Quantitative approach for the risk assessment of African swine fever and Classical swine fever introduction into the United States through legal imports of pigs and swine products.

The US livestock safety strongly depends on its capacity to prevent the introduction of Transboundary Animal Diseases (TADs). Therefore, accurate and updated information on the location and origin of those potential TADs risks is essential, so preventive measures as market restrictions can be put on place. The objective of the present study was to evaluate the current risk of African swine fever (ASF) and Classical swine fever (CSF) introduction into the US through the legal importations of live pigs and swine products using a quantitative approach that could be later applied to other risks. Four quantitative stochastic risk assessment models were developed to estimate the monthly probabilities of ASF and CSF release into the US, and the exposure of susceptible populations (domestic and feral swine) to these introductions at state level. The results suggest a low annual probability of either ASF or CSF introduction into the US, by any of the analyzed pathways (5.5*10-3). Being the probability of introduction through legal imports of live pigs (1.8*10-3 for ASF, and 2.5*10-3 for CSF) higher than the risk of legally imported swine products (8.90*10-4 for ASF, and 1.56*10-3 for CSF). This could be caused due to the low probability of exposure associated with this type of commodity (products). The risk of feral pigs accessing to swine products discarded in landfills was slightly higher than the potential exposure of domestic pigs through swill feeding. The identification of the months at highest risk, the origin of the higher risk imports, and the location of the US states most vulnerable to those introductions (Iowa, Minnesota and Wisconsin for live swine and California, Florida and Texas for swine products), is valuable information that would help to design prevention, risk-mitigation and early-detection strategies that would help to minimize the catastrophic consequences of potential ASF/CSF introductions into the US

Correction: Quantitative approach for the risk assessment of African swine fever and Classical swine fever introduction into the United States through legal imports of pigs and swine products.

[This corrects the article DOI: 10.1371/journal.pone.0182850.]

Quantitative approach for the risk assessment of African swine fever and Classical swine fever introduction into the United States through legal imports of pigs and swine products.

The US livestock safety strongly depends on its capacity to prevent the introduction of Transboundary Animal Diseases (TADs). Therefore, accurate and updated information on the location and origin of those potential TADs risks is essential, so preventive measures as market restrictions can be put on place. The objective of the present study was to evaluate the current risk of African swine fever (ASF) and Classical swine fever (CSF) introduction into the US through the legal importations of live pigs and swine products using a quantitative approach that could be later applied to other risks. Four quantitative stochastic risk assessment models were developed to estimate the monthly probabilities of ASF and CSF release into the US, and the exposure of susceptible populations (domestic and feral swine) to these introductions at state level. The results suggest a low annual probability of either ASF or CSF introduction into the US, by any of the analyzed pathways (5.5*10-3). Being the probability of introduction through legal imports of live pigs (1.8*10-3 for ASF, and 2.5*10-3 for CSF) higher than the risk of legally imported swine products (8.90*10-4 for ASF, and 1.56*10-3 for CSF). This could be caused due to the low probability of exposure associated with this type of commodity (products). The risk of feral pigs accessing to swine products discarded in landfills was slightly higher than the potential exposure of domestic pigs through swill feeding. The identification of the months at highest risk, the origin of the higher risk imports, and the location of the US states most vulnerable to those introductions (Iowa, Minnesota and Wisconsin for live swine and California, Florida and Texas for swine products), is valuable information that would help to design prevention, risk-mitigation and early-detection strategies that would help to minimize the catastrophic consequences of potential ASF/CSF introductions into the US

Pig and swine products imports to the US and disease status of the countries of origin.

<p>Pig and swine products imports to the US and disease status of the countries of origin.</p

Advanced sensitivity analysis for the models of ASF/CSF risk of introduction by pigs imports.

<p>The spider graph plots the percent change of the selected input parameters against the output results.</p

Risk of exposure to legal imports of swine products.

<p>The graduated color map represents the risk from the highest (darker) to the lowest (lighter) of US susceptible swine populations being exposed to the legally imported swine products.</p

Final risk of ASF/ CSF introduction by legal imports of live swine into the US.

<p>The graduated color map represents the final risk (release*exposure) from the highest (darker) to the lowest (lighter).</p

Risk maps of ASFv and CSFv release by legal imports of swine products.

<p>(A) Risk of ASFv release into the US. (B) Risk of CSFv release in to the US. The graduated color maps represent the risk from the highest (darker) to the lowest (lighter).</p

Final risk of ASF and CSF introduction by legal imports of swine products.

<p>The graduated color maps represent the final risk (release *exposure) from the highest (darker) to the lowest (lighter). (A) Risk of ASF introduction into the US by legal imports of swine products. (B) Risk of CSF introduction into the US by legal imports of swine products.</p

Advanced sensitivity analysis for the models of ASF/CSF risk of introduction by swine products imports.

<p>The spider graph plots the percent change of the selected input parameters against the output results.</p