Infectious diseases and social distancing under state-dependent probabilities

Area of Science:

• Epidemiology
• Mathematical Modeling
• Infectious Disease Dynamics

Background:

• Infectious diseases pose significant public health challenges.
• Stochastic shocks can introduce new disease strains and alter pathogen characteristics.
• Understanding disease spread dynamics is crucial for effective public health interventions.

Purpose of the Study:

• To analyze the impact of infectious diseases and social distancing within an extended SIS framework.
• To investigate the role of state-dependent stochastic shocks on disease prevalence and pathogen evolution.
• To evaluate how social distancing affects long-term epidemiological outcomes.

Main Methods:

• Utilized an extended Susceptible-Infectious-Susceptible (SIS) epidemiological model.
• Incorporated stochastic shocks with state-dependent probabilities.
• Analyzed the long-run epidemiological outcomes using invariant probability distributions.

Main Results:

• Social distancing reduces the variability of disease prevalence by decreasing the support of the steady-state distribution.
• Social distancing can shift the support of the distribution rightward, potentially increasing the number of infectives.
• Despite potential increases in infective numbers, social distancing concentrates the distribution's mass towards lower prevalence levels.

Conclusions:

• Social distancing remains an effective control measure for infectious diseases.
• The interplay between disease prevalence, stochastic shocks, and social distancing influences epidemiological outcomes.
• Mathematical modeling provides insights into the complex dynamics of disease control.