Michał Maćkowski, Piotr Brzoza
Accessible Tutoring Platform Using Audio-Tactile Graphics Adapted for Visually Impaired People
Sensors (Basel, Switzerland)
epidemic propagation networks represents important departure traditional massaction models however highdimensionality exact models poses challenge mathematical analysis parameter inference using meanfield models pairwise model pwm highdimensionality becomes tractable models used extensively model analysis limited work context statistical inference paper explore extent pwm susceptibleinfectedrecovered sir epidemic used infer disease networkrelated parameters data epidemics loosely categorised population level eg daily new cases individual level eg recovery times understand network inference influenced type data employed widelyused mle approach populationlevel data dynamical survival analysis dsa individuallevel data scenarios model mismatch data generated via simulations methods perform well despite strong dependence parameters contrast realworld data footandmouth h1n1 covid19 whereas dsa method appears fairly robust potential model mismatch produces parameter estimates epidemiologically plausible results mle method revealed several issues pertaining parameter unidentifiability lack robustness exact knowledge key quantities population size andor proportion reporting taken together however findings suggest networkbased meanfield models used formulate approximate likelihoods coupled efficient inference scheme make possible learn parameters disease dynamics also underlying network