Predicting the time to detect moderately virulent African swine fever virus in finisher swine herds using a stochastic disease transmission model

Sasidhar Malladi; Amos Ssematimba; Peter J. Bonney; Kaitlyn M. St. Charles; Timothy Boyer; Timothy Goldsmith; Emily Walz; Carol J. Cardona; Marie R. Culhane

doi:10.1186/s12917-022-03188-6

Predicting the time to detect moderately virulent African swine fever virus in finisher swine herds using a stochastic disease transmission model

Sasidhar Malladi, Amos Ssematimba, Peter J. Bonney, Kaitlyn M. St. Charles, Timothy Boyer, Timothy Goldsmith, Emily Walz, Carol J. Cardona, Marie R. Culhane

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

Background: African swine fever (ASF) is a highly contagious and devastating pig disease that has caused extensive global economic losses. Understanding ASF virus (ASFV) transmission dynamics within a herd is necessary in order to prepare for and respond to an outbreak in the United States. Although the transmission parameters for the highly virulent ASF strains have been estimated in several articles, there are relatively few studies focused on moderately virulent strains. Using an approximate Bayesian computation algorithm in conjunction with Monte Carlo simulation, we have estimated the adequate contact rate for moderately virulent ASFV strains and determined the statistical distributions for the durations of mild and severe clinical signs using individual, pig-level data. A discrete individual based disease transmission model was then used to estimate the time to detect ASF infection based on increased mild clinical signs, severe clinical signs, or daily mortality. Results: Our results indicate that it may take two weeks or longer to detect ASF in a finisher swine herd via mild clinical signs or increased mortality beyond levels expected in routine production. A key factor contributing to the extended time to detect ASF in a herd is the fairly long latently infected period for an individual pig (mean 4.5, 95% P.I., 2.4 - 7.2 days). Conclusion: These transmission model parameter estimates and estimated time to detection via clinical signs provide valuable information that can be used not only to support emergency preparedness but also to inform other simulation models of evaluating regional disease spread.

Original language	English (US)
Article number	84
Journal	BMC Veterinary Research
Volume	18
Issue number	1
DOIs	https://doi.org/10.1186/s12917-022-03188-6
State	Published - Dec 2022

Bibliographical note

Funding Information:
The authors thank Willie Loeffen and Wageningen Bioveterinary Research, Wageningen University, the Netherlands for kindly providing the experimental transmission data used in this study. The authors acknowledge the Minnesota Supercomputing Institute (MSI) and the University of Minnesota for providing resources that contributed to the research results reported within this paper.

Funding Information:
PJB, SM, AS, MRC, and CJC acknowledge funding of their work by a cooperative agreement between the Center for Epidemiology and Animal Health (CEAH) of the United States Department of Agriculture (USDA), Animal and Plant Health Inspection Service (APHIS) Veterinary Services (VS) and the University of Minnesota (UMN) USDA Award #: AP20VSCEAH00C054| (Quantitative Analysis to Manage Animal Transboundary /Emerging and Domestic Disease Emergencies and Support Risk-based Decision-Making.)

Publisher Copyright:
© 2022, The Author(s).

Keywords

African Swine Fever
Clinical signs detection
Modeling
Moderately virulent strain
Mortality triggers
Surveillance
Swine Diseases/diagnosis
African Swine Fever Virus
African Swine Fever/diagnosis
Animals
Swine
Bayes Theorem
Disease Outbreaks/veterinary

PubMed: MeSH publication types

Journal Article

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1186/s12917-022-03188-6

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Malladi, S., Ssematimba, A., Bonney, P. J., St. Charles, K. M., Boyer, T., Goldsmith, T., Walz, E., Cardona, C. J., & Culhane, M. R. (2022). Predicting the time to detect moderately virulent African swine fever virus in finisher swine herds using a stochastic disease transmission model. BMC Veterinary Research, 18(1), Article 84. https://doi.org/10.1186/s12917-022-03188-6

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title = "Predicting the time to detect moderately virulent African swine fever virus in finisher swine herds using a stochastic disease transmission model",

abstract = "Background: African swine fever (ASF) is a highly contagious and devastating pig disease that has caused extensive global economic losses. Understanding ASF virus (ASFV) transmission dynamics within a herd is necessary in order to prepare for and respond to an outbreak in the United States. Although the transmission parameters for the highly virulent ASF strains have been estimated in several articles, there are relatively few studies focused on moderately virulent strains. Using an approximate Bayesian computation algorithm in conjunction with Monte Carlo simulation, we have estimated the adequate contact rate for moderately virulent ASFV strains and determined the statistical distributions for the durations of mild and severe clinical signs using individual, pig-level data. A discrete individual based disease transmission model was then used to estimate the time to detect ASF infection based on increased mild clinical signs, severe clinical signs, or daily mortality. Results: Our results indicate that it may take two weeks or longer to detect ASF in a finisher swine herd via mild clinical signs or increased mortality beyond levels expected in routine production. A key factor contributing to the extended time to detect ASF in a herd is the fairly long latently infected period for an individual pig (mean 4.5, 95% P.I., 2.4 - 7.2 days). Conclusion: These transmission model parameter estimates and estimated time to detection via clinical signs provide valuable information that can be used not only to support emergency preparedness but also to inform other simulation models of evaluating regional disease spread.",

keywords = "African Swine Fever, Clinical signs detection, Modeling, Moderately virulent strain, Mortality triggers, Surveillance, Swine Diseases/diagnosis, African Swine Fever Virus, African Swine Fever/diagnosis, Animals, Swine, Bayes Theorem, Disease Outbreaks/veterinary",

author = "Sasidhar Malladi and Amos Ssematimba and Bonney, {Peter J.} and {St. Charles}, {Kaitlyn M.} and Timothy Boyer and Timothy Goldsmith and Emily Walz and Cardona, {Carol J.} and Culhane, {Marie R.}",

note = "Funding Information: The authors thank Willie Loeffen and Wageningen Bioveterinary Research, Wageningen University, the Netherlands for kindly providing the experimental transmission data used in this study. The authors acknowledge the Minnesota Supercomputing Institute (MSI) and the University of Minnesota for providing resources that contributed to the research results reported within this paper. Funding Information: PJB, SM, AS, MRC, and CJC acknowledge funding of their work by a cooperative agreement between the Center for Epidemiology and Animal Health (CEAH) of the United States Department of Agriculture (USDA), Animal and Plant Health Inspection Service (APHIS) Veterinary Services (VS) and the University of Minnesota (UMN) USDA Award #: AP20VSCEAH00C054| (Quantitative Analysis to Manage Animal Transboundary /Emerging and Domestic Disease Emergencies and Support Risk-based Decision-Making.) Publisher Copyright: {\textcopyright} 2022, The Author(s).",

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doi = "10.1186/s12917-022-03188-6",

language = "English (US)",

volume = "18",

journal = "BMC Veterinary Research",

issn = "1746-6148",

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AU - Malladi, Sasidhar

AU - Ssematimba, Amos

AU - Bonney, Peter J.

AU - St. Charles, Kaitlyn M.

AU - Boyer, Timothy

AU - Goldsmith, Timothy

AU - Walz, Emily

AU - Cardona, Carol J.

AU - Culhane, Marie R.

N1 - Funding Information: The authors thank Willie Loeffen and Wageningen Bioveterinary Research, Wageningen University, the Netherlands for kindly providing the experimental transmission data used in this study. The authors acknowledge the Minnesota Supercomputing Institute (MSI) and the University of Minnesota for providing resources that contributed to the research results reported within this paper. Funding Information: PJB, SM, AS, MRC, and CJC acknowledge funding of their work by a cooperative agreement between the Center for Epidemiology and Animal Health (CEAH) of the United States Department of Agriculture (USDA), Animal and Plant Health Inspection Service (APHIS) Veterinary Services (VS) and the University of Minnesota (UMN) USDA Award #: AP20VSCEAH00C054| (Quantitative Analysis to Manage Animal Transboundary /Emerging and Domestic Disease Emergencies and Support Risk-based Decision-Making.) Publisher Copyright: © 2022, The Author(s).

PY - 2022/12

Y1 - 2022/12

N2 - Background: African swine fever (ASF) is a highly contagious and devastating pig disease that has caused extensive global economic losses. Understanding ASF virus (ASFV) transmission dynamics within a herd is necessary in order to prepare for and respond to an outbreak in the United States. Although the transmission parameters for the highly virulent ASF strains have been estimated in several articles, there are relatively few studies focused on moderately virulent strains. Using an approximate Bayesian computation algorithm in conjunction with Monte Carlo simulation, we have estimated the adequate contact rate for moderately virulent ASFV strains and determined the statistical distributions for the durations of mild and severe clinical signs using individual, pig-level data. A discrete individual based disease transmission model was then used to estimate the time to detect ASF infection based on increased mild clinical signs, severe clinical signs, or daily mortality. Results: Our results indicate that it may take two weeks or longer to detect ASF in a finisher swine herd via mild clinical signs or increased mortality beyond levels expected in routine production. A key factor contributing to the extended time to detect ASF in a herd is the fairly long latently infected period for an individual pig (mean 4.5, 95% P.I., 2.4 - 7.2 days). Conclusion: These transmission model parameter estimates and estimated time to detection via clinical signs provide valuable information that can be used not only to support emergency preparedness but also to inform other simulation models of evaluating regional disease spread.

AB - Background: African swine fever (ASF) is a highly contagious and devastating pig disease that has caused extensive global economic losses. Understanding ASF virus (ASFV) transmission dynamics within a herd is necessary in order to prepare for and respond to an outbreak in the United States. Although the transmission parameters for the highly virulent ASF strains have been estimated in several articles, there are relatively few studies focused on moderately virulent strains. Using an approximate Bayesian computation algorithm in conjunction with Monte Carlo simulation, we have estimated the adequate contact rate for moderately virulent ASFV strains and determined the statistical distributions for the durations of mild and severe clinical signs using individual, pig-level data. A discrete individual based disease transmission model was then used to estimate the time to detect ASF infection based on increased mild clinical signs, severe clinical signs, or daily mortality. Results: Our results indicate that it may take two weeks or longer to detect ASF in a finisher swine herd via mild clinical signs or increased mortality beyond levels expected in routine production. A key factor contributing to the extended time to detect ASF in a herd is the fairly long latently infected period for an individual pig (mean 4.5, 95% P.I., 2.4 - 7.2 days). Conclusion: These transmission model parameter estimates and estimated time to detection via clinical signs provide valuable information that can be used not only to support emergency preparedness but also to inform other simulation models of evaluating regional disease spread.

KW - African Swine Fever

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KW - Moderately virulent strain

KW - Mortality triggers

KW - Surveillance

KW - Swine Diseases/diagnosis

KW - African Swine Fever Virus

KW - African Swine Fever/diagnosis

KW - Animals

KW - Swine

KW - Bayes Theorem

KW - Disease Outbreaks/veterinary

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Predicting the time to detect moderately virulent African swine fever virus in finisher swine herds using a stochastic disease transmission model

Abstract

Bibliographical note

Keywords

PubMed: MeSH publication types

UN SDGs

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