Analysis of static and temporal trade animal network for evaluation of simulation of disease quality

• Ano de defesa: 2020
• Autor(a) principal: Cardenas, Nicolas Cespedes
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Analise de redes; Animal surveillance; Epidemiologia; Epidemiology; Modelos de espalhamento; Network analysis; Spread disease models; Vigilância animal
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/10/10134/tde-06052021-092751/

Social network analysis (SNA) is a powerful tool to describe the impact of the animal trade on the spread of pathogens, and to generate essential patterns that can be used to understand, prevent and mitigate possible outbreaks. This study aimed to describe and analyze animal movement networks between pig properties in the state of Santa Catarina (SC) and cattle in the state of Rio Grande do Sul (RS), to guide the surveillance of diseases transmitted by contact based on interactions between properties (farms and farms), using different models of disease spread with specific objectives for each state. For the state of SC the static and temporal network was described, the internal communities of commerce were calculated, and the effectiveness of control actions through analysis of contact chains. Based on this information, two indexes were proposed at the municipal level to classify them according to their participation in the network. For the state of RS, the static and temporal transport network of cattle and buffaloes and the types of connected components of the network were described. A Susceptible-Occult-Reactive-Infectious scattering model (SORI) was implemented to represent the dynamics of bovine tuberculosis (TB) in the population, in addition, control actions were tested using the proposed model. The results showed highly connected networks with well-defined temporal and spatial dynamics. The simulation models demonstrated that it is possible to reduce the size of epidemic outbreaks, strategically selecting properties, based on SNA metrics such as “degree” and “betweenness”. This information is important for targeting risk-based infectious disease surveillance systems.