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

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.

Variation in microparasite free-living survival and indirect transmission can modulate the intensity of emerging outbreaks.

Variation in free-living microparasite survival can have a meaningful impact on the ecological dynamics of established and emerging infectious diseases. Nevertheless, resolving the importance of indirect and environmental transmission in the ecology of epidemics remains a persistent challenge. It requires accurately measuring the free-living survival of pathogens across reservoirs of various kinds and quantifying the extent to which interaction between hosts and reservoirs generates new infections. These questions are especially salient for emerging pathogens, where sparse and noisy data can obfuscate the relative contribution of different infection routes. In this study, we develop a mechanistic, mathematical model that permits both direct (host-to-host) and indirect (environmental) transmission and then fit this model to empirical data from 17 countries affected by an emerging virus (SARS-CoV-2). From an ecological perspective, our model highlights the potential for environmental transmission to drive complex, nonlinear dynamics during infectious disease outbreaks. Summarizing, we propose that fitting alternative models with indirect transmission to real outbreak data from SARS-CoV-2 can be useful, as it highlights that indirect mechanisms may play an underappreciated role in the dynamics of infectious diseases, with implications for public health.