Lumpy skin disease(LSD)is a transboundary disease affecting cattle and has a detrimental effect on the cattle industries in numerous countries in Africa,Europe and Asia.In 2021,LSD outbreaks have been reported in almo...Lumpy skin disease(LSD)is a transboundary disease affecting cattle and has a detrimental effect on the cattle industries in numerous countries in Africa,Europe and Asia.In 2021,LSD outbreaks have been reported in almost all of Thailand's provinces.Indeed,fitting LSD occurrences using mathematical models provide important knowledge in the realm of animal disease modeling.Thus,the objective of this study is to fit the pattern of daily new LSD cases and daily cumulative LSD cases in Thailand using mathematical models.The first-and second-order models in the forms of Lorentzian,Gaussian and Pearson-type VII models are used to fit daily new LSD cases whereas Richard's growth,Boltzmann sigmoidal and Power-law growth models are utilized to fit the curve of cumulative LSD cases.Based on the root-mean-squared error(RMSE)and Akaike information criterion(AIC),results showed that both first and second orders of Pearson-type VII models and Richard's growth model(RGM)were fit to the data better than other models used in the present study.The obtained models and their parameters can be utilized to describe the LSD outbreak in Thailand.For disease preparedness purposes,we can use the first order of the Pearson-type VII model to estimate the time of maximum infected cases occurring when the growth rate of infected cases starts to slow down.Furthermore,the period when the growth rate changes at a slower rate,known as the inflection time,obtained from RGM allows us to anticipate when the pandemic has peaked and the situation has stabilized.This is the first study that utilizes mathematical methods to fit the LSD epidemics in Thailand.This study offers decision-makers and authorities with valuable information for establishing an effective disease control strategy.展开更多
文摘Lumpy skin disease(LSD)is a transboundary disease affecting cattle and has a detrimental effect on the cattle industries in numerous countries in Africa,Europe and Asia.In 2021,LSD outbreaks have been reported in almost all of Thailand's provinces.Indeed,fitting LSD occurrences using mathematical models provide important knowledge in the realm of animal disease modeling.Thus,the objective of this study is to fit the pattern of daily new LSD cases and daily cumulative LSD cases in Thailand using mathematical models.The first-and second-order models in the forms of Lorentzian,Gaussian and Pearson-type VII models are used to fit daily new LSD cases whereas Richard's growth,Boltzmann sigmoidal and Power-law growth models are utilized to fit the curve of cumulative LSD cases.Based on the root-mean-squared error(RMSE)and Akaike information criterion(AIC),results showed that both first and second orders of Pearson-type VII models and Richard's growth model(RGM)were fit to the data better than other models used in the present study.The obtained models and their parameters can be utilized to describe the LSD outbreak in Thailand.For disease preparedness purposes,we can use the first order of the Pearson-type VII model to estimate the time of maximum infected cases occurring when the growth rate of infected cases starts to slow down.Furthermore,the period when the growth rate changes at a slower rate,known as the inflection time,obtained from RGM allows us to anticipate when the pandemic has peaked and the situation has stabilized.This is the first study that utilizes mathematical methods to fit the LSD epidemics in Thailand.This study offers decision-makers and authorities with valuable information for establishing an effective disease control strategy.
