Effects of environmental variability on superspreading transmission events in stochastic epidemic models

Abstract

Superspreaders (individuals with a high propensity for disease spread) have played a pivotal role in recent emerging and re-emerging diseases. In disease outbreak studies, host heterogeneity based on demographic (e.g. age, sex, vaccination status) and environmental (e.g. climate, urban/rural residence, clinics) factors are critical for the spread of infectious diseases, such as Ebola and Middle East Respiratory Syndrome (MERS). Transmission rates can vary as demographic and environmental factors are altered naturally or due to modified behaviors in response to the implementation of public health strategies. In this work, we develop stochastic models to explore the effects of demographic and environmental variability on human-to-human disease transmission rates among superspreaders in the case of Ebola and MERS. We show that the addition of environmental variability results in reduced probability of outbreak occurrence, however the severity of outbreaks that do occur increases. These observations have implications for public health strategies that aim to control environmental variables.

A Hybrid Epidemic Model to Explore Stochasticity in COVID-19 Dynamics

“… 2005 ; Shakiba et al. 2021 ). The term “superspreader” refers to those who are capable of transmitting an infection to a disproportionately large number of individuals, whereas the majority of the population would only transmit to a few or none (Stein 2011 ).…”

“… 2018 ; Shakiba et al. 2021 ). A continuous-time Markov chain (CTMC) model with discrete random variables allowed us to identify the influence of demographic variability, or more specifically, the variability in transitions between states due to disease transmission, recovery, and disease-related deaths (as opposed to environmental variability) (Edholm et al.…”

“…We incorporated time-dependent environmental variability into the disease transmission, developing two alternative models: a time-nonhomogeneous stochastic process (NHP) model with discrete random variables (extension of the CTMC model), and a stochastic differential equation (SDE) model with continuous random variables (Shakiba et al. 2021 ). Through these previous works, we demonstrated the importance of incorporating stochasticity to more realistically capture the epidemiological dynamics.…”

“…From the NHP and SDE, we found that increased environmental variability results in more severe, but less frequent outbreaks (Shakiba et al. 2021 ).…”

“… 2000 ; Shakiba et al. 2021 ; Truscott and Gilligan 2003 ; Varughese and Fatti 2008 ), our hybrid model differs from some of the existing hybrid models with CTMCs and SDEs as these are time-homogeneous processes with constant parameters, and they are often restricted to the SIR framework (Rebuli et al. 2017 ; Safta et al.…”