ABSTRACT: Molecular epidemiology (ME) is a branch of epidemiology developed by merging molecular biology into epidemiological studies. In this paper, the authors try to discuss the ways that molecular epidemiology studies identify infectious diseases' causation and pathogenesis, and unravel infectious agents' sources, reservoirs, circulation pattern, transmission pattern, transmission probability, and transmission order. They bring real-world examples of research works in each area to make each study design more understandable. They also address some research areas and study design aspects that need further attention in future. They close with some thoughts about future directions in this field and emphasize on the need for training competent molecular epidemiology specialists that are capable of dealing with rapid advances in the field.
Project description:BACKGROUND: Computational biology is often associated with genetic or genomic studies only. However, thanks to the increase of computational resources, computational models are appreciated as useful tools in many other scientific fields. Such modeling systems are particularly relevant for the study of complex systems, like the epidemiology of emerging infectious diseases. So far, mathematical models remain the main tool for the epidemiological and ecological analysis of infectious diseases, with SIR models could be seen as an implicit standard in epidemiology. Unfortunately, these models are based on differential equations and, therefore, can become very rapidly unmanageable due to the too many parameters which need to be taken into consideration. For instance, in the case of zoonotic and vector-borne diseases in wildlife many different potential host species could be involved in the life-cycle of disease transmission, and SIR models might not be the most suitable tool to truly capture the overall disease circulation within that environment. This limitation underlines the necessity to develop a standard spatial model that can cope with the transmission of disease in realistic ecosystems. RESULTS: Computational biology may prove to be flexible enough to take into account the natural complexity observed in both natural and man-made ecosystems. In this paper, we propose a new computational model to study the transmission of infectious diseases in a spatially explicit context. We developed a multi-agent system model for vector-borne disease transmission in a realistic spatial environment. CONCLUSION: Here we describe in detail the general behavior of this model that we hope will become a standard reference for the study of vector-borne disease transmission in wildlife. To conclude, we show how this simple model could be easily adapted and modified to be used as a common framework for further research developments in this field.
Project description:Transmission events are the fundamental building blocks of the dynamics of any infectious disease. Much about the epidemiology of a disease can be learned when these individual transmission events are known or can be estimated. Such estimations are difficult and generally feasible only when detailed epidemiological data are available. The genealogy estimated from genetic sequences of sampled pathogens is another rich source of information on transmission history. Optimal inference of transmission events calls for the combination of genetic data and epidemiological data into one joint analysis. A key difficulty is that the transmission tree, which describes the transmission events between infected hosts, differs from the phylogenetic tree, which describes the ancestral relationships between pathogens sampled from these hosts. The trees differ both in timing of the internal nodes and in topology. These differences become more pronounced when a higher fraction of infected hosts is sampled. We show how the phylogenetic tree of sampled pathogens is related to the transmission tree of an outbreak of an infectious disease, by the within-host dynamics of pathogens. We provide a statistical framework to infer key epidemiological and mutational parameters by simultaneously estimating the phylogenetic tree and the transmission tree. We test the approach using simulations and illustrate its use on an outbreak of foot-and-mouth disease. The approach unifies existing methods in the emerging field of phylodynamics with transmission tree reconstruction methods that are used in infectious disease epidemiology.
Project description:Leprosy is a debilitating, infectious disease caused by Mycobacterium leprae. Despite the availability of multidrug therapy, transmission is unremitting. Thus, early identification of M. leprae infection is essential to reduce transmission. The immune response to M. leprae is determined by host genetics, resulting in paucibacillary (PB) and multibacillary (MB) leprosy associated with dominant cellular or humoral immunity, respectively. This spectral pathology of leprosy compels detection of immunity to M. leprae to be based on multiple, diverse biomarkers. In this study we have applied quantitative user friendly lateral flow assays (LFAs) for four immune markers (anti-PGL-I antibodies, IL-10, CCL4 and IP-10) for whole blood samples from a longitudinal BCG vaccination field-trial in Bangladesh. Different biomarker profiles, in contrast to single markers, distinguished M. leprae infected from non-infected test groups, patients from household contacts (HHC) and endemic controls (EC), or MB from PB patients. The test protocol presented in this study merging detection of innate, adaptive cellular as well as humoral immunity, thus provides a convenient tool to measure specific biomarker profiles for M. leprae infection and leprosy utilizing a field-friendly technology.
Project description:<h4>Introduction</h4>Wild birds and especially migratory species can become long-distance vectors for a wide range of microorganisms. The objective of the current paper is to summarize available literature on pathogens causing human disease that have been associated with wild bird species.<h4>Methods</h4>A systematic literature search was performed to identify specific pathogens known to be associated with wild and migratory birds. The evidence for direct transmission of an avian borne pathogen to a human was assessed. Transmission to humans was classified as direct if there is published evidence for such transmission from the avian species to a person or indirect if the transmission requires a vector other than the avian species.<h4>Results</h4>Several wild and migratory birds serve as reservoirs and/or mechanical vectors (simply carrying a pathogen or dispersing infected arthropod vectors) for numerous infectious agents. An association with transmission from birds to humans was identified for 10 pathogens. Wild birds including migratory species may play a significant role in the epidemiology of influenza A virus, arboviruses such as West Nile virus and enteric bacterial pathogens. Nevertheless only one case of direct transmission from wild birds to humans was found.<h4>Conclusion</h4>The available evidence suggests wild birds play a limited role in human infectious diseases. Direct transmission of an infectious agent from wild birds to humans is rarely identified. Potential factors and mechanisms involved in the transmission of infectious agents from birds to humans need further elucidation.
Project description:In this introduction to the Special Issue on methods for modelling of infectious disease epidemiology we provide a commentary and overview of the field. We suggest that the field has been through three revolutions that have focussed on specific methodological developments; disease dynamics and heterogeneity, advanced computing and inference, and complexity and application to the real-world. Infectious disease dynamics and heterogeneity dominated until the 1980s where the use of analytical models illustrated fundamental concepts such as herd immunity. The second revolution embraced the integration of data with models and the increased use of computing. From the turn of the century an emergence of novel datasets enabled improved modelling of real-world complexity. The emergence of more complex data that reflect the real-world heterogeneities in transmission resulted in the development of improved inference methods such as particle filtering. Each of these three revolutions have always kept the understanding of infectious disease spread as its motivation but have been developed through the use of new techniques, tools and the availability of data. We conclude by providing a commentary on what the next revoluition in infectious disease modelling may be.
Project description:Compartmental model diagrams have been used for nearly a century to depict causal relationships in infectious disease epidemiology. Causal directed acyclic graphs (DAGs) have been used more broadly in epidemiology since the 1990s to guide analyses of a variety of public health problems. Using an example from chronic disease epidemiology, the effect of type 2 diabetes on dementia incidence, we illustrate how compartmental model diagrams can represent the same concepts as causal DAGs, including causation, mediation, confounding, and collider bias. We show how to use compartmental model diagrams to explicitly depict interaction and feedback cycles. While DAGs imply a set of conditional independencies, they do not define conditional distributions parametrically. Compartmental model diagrams parametrically (or semiparametrically) describe state changes based on known biological processes or mechanisms. Compartmental model diagrams are part of a long-term tradition of causal thinking in epidemiology and can parametrically express the same concepts as DAGs, as well as explicitly depict feedback cycles and interactions. As causal inference efforts in epidemiology increasingly draw on simulations and quantitative sensitivity analyses, compartmental model diagrams may be of use to a wider audience. Recognizing simple links between these two common approaches to representing causal processes may facilitate communication between researchers from different traditions.
Project description:Simple deterministic models are still at the core of theoretical epidemiology despite the increasing evidence for the importance of contact networks underlying transmission at the individual level. These mean-field or 'compartmental' models based on homogeneous mixing have made, and continue to make, important contributions to the epidemiology and the ecology of infectious diseases but fail to reproduce many of the features observed for disease spread in contact networks. In this work, we show that it is possible to incorporate the important effects of network structure on disease spread with a mean-field model derived from individual level considerations. We propose that the fundamental number known as the basic reproductive number of the disease, R0, which is typically derived as a threshold quantity, be used instead as a central parameter to construct the model from. We show that reliable estimates of individual level parameters can replace a detailed knowledge of network structure, which in general may be difficult to obtain. We illustrate the proposed model with small world networks and the classical example of susceptible-infected-recovered (SIR) epidemics.
Project description:For infectious diseases, a genetic cluster is a group of closely related infections that is usually interpreted as representing a recent outbreak of transmission. Genetic clustering methods are becoming increasingly popular for molecular epidemiology, especially in the context of HIV where there is now considerable interest in applying these methods to prioritize groups for public health resources such as pre-exposure prophylaxis. To date, genetic clustering has generally been performed with ad hoc algorithms, only some of which have since been encoded and distributed as free software. These algorithms have seldom been validated on simulated data where clusters are known, and their interpretation and similarities are not transparent to users outside of the field. Here, I provide a brief overview on the development and inter-relationships of genetic clustering methods, and an evaluation of six methods on data simulated under an epidemic model in a risk-structured population. The simulation analysis demonstrates that the majority of clustering methods are systematically biased to detect variation in sampling rates among subpopulations, not variation in transmission rates. I discuss these results in the context of previous work and the implications for public health applications of genetic clustering.
Project description:In the past decade, crinviruses have gained interest due to their rapid widespread and destructive nature for cucurbit cultivation. Several members of the genus Crinivirus are considered emerging viruses. Currently, four criniviruses: Beet pseudo-yellows virus, Cucurbit chlorotic yellows virus, Cucurbit yellow stunting disorder virus and Lettuce infectious yellows virus have been reported to infect field- or greenhouse- grown cucurbits. Apart from their cucurbit hosts, criniviruses infect other cash crops and weeds. Criniviruses are exclusively transmitted by whiteflies. The virion titer and the vector genus or species complex are predominant factors affecting virus transmission. These criniviruses maintain genetic stability with limited intra-species variability. They share similar core genome structure and replication strategies with some variations in the non-core proteins and downstream replication processes. Management of the diseases induced by criniviruses relies on integrated disease management strategies and on resistant varieties, when available. This review will cover their epidemiology, molecular biology, detection and management.
Project description:The Middle East respiratory syndrome coronavirus (MERS-CoV) is a novel zoonotic pathogen. In 2012, the infectious outbreak caused by MERS-CoV in Saudi Arabia has spread to more than 1600 patients in 26 countries, resulting in over 600 deaths.Without a travel history, few clinical and radiological features can reliably differentiate MERS from SARS. But in real world, comparing with SARS, MERS presents more vaguely defined epidemiology, more severe symptoms, and higher case fatality rate. In this review, we summarize the recent findings in the field of MERS-CoV, especially its molecular virology, interspecies mechanisms, clinical features, antiviral therapies, and the further investigation into this disease. As a newly emerging virus, many questions are not fully answered, including the exact mode of transmission chain, geographical distribution, and animal origins. Furthermore, a new protocol needs to be launched to rapidly evaluate the effects of unproven antiviral drugs and vaccine to fasten the clinical application of new drugs.