# Network of ship movements and its potential in spreading pathogenic infectious salmon anemia virus among Norwegian fish farms

Hélène Duault, Maximilien Bailly, Mingli Zhao, Sarah C Hill, Guillaume Fournié

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Hélène Duault, Maximilien Bailly, Mingli Zhao, Sarah C Hill, Guillaume Fournié. Network of ship movements and its potential in spreading pathogenic infectious salmon anemia virus among Norwegian fish farms. Preventive Veterinary Medicine, 2025, 245, pp.106667. 10.1016/j.prevetmed.2025.106667. hal-05232513

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# Network of ship movements and its potential in spreading pathogenic infectious salmon anemia virus among Norwegian fish farms

Hélène Duault<sup>a,b,*</sup> <small>●</small>, Maximilien Bailly<sup>a,b</sup>, Mingli Zhao<sup>c</sup>, Sarah C. Hill<sup>c</sup>, Guillaume Fournié<sup>a,b,c</sup>

## ARTICLE INFO

### Keywords:

Network analysis

Infectious salmon anemia

## ABSTRACT

In Norway, infectious salmon anemia (ISA) is a notifiable and economically important disease. Accurately understanding between-farm transmission remains essential for ISA control and prevention. Using a network approach, our objective was to assess whether ship movements could potentially contribute to the transmission of pathogenic variants of ISA virus (ISAV-HPRΔ) between farms.

We described yearly static ship contact (reconstructed according to delay between visits: Δ = 1, 8 and 15 days) and company affiliation networks. We assessed the relevance of salmon production areas as subdivisions of these networks. Finally, we identified ship movements that could have resulted in ISAV-HPRΔ transmission between confirmed ISA-HPRΔ cases and explored whether either network was associated with the spatiotemporal distribution of these cases using a permutation test.

Connectivity was high in ship contact networks, with the largest strongly connected component encompassing ≥ 72 % of farms. Farms' affiliations and locations in the same or differing production areas influenced their likelihood of being connected, however increasing Δ enabled the connection of distant regions. Both networks were associated with the distribution of ISA cases.

Ship movements and sharing of equipment or personnel are therefore potential viral transmission pathways. While the ship contact network was well structured by production areas, inadequate disinfection of ships could lead to longer ISAV survival times, thus resulting in long-distance ISAV-HPRΔ transmission events throughout the country. This study highlighted the need to further investigate the role of ships and fomites in fish disease spread; the use of genetic data could provide additional insights.

## 1. Introduction

Caused by an orthomyxovirus, infectious salmon anemia (ISA) was first described in juvenile Atlantic salmon (*Salmo salar*) in Norway in 1984 (Rimstad and Mjaaland, 2002; Thorud and Djupvik, 1988). Since then, ISA outbreaks have also been reported in other major Atlantic salmon-producing countries such as Canada (Mullins et al., 1998), United Kingdom (Scotland) (Rodger et al., 1998), United States of America (Maine) (Bouchard et al., 2001) and Chile (Godoy et al., 2008). The presence or absence of a deletion in the highly polymorphic region (HPR) of gene segment 6 of ISA virus (ISAV) defines two main phenotypic variants: virulent ISAV-HPRΔ and non-virulent ISAV-HPR0 (Dean et al., 2022; EFSA Panel on Animal Health and Welfare (AHAW), 2012).

ISAV-HPR0 typically causes localized infection in salmon gills, occasionally spreading to other organs, without showing disease signs (Christiansen et al., 2011). However, ISAV-HPRΔ infections are systemic, causing severe anemia and high mortality (Rimstad et al., 2011; Rimstad and Mjaaland, 2002). This makes ISA an economically important disease in the salmon farming industry. Both types of infections are reportable to the WOAH, however in Norway, only ISA-HPRΔ is noticeable and its detection triggers mandatory restrictions on fish movements and the depopulation of infected farms (Sommerset et al., 2023).

In Norway, the annual ISA-HPRΔ incidence peaked at 80 new cases (i.e. infected farms) in 1991. The subsequent implementation of preventive and control measures, such as regulations on transportation of live fish and enhanced biosecurity in hatcheries and slaughterhouses,

<footnote><sup>a</sup> Université de Lyon, INRAE, VetAgro Sup, UMR EPIA, Marcy l'Etoile 69280, France</footnote>

<footnote><sup>b</sup> Université Clermont Auvergne, INRAE, VetAgro Sup, UMR EPIA, Saint Genes Champanelle 63122, France</footnote>

<footnote><sup>c</sup> Department of Pathobiology and Population Sciences, Royal Veterinary College, Hawkshead Lane, Hatfield AL9 7TA, UK</footnote>

<footnote>* Corresponding author at: Université de Lyon, INRAE, VetAgro Sup, UMR EPIA, Marcy l'Etoile 69280, France.</footnote>

<footnote>E-mail address: duault.hel@gmail.com (H. Duault).</footnote>
led to a substantial decline in cases (Rimstad et al., 2011; Rimstad and Mjaaland, 2002). From 1993–2019, yearly incidence ranged from 1 to 20 cases, averaging 12 between 2013 and 2019 (Sommerset et al., 2023, 2022). However, there was a surge in cases in 2020 and 2021, with 23 and 25 cases respectively (Sommerset et al., 2023), underscoring the need to better understand, and prevent, disease spread (Aldrin et al., 2021; Sommerset et al., 2022).

While vertical ISAV transmission has been suggested to occur between countries *via* infected eggs (Marshall et al., 2014; Nylund et al., 2007; Vike et al., 2009), horizontal transmission seems to be the most important mechanism for ISAV spread between neighboring farms (Aldrin et al., 2021, 2011). Observational (Gustafson et al., 2007; Jarp and Karlsen, 1997; Vågsholm et al., 1994) and experimental (Thorud and Djupvik, 1988) studies have suggested that ISAV can be passively transmitted through water. Tapia et al. studied ISAV's in-vitro survival time in different water types and temperatures, and estimated a median survival time of 8 days (95 % CI: 8–15) in sterile seawater (30.84 ‰) (Tapia et al., 2013). Conversely, Vike et al. estimated that ISAV survival time was less than 24 h in sterile seawater (35 ‰) and decreased with exposure to ultraviolet light radiation (Vike et al., 2014). Thus, Vike et al. hypothesized that a distance of 2.5 km between marine farms in Norway and Chile should be sufficient to prevent passive ISAV transmission through seawater (Vike et al., 2014). However, horizontal ISAV transmission could also occur *via* biological vectors. Indeed, while Atlantic salmon is the only species in which ISA has been observed in the field, ISAV can cause long-term infection in other salmonids (EFSA Panel on Animal Health and Welfare (AHAW), 2012); these species as well as escaped farmed and wild Atlantic salmon could be a source of infection for farms (Aldrin et al., 2011; Rimstad et al., 2011).

Human activities can also contribute to horizontal ISAV transmission, sometimes over large distances (Murray, 2013). These include the transport of infected fish, the release of contaminated ballast water from supply boats, movements of contaminated equipment. During the seawater production period, specific activities such as the transfer of smolts, treatments against sea lice and harvesting, are conducted by well-boats (Lyngstad et al., 2015), which are boats designed to carry live fish. In Scotland (Murray et al., 2002) and in Chile (Mardones et al., 2014), the risk of ISAV infection was associated with the movements of live fish by well-boats. A 2015 technical report concluded that well-boats may contribute to fish disease spread in Norway, but the extent of their impact remains unclear (Lyngstad et al., 2015).

Recent epidemiological studies have used real-time positioning vessel data to investigate pancreas disease (PD) transmission in Norway, another viral disease of Atlantic salmon (Amirpour Haredasht et al., 2019; Öhlschuster et al., 2023). However, the network created by these ship movements between Norwegian fish farms has not yet been comprehensively described. In network analysis, epidemiological units (e.g., farms) are viewed as nodes, and interactions that may transmit pathogens between nodes as edges (Bansal et al., 2007). This method has been used to assess the potential role of contact networks in spreading pathogens among domestic animal populations (Brown et al., 2022; Cardenas et al., 2021; Fielding et al., 2019; Jones et al., 2019; Lentz et al., 2016). Our aim was to describe the network resulting from ship movements between Norwegian fish farms and assess whether it could potentially contribute to the transmission of ISAV-HPRΔ between farms.

## 2. Material and methods

### 2.1. Data

BarentsWatch is an open-access information system launched in 2012 and managed by the Norwegian government that routinely compiles and shares information on Norwegian coastal and marine areas. These include historical and real-time data from the Directorate of Fisheries and the Norway Food Safety Authority on the location, company affiliation, activity and health status of fish farms (e.g. ISAV-HPRΔ

suspicion and confirmation), and from the Norwegian Coastal Administration on vessel movements. Vessel itineraries are based on Automatic Identification System (AIS) data. Vessels equipped with AIS transponders transmit information on ship speed and course to nearby ships and to satellites. The fish farms estimated to be visited every week by each ship, identified by its uniquely coded Maritime Mobility Service Identity, are recorded, as well as the time of these visits. To decide whether a ship visited a farm, an automated analysis checking ship speed within a buffer area around the farm is employed (for more details, see https://www.barentswatch.no/fiskehelse). Until the 35th week of 2020, only well-boats were included in BarentsWatch. Since then, all ships (except for fishing vessels under 15 m and recreational vessels under 45 m), well-boat or not, visiting at least one fish farm in a given week, are included. Criteria used to define a farm as active or inactive, i.e. populated or not currently holding fish, depend on its type (on-land vs at-sea). Juveniles are kept in fresh-water tanks in on-land farms until they undergo smoltingification, at which point the salmon are transferred to sea pens (net cages) (Afewerki et al., 2023). On-growing sea farms, which are legally required to report the weekly abundance of salmon lice parasites, are considered inactive if they go ≥ 4 weeks without reporting. However, there are no requirements to report sea lice in juveniles since they are kept in fresh water where the parasite is never present. Therefore, we considered on-land farms as active throughout a year if they were visited at least once. We analyzed BarentsWatch fish health and vessel track data from January 1st 2021 to December 31st 2023.

### 2.2. Static networks

For each calendar year of the study period 2021–23, we built static networks with all active farms licensed for Atlantic salmon or rainbow trout (Oncorhynchus mykiss) as nodes. There was a directed edge from node *i* to node *j*, if a ship visited *i* and then *j* within a given timeframe (Δ). Considering the conflicting ISAV survival times estimated by various studies (Rimstad and Mjaaland, 2002; Tapia et al., 2013; Vike et al., 2014), we considered multiple possible timeframes: Δ = 1, 8 and 15 days. The weight of an edge from node *i* to node *j* was the number of times any ship visited *i* then *j* in a year.

Given the potential epidemiological role of well-boats in ISAV transmission (Mardones et al., 2014; Murray et al., 2002), we reconstructed two types of networks: one encompassing all ships included in the BarentsWatch database and another focusing solely on well-boats. While we did have information on whether a ship was classified as a well-boat or not, the purpose of each visit (e.g. harvesting, delousing or non-aquaculture related causes) remains unknown. Therefore, we did not attempt to assess the risk associated with different types of boats or activities.

Yearly networks were described using the metrics defined in Table 1. We estimated the correlation between in- and out-strength (degree) by computing the Pearson correlation coefficient. To test whether a network exhibited small-world properties, i.e. high clustering and small average path length (Watts and Strogatz, 1998), we compared the average path length and the clustering coefficient of an observed LSCC (largest strongly connected component) with those from a 1000 random networks of equivalent size, generated by the Erdös-Rényi model (Erdös and Rényi, 1959).

Companies could own aquaculture licenses for multiple active farms over the study period. In addition to ship movements, affiliation to the same company could lead to indirect ISAV transmission between farms *via* the sharing of equipment or personnel. Farms could be licensed to multiple companies for a given week. These companies might share additional aquaculture sites but not necessarily all of them, and some might also share other sites with different companies, which complicates how they are related to one another and meant we could not simply consider them as part of a larger organisation. We therefore considered an undirected company network, where an edge was constructed between two farms when they shared at least one company during the
<table><caption>**Table 1**

Parameters used to describe the network (name and definition).</caption><thead><tr><th>Parameter</th><th>Definition</th><th>Reference</th></tr></thead><tbody><tr><td>Number of nodes</td><td>Number of farms that were active in a given year.</td><td></td></tr><tr><td>Number of edges</td><td>Number of directed pairs of farms visited by the same ship within a given timeframe in a given year.</td><td></td></tr><tr><td>Density</td><td>The ratio between the number of actual and possible edges.</td><td></td></tr><tr><td>Weighted density</td><td>The ratio between the number of ship visits and the number of possible edges.</td><td></td></tr><tr><td>Largest weakly connected component (LWCC)</td><td>Largest subset of farms, expressed as the percentage of active farms, in which any farm can reach any other through a network path, disregarding edge directions.</td><td>(Kao et al., 2006)</td></tr><tr><td>Largest strongly connected component (LSCC)</td><td>Largest subset of farms, expressed as the percentage of active farms, in which any farm can reach any other through at least one directed path.</td><td>(Kao et al., 2006)</td></tr><tr><td>Average (un) weighted path length</td><td>Mean of the shortest (un)weighted paths between all pairs of farms in the LSCC.</td><td>(Watts and Strogatz, 1998)</td></tr><tr><td>Clustering coefficient</td><td>Proportion of closed triplets of nodes within the LSCC, ignoring direction and weight of edges.</td><td>(Watts and Strogatz, 1998)</td></tr><tr><td>In- (out-) degree</td><td>Number of farms that are connected to a given farm by at least one arriving (departing) ship.</td><td></td></tr><tr><td>In- (out-) strength</td><td>Number of times any ship arrived at (departed from) a given farm after (before) visiting another.</td><td>(Barrat et al., 2004)</td></tr><tr><td>Edge loyalty</td><td>(E<sup>-1</sup> ∩ E') / (E<sup>-1</sup> ∪ E'), with E' as the edges in year y.</td><td>(Cardenas et al., 2021; Schulz et al., 2017)</td></tr><tr><td>Node loyalty</td><td>(V<sup>-1</sup> ∩ V') / (V<sup>-1</sup> ∪ V'), with V' as the nodes active in year y.</td><td>(Cardenas et al., 2021; Schulz et al., 2017)</td></tr><tr><td>Assortativity by strength (degree)</td><td>Tendency of farms to connect to other farms with similar strength (degree).</td><td></td></tr></tbody></table>

same week. The weight of an edge corresponded to the number of weeks that both farms shared at least one company. We assessed the correlation between the ship movement network and company network by comparing the proportion of ship movements occurring between farms while they were affiliated to the same company to the ones obtained in 100 permuted networks, where companies were randomly re-assigned.

Thirteen contiguous Norwegian production areas, in each of which salmon production is regulated independently, were introduced in 2017 (Hersoug, 2021). To assess the relevance of these production areas as subdivisions of the contact network, we compared the density calculated within and between production areas in the observed unweighted network to those from 1000 permuted networks. In these permuted networks, farm locations were randomly re-assigned (i.e. node-label swapping). Secondly, we used the Louvain community detection algorithm on the weighted network (Blondel et al., 2008) to define groups of nodes maximizing the modularity, i.e. resulting in high density within groups and low density between groups (Clauset et al., 2004; Newman, 2006). The value of the weighted modularity associated with the network partition obtained by applying the Louvain algorithm was compared to the value obtained by considering production areas as communities. Finally, we estimated how well the community mapping reflected the distribution of farms in production areas, by computing the ratio between the average Shannon diversity index (Spellerberg and Fedor, 2003) over all production areas and the index for the entire network. The Shannon diversity index ($H'$) in a production area j was expressed as $H'_{j} = -\sum_{i} p_{ij} \log p_{ij}$ with $p_{ij}$ the proportion of nodes in production area j belonging to community i. The lower the ratio, the lower the diversity in community membership within production areas, reflecting a closer alignment between the community-based and

production area-based partitions of farms. We compared the ratios computed on the observed and 1000 permuted networks, in which farm locations were randomly re-assigned.

Similarly, we then assessed the relevance of production areas in the undirected company network.

## 2.3. Temporal networks

Taking into account the timing of ship movements, we tested the association between ship movements and the distribution of ISA-HPRΔ cases. First, we assessed the potential for transmission between each pair of cases. In a stochastic transmission model, Aldrin *et al.* estimated that the average time from infection onset to detection on farms was 4.6 months (CI: 3.6–5.6) (Aldrin *et al.*, 2021). Moreover, the incubation period in an experimentally-infected Atlantic salmon is usually 10–20 days (Rimstad and Mjaaland, 2002). Therefore, we assumed that a farm became infected 110–170 days prior to a suspicion of ISA-HPRΔ (referred to as the *introduction window*), and that a farm was infectious from 160 days before suspicion until mandatory slaughter (Fig. 1). We also considered two shorter infectious periods starting from 130 and 100 days before suspicion. Due to the paucity of information on ISAV-HPR0 cases, our analysis is limited to assessing only potential direct transmission of ISAV-HPRΔ between farms (see Discussion).

For farm A to be considered a potential infector of farm B, A's infectious period needed to either overlap with B's introduction window, or end less than Δ days before the start of B's introduction window. We computed the number of cases with at least one identified potential infector, and the number of potential infector-infected pairs. We also estimated the minimum seaway distances between a confirmed case and its potential infectors.

We then identified ship movements that could have transmitted ISA-HPRΔ between confirmed cases. If a ship left farm A when A was considered infectious and reached farm B during B's introduction window, we considered this visit as a potential transmission event from A to B. Note that the detection of a potential transmission event does not imply a confirmed infection route, but indicates that, based on the timing of reported cases and ship movements alone, transmission via ship contact was epidemiologically plausible. We calculated the number of cases reached by at least one such potential transmission event (indicator 1), the number of potential infector-infected pairs linked by at least one potential transmission event (indicator 2), the seaway distances between them and the number of potential transmission events (indicator 3). Chains of potentially linked cases were defined as sets of cases connected by one or more potential transmission events, accounting for plausible infection timing based on the inferred infectious period and introduction window of each case. All secondary cases in a given chain were connected – either directly or indirectly through intermediary cases – to the first case of the chain.

These metrics were then compared to those computed on 1000 permuted networks generated by randomly re-assigning the date of ISA-HPRΔ suspicion (and slaughter) among farms that were active at time of suspicion (i.e. node-label swapping).

To control for the possible effect of production areas in the network, we also generated permuted networks by randomly assigning the date of ISA-HPRΔ suspicion (and slaughter) only among farms that were active within the same production area, at time of suspicion.

We then similarly assessed the association between companies and the distribution of ISA-HPRΔ cases (using only indicator 1 and 2). If farm A and farm B shared at least one company when A was considered infectious and during B's introduction window, we considered A as a potential infector of B.

BarentsWatch data were imported and analyzed in R v4.3.2 (R Core Team, 2023). Network analysis was performed using the *igraph* package (Csárdi et al., 2023). Seaway distances were estimated using the *gdistance* R package (Etten, 2017) and a function modified from (Assis et al., 2013).
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Possible introduction window and infectious period according to known detection and culling dates. Incubation period and length of time from infection onset to detection were defined based on the literature.</figcaption></figure>

# 3. Results

## 3.1. Ship network description

The number of ships included in the network (i.e. ships registered in the database as linking at least two farms) almost tripled from 2021 to 2023, whereas the number of well-boats, which overall accounted for 6% of tracked ships, and active farms remained stable over time (Table 2). While the median number of visits per ship was higher for well-boats, a non well-boat could make up to 4044 visits over the three-year period.

For a given timeframe (Δ) and ship category (i.e. all ships or exclusively well-boats), network structural features remained consistent throughout the study period (Fig. 2). Despite the drastic increase in the number of ships over the study period, the number of edges only slightly increased. Including all ships instead of only well-boats and/or extending the timeframe (Δ) from 1 to 8 days considerably increased the number of edges, enhancing the density and level of clustering while shortening path lengths (Fig. 2). However, even in the lesser connected well-boat networks, the LSCC encompassed over 72% of active farms and 89–99% of the LWCC. Across all network specifications, the level of clustering was much higher than in random networks and, as Δ increased, the average shortest unweighted path length in the LSCC aligned closely with that observed in comparable random networks, indicative of small-world properties. However, the average shortest weighted path length was 1.3-to-3.7 times as high in the observed LSCC

<table><caption>**Table 2**
Number of farms, ships and visits (Δ= 15 days).</caption><thead><tr><th>Parameter</th><th>2021-2023</th><th>2021</th><th>2022</th><th>2023</th></tr></thead><tbody><tr><td>Number of active farms</td><td>1243</td><td>1136</td><td>1135</td><td>1129</td></tr><tr><td>Number of active farms connected by a ship (%)</td><td>1162 (93.5 %)</td><td>1040 (91.5 %)</td><td>1045 (92.1 %)</td><td>1047 (92.7 %)</td></tr><tr><td>Number of active farms connected by a well-boat (%)</td><td>1107 (89.1 %)</td><td>969 (85.3 %)</td><td>980 (86.3 %)</td><td>985 (87.2 %)</td></tr><tr><td>Total number of ships in the database</td><td>2962</td><td>1228</td><td>1686</td><td>2121</td></tr><tr><td>Number of all ships included in the network (well-boats)</td><td>1592 (103)</td><td>526 (87)</td><td>1022 (84)</td><td>1411 (96)</td></tr><tr><td>Median number of visits per any ship (range)</td><td>121.5 (1 – 4044)</td><td>108 (1 – 1328)</td><td>80 (1 – 1459)</td><td>79 (1 – 1371)</td></tr><tr><td>Median number of visits per well-boat (range)</td><td>401 (1 – 1494)</td><td>165 (3 – 627)</td><td>159 (1 – 467)</td><td>138.5 (1 – 400)</td></tr></tbody></table>

as in the corresponding random networks, meaning that geographically distant farms were only connected through a few ship visits in the observed network.

Node loyalty was high for all network specifications, with > 85% of farms remaining active from one year to the next (Table S1). The inclusion of all ships, as opposed to only well-boats, more than doubled edge loyalty. Similarly, extending the timeframe Δ slightly increased edge loyalty and the median number of ships arriving from or departing to other farms (in and out-strength) (Figure S1). Farms visited by a high (or low) number of ships arriving from other farms typically also had a high (or low) number of ships departing to other farms (Pearson correlation coefficient > 0.95, Table S2). The generally positive but low assortativity by strength (degree) indicated a weak tendency of farms to connect to other farms with similar strength (degree). In and out-strength distributions were right-skewed and when Δ = 15 days, over 100 farms had ≤ 10 ships (vs. less than 11 farms had ≥20,000 ships) arriving from and departing to other farms (Figure S1).

## 3.2. Communities, production areas and company affiliations

While the modularity from Louvain's community partition was consistently higher than that based on production areas, the difference was small, particularly for Δ = 1 day (Fig. 3.A and Figure S2). Moreover, the composition of production areas in relation to Louvain's communities was always less diverse based on Shannon indices than expected by chance (p < 0.001), indicating a significant overlap between Louvain's community partitions and production areas (Fig. 3.D and Figure S3). This implies that the likelihood of ship movements between any two farms was strongly influenced by their location in the same, or differing, production areas. Indeed, the density within and between production areas were respectively significantly higher and lower than expected by chance (p < 0.001, Fig. 3.B and Figure S4). Fig. 4 shows the weighted density within and between production areas over the study period, with production areas numbered 1–13 from South to North (Fig. 3.C). The highest weighted density was always recorded within production areas, corresponding to the diagonal in Fig. 4. Although Δ = 1 day restricted between-area visits to neighboring or near-neighboring production areas, increasing Δ enhanced between-area density, notably connecting distant regions and shortening path length across the network.

Active farms were affiliated to a total of 358 companies during the study period and the number of companies a farm was affiliated with ranged from 1 to 11 (median: 2, Figure S5). The distribution of the number of farms per company was right-skewed: 39.4% (n = 141) of companies operated in only one farm, while the largest company
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Characteristics of yearly networks. Networks were constructed considering all ships or only well-boats and $\Delta = 1$, 8 or 15 days. LSCC stands for largest strongly connected component. Average (un)weighted path length and clustering coefficient were estimated for the LSCC. Results from the 1000 random networks are represented by the black intervals (small ranges render the minimum and maximum values barely distinguishable) in the bottom panels.</figcaption></figure>

managed up to 181 farms (median: 3, Figure S5). The LWCC of this undirected network encompassed 81.8 % of active farms, the remaining farms were either disconnected (9.5 %) or part of a small component of 2–6 farms (8.7 %). Sixty-one percent of all ship movements occurred between farms affiliated to the same company, a higher proportion than expected by chance (2.7–3.2 %, $p < 0.01$, Table S3). This implies that the likelihood of ship movements between any two farms was not only influenced by their locations but also the companies they were affiliated with. The modularity of the company network was markedly lower when computed by production areas compared to Louvain's community partition (Figure S6.A). While there was some overlap between Louvain's community partition and production areas (Figure S6.D), high weighted densities were observed not only within production areas but also between them (Figure S6.C).

### 3.3. Temporal network

Fifty-eight ISA-HPRΔ cases were confirmed and reported to Bare-ntsWatch over the study period in production areas 2–12, with 2–9 cases per area (median: 5) (Fig. 5). Based on detection times and reconstructed introduction windows and infectious periods, all cases had at least one potential infector, with the median number of potential infectors per case being 15 (range: 1–23) (Figure S7.A) and a total of 844 potential infector-infected pairs.

Out of the 92,881 ship visits departing from farms during their plausible infectious period (referred to as *infectious farms*), a maximum of 783 (0.8 %) represented possible transmission events: 117 direct, without any farm visited between a potential infector and a case, and 666 indirect events. These 783 possible transmission events involved 85

possible infector-infected pairs, linking 38 cases to at least one possible infector when considering all ships (65 % of all cases), and only 9 cases when only considering well-boats (16 %) (Table S4). These were facilitated by 66 ships. A few well-boats (9/66, 14 %) were involved, accounting for 17 (2 %) of these possible transmission events but 11 (13 %) of possible infector-infected pairs (Table S4).

The numbers of potential infectors (median: 2 [range: 1–6]) and transmission events (3 [1–295]) per case were overdispersed, with 4 (10 %, n = 38) cases, out of the 38 linked to at least one possible infector, accounting for 64 % of all potential transmission events (Figure S7.B and C). These potential transmission events also involved 11 additional cases that were identified only as potential sources of infection for others. No chain of potentially linked cases involved more than 8 cases (Figure S8). While most potential transmission events occurred within the same production area (650/783, 83 %), 51 out of the 85 (60 %) possible infector-infected pairs were across different areas (links in light blue in Fig. 6). When $\Delta$ increased from 1 to 15 days, the maximum distance for potential transmission events was multiplied by 8 (from 200.6 km to 1694.4 km). The median seaway distance from a confirmed case to its closest possible infector was higher in the possible transmission network than only based on detection times (86.5 km [range: 1.2 – 1694.4] vs. 57.8 km [1.2 – 840.7]) (Figure S7.D), meaning that the possible infector identified using ship movement data was not necessarily the closest possible infector based only on the timing of notifications.

The number of cases with at least one possible infector, the number of possible infector-infected pairs and the number of possible transmission events were significantly greater than expected by chance for the “all ships” networks ($p < 0.03$, Fig. 5). For the well-boat network
<figure><img src="image_4.png" /><figcaption>B. Density</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>C. Map of Norway</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>D. Diversity index</figcaption></figure>

<figure><img src="image_7.png" /></figure>

**Fig. 3.** Production areas in the 2023 “all ships” network. A. Modularity estimated from different divisions of the network (according to production areas -in purple or communities detected by the Louvain algorithm -in green-). B. Density estimated within (in blue) and between (in orange) production areas. C. Map of Norway showing the 13 production areas and the active farms within them. D. Ratio between the average Shannon diversity index of all 13 production areas and the Shannon diversity index of the entire network. In the three graphs (A, B and D), results are shown according to Δ and the black intervals represent results expected by chance.

with Δ ≥ 8, the number of cases with at least one possible infector and the number of possible infector-infected pairs also remained greater than expected by chance. This was the case when controlling (5.B) or not (5.A) for the distribution of farms across production areas. While smaller infectious periods decreased the value of the three indicators (Table S4), they remained significantly greater than expected by chance for the “all ships” networks and for the first two indicators in the well-boat network, when Δ = 15 (p < 0.05, Figure S9 and S10).

When considering company affiliation, 27 ISA-HPRΔ cases had at least one possible infector identified, including 23 of the 38 cases identified when considering ship-mediated transmission, and 4 additional cases that could not be linked to a potential infector through ship movements. Only 25 (out of 85, 29 %) infector-infected pairs connected by ship movements were affiliated to the same company at the time of potential transmission. The company network identified 16 additional possible infector-infected pairs. Most infector-infected pairs sharing the same company affiliation were within the same production area, with only 14 pairs (out of 41, 34 %) spanning different production areas. The number of cases with at least one possible infector and the number of possible infector-infected pairs were significantly greater than expected by chance (p ≤ 0.03) and remained so, even when considering smaller

infectious periods (p ≤ 0.04) (Figure S11).

4. Discussion

In this work, we first constructed directed and weighted ship contact networks between Norwegian fish farms for 2021–2023 using open-access data. We described yearly static networks, underscoring the influence of Atlantic salmon production areas on node connectivity. We then used the temporal network to explore the possible association between ship movements and the distribution of open-access ISA-HPRΔ cases. Finally, we assessed company affiliation as an alternative pathway for ISAV-HPRΔ transmission between farms.

In the network, the number of well-boats remained stable, however the total number of ships tripled over the study period. While this drastic increase concerned only ships that linked at least two farms, the total number of ships in the database almost doubled over the same period. This could reflect an actual increase in the number of ships in Norway as has been observed over the past decade (42 % annual increase from 2009 to 2018 according to the NOAA Artic Report Card, 2022 (Druckenmiller et al., 2022)) and/or be due to the resumption of activities post-COVID19, e.g. global fishing had not yet recovered to
<figure><img src="image_8.png" /><figcaption>Fig. 4. Weighted density between and within production areas over the study period (2021–2023). 1, 8 and 15 are the values considered for Δ (in days).</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 5. Identification of possible infectors of confirmed ISA cases. A. Map of the confirmed cases colored according to whether (i) they were not part of the detected possible ship-mediated transmission network, (ii) they were possible infectors for other nodes but did not have possible infectors, or (iii) they had at least one possible injector within Δ = 15 days. The 85 possible injector-infected pairs are linked with a dashed line colored according to whether they are from the same production area. We strongly caution that the detection of a *possible* pathway through which ISAV-HPRΔ could plausibly have transmitted *via* ship movement given the publicly available ship and infection data does not imply that this pathway was a true infection route. B. Number of confirmed cases according to production area.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Results from the permutation tests on the temporal network. Values correspond to estimated p-values. Values obtained in the observed network are in red and those from the 1000 permuted networks are in black (95 % interval and median value). A. Without controlling for production areas. B. Controlling for production areas.</figcaption></figure>

pre-pandemic levels by 2021 (Paolo et al., 2024). However, this increasing number of ships had little to no effect on the network structural features that remained consistent throughout the study period.

The static ship contact networks were highly connected with a high proportion of farms being reachable to one another through directed paths. However, the networks also showed a strong community structure, that potentially hinders rapid epidemic spreading through the network (Green et al., 2009; Wu and Liu, 2008) by promoting within-community transmission (Liu and Hu, 2005). Organisation into communities has previously been observed in networks of live fish movements in Scotland (modularity of 0.75) (Green et al., 2012, 2009) and Ireland (Yatabe et al., 2015), but the composition of these communities was not associated with the spatial distribution of farms (Green et al., 2012, 2009; Yatabe et al., 2015). In Norway, the definition of independent production areas is based on clusters obtained from hydrodynamic models that aimed to minimize water current connectivity between farms in different production areas (Kristoffersen et al., 2018). The strong influence of Atlantic salmon production areas on the structure of the ship movement network coupled with the way these areas were defined suggests that ship-mediated transmission as well as passive ISAV-HPRΔ transmission through water would be more likely to occur within production areas. Moreover, while the network constructed based on company affiliations was less structured by production areas, most possible indirect ISAV-HPRΔ transmission events occurred within

production areas. However, the uncertainty regarding ISAV survival times meant that long-distance ship-mediated transmission in Norway could not be discounted and a high proportion of infector-infected pairs connected by ship movements were across different areas. This highlights the importance of appropriate biosecurity measures since inadequate disinfection of ships could lead to longer ISAV survival times, which could in turn result in long-distance ISAV-HPRΔ transmission events throughout the country.

The associations between animal and/or fomite movements and outbreak patterns have been scarcely explored, in part because of the scarcity of similar movement data from other systems. In Vietnam, the number of live bird trade links between epidemiological units (communes) with birds infected by avian influenza virus during 2003–2006 was higher than expected by chance, supporting live bird trade as a possible transmission pathway (Soares Magalhães et al., 2010). Similarly, by comparing the “mean number of infected cases among the neighbors of an infected node” computed on observed and permuted networks, bovine tuberculosis spread was linked to cattle trade in Uruguay (VanderWaal et al., 2016) and in the South-West of France (Bouchez-Zacria et al., 2018). To account for the relative strength of contacts, a path-based approach that considers the weighted path lengths between infected cases has been proposed (VanderWaal et al., 2016) and subsequently implemented on behavioral data. Thus, Sandel et al. built a weighted network (weight equal to the number of times
individuals were less than 5 m from each other) and found that male chimpanzees with respiratory clinical signs in Kibale National Park, Uganda, were significantly closer in the network to one another than expected by chance (Sandel et al., 2020). Due to data limitations and uncertain timings of infection, unlike here, these studies could not account for the directionality nor the timing of transmission.

Our approach is close to that adopted by Guinat *et al.* in their analysis of the French live-duck movement network (Guinat *et al.*, 2020), for which at-risk movements between premises were defined based on the date of avian influenza suspicion in each case. In our study, ship movements and companies in Norway were associated with the distribution of ISA-HPRΔ cases for all assumed lengths of farm's infectious period. Ship movements and sharing of equipment or personnel are therefore potential viral transmission pathways between farms. However, estimating the relative strength of this association is not possible with this permutation-based approach (VanderWaal *et al.*, 2016). The role played by alternative transmission pathways, such as passive transmission through seawater (Gustafson *et al.*, 2007; Thorud and Djupvik, 1988) or *via* biological vectors (Rimstad *et al.*, 2011), should be investigated while controlling for the ship movement and company network structure. These pathways might explain cases for which no potential infector was identified in either network, as well as provide alternative explanations for spread when potential infectors were identified.

Another explanation for these cases could be that the ISAV-HPRΔ on these farms represent primary outbreaks, i.e., events in which ISAV-HPRΔ evolves directly from an ancestral ISAV-HPR0 virus already present at the farm. Aldrin *et al.* hypothesized that nearly 40 % of outbreaks reported in Norway prior to 2020 were primary outbreaks (Aldrin *et al.*, 2021). ISAV-HPR0 is not routinely tested for because it does not cause severe disease, and we have minimal information on farms that are infected by this variant. Given current surveillance processes, we focused on better understanding potential direct ISAV-HPRΔ transmissions, which could account for the remaining 60 % of outbreaks. However, the potential occurrence of these primary outbreaks could be in future confirmed or refuted using genetic data.

Genetic data could also help further investigate the evidence for linkages between possible transmission events identified using the ship movement and company networks. Previously, the strong correlation between the distribution of communities defined by animal mobility and phylogenetic clades provided further support for the role of animal movement in Peste des Petits Ruminants Virus transmission in West Africa (Bataille *et al.*, 2021). Phylogenetic analysis has also been used to investigate possible transmission pathways for Bovine Viral Diarrhoea Virus (Booth *et al.*, 2013).

We were limited by practical constraints due to the level of detail available in the BarentsWatch database. We assumed that farms sharing at least one company also shared equipment and personnel. However, some farms were affiliated with multiple companies in a given week, and without further information on the roles of these companies, we could not assess the transmission risk associated with their specific activities. In addition, the purpose for each ship visit is not included in this database. Transfer of infected fish and contaminated ballast water, equipment, feed or personnel, likely represent different levels of ISAV transmission risk. This emphasizes the value of ascribing a reason to each ship visit in order to investigate differences in networks formed by ships that perform different roles within the fish farming industry. Secondly, it would allow the confirmation that these visits occurred and were not misclassified due to the automated analysis, in particular for non well-boats. Finally, identifying whether certain types of contact are more represented amongst possible infector-infected pairs would be the first step in identifying which movements may be associated with higher ISAV-HPRΔ transmission risk.

The ship movement network between Norwegian fish farms was highly connected and strongly structured by Atlantic salmon production areas. The fact that ship movements and sharing equipment or personnel

could represent potential ISAV-HPRΔ transmission pathways highlighted the need to further investigate the role of ships and fomites in fish pathogen spread. In future studies, the joint analysis of genetic and network data could generate further insights.

## Funding sources

H.D. and G.F. are supported by the French National Research Agency and the French Ministry of Higher Education and Research. S.C.H. was supported by a Sir Henry Wellcome Postdoctoral Fellowship from the Wellcome Trust (220414/Z/20/Z) [https://welcome.org/]. M.Z. was supported by a BBSRC Responsive Mode grant, BB/W006294/1.

## CRediT authorship contribution statement

**Hélène Duault:** Writing – original draft, Visualization, Formal analysis, Data curation. **Maximilien Bailly:** Writing – original draft, Formal analysis, Data curation. **Guillaume Fournié:** Writing – review & editing, Supervision, Methodology, Conceptualization. **Mingli Zhao:** Writing – review & editing, Conceptualization. **Sarah C. Hill:** Writing – review & editing, Supervision, Data curation, Conceptualization.

## Declaration of Competing Interest

The authors declare that they have no competing interests.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.prevetmed.2025.106667.

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