the risk posed by waterfowl in neighbouring wetlandspj is proportional to the value:

Where:

i = serial number of wetlands i = 1, ..., 12; j = Euclidean distance of pixel j from the wetland i;

b = mean number of wild waterfowl passing through the wetland i during the migration period

at = multiplication factor for the effective pasturing area available to migratory birds, calculated as the ratio between the area of dry land in 10 Km buffer around the waterfowl settlement and the area of a circle of 10 Km radius.

The factor at takes into account the location of the migratory waterfowl settlements, often very close to the Cuban coastline. In this case, the bird population will spread over the dry land only, so the density has been rescaled. The migratory waterfowl (MW) settlements reported in Western part of Cuba are 13 (Table 1) but that in Peninsula de Hicacos (Matanzas province) was excluded from the analysis as it is in a very thin and long peninsula and is in a touristic area (Acosta and Mugica, 2006). The geographical analysis has been performed using Python scripts in ESRI® ArcGIS 10, on data projected in the reference system NAD 1927— UTM 17.

4. Selection criteria

The selection of farms to be sampled throughout active surveillance, during seasonal waterfowl migration, was based on the index resulting from statistical analysis, which considered proximity to the migratory waterfowl settlement and density of MW. The values of the index pj in each pixel have been normalized between 0 and 1. The risk for each farm, based on its location, was

(Table 1);

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considered proportional to the value of pj in the pixel corresponding to the centroid of the poultry farm. Any farm with an index (pj) higher than 0.25 has been selected to be surveyed. The aim of this risk-based surveillance system was to improve the sensitivity of surveillance for incursions of avian influenza (AI) in Cuba. In order to increase the chances and the timeliness of detection of possible incursions, the choice of farms to be included in the system was based on their vulnerability. Therefore, the presence of two other vulnerability factors, besides the exposition to migratory waterfowl was considered: the presence of (1) inadequate anti-bird netting or (2) drinking water supply from sources exposed to the contact with wild birds. The presence of either of these two biosecurity flaws was considered an ancillary criterion since in the absence of infection in MW, they would have been unable to trigger an outbreak. Their intended use was to provide further information for the selection of farms to survey in case of widespread ties in the scoring determined by the proximity with wetlands (index pj).

RESULTS

1. Risk distribution pattern

The geographical distribution of the index pj (Figure 2 and Figure s3 in supplementary material) shows that the highest risk of introduction of AI is concentrated predominantly along the southern coastline of the study area, where most of wetlands with largest values of MW census are located.

The farms located closer to MW settlements had the highest values for the risk indicator pj.. Those with a pj value greater than 0.25 were chosen for targeted surveillance during the risk period. There were no ties in the distribution of pj values; therefore the selection of farms for risk-based surveillance did not make use of the two ancillary criteria on biosecurity.

2. Active surveillance strategy

According to selection criteria, 28 poultry farms were chosen. These did not include any in Havana province nor in the Isla de la Juventud municipality because the value of the risk indicator was below the threshold criterion in these locations (< 0.25). Since these areas are densely populated, a possible introduction of AI could have significant consequences for public health, therefore 3 more farms were added from these provinces to the selected poultry flocks. They were chosen on the basis of their risk value, close or immediately below 0.25. Thus, the target poultry farms to be sampled were 31 (identified in Figure 2 and Figure s3 in supplementary material with light dots).

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DISCUSSION

The risk-based surveillance is characterized by the selection for sampling of populations or subpopulations where the disease presence is more likely and where prior risk factors exist (Cameron, 2012; Salman, 2003; Thrusfield, 2005). This may increase the probability of diseases detection. This type of monitoring is also used to document the absence of a disease in a population with a high degree of confidence (Salman, 2003).

Several risk factors are considered relevant as risk of AI transmission to poultry (Iglesias et al, 2010). In areas where the AI is exotic, the most relevant risk factors to be considered are those relating to the introduction of the infection. The main sources of infection for a free poultry population are migratory birds (especially waterfowl) and the trade of animals. In the Cuban situation, the main potential source of infection is migratory birds and the proximity to waterfowl settlements may enhance the probability of contact reservoir-poultry. Also important are breaches in biosecurity (FAO, 2008), which favor the direct or indirect contact between migratory birds and domestic poultry.

Concerning the other possible route of introduction of AI, due to the water isolated condition of the country, there is a moderate to low level of poultry and poultry product importation, always under strict veterinary control, so migratory waterfowl remain the most probable source of AIV introduction.

On the other hand, it is demonstrated that H5 and H7 avian influenza viruses, including highly pathogenic strains, have the ability to persist in water with wide variety of temperature and salinity for extended periods of time (Brown et al, 2007), hence it also emphasized the importance of the poultry water intake from natural lakes or ponds as a factor that must enhance the probability of AIV introduction in poultry farms.

Considering that the development of HI antibodies in detectable amounts requires at least seven days post infection (EFSA, 2008), and the likely delay in AI transmission from MW to poultry (Ducatez et al, 2008), the authors proposed to start active surveillance in the study area 3 weeks after the onset of Anseriformes migration. Additionally, sampling must be repeated twice in the same selected poultry farms at 15 days interval (Comin et al, 2012; EFSA, 2008) to cover the whole migration season. In this way, the antibody detectability would be favoured in case of either a posterior AI introduction or enhancement of a previous seroprevalence under the sensitivity level.

The current surveillance program for AI in Cuba consists of a passive component based on direct virus detection (IMV, 2006) in clinical suspects, mainly by real-time reverse transcription-

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PCR. The active component of this program is based on a yearly random sampling of the 25% of poultry farm, with a within flock target prevalence of 30% (Ferrer et al, 2013). However, depending on the dynamics of infection in the affected holding (Gonzalez etal, 2010) and the subsequent transmission between farms, the early detection may not be very efficient. It is expected that complementing the existing active surveillance with the targeted sampling of poultry holdings at high risk of contact with waterfowl, during the migration periods, would increase the chances for detecting AI introduction.

In Cuba as in several American countries, rice fields are important for water birds (Acosta etal,., 2010; Mugica et al, 2006). However, rice cultivation shows seasonality and the places could vary according to land uses; therefore further studies are required to establish its importance relative to wetland for the transit and resting of MW during migration seasons. Anyway, considering that rice cultivation in the study region is mainly concentrated in the south (Martin et al, 2009) it can be hypothesized that the inclusion of rice padding in the model would lead to minor modifications of the risk map for AI introduction.

This work also considered commercial poultry farms for sampling as sentinels instead of backyard poultry during targeted surveillance. The role of backyard poultry in the spread of the AI is disputed according to several consideration of population size and breeds (Goutard et al, 2012). Backyard poultry are important in Asian countries in which they represent over 80% of the poultry population (Lee et al, 2008; Sedyaningsih et al, 2007). In Cuba, backyard poultry represent only 12% of the total poultry population. In densely populated poultry area of Northern Italy, backyard free-range farming is at high risk for introduction of avian influenza (Terregino et al, 2007), nonetheless, Bavinck et al (2009), demonstrated that the probability of infection was much smaller for backyard flocks than for commercial farms in the 2003 Dutch epidemic by H7N7. For this reason and for the low proportion of backyard poultry in respect to the commercial farming, the risk-based surveillance in Cuba considered only commercial poultry industry.

CONCLUSIONS

The model identified the areas with higher risk for AIV introduction from MW, for the selection of poultry premises where to apply the risk-based surveillance. Since the incursions of HPAI do not occur with a regular frequency and several years may elapse before the introduction of the

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virus, the evaluation of the effectiveness of this approach would require its application for several migration seasons before sufficient reliable data are collected.

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457 States. December 19. p: 1-7 (Available in

458 http://www.redmond.gov/common/pages/UserFile.aspx?fileId=59344. Last access on

459 March 4, 2014).

460 WAHIS 2014. Query: Terrestrial animals - Low pathogenic avain influenza (poultry) (2006 -) -

461 years 2006-2014 Available from:

462 http://www.oie.int/wahis_2/public/wahid.php/Diseaseinformation/Diseasetimelines;

463 accessed on March 3, 2014.

464 WHO 2013. Overview of the emergence and characteristics of the avian influenza A(H7N9)

465 virus. 31 May 2013. Available from:

466 http://www.who.int/influenza/human_animal_interface/influenza_h7n9/WHO_H7N9

467 _review_31May13.pdf?ua=1; accessed on March 3, 2014.

468 WHO 2014. WHO Risk Assessment of human infection with avian influenza A(H7N9) virus.

469 Available from:

470 http://www.who.int/influenza/human_animal_interface/influenza_h7n9/Risk_Assess

471 ment/en/; accessed on March 3, 2014.

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474 Table 1. Migratory waterfowl settlements in the Western part of Cuba: location, abundance of wild

475 birds from genera (Charadriiformes, Blanco, 2006, and Anseriformes, Acosta and Mugica, 2006)

476 associated to avian influenza transmission.

Progressive Province Name of the Setting position Number of birds passing during migration Criteria of Number of

number setting Long W Lat N abundance species

1 Amarillas -80,54,14 22,28,53 50 000- 100 000 High 29

2 Las Salinas, C. Zapata -81,14,19 22,12,10 50 000- 100 000 High 74

3 Matanzas Península de Hicacos -81,10,59 23,10,59 11 000- 49 000 Medium 67

4 Salinas de Bidos -80,44,00 23,04,18 11 000- 49 000 Medium 49

5 Laguna del Concunil -80,50,17 23,03,48 11 000- 49 000 Medium 24

6 Mayabeque Batabanó -82,17,32 22,41,16 11 000- 49 000 Medium 14

7 Artemisa Guanimar -82,33,45 22,41,27 11 000- 49 000 Medium 22

8 La Habana Triscornia -82,19,25 23,08,28 1 000- 10 000 Low 33

9 Alonso Rojas -83,25,16 22,16,18 50 000- 100 000 High 52

10 Pinar del Río Sur los Palacios -83,15,46 22,25,47 50 000- 100 000 High 52

11 Guanahacabibes -84,56,18 21,53,24 1 000- 10 000 Low 55

12 I. Juventud Ciénaga de Lanier -82,48,36 21,35,45 11 000- 49 000 Medium 29

13 Los Indios -83,00,56 21,41,54 1 000- 10 000 Low 19

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478 Figure 1. Geographical distribution of poultry farms (black dots) and geographical location of

479 wetlands (centroid of the area) in the western part of Cuba. The size of the points locating the

480 wetlands is proportional to the number of waterfowl transiting during migration.

482 Figure 2 Risk map of introduction of Avian Influenza through migratory waterfowls (settlements with

483 dark dots) in the Western region of Cuba and selected farms (light dots) for active surveillance

484 programme.

» >#731

v -V * %

i • .

0 25 50 100 Km

1_i_i_i_I_I_I_I_I

• Poultry farms Wetland locations

Number of waterfowl transiting during migration

♦ 1 000 -10 000 ^ 11000-49 000

50 000 - 100 000

Habana

Artemisa

Mayabeque Matanzas

Pinar 1 \lsla de la

del Rio

Juventud

Poultry farms

• Farms

• Selected farms Hydrography

^ Migratory bird settlements