{"id":50873,"date":"2024-12-11T14:00:22","date_gmt":"2024-12-11T14:00:22","guid":{"rendered":""},"modified":"2024-12-11T14:00:22","modified_gmt":"2024-12-11T14:00:22","slug":"%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-50873","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-50873\/","title":{"rendered":"\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 \u06cc\u06a9\u0633\u0627\u0646 \u0633\u0627\u0632\u06cc \u0645\u062f\u0644 \u0647\u0627\u06cc \u0628\u06cc\u0645\u0627\u0631\u06cc \u0647\u0627\u06cc \u0639\u0641\u0648\u0646\u06cc \u0627\u0646\u0633\u0627\u0646\u06cc \u0648 \u0622\u06af\u0627\u0647\u06cc \u062f\u0631 \u0632\u0645\u0627\u0646 \u0648\u0627\u0642\u0639\u06cc \u0627\u0632 \u0627\u062b\u0631\u0628\u062e\u0634\u06cc \u0645\u062f\u0627\u062e\u0644\u0647 \u062f\u0631 \u0633\u0637\u062d \u062c\u0645\u0639\u06cc\u062a \u0648 \u0632\u06cc\u0631\u062c\u0645\u0639\u06cc\u062a"},"content":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n\n
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\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\n ProfileRachel L. Seibel, ProfileMichael J. Tildesley, ProfileEdward M. Hill<\/td>\n<\/tr>\n
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\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n

During infectious disease outbreaks, humans often base their decision to adhere to an intervention strategy on their personal opinion towards the intervention, perceived risk of infection and intervention effectiveness. However, due to data limitations and inference challenges, infectious disease models usually omit variables that may impact an individual\u2019s decision to get vaccinated and their awareness of the intervention\u2019s effectiveness of disease control within their social contacts as well as the overall population. We constructed a compartmental, deterministic Susceptible-Exposed-Infectious-Recovered (SEIR) disease model that includes a behavioural function with parameters influencing intervention uptake. The behavioural function accounted for an initial subpopulation opinion towards an intervention, their outbreak information awareness sensitivity and the extent they are swayed by the real-time intervention effectiveness information (at a subpopulation- and population-level) \u2014 the selected real-time response construct mimicked a situation of reductions in vaccine uptake due to a negative behavioural response arising from imperfect vaccine protection resulting in breakthrough infections and deaths in vaccinated individuals. Applying the model to vaccination uptake and three human pathogens – pandemic influenza, SARS-CoV-2 and Ebola virus – we explored through model simulation how these intervention adherence decision parameters and behavioural heterogeneity in the population impacted epidemiological outcomes. From our model simulations we found that differences in preference towards outbreak information were pathogen-specific. In other words, in some pathogen systems, different types of outbreak information awareness at different outbreak stages may be more informative to an information-sensitive population and lead to less severe epidemic outcomes. In both behaviourally-homogeneous and behaviourally-heterogeneous populations, pandemic influenza showed patterns distinct from SARS-CoV-2 and Ebola for cumulative epidemiological metrics of interest. Furthermore, there was notable sensitivity in outbreak size under different assumptions regarding the population split in behavioural traits. Outbreak information preference was sensitive to vaccine efficacy, which demonstrates the importance of considering human behaviour during outbreaks in the context of the perceived effectiveness of the intervention. Incorporating behavioural functions that modify infection control intervention adherence into epidemiological models can aid our understanding of adherence dynamics during outbreaks. Ultimately, by parameterising models with what we know about human behaviour towards vaccination (and other infection control interventions) adherence, such models can help assist decision makers during outbreaks. Such progress will be particularly important for emerging infectious diseases when there is initially little information on the disease dynamics and intervention effectiveness.<\/p>\n

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