RESUMO
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.
RESUMO
In this study, a robust control technique is investigated for the reference tracking of uncertain time-delayed systems in the existence of the actuator saturation. Due to emerging of some control complexities, as well as the input limitations, time-varying delay, uncertainty, and external disturbance, such a tracking goal would be realized through suitable design of the composite nonlinear feedback (CNF) controller. Thus, considering the mentioned limitations, a Lyapunov-based procedure is used to determine the control law. Then, the parameters of the CNF input are derived by using the solution of a linear matrix inequality (LMI) problem. The planned tracking idea is numerically implemented in two uncertain control systems. Some performance characteristics (i.e., the tracking error, boundedness, and transient responses) are compared with similar ones. Accordingly, the simulations illustrate the efficiency of the suggested control procedure over the existing CNF approaches.
RESUMO
Climate change is a crucial cause of health issues, as reported in many studies. Temperature is one of the important factors related to extreme weather. Chiang Mai, the center of the north of Thailand, is also affected by temperature changes that have led to many outpatient visits. Better information will help the health service to be well-prepared. This research applied typical meteorological data and solar radiation into the distributed lag nonlinear model and a quasi-Poisson regression model. The "hot effect" and "cold effect" on outpatient visits caused by respiratory diseases, dermatophytosis, and intestinal infectious diseases in a public Chiang Mai hospital between January 2015 and December 2019 were then investigated. Of the 185,202 cases, results showed that all of the diseases mentioned had more than 10% of relative risk (RR) in cold effects. However, the RR of dermatophytosis was found to be 114%, a very high risk. In the case of hot effects, the patients of the age 19-29 have relatively high RR over 20% for respiratory diseases and dermatophytosis. It was also observed that cold effects lasted longer than hot effects.