Analysis of the swine movement network in Mexico: A perspective for disease prevention and control

Alejandro Zaldivar-Gomez; Jose Pablo Gomez-Vazquez; Beatriz Martínez-López; Gerardo Suzán; Oscar Rico-Chávez

doi:10.1371/journal.pone.0309369

. 2024 Aug 30;19(8):e0309369. doi: 10.1371/journal.pone.0309369

Analysis of the swine movement network in Mexico: A perspective for disease prevention and control

Alejandro Zaldivar-Gomez ¹, Jose Pablo Gomez-Vazquez ², Beatriz Martínez-López ², Gerardo Suzán ¹, Oscar Rico-Chávez ^1,^*

Editor: Clement Adebajo Meseko³

PMCID: PMC11364239 PMID: 39213331

Abstract

Pig farming in Mexico is critical to the economy and food supply. Mexico has achieved advancements in swine health and established an electronic database that records swine movements (Sistema Nacional de Avisos de Movilización, SNAM). In this study, we characterized swine movement patterns in México between 2017 and 2019 to identify specific areas and periods that require concentrated efforts for effective epidemiological surveillance and disease control. We employed a Social Network Analysis (SNA) methodology to comprehensively describe and analyze the intricate patterns of pig movement. In addition, we sought to integrate swine population density into the analysis. We used metrics to characterize the network structure and identify the most critical nodes in the movement network. Cohesion metrics were used to identify commercial communities characterized by a high level of interconnectivity in swine movements between groups of nodes. Of a cumulative count of 662,255 pig shipments, 95.9% were attributed to slaughterhouse shipments. We observed that 54% of all Mexican municipalities were part of the network; however, the density of the movement network was less than 0.14%. We identified four Swine Production Centers in Mexico with high interconnectivity in the movement network. We detected moderate positive correlations (ρ ≥0.4 and <0.6, p < 0.001) between node metrics and swine population indicators, whereas the number of commercial swine facilities showed weak correlations with the node metrics. We identified six large, geographically clustered commercial communities that aligned with the Swine Production Centers. This study provides a comprehensive overview of swine movement patterns in Mexico and their close association with swine production centers, which play a dual role as producers and traders within the swine industry of Mexico. Our research offers valuable insights for policymakers in developing disease prevention and control strategies.

Introduction

In recent years, the swine industry in Mexico has undergone significant expansion, emerging as a critical sector within the economy. This sector is an essential component of Mexican meat production, accounting for approximately 7% of the total production [1]. This makes it the third most important contributor to the country’s meat production, after the poultry and beef sectors [2]. From 2012 to 2020, the number of swine in Mexico increased from 15.9 to 18.8 million head, indicating a growth of 18.5% [3]. The rise in the pork business can be attributed to several causes, such as the rising demand for pork, pig farm technology upgrades, and more efficient production procedures [4–8].

In Mexico, pig farms are classified into three different production systems based on the use of technology and infrastructures: i) technified, ii) semi-technified, and iii) backyard [7, 9, 10]. The technified production system encompasses 40–50% of the pig population in Mexico which is estimated at 18.4 million animals and contributes to 75% of the annual pork production (1,200 tons) [11]. Technologically advanced farms in Mexico have achieved production parameters comparable to those of nations such as the United States and Canada, which rank third and seventh in global pork production, respectively [12–14]. Moreover, the sanitary status of the pig sector in Mexico has made significant progress, with the eradication of Classical Swine Fever (CSF) and Aujeszky’s disease in 2015 [15].

Despite the success achieved by the pig sector in Mexico, there are challenges related to biosecurity and the control of endemic diseases, such as Porcine Reproductive and Respiratory Syndrome virus (PRRSV) or the Porcine Circovirus (PCV) [16, 17]. Moreover, this sector has additional sanitary challenges due to the risk of introducing the African Swine Fever virus (ASFV) in the Americas [18]. Outbreak of those diseases can have significant economic effects, leading to the loss of animals, decreased production and exports, and the imposition of trade restrictions by other countries [19, 20].

The movement of pigs between farms or the presence of fomites in the vehicles used to transport swine can potentially spread endemic or emerging diseases [21–24]. The analysis of swine movement and its close relationship with production sites play a key role in the promotion of animal health and disease control in the swine industry [25]. Animal movement control has been used to monitor the movement of pigs from origin to destination [26, 27]. This strategy promotes disease detection and control and supports the implementation of compartments, as was recently recommended in countries affected by ASF [28].

In Mexico, the movement of animals is regulated by federal Veterinary Services (Servicio Nacional de Sanidad, Inocuidad y Calidad Agroalimentaria, SENASICA). This regulation includes the individual identification of animals, the supervision of sanitary requirements during transport, and the registration of shipments [29]. In the case of swine movements in Mexico, all shipments are registered in an electronic database (Sistema Nacional de Avisos de Movilización, SNAM) [30]. This platform allows for a nationwide centralized and standardized registry of pig movements, generating valuable data to analyze pig movement patterns. Analyzing the pig movement patterns in Mexico will facilitate evidence-based decision-making regarding health-related concerns and promote the sustainable development of the Mexican swine industry.

Social Network Analysis (SNA) has demonstrated its usefulness in characterizing pig movement patterns and assessing their influence on the spread of diseases [31–34]. In this conceptual framework, farms and other facilities are represented as nodes, and the movements of pigs between these nodes are represented as edges. SNA thus facilitates understanding of the interactions between nodes such as farms, slaughterhouses, and livestock fairs, through movement data. SNA has been effectively used in the analysis of pig movements in Europe [35–37], the United States of America [38, 39], and recently in some countries in South America, such as Uruguay [40], Ecuador [41] and Argentina [42]. In addition, SNA has proven to be a useful tool for identifying commercial communities formed by a group of farms or other types of sites. These communities are characterized by strong interactions due to more frequent trade of animals among them compared to the rest of the network [43]. Another application of SNA is to assist in the parameterization of transmission models to assess the risk of disease spread [44, 45].

Despite advances in animal identification and the digitalization of movement records in Mexico, these data have yet to be used for the analysis of livestock movement patterns. To date, research has mostly been focused on analyzing a single link of the supply chain in Mexico, namely, from slaughter centers to the final consumer [46, 47]. However, to study mobilization between farms or to marketing centers, it is essential to broaden this approach.

In this study, we looked at pig movement patterns at the municipal level in Mexico from 2017 to 2019. We defined municipalities as the third level of political administration, below the state and national levels. We sought to understand the integration of these high-density areas in the commercialization chain, providing information to improve the animal health policies within the swine industry in Mexico. Descriptive statistical and SNA techniques were applied to analyze the spatiotemporal dynamics of pig mobilization and identify the periods of the year with the highest frequency of movements and the areas where pig movements (both outgoing and incoming) are concentrated. We characterized the relationships of the movements, identifying links and critical nodes and the commercial communities present in the movement network. Finally, we compared the results of the node metrics with swine population and conventionally used swine facility indicators.

Materials and methods

Data source

The primary data sources employed in this study were official records obtained from the SNAM, which contained comprehensive information on pig movements in Mexico from 2017 to 2019. The SNAM collects detailed information on each pig shipment, including the origin and destination of the pigs.

The swine population distribution data were obtained from a National Livestock Register (Padron Ganadero Nacional, PGN) [48]. This database is an official record at the municipal level of livestock farms in Mexico and the livestock service providers that support these activities. We also collected information on the distribution of swine marketing centers in Mexico, including livestock fairs registered in the PGN and the pig slaughterhouses, published by SENASICA [49, 50]. All datasets were obtained with updates through 2019. This ensures that the analyses are performed on a similar time scale.

No sensitive or private information was collected from any person during this study. Data was collected in accordance with the regulations of the Mexican government regarding confidentiality and the protection of personal data. To protect the privacy of the information of swine producers, movements were tabulated at the municipality level rather than at the farm level. However, this decision limited the capacity to track individual animals or the farm of origin and destination of each shipment.

Database construction

The three datasets were geocoded using an official catalog of municipalities published by the National Statistical Office (Instituto Nacional de Estadística y Geografia, INEGI) [51]. Each record in the database corresponds to a movement of pigs and contains information about the municipality of origin and destination, the registration date, the purpose of the movement, and the number of heads shipped. We detected some movements with apparently extreme values, including shipments containing over 500 pigs. These records could represent multiple shipments; however, they were kept in the analysis as originally entered to maintain the representativeness of the movements.

The purpose of movement is classified into four categories, corresponding to the main stages of swine production in Mexico. These are: i) slaughterhouse, related to the shipment of swine for slaughter and processing; ii) fattening, which includes the shipments of pigs to specialized facilities where animals are fattened to finishing; iii) breeding, for reproductive purposes, such as the sale of breeding sows or boars; and iv) livestock fairs, to commercial events or livestock exhibitions.

Statistical analysis of the movements and distribution of swine in Mexico

We applied an exploratory analysis to describe the number of shipments and pigs involved. The weekly frequency of movements and the average shipment size were analyzed to describe the temporal trend, which was evaluated using the Mann-Kendall Trend Test [52]. We examined the seasonality of shipments by the monthly frequency and the purpose of movements. A one-factor analysis of variance (ANOVA) was conducted to assess potential variations in shipment sizes based on the purpose of movement. Subsequently, post-hoc pairwise comparisons were performed using the Tukey method to identify and compare the differences among group means. All statistical analyses were performed in R version 4.1.2. [53].

We used a hotspot analysis to identify areas with high swine density in Mexico. Hotspot analysis is a spatial analysis and mapping technique that aims to identify spatial clustering [54]. The Global and Local Moran’s Index (I) was used to assess the degree of spatial autocorrelation of swine density at the municipality level and detect hotspots. The degree of spatial autocorrelation in the dataset is indicated by Moran’s I values, which range from -1 to 1 [55]. A value of I close to 1 indicates that locations with similar values tend to be close to each other. Conversely, values approaching -1 indicate high dispersion, while values near 0 suggest random distribution. Additionally, we created a Kernel density map to assess the spatial pattern of swine density, with the purpose of comparing it to swine movements [56]. The hotspot and Kernel density maps were constructed using QGIS software version 3.24.0-Tisler [57].

Network analysis of swine movements

Directed networks were used to analyze the movements of pigs. The municipalities were considered nodes in these networks, while pig movements were represented as connections or edges between municipalities. These edges were weighted by the number of shipments between pairs of nodes. The complete network of swine movements was fragmented into subnetworks according to the purpose of movement, allowing for a more detailed and focused analysis of pig movement patterns within specific production processes. To analyze the stability of the pig movement network during the analysis period, the complete network was divided into weekly intervals.

All networks of swine movements were described using the number of nodes (municipalities) and edges (movements). The number of shipments between nodes, the average size of shipments and the Euclidean distance between nodes were calculated to describe the networks. Euclidean distance between pairs of nodes was calculated from the centroids of each municipality.

We calculated metrics to evaluate the structure and connectivity of all networks of swine movements. These metrics have been described in the scientific literature and used to analyze livestock movement patterns [31, 58–60]. Density and diameter metrics were used to evaluate the degree of connectivity and size of all networks of swine movements. Network density is a metric that quantifies the level of interconnection between nodes relative to the total number of potential connections. The diameter quantifies the extent of indirect paths between pig movements, as it represents the maximum length of the shortest path between nodes.

The reciprocity, transitivity, and assortativity metrics were used to analyze the interaction of pig movements between nodes. Reciprocity assesses the degree of symmetry of the flow of shipments between nodes by calculating the proportion of connections with mutual shipments. Meanwhile, transitivity quantifies the tendency for nodes to cluster or establish tightly interconnected groups within the network. Finally, the assortativity coefficient was used to evaluate the similarity between the connected nodes according to the size of their swine populations. Hence, a positive assortativity coefficient implies a network bias towards a prevalence of connections among municipalities with similar swine populations, while a negative coefficient indicates differing swine populations among connected municipalities.

The impact of nodes in all networks of swine movement was evaluated using degree centrality metrics. A high in-degree indicates that a node receives pig shipments from multiple nodes, while a high out-degree indicates that the node sends shipments to other nodes. Also, the influence of municipalities as hubs and authorities was evaluated. The identification of hubs and authorities is carried out by considering the out-degree and in-degree of a municipality, as well as the number of connections it has to other municipalities with high connectivity. Betweenness was used to quantify the frequency at which a particular municipality acts as an intermediary between pairs of other nodes. The igraph package [61] was employed for the construction and analysis of all networks of swine movements.

The Spearman rank correlation coefficient was employed to assess the relationship between the results of node metrics and the variables associated with swine population and commercial facilities. The variables related to the swine population were pig density, the number of farms (categorized into technified, semi-technified, and backyard farms), and the number of technified farms per municipality. Swine commercial facility variables included the number of livestock fairs and pig slaughtering establishments per municipality.

Detection of commercial communities

The Walktrap algorithm was used to identify commercial communities in the complete network of swine movements [62]. These communities are densely connected nodes, defined by pig shipments [63]. The algorithm is efficient in identifying large networks and uses short random walks between nodes to calculate distances between nodes [64, 65]. A weighted random walk selection process was applied to identify communities with the strongest connections, based on the number of shipments per connection. A threshold of 20 or more nodes was set for a large community to be considered. Below this threshold, communities with 3 or fewer nodes were found. In addition, the number of internal and external connections between communities was compared.

Results

Descriptive analysis of swine movements

In the 2017–2019 study period, a total of 662,255 pig shipments and 92,224,219 pigs shipped were registered in the SNAM. The most frequent recorded purpose of pig shipments was to slaughterhouses, accounting for 95.9% of movements, 3.1% of movements were for fattening, 0.90% for breeding, and 0.03% for livestock fairs. The average size of the shipments was 139 pigs; the 50^th, 75^th, and 95^th percentiles were 140, 210, and 250 pigs, respectively; the maximum number of head mobilized was 5,000 (Table 1). The ANOVA results revealed statistically significant differences with in at least one of the groups based on the purpose of movement. All combinations of the analyzed groups showed statistically significant differences between them, as determined by the Tukey method.

Table 1. Size of pig shipments in Mexico by purpose of movement.

Purpose of movement	Head per shipment			ANOVA^a
Purpose of movement	Mean (range)	SD	Median	F	p
Slaughterhouse	126.5 (1–5,000)	92.1	132	75,718	< 0.001
Fattening	550.4 (1–4,517)	496.1	400
Livestock fair	22.7 (1–600)	67	5
Breeding	74.4 (1–1,600)	123.8	15

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^a The mean differences calculated by Tukey method show that the four groups differ from each other.

A gradual, but steady increase in the weekly frequency of pig movements was evident over the study period without complete stabilization (Fig 1A), which was confirmed by significant positive result of the Mann-Kendall Trend Test (τ = 0.492, P < 0.001). Similarly, a gradual increase in the average size of shipments was detected, with significant fluctuations (Fig 1B). A seasonal variation in the frequency of pig movements was observed. The highest peaks of shipments were observed from September to December, while January to April had the lowest levels. A significant reduction of 24,373 shipments was observed during this period, which represents a decrease of 10.5% compared to the peak. This seasonal trend was evident in pig movements for fattening and slaughterhouses (Fig 1C).

Shipments for fattening showed the most growth in terms of average shipment size between 2017 to 2019, with a 34% increase from 372 to 702 pigs per shipment. In comparison, these shipments were almost four times larger than those directed to slaughterhouses. Shipments for breeding showed a moderate increase, from 67 to 80 pigs per shipment. The least growth in shipment size was for slaughter, from 123 to 130 pigs per shipment. Significant differences in shipment size were identified through ANOVA analysis, and the Tukey test further indicated that all pairwise comparisons were statistically significant, except for movements for livestock fairs.

Geographical distribution of pig farms and swine movements in Mexico

The analysis of the geographic distribution of swine movements revealed a concentration of incoming and outgoing shipments in the central region of Mexico, with several important regions in the periphery of the country (Fig 2A). The spatial patterns of these movements are closely linked to the areas of highest swine density in Mexico (Fig 2B and 2C).

Fig 2 — The nodes represent the centroids of the municipalities with outgoing shipments (navy blue) and incoming shipments (green) (A). The red lines represent edges with a high frequency of pig shipments (> 310 shipments during the study period [95th percentile]), while the blue lines represent regular movements (range: 3–310). Edges and nodes with fewer than 3 shipments are not displayed to improve visual clarity. Kernel density maps of swine population (B) and farm distribution in 2019 (C). Note: The map of the administrative boundaries was obtained from the Marco Geoestadistico, provided by the Instituto Nacional de Estadistica y Geografia (INEGI). Source: https://www.inegi.org.mx/temas/mg/#descargas. To prepare the base map, we used the QuickMapServices plugin in QGIS, obtaining the data from NextGIS. The source link can be accessed at: https://qms.nextgis.com/.

The Global Moran’s Index revealed spatial autocorrelation (I = 0.229, P < 0.05) in swine density in Mexico. Four hotspots of municipalities were then identified by the Local Moran’s Index (S1 Fig). The first hotspot (c₁) is located in the central-western region of Mexico. It includes 49 municipalities in Jalisco, Michoacán, Guanajuato, and Querétaro. The second hotspot (c₂) is in the central-eastern region of Mexico comprising 12 municipalities in Puebla and Veracruz. The third hotspot (c₃) is in the northern region and includes six municipalities in Sonora. Finally, the fourth hotspot (c₄) is in the southeastern region and consists of 17 municipalities in Yucatan. The rest of the municipalities of Mexico had no hotspots with high swine density. The names of the municipalities within each hotspot and their respective states are listed in S1 Table. These four hotspots are estimated to concentrate approximately 55.7% of the pigs in Mexico. In addition, these hotspots revealed a high flow of incoming and outgoing movements (Table 2).

Table 2. Description of swine shipments and pig population in hotspots of municipalities with high swine density and the rest of the municipalities in Mexico.

Variables	Hotspots^a					Other municipalities
Variables	c₁	c₂	c₃	c₄	Total	Other municipalities
Number of municipalities	49	12	6	17	84	2,386
*Swine population* ^b
Swine farms	3,086	322	433	237	4,078	89,096
Swine farm density (farms/km²)	0.12	0.1	0.01	0.06	0.07	0.05
Swine population	4,270,602	923,750	1,817,753	455,255	7,467,360	5,939,945
Swine poulation density (km2)	170.9	279.2	65.2	116.0	124.3	3.11
Mean Local Moran Index	7.73	4.56	1.21	2.24	5.7	0.03
*Shipments mobilized*
Internal	64,167	10,290	47,192	17,051	138,700	146,540
Incoming	40,722	3,341	21,490	22,628	88,181	311,648
Outgoing	102,103	64,988	5,165	7,480	179,736	197,279
*Shipped pigs*
Internal	7,608,005	350,285	7,330,123	2,509,488	17,797,901	15,984,463
Incoming	7,722,874	412,388	6,483,160	3,194,527	17,812,949	49,179,333
Outgoing	12,055,324	9,369,004	2,959,029	678,621	25,061,978	33,379,875

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^a S1 Table lists the names of the municipalities that make up the hotspots.

^b Swine population data updated to 2019.

As shown in Fig 3A, different movement flow patterns of the pigs were identified between all hotspots. In both c₁ and c₂, the outgoing pig shipments individually exceeded to their incoming shipments. The outgoing movements from c1 represent 61.4% of all its movements, while for c2, they account for 86.3%. On the other hand, c₃ and c₄ were characterized by their internal movements, suggesting significant internal trade of pigs within the municipalities comprising each hotspot. These internal movements accounted for 90.1% and 69.5% of the total pig movement in these hotspots. Finally, the movement of pigs between these hotspots was not a significant factor in terms of quantity.

The flows patterns for different purposes, such as shipments to slaughterhouses or for fattening, were similar in terms of origin and destination, but there were differences in the numbers of shipments for each hotspot (Fig 3B–3E).

Description of the swine movement network

The complete network of swine movements connected 54% (1,334) of all municipalities. At the end of 2019, there were 93,174 pig farms registered in PGN, with 25.3% of these farms are located in municipalities without a connection to the network. Therefore, there is a high probability that these farms did not have a report of pig movements in the SNAM.

The complete network of swine movements had a total of 662,255 shipments (edges) grouped into 8,328 pairs of movements between nodes (Table 3). The sub-network of swine movements to slaughterhouses connected 44.6% of the municipalities and was the most extensive network in terms of geographical coverage. The sub-network for breeding ranked second (33.9% of the municipalities), followed by fattening (23.5%) and finally livestock fairs (3.8%).

Table 3. Summary of attributes and metrics of swine movement networks.

Variables	Complete network	Sub-networks
Variables	Complete network	Slaughterhouse	Fattening	Livestock fairs	Breeding farms
*Network atributes*
Number of municipalities (nodes)	1,334	1,101	581	93	838
Nodes with loops	92	78	38	4	29
Number of shipments (edges)	662,255	635,397	20,678	203	5,977
Pairs of movements between nodes	8,328	6,329	1,215	103	2,014
*Shipments*
Median	3	6	2	1	1
Mean	79.5	100.4	17	2	3
95^th percentile	310	412.6	67.6	5	7
Maximum	12,164	12,125	2,029	13	254
Euclidean distance (edge length), km
Median	199.3	188.5	155.4	111.5	231.5
Mean	300	262.7	200.1	205.8	405.9
95^th percentile	948.2	731	966	859.5	1,349.70
Maximum	2,375.50	2,081.30	1,381.80	1,339.30	2,375.50
*Graph level metrics*
Nerwork density (%)	0.14%	0.10%	0.02%	0.00%	0.03%
Reciprocity (%)	6.46%	6.20%	9.20%	9.70%	3.40%
Transitivity	0.13	0.14	0.08	0.03	0.03
Diameter	10	10	11	4	11
Assortativity	0.02	0.03	0.03	0.09	-0.02
*Node-level metrics (In-degree)*
Mean	3.34	2.54	0.48	0.4	0.81
95^th percentile	16	13	3	0	4
Maximum	101	96	25	10	17
*Node-level metrics (Out-degree)*
Mean	3.34	2.54	0.48	0.04	0.81
95^th percentile	19	14	2	0	1
Maximum	301	219	108	25	191
*Node-level metrics (Betweenness)*
Mean	398.7	295	41.56	0.17	56.25
95^th percentile	743	326	0	0	0
Maximum	49,939	50,839	13,353	264	16,047

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A total of 92 municipalities connected to the complete network demonstrated the presence of feedback loops or internal movements. This represents 6.9% of all municipalities. These feedback loops account for 11.6% of all pig shipments and 6.8% of all pigs shipped during the study period. Therefore, we included the looped shipments in subsequent analyses to preserve representativeness.

On average, 79.5 shipments occurred between each pair of nodes in the complete network of swine movements. The average of pig shipments between nodes exceeded the median, indicating a highly right-skewed distribution in all networks of swine movements (Table 3). The pig shipments were transported an average distance of 300 km. Except for pig shipments to livestock fairs, statistically significant differences were observed in the average distance between nodes according to the purpose of movement (F = 173.8, P < 0.001).

Description of network level metrics

Table 3 summarizes the metrics results for all networks of swine movements. The study found that the density of swine networks ranged from 0.001% to 0.14%, with the most significant proportion of bidirectional connections in sub-networks for fattening and livestock fairs. The sub-network to slaughterhouses showed the highest transitivity value, suggesting interconnected municipalities. There was no evidence of preferential connections between municipalities with similar pig populations.

In the temporal analysis, the complete network and slaughterhouse sub-network gradually increased in density but showed no significant changes in reciprocity, transitivity, or diameter values, as illustrated in S2 Fig. In fattening and breeding sub-networks, intermittent peaks in reciprocity and transitivity values suggest that short periods of swine movement generate more symmetrical and cohesive temporal interactions between municipalities.

Description of node-level metrics

In the complete network of swine movements, the municipalities with the highest in-degree were located in the central region of Mexico, mainly in municipalities of c₁. Furthermore, in all hotspots (c₁, c₂, c₃, c₄), there were municipalities with the highest out-degree, which also had a high betweenness. The distribution of the degree centrality and betweenness metrics results in the complete network of swine movements are show in more detail in Fig 4.

Fig 4 — *In-degree* (A), *out-degree* (b), and *betweenness* (c) values. Note: The map of the administrative boundaries was obtained from the Marco Geoestadistico, provided by the Instituto Nacional de Estadistica y Geografia (INEGI). Source: https://www.inegi.org.mx/temas/mg/#descargas.

A general trend was observed in all networks, with municipalities showing high out-degree values and betweenness values. A similar trend was observed between in-degree and betweenness; however, only a few municipalities presented high values for both metrics. Although some changes in the value of the metrics were observed in all sub-networks, the same trend in the results and their location in the hotspots was maintained.

For all networks, there were no consistent patterns; municipalities with high in-degree were not also systematically identified as hubs, nor were municipalities with high out-degree generally identified as authorities, suggesting greater complexity in the structure of the movement networks.

Correlation between node metrics and indicators of swine population and commercial facilities

Moderately strong positive correlations (ρ ≥ 0.4 and < 0.6, p < 0.001) were detected between population indicators and node metrics in all networks of swine movements, except in the sub-network of transport to livestock fairs. Swine density and number of technified farms showed the strongest correlations. In contrast, we observed weak correlations with commercial facilities indicators. S2 Table shows the complete results of the correlation analysis.

Detection of commercial communities

We applied the random walk selection of the Walktrap algorithm, and we identified 11 communities, of which six were large (Fig 5). The weighted selection of the algorithm identified the same number of large communities but reduced the number of nodes per community by eliminating weaker edges or those with fewer shipments (S3 Table).

Discussion

This study provides the first comprehensive overview of swine movement patterns and trade networks in Mexico, encompassing movements to farms, slaughterhouses, and livestock fairs. Previous research has focused on a small fraction of pig movement to slaughterhouse [66]. The use of official records of swine movements enables the generation of data that can inform decision-makers regarding the monitoring of disease control and biosecurity measures [67].

The most common reason for the movement of pigs in Mexico is for slaughter, a finding that is not consistent with previous research in other countries [68–70]. The high number of shipments to the slaughterhouse can be attributed to the volume of production of technified farms and their biosecurity protocols, which restrict the movements of pigs between farms or facilities [17, 71, 72]. This discrepancy indicates the presence of a unique pattern of movement within the Mexican pig sector, warranting further investigation.

Our results confirm that swine movements in Mexico increased in terms of both the frequency and the size of shipments over the study period. This trend can be attributed to the growing domestic pork demand in Mexico and the expanding volume of exports [73–75]. It is expected to continue and possibly accelerate in the future. As a consequence, new challenges in terms of biosecurity, logistics and disease control throughout the supply chain will probably emerge [76–80].

While there has been an improvement in SNAM coverage with more farmers registering their pig movements, there is still a significant gap due to a lack of awareness, resources, and renounce to adopt digital platforms. These challenges need to be addressed to encourage greater participation in traceability in the Mexican pork industry.

The seasonal movement patterns of pigs found in our study are consistent with the seasonality of swine production in Mexico [81]. The seasonal movement patterns are characterized by a high season towards the end of the year (October-December) due to the festivals and celebrations that occur during this period. The period of lowest pig movements occurs in spring (March-April), which coincides with a religious period when the Catholic population in Mexico reduces its meat consumption [82]. Another possible explanation for these seasonal movement patterns is the adaptive production strategy employed by the pig farms, as well as market demand fluctuations, climatic conditions, and feed availability [83, 84]. This could be interesting to explore in future research.

Our analysis of the geographic distribution of pig farms identified four hotspots of high-density municipalities (c₁, c₂, c₃ and c₄), considered the main centers of swine production in Mexico. These hotspots represent critical points for pig movement, especially for fattening, breeding, or livestock fairs. Multiple factors, including geography, demand for pork, and production practices, influence these profiles. For example, pig production in the hotspots of Sonora and Yucatán (c₃ and c₄) focuses on fattening and slaughtering pigs within the same region, with a significant portion of the pork meat being exported, particularly to Asia [75, 85]. In contrast, the hotspots identified in the central region of Mexico (c₁ and c₂) focus on fattening and transportation of live pigs to other municipalities, mainly to the most populated areas of Mexico.

Analysis of the complete network of swine movements revealed a complex interconnection between municipalities in Mexico. This is the result of the exchange of pigs within the hotspots (c₁, c₂, c₃, and c₄) and to the rest of the municipalities. The low density in all networks of swine movements could indicate the presence of limited or underutilized edges between origin and destination municipalities.

The network diameter and the average Euclidean distance between nodes indicate a comprehensive geographic coverage of swine movements. The geographic coverage of this network, which is predominantly local, particularly associated to the hotspots, could explain the low density of connections, thereby facilitating more efficient mobilization coverage. However, we also identified outliers in the Euclidean distance data. These outliers could be due to exceptional movements, such transport for high-value pigs.

This interconnection poses health and biosecurity challenges due to the high concentration of farms and shipments, mainly in the swine production centers in Mexico [42, 86–88]. Although a low network density could limit the spread of disease, the existing links between nodes due to the regular movement of pig shipments represents a potential pathway for the spread of disease [89].

The most frequent movements within the complete network are directed towards slaughterhouse, traditionally considered "dead-ends" regarding disease transmission. Despite this, there is evidence that pathogens are being spread to farms near slaughterhouses or through transport vehicles, especially those transmitted by indirect contact or mechanical vectors [90, 91]. This observation underscores the necessity of enhancing surveillance and biosecurity measures at slaughterhouses, especially in municipalities associated with the swine density hotspots.

In sub-networks of swine movements, bidirectional connections were detected during short periods in the fattening and breeding movements. These recurrent exchanges suggest that these pig movements occur only when there is a specific association between breeding and finishing locations or involve multi-site pig farms [39].

Our analysis of swine movement network reveals several limitations that must be considered for an accurate interpretation of the results. One such limitation is the inability to track movements at the local level. This is evidenced by the low number of loops found in the municipalities, indicating that internal mobilization was not reported to SNAM. The absence of such local data could result in an underestimation of the complexity and real extent of swine mobilization networks.

Another limitation is the lack of information to identify the type of pig farms involved in the mobilization network. This includes whether the farms are technified, semi-technified or backyard. Although the correlation analysis indicated that technified farms may have influenced the configuration of outgoing and incoming connections of the municipalities, further analyses are required to determine the impact of other farms in the network.

The observation of outliners in shipments larger than 500 head to the SNAM may introduce bias in the identification of critical network nodes and affect network metrics. Although previous studies have documented multiple pig movements in a single transaction [42, 92], it is important to improve the quality of data collection for these movements, even if this practice is not typical.

Conclusions

The study examines swine movement patterns in Mexico, using official movement records as the data source. Provides a comprehensive overview of the sector, capturing the general trends and dynamics of these movements. The analysis is particularly relevant in Mexico, where the swine sector faces threats from endemic and transboundary diseases such as ASF. While the results are limited to a specific period and primarily reflect the large-scale swine industry, which is less likely to reflect smallholder movements, further research is needed to better understand their dynamics.

The results of this study could help enhance the accuracy of official movement records and improve the assessment of national disease transmission risks. Furthermore, the study identifies significant nodes and commercial communities in Mexico that can be targeted in disease response planning. Our findings indicate that the Mexican swine sector is characterized by the concentration of four high-density swine production centers, which have a significant influence on the swine movement network and are critical for establishing strategic surveillance and disease control points.

Supporting information

S1 Fig. Hot and cold spot map illustrating the spatial distribution of municipalities with high swine density in Mexico.

This map highlights areas in red indicating concentrated swine populations (hot spots) and regions in navy blue with lower swine density (cold spots). The map reveals areas in transition, with Low-High (light blue) and High-Low (light red) gradients. The outlined squares in the figure denote the location of swine production centers (c₁, c₂, c₃, and c₄). Note: The map of the administrative boundaries was obtained from the Marco Geoestadistico, provided by the Instituto Nacional de Estadistica y Geografia (INEGI). Source: https://www.inegi.org.mx/temas/mg/#descargas.

(TIF)

pone.0309369.s001.tif^{(1.2MB, tif)}

S2 Fig. Metrics of the complete network of swine movements and sub-networks by purpose of movement calculated weekly during 2017–2019.

The following metrics are described: density (A), reciprocity (B), transitivity (C), and diameter (D).

(TIF)

pone.0309369.s002.tif^{(440.4KB, tif)}

S1 Table. List of Municipalities comprising the four hotspots with high swine density identified in Mexico.

(PDF)

pone.0309369.s003.pdf^{(65.2KB, pdf)}

S2 Table. Spearman range correlation analysis between the metrics of nodes and indicators of swine population and commercial facilities.

(PDF)

pone.0309369.s004.pdf^{(72.1KB, pdf)}

S3 Table. Results of detection of commercial communities in the complete network of swine movements from 2017 to 2019.

(PDF)

pone.0309369.s005.pdf^{(48KB, pdf)}

Acknowledgments

We acknowledge the support of the Servicio Nacional de Sanidad, Inocuidad y Calidad Agroalimentaria (SENASICA) for providing the swine movement data essential for our analysis.

Data Availability

The data underlying the results presented in the study are available from Figshare repository (https://doi.org/10.6084/m9.figshare.c.7253860.v2).

Funding Statement

The first author, Alejandro Zaldivar-Gomez was supported by the Programa de Becas para Estudios de Posgrado, approved by the Consejo Nacional de Ciencia y Tecnología (CONACYT) (Grant number: 750549). There was no further external funding provided for this study.

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PLoS One. doi: 10.1371/journal.pone.0309369.r001

Decision Letter 0

Jean-François Carod

4 Apr 2024

PONE-D-23-44053Analysis of the swine movement network in Mexico: A perspective for disease prevention and control.PLOS ONE

Dear Dr. Rico-Chávez,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Additional Editor Comments:

Thanks for your submission "Analysis of swine movement network in Mexico: A perspective for disease prevention and control" , please consider both of the reviewers comments and take action for a resubmission including the major changes requested.

Yours sincerelly,

Dr Jf Carod

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The article provides relevant information about the swine network in Mexico, which is important for national improvements of the sanitary system and general risk analysis. The article provides extensive network analysis. My general recommendation is to condense the scientific text to enhance readability and maintain focus.

Dear authors, please find below some recommendations based on the text provided, aimed at enhancing the quality of your hard work:

Line 57: Could you explain better this percentages? The pig population was counted based on the number of farms or number of pigs (could you include the numbers of the estimation), percentage of production refers also to metric tons of meat or number of slaughtered animals?

Line 110: Municipal is a third or fourth level political organization. Federal, State and Municipal? Could you explain this.

Line 143: Spanish original names could be italic, or a short English name followed by the Spanish on parenthesis.

Line 216: Please consider indicate that you used hubs and authorities score, just for general audience readers.

Line 223: What about the semi-technified and backyard?

Line 228-241: Could you merge these two paragraphs.

Line: 238: Could you rephrase for better readability, why 20?

Line 265: Could you include the percentage of reduction.

Line 264: According to Fig.1C, the movement from livestock fairs and fattening reduces between Jun, Jul, Ago any explanation for this reduction (On the discussion the reduction is explained around Mar-Apr).

Line 278: According to Fig. 1C the number of shipments per month to livestock fairs, are extremely low, considering the 600.000 movements, it shows that the clients of that fairs should be backyard producers without registering those movements, is that appreciation, correct? Could you comment about that?

Line 288 Fig. 2. Could you comment about the long-distance movements (e.g. Line Merida – Chihuahua or Hemosillo >2500 km).

Table 2: Please consider including on Swine populationb the density (pigs/farms per Km2) to have a direct density comparison between hotspots, also could you consider including the local Moran index on the text or on the table for the hotspots, it could be useful (I suppose that local index should be higher than global index).

Line 323: When superimposing Fig2 and SM1 seems that there are some movements close to the c3 and c4 hotspots, please consider looking further if they are related or just errors on the data as my previous comment about line 288.

Line 317-323: Please consider include a percentage to the 15,148 considering the reference of total movement in the hotspots, some are explained as numbers and other as percentages, that makes harder to read.

Line 333: Please consider explain what you refer when talking about similarities and discrepancies.

Line 338: Please consider indicate the total number of farms registered on the PGN. Do you consider that they are backyard farms?

Table 3: Euclidean distance: 95th percentile and maximum distances are quite high biological plausibility of terrestrial transport, consider checking the validity of those movements, or maybe they are aerial transport maybe for high valuable pigs.

Line 439: Please consider rephrasing the loops explanation.

Line 366: Please consider which was the first assumption and based on what analysis.

404: Please consider separating methods and results in this paragraph.

Line 420: Please consider rephasing this paragraph.

Line 425: Please consider explaining better why on the map there are NA. My understanding is that 11 communities were identified, the largest 6 are plotted on the map and the other 5 (weaker) are plotted as NA.

Line 428: Please consider providing a clearer explanation of “geographically clearly defined” when looking at the map (Fig. 5B) as the colours appear quite scattered, particularly the blue ones. Additionally, regarding the “correspondence” of hotspots to the communities: C4 corresponds to the “red” community, but one municipality is on the opposite northeast side of the country. C2 is divided between the “blue”, “green”, “yellow”, NA (hard to tell); C1 is within the blue community, with some NA as well. Maybe a count of the match could help or use “approximate” in reference to the hotspots.

Line 459: Please consider that this paragraph only addresses the industrial perspective, whereas backyard producers might find it easier to slaughter animals on-site. There is evidence of movements to and from markets and maintaining them for ~1o monthly movements seems economically inefficient (please consider include the number of those markets). It is possible that other users of those livestock fairs are not registering their movements, as indicated in 578. Perhaps it could be interesting to keep this as an open question in the discussion for further research projects.

Line 467: Please consider that movements to slaughterhouses are usually considered as “dead-ends” with respect to disease transmission. How the network without dead ends would look like for using in a disease transmission model?

Line 476: Please consider rephrasing this paragraph.

Line 551: Please consider resume this long paragraph.

Reviewer #2: The paper "Analysis of swine movement network in Mexico: A perspective for disease prevention and control" uses social network analysis (SNA) methods to investigate patterns in Mexico's officially recorded swine movement data. The authors provide the analysis results through plots and tables and discuss their implications. While this kind of analysis is not new, the authors claim this is an original application of SNA in Mexico's swine movement networks. Here is my feedback based on what I understood from reading the paper.

1. The analysis is presented at the municipality level (as nodes) and not the farm level. Hence, there are limitations in interpreting the results. These limitations should be clarified to the readers.

2. The author's estimate of about 95.9% being attributed to slaughterhouse movements could be misleading if it is just based on shipment counts rather than pig population. Heads in fattening shipments are almost four times more than slaughterhouse shipments. This should be clarified in the wording.

3. Some of the statements in the paper are too trivial. For example, statements such as "movements occur in the same areas where swine production is concentrated..." do not provide any interesting insight. The authors should limit these statements and make the write-up concise.

4. Lines 191-193: The authors use and report Euclidean distance to measure distances between municipalities/nodes. However, shipment distances computed from road networks will likely better fit this context.

5. The discussion section is too long, where the authors provide a lot of commentary, but for the reader, it is difficult to extract information that could be used in later studies. For example, what are the top 2 or 3 insights/findings that are novel contributions of this paper?

6. Lines 262-264: Grammar issues.

**********

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Reviewer #1: Yes: Alfredo Acosta

Reviewer #2: No

**********

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PLoS One. 2024 Aug 30;19(8):e0309369. doi: 10.1371/journal.pone.0309369.r002

Author response to Decision Letter 0

10 Jun 2024

5. Review Comments to the Author

Reviewer #1: The article provides relevant information about the swine network in Mexico, which is important for national improvements of the sanitary system and general risk analysis. The article provides extensive network analysis. My general recommendation is to condense the scientific text to enhance readability and maintain focus.

Dear authors, please find below some recommendations based on the text provided, aimed at enhancing the quality of your hard work:

Thank you for your feedback. We have included the total pig population and pork production in Mexico. Please see the lines 56 – 59.

Line 110: Municipal is a third or fourth level political organization. Federal, State and Municipal? Could you explain this.

We made the changes suggested. Please see the lines 110-112.

Line 143: Spanish original names could be italic, or a short English name followed by the Spanish on parenthesis.

We used short English names followed by the Spanish names in parentheses. All changes were made in lines 24, 81, 85, 129, and 145.

Line 216: Please consider indicate that you used hubs and authorities score, just for general audience readers.

We have added an explanation about this topic and rephrased the text. Please see the lines 217 – 219.

Line 223: What about the semi-technified and backyard?

The swine population variables include data from technified, semi-technified, and backyard farms. However, we created a subset of technified farm because these types of farms frequently report to the SNAM database. Distinction between these categories of farms is explained in lines 224 - 227.

Line 228-241: Could you merge these two paragraphs.

We have rewritten the paragraph to improve readability. See lines 231-238.

Line: 238: Could you rephrase for better readability, why 20?

We observed that some communities ranged from 1 to 3 nodes, while the others had more than 27 nodes. To address this discrepancy, we established a cut-off point of 20 nodes, categorizing communities with fewer than 20 nodes as small and those with 20 or more nodes as large. Please see the explanation in the manuscript in lines 236-238.

Line 265: Could you include the percentage of reduction.

We made the adjustment in the manuscript. Please see the line 261-263.

In the section of discussion, we have focused on explaining the reduction in swine movements observed during the March to April period. We attribute this decline to the influence of religious traditions, as corroborated by the available data.

The observed pattern of movements to fairs from June to August may be influenced by other factors, such as the scheduling of fairs or an economic factor, but we don't have the data to discuss this pattern. We made a clarification in the manuscript. Please see the lines 442 - 447

Although it is very likely that backyard producers do not participate in the registration of swine movements, we are currently unable to identify the type of farm in movement data. We compared the number of farms from the National Livestock Register (PGN) with network metrics and found a correlation specifically with technified farms. This provides indirect evidence of the influence that technified farms have on the configuration of the network. However, further research is needed in the future.

In the discussion we noticed these limitations. See the lines 495 – 499.

Line 288 Fig. 2. Could you comment about the long-distance movements (e.g. Line Merida – Chihuahua or Hemosillo >2500 km).

Long-distance movements are exceptional and involve a small number of animals and shipments. They are associated with the sale of animals for breeding purposes or high-value pigs. We have included an explanation of long-distance movements in the discussion to ensure the reader is fully aware of these factors. See the lines 468-470.

Table 2: Please consider including on Swine population the density (pigs/farms per Km2) to have a direct density comparison between hotspots, also could you consider including the local Moran index on the text or on the table for the hotspots, it could be useful (I suppose that local index should be higher than global index).

Thank you for the suggestion. We included the density of farms and pigs per km2 in Table 2. Regarding the local Moran Index, we calculate the average value per hotspot, as it is calculated individually for each municipality. The local Moran index is indeed higher than the value of the global index (I = 0.229, P < 0.05), indicating spatial clustering in the dataset.

We have reviewed the database, and we can confirm that the movements observed near the c3 and c4 hotspots are correctly recorded. As we mentioned in the previous commentary, these movements are exceptional with few animals and are due to shipments of high value pigs. We have included an explanation of this long-distance movement in the discussion. See the lines 468-470.

We have made the suggested adjustments. See lines 345 – 349.

Line 333: Please consider explain what you refer when talking about similarities and discrepancies.

The differences and similarities were clarified in the manuscript and the paragraph was reduced for better reading on lines 327 -329.

Line 338: Please consider indicate the total number of farms registered on the PGN. Do you consider that they are backyard farms?

We have made the suggested adjustments. Please see the line 333.

We checked the database and found the records for these movements are valid. As mentioned in the previous commentaries, these movements are exceptional with very few shipments during the analysis period. The aerial transport is possible, but we cannot validate this type of information.

Although these movements are exceptional, we decided to keep the records in the analysis to have the highest representativeness of the movements. We also explain this long-distance movement in the manuscript. See the lines 468-470.

Line 439: Please consider rephrasing the loops explanation.

Thank you for your feedback. We have created a more concise and readable version of this paragraph. Please see the lines 415 – 419.

Line 366: Please consider which was the first assumption and based on what analysis.

We appreciate your feedback. The original text was unclear, so we have revised it to make it more concrete. Please see lines 359-364.

Line 404: Please consider separating methods and results in this paragraph.

The fragment of methods was placed in lines 217 – 219, while the results were placed in line 390-391.

Line 420: Please consider rephasing this paragraph.

The paragraph has been revised to enhance clarity and conciseness. The updated version is lines 396-400.

Thanks for your comment. We had an error in identifying small communities as NA on the map. We updated the map to show only the label of the large communities for improve clarity and interpretation. We also update the caption to inform readers about this issue (line 409-410).

Although there is a geographic overlap between the hotspots and the identified communities, the correlation is not perfectly defined or clear. Therefore, we decided to omit this statement from the manuscript to avoid confusion.

We recognize the limitations in describing the movement profile of small producers due to their lack of regularity in reporting their mobilization. The data gives us a good starting point to understand the swine movement patterns in Mexico and could inspire more focused research in the future.

The number of these markets has been included in the correlation analysis with the number of livestock fairs. This helped us to better understand their influence on the pig movement network.

A network without “dead-ends” would involve movements that eventually return to the original location or continue circulating within the network. This type of network could potentially result in increased disease transmission dynamics, as infected animals could reintroduce the pathogen back into the network instead of being removed from it through slaughter.

However, this is not common on farms in Mexico because animals are usually sent to slaughterhouses or points of sale after a certain fattening period. This creates a unidirectional movement that does not include a return to the farm of origin. This argument is further supported by the low percentage of reciprocity observed in the networks (Please see the Table 3).

We made the paragraph more summarized. See the lines 477-482.

Line 476: Please consider rephrasing this paragraph.

In response to the other reviewer's request for a more concise discussion, we have decided to remove this paragraph.

Line 551: Please consider resume this long paragraph.

To be more concrete in the discussion, we resume the paragraph. See the lines 472 – 475.

Reviewer #2: The paper "Analysis of swine movement network in Mexico: A perspective for disease prevention and control" uses social network analysis (SNA) methods to investigate patterns in Mexico's officially recorded swine movement data. The authors provide the analysis results through plots and tables and discuss their implications. While this kind of analysis is not new, the authors claim this is an original application of SNA in Mexico's swine movement networks. Here is my feedback based on what I understood from reading the paper.

We appreciate your feedback. Thank you for your feedback. These limitations have already been addressed in the manuscript. It is clarified that the analysis is presented at the municipality level rather than the farm level, and the implications for interpreting the results are explained. (Please see the lines 110 - 112).

We have added the suggested clarification in the manuscript. Please see the lines 270-271.

Thank you for your feedback. We've made the manuscript more concise and removed trivial statements.

Thank you for your feedback. However, this approach presents a significant challenge due to the lack of precise farm location data. We use the centroid of municipalities to calculate the Euclidean distance. Consequently, using road networks would not improve the accuracy of our distance estimates. Given the differences in the area of municipalities, it would be difficult to accurately estimate the most appropriate road routes. This could lead to confusion and redundancy, as all trips would originate from the same node and geographic location. In summary, we believe this recommendation is not feasible.

We agree with your opinion and have adjusted the discussion to make it more concise.

In response to your question, we have identified 3 contributions of this paper:

1. This study describes for the first-time swine movement patterns in Mexico, providing valuable information that can strengthen disease control and prevention strategies and improve the effectiveness of swine traceability procedures.

2. Identification of significant nodes and commercial communities: The analysis identified four main hotspots that concentrate the largest pig population in Mexico, revealing a complex flow of pig movements that also have connections with the rest of the municipalities in the country.

3. Our results mainly reflect the large-scale swine industry in Mexico, but more research is needed to understand the dynamics of smallholder swine movements.

6. Lines 262-264: Grammar issues.

We have considered your recommendation and have made the text more concise. Please see lines 259-260.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0309369.s006.docx^{(29.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0309369.r003

Decision Letter 1

Clement Adebajo Meseko

12 Aug 2024

Analysis of the swine movement network in Mexico: A perspective for disease prevention and control.

PONE-D-23-44053R1

Dear Dr. Rico-Chávez,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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PLoS One. doi: 10.1371/journal.pone.0309369.r004

Acceptance letter

Clement Adebajo Meseko

22 Aug 2024

PONE-D-23-44053R1

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Hot and cold spot map illustrating the spatial distribution of municipalities with high swine density in Mexico.

(TIF)

pone.0309369.s001.tif^{(1.2MB, tif)}

S2 Fig. Metrics of the complete network of swine movements and sub-networks by purpose of movement calculated weekly during 2017–2019.

The following metrics are described: density (A), reciprocity (B), transitivity (C), and diameter (D).

(TIF)

pone.0309369.s002.tif^{(440.4KB, tif)}

S1 Table. List of Municipalities comprising the four hotspots with high swine density identified in Mexico.

(PDF)

pone.0309369.s003.pdf^{(65.2KB, pdf)}

S2 Table. Spearman range correlation analysis between the metrics of nodes and indicators of swine population and commercial facilities.

(PDF)

pone.0309369.s004.pdf^{(72.1KB, pdf)}

S3 Table. Results of detection of commercial communities in the complete network of swine movements from 2017 to 2019.

(PDF)

pone.0309369.s005.pdf^{(48KB, pdf)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0309369.s006.docx^{(29.4KB, docx)}

Data Availability Statement

The data underlying the results presented in the study are available from Figshare repository (https://doi.org/10.6084/m9.figshare.c.7253860.v2).

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PERMALINK

Analysis of the swine movement network in Mexico: A perspective for disease prevention and control

Alejandro Zaldivar-Gomez

Jose Pablo Gomez-Vazquez

Beatriz Martínez-López

Gerardo Suzán

Oscar Rico-Chávez

Roles

Abstract

Introduction

Materials and methods

Data source

Database construction

Statistical analysis of the movements and distribution of swine in Mexico

Network analysis of swine movements

Detection of commercial communities

Results

Descriptive analysis of swine movements

Table 1. Size of pig shipments in Mexico by purpose of movement.

Fig 1. Temporal trend of swine movements in Mexico from 2017 to 2019.

Geographical distribution of pig farms and swine movements in Mexico

Fig 2. The distribution of swine movements and pig density in Mexico between 2017 and 2019.

Table 2. Description of swine shipments and pig population in hotspots of municipalities with high swine density and the rest of the municipalities in Mexico.

Fig 3. Swine movement flow between hotspots in Mexico.

Description of the swine movement network

Table 3. Summary of attributes and metrics of swine movement networks.

Description of network level metrics

Description of node-level metrics

Fig 4. Distribution of node-level metrics for the complete network of swine movements.

Correlation between node metrics and indicators of swine population and commercial facilities

Detection of commercial communities

Fig 5. Members of the large communities identified by the Walktrap algorithm.

Discussion

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Jean-François Carod

Roles

Author response to Decision Letter 0

Decision Letter 1

Clement Adebajo Meseko

Roles

Acceptance letter

Clement Adebajo Meseko

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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