Identifying effective surveillance measures for swine pathogens using contact networks and mathematical modeling

Abstract

Infectious diseases in livestock have detrimental effects on the health of animals, the livelihood of farmers, and the meat industry. Understanding the specific pathways of disease spread and evaluating the effectiveness of surveillance measures is critical to preventing large outbreaks. Direct livestock transport, transport tours-where a single truck moves livestock between multiple farms in a single journey-and contacts that livestock have with their surrounding environment have been identified as drivers of disease dissemination. The objective of this study was to assess the role of these different pathways in the transmission of several swine pathogens and to evaluate the efficacy of surveillance strategies in identifying outbreaks. To achieve this, we built contact networks for these modes of disease transmission based on empirical data from the Swiss swine production sector. We developed a stochastic, susceptible-infectious-recovered (SIR) type, herd-based model to simulate the spread of multiple pathogens within farms and between farms along the networks. We parameterized the model for Porcine Reproductive and Respiratory Syndrome (PRRS) virus, African Swine Fever (ASF) virus, and Actinobacillus pleuropneumonia (APP): three pathogens with distinct clinical patterns, modes of transmission, and contact transmission rates. The model provides insight into the contribution of different contact types to disease dispersion. Our findings highlight that direct truck transport and local spread are the main routes of between-farm transmission. In addition, we analyzed the ability of surveillance measures to detect outbreaks from these distinct pathogens spreading along the contact networks. Farmer-based surveillance programs were the only measures that consistently identified outbreaks of APP and PRRS, and they were able to identify ASF outbreaks almost 8 weeks or more before active slaughterhouse- and network-based surveillance. Our model outcomes give evidence of the prominent transmission pathways and surveillance measures, which could help establish programs to prevent the spread of swine infectious diseases.

Fig 1. Map of Switzerland.

Indication of the pig holdings and the regions with APP or PRRS cases between 2014 and 2019.

Fig 2. Schematic of the SEIAR model showing movement rates between compartments.

Model state variables are shown in Table 3 and parameters in Table 4.

Fig 3. Diagrams for two scenarios of truck tours.

(a) Solid line indicates direct pig movement. (b) dashed line indicates truck temporal movement.

Fig 4. Example contact network for one day.

Fig 5. Median, 95^th percentile, and maximum cumulative infected number of farms for each disease without incorporating surveillance and a date of disease introduction in May, 2019.

(A) All 1,000 simulation runs were included. (B) Only simulation runs with large outbreaks, as defined by 10 or more cumulative infected farms.

Fig 6. Seasonal changes.

Median cumulative infected farms by date of pathogen introduction for simulations with large outbreaks.

Fig 7. Proportion of new cases transmitted via each transmission pathway for only simulations with large outbreaks and a disease introduction of May 2019.

(A) Baseline parameters including within farm spread (B) Baseline parameters excluding within-farm spread.

Fig 8. Earliest date at which an infected pig was detected.

Each circle represents the first date a positive case was detected for the simulation run. The boxplot shows the distribution of these dates, with the box representing the interquartile range (Q1 to Q3) and the line indicating the median. The only simulations included are those that lead to large outbreaks at the end of the 8 months simulation period in the scenario excluding control measures or surveillance. The disease introduction is May 2019.