Pages that link to "Item:Q2232166"
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The following pages link to Continuous-time stochastic processes for the spread of COVID-19 disease simulated via a Monte Carlo approach and comparison with deterministic models (Q2232166):
Displaying 11 items.
- Detecting time-changes in \(PM_{10}\) during covid pandemic by means of an Ornstein Uhlenbeck type process (Q1980127) (← links)
- A stochastic epidemic model of COVID-19 disease (Q2132321) (← links)
- The stochastic \(\theta\)-SEIHRD model: adding randomness to the COVID-19 spread (Q2170821) (← links)
- (Q4991300) (← links)
- Stationary distribution and long-time behavior of COVID-19 model with stochastic effect (Q5057717) (← links)
- The role of mobility in the dynamics of the COVID-19 epidemic in Andalusia (Q6165602) (← links)
- A multi-strain model with asymptomatic transmission: application to COVID-19 in the US (Q6174200) (← links)
- Well‐posedness for a diffusion–reaction compartmental model simulating the spread of COVID‐19 (Q6178206) (← links)
- A Continuous-Time Markov Chain Model for the Spread of COVID-19 (Q6558991) (← links)
- Forest fire spreading: a nonlinear stochastic model continuous in space and time (Q6590517) (← links)
- Data-driven modeling of impact of differential efficacy of COVID-19 vaccines in two socio-economically contrasting cities: New York, USA and Bogotá, Colombia (Q6605942) (← links)
