Comparison of modelling methods for defining the optimum control effort for peste des petits ruminants (PPR)
Peste des petits ruminants (PPR), a major disease of small ruminants, is spreading across Africa and Asia. The development of effective decision-support tools for PPR control requires an improved understanding of the disease's epidemiology. Models are needed to define/test disease control methods and to gain a better understanding of PPR virus ecology. Several ruminant species appear susceptible to the disease in varying degrees, including sheep and goats which express clinical symptoms. Other domestic and wild animal species are known to generate immunological responses to infection but their role in PPR epidemiology remains unknown. Data from serological surveys in goats, sheep, cattle and buffaloes were used to calculate the forces of infection by means of catalytic models combined to @risk® probabilistic functions. Contact parameters were computed from “Who Acquires Infection From Whom” (WAIFW) matrices using probability distributions applied to each input parameter. R0 distribution was computed after solving the Next-Generation-Matrix and fit a BetaGeneral distribution (•1=3.3; •2=8.7; min=1.4; max=23.4). R0 mean and median were respectively equal to 7.4 and 7.1. The resulting estimates of R0 defined the control effort needed to eliminate the infection from a homogeneous host population. A second step based on a deterministic compartmental model, stratified by metapopulation and stochastic simulation for the contact and transition parameters consequently was implemented to define the levels of vaccination required in small-ruminants. Another approach based on the type-reproduction number T also was used to define the critical control effort for heterogeneous populations and was compared with the previous one
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dig-cirad-fr-6000482022-01-24T07:56:22Z http://agritrop.cirad.fr/600048/ http://agritrop.cirad.fr/600048/ Comparison of modelling methods for defining the optimum control effort for peste des petits ruminants (PPR). Roger François, Martinez M., Pfeiffer Dirk, Libeau Geneviève, Diallo Adama, Heesterbeek H.. 2006. In : Proceedings of the 11th Symposium of the International Society of Veterinary Epidemiology and Economics, Cairns, Australia. ISVEE. Cairns : ISVEE, Résumé, 1 p. (ISVEE Proceedings, 1177-360X, 11) International Symposium on Veterinary Epidemiology and Economics. 11, Cairns, Australie, 6 Août 2006/11 Août 2006.http://www.sciquest.org.nz/node/64030 <http://www.sciquest.org.nz/node/64030> Researchers Comparison of modelling methods for defining the optimum control effort for peste des petits ruminants (PPR) Roger, François Martinez, M. Pfeiffer, Dirk Libeau, Geneviève Diallo, Adama Heesterbeek, H. eng 2006 ISVEE Proceedings of the 11th Symposium of the International Society of Veterinary Epidemiology and Economics, Cairns, Australia Peste des petits ruminants (PPR), a major disease of small ruminants, is spreading across Africa and Asia. The development of effective decision-support tools for PPR control requires an improved understanding of the disease's epidemiology. Models are needed to define/test disease control methods and to gain a better understanding of PPR virus ecology. Several ruminant species appear susceptible to the disease in varying degrees, including sheep and goats which express clinical symptoms. Other domestic and wild animal species are known to generate immunological responses to infection but their role in PPR epidemiology remains unknown. Data from serological surveys in goats, sheep, cattle and buffaloes were used to calculate the forces of infection by means of catalytic models combined to @risk® probabilistic functions. Contact parameters were computed from “Who Acquires Infection From Whom” (WAIFW) matrices using probability distributions applied to each input parameter. R0 distribution was computed after solving the Next-Generation-Matrix and fit a BetaGeneral distribution (•1=3.3; •2=8.7; min=1.4; max=23.4). R0 mean and median were respectively equal to 7.4 and 7.1. The resulting estimates of R0 defined the control effort needed to eliminate the infection from a homogeneous host population. A second step based on a deterministic compartmental model, stratified by metapopulation and stochastic simulation for the contact and transition parameters consequently was implemented to define the levels of vaccination required in small-ruminants. Another approach based on the type-reproduction number T also was used to define the critical control effort for heterogeneous populations and was compared with the previous one conference_item info:eu-repo/semantics/conferenceObject Conference info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/600048/1/ID600048.pdf text Cirad license info:eu-repo/semantics/openAccess https://agritrop.cirad.fr/mention_legale.html http://www.sciquest.org.nz/node/64030 info:eu-repo/semantics/altIdentifier/purl/http://www.sciquest.org.nz/node/64030 |
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Peste des petits ruminants (PPR), a major disease of small ruminants, is spreading across Africa and Asia. The development of effective decision-support tools for PPR control requires an improved understanding of the disease's epidemiology. Models are needed to define/test disease control methods and to gain a better understanding of PPR virus ecology. Several ruminant species appear susceptible to the disease in varying degrees, including sheep and goats which express clinical symptoms. Other domestic and wild animal species are known to generate immunological responses to infection but their role in PPR epidemiology remains unknown. Data from serological surveys in goats, sheep, cattle and buffaloes were used to calculate the forces of infection by means of catalytic models combined to @risk® probabilistic functions. Contact parameters were computed from “Who Acquires Infection From Whom” (WAIFW) matrices using probability distributions applied to each input parameter. R0 distribution was computed after solving the Next-Generation-Matrix and fit a BetaGeneral distribution (•1=3.3; •2=8.7; min=1.4; max=23.4). R0 mean and median were respectively equal to 7.4 and 7.1. The resulting estimates of R0 defined the control effort needed to eliminate the infection from a homogeneous host population. A second step based on a deterministic compartmental model, stratified by metapopulation and stochastic simulation for the contact and transition parameters consequently was implemented to define the levels of vaccination required in small-ruminants. Another approach based on the type-reproduction number T also was used to define the critical control effort for heterogeneous populations and was compared with the previous one |
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conference_item |
author |
Roger, François Martinez, M. Pfeiffer, Dirk Libeau, Geneviève Diallo, Adama Heesterbeek, H. |
spellingShingle |
Roger, François Martinez, M. Pfeiffer, Dirk Libeau, Geneviève Diallo, Adama Heesterbeek, H. Comparison of modelling methods for defining the optimum control effort for peste des petits ruminants (PPR) |
author_facet |
Roger, François Martinez, M. Pfeiffer, Dirk Libeau, Geneviève Diallo, Adama Heesterbeek, H. |
author_sort |
Roger, François |
title |
Comparison of modelling methods for defining the optimum control effort for peste des petits ruminants (PPR) |
title_short |
Comparison of modelling methods for defining the optimum control effort for peste des petits ruminants (PPR) |
title_full |
Comparison of modelling methods for defining the optimum control effort for peste des petits ruminants (PPR) |
title_fullStr |
Comparison of modelling methods for defining the optimum control effort for peste des petits ruminants (PPR) |
title_full_unstemmed |
Comparison of modelling methods for defining the optimum control effort for peste des petits ruminants (PPR) |
title_sort |
comparison of modelling methods for defining the optimum control effort for peste des petits ruminants (ppr) |
publisher |
ISVEE |
url |
http://agritrop.cirad.fr/600048/ http://agritrop.cirad.fr/600048/1/ID600048.pdf |
work_keys_str_mv |
AT rogerfrancois comparisonofmodellingmethodsfordefiningtheoptimumcontroleffortforpestedespetitsruminantsppr AT martinezm comparisonofmodellingmethodsfordefiningtheoptimumcontroleffortforpestedespetitsruminantsppr AT pfeifferdirk comparisonofmodellingmethodsfordefiningtheoptimumcontroleffortforpestedespetitsruminantsppr AT libeaugenevieve comparisonofmodellingmethodsfordefiningtheoptimumcontroleffortforpestedespetitsruminantsppr AT dialloadama comparisonofmodellingmethodsfordefiningtheoptimumcontroleffortforpestedespetitsruminantsppr AT heesterbeekh comparisonofmodellingmethodsfordefiningtheoptimumcontroleffortforpestedespetitsruminantsppr |
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