Hepatitis C virus modelled as an indirectly transmitted infection highlights the centrality of injection drug equipment in disease dynamics

Hepatitis C Virus Dynamic Transmission Models Among People Who Inject Drugs

Background

Transmission dynamic model is a concrete structure to describe and investigate the complex system of host-pathogen interactions. Hepatitis C virus (HCV) is a blood-borne virus that is transmitted from infectious to susceptible individuals when they come into contact with HCV-contaminated equipment. Injecting drug use is the most known transmission route, and about 80% of new HCV cases have been confirmed as having acquired HCV infection via drug injection.

Objective

The main objective of this review paper was to review the importance of HCV dynamic transmission model, that enables the readers to understand the mechanism how HCV is transmissible from infectious to susceptible hosts and the effective controlling strategies.

Methods

PubMed Central, Google Scholar, and Web of Science electronic databases have been used to search data by using key terms like "HCV transmission model among people who inject drug (PWID)", HCV potential herd immunity", and "basic reproductive number for HCV transmission in PWID." Data from research findings other than English version have been excluded from being used, and the most recently published data have been considered to be included.

Conclusion

HCV belongs to the Hepacivirus genus within the Flaviviridae family. HCV infection is acquired when the susceptible individuals in populations come into contact with medical equipment such as shared syringes and needles, or swabs contaminated with infected blood. Construction of HCV transmission dynamic model is very significant in order to predict the duration and magnitude of its epidemic and to evaluate the potential impact of intervention. Comprehensive harm reduction and care/support service strategies are the best approach for intervention regarding HCV infection transmission among PWID.

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.

The Epidemiological Signature of Pathogen Populations That Vary in the Relationship between Free-Living Parasite Survival and Virulence

The relationship between parasite virulence and transmission is a pillar of evolutionary theory that has implications for public health. Part of this canon involves the idea that virulence and free-living survival (a key component of transmission) may have different relationships in different host-parasite systems. Most examinations of the evolution of virulence-transmission relationships-Theoretical or empirical in nature-Tend to focus on the evolution of virulence, with transmission being a secondary consideration. Even within transmission studies, the focus on free-living survival is a smaller subset, though recent studies have examined its importance in the ecology of infectious diseases. Few studies have examined the epidemic-scale consequences of variation in survival across different virulence-survival relationships. In this study, we utilize a mathematical model motivated by aspects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) natural history to investigate how evolutionary changes in survival may influence several aspects of disease dynamics at the epidemiological scale. Across virulence-survival relationships (where these traits are either positively or negatively correlated), we found that small changes (5% above and below the nominal value) in survival can have a meaningful effect on certain outbreak features, including R0, and on the size of the infectious peak in the population. These results highlight the importance of properly understanding the mechanistic relationship between virulence and parasite survival, as the evolution of increased survival across different relationships with virulence may have considerably different epidemiological signatures.

Variation in SARS-CoV-2 free-living survival and environmental 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 environmental transmission in the ecology of epidemics remains a persistent challenge, requires accurate 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, non-linear dynamics during infectious disease outbreaks. Summarizing, we propose that fitting such models with environmental transmission to real outbreak data from SARS-CoV-2 transmission highlights that variation in environmental transmission is an underappreciated aspect of the ecology of infectious disease, and an incomplete understanding of its role has consequences for public health interventions.