The Infectious Disease Ontology in the Age of COVID-19

The Infectious Disease Ontology (IDO) is a suite of interoperable ontology modules that aims to provide coverage of all aspects of the infectious disease domain, including biomedical research, clinical care, and public health. IDO Core is designed to be a disease and pathogen neutral ontology, covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is then extended by a collection of ontology modules focusing on specific diseases and pathogens. In this paper we present applications of IDO Core within various areas of infectious disease research, together with an overview of all IDO extension ontologies and the methodology on the basis of which they are built. We also survey recent developments involving IDO, including the creation of IDO Virus; the Coronaviruses Infectious Disease Ontology (CIDO); and an extension of CIDO focused on COVID-19 (IDO-CovID-19).We also discuss how these ontologies might assist in information-driven efforts to deal with the ongoing COVID-19 pandemic, to accelerate data discovery in the early stages of future pandemics, and to promote reproducibility of infectious disease research

A Hybrid Agent-Based and Equation Based Epidemiological Model for the Spread of Infectious Diseases

Infectious disease models are essential in understanding how an outbreak might occur and how best to mitigate an outbreak. One of the most important factors in modelling a disease is choosing an appropriate model and determining the assump tions needed to create the model. The main research questions this thesis addresses are how do we create a model for the spread of infectious diseases that captures heterogeneous agents without using an inordinate amount of computing power and how can we use that model to plan for future infectious disease outbreaks. We start our work by analysing and comparing equation based and agent based models and determine that an agent-based model’s stochasticity and ability to capture emerging results (complex and hard to explain results from interactions of agents) means that the agent-based model has an advantage in modelling the in dividual actions and complexities that make one infectious disease outbreak differ from another. Focusing on agent-based models, we take the model in two direc tions adding complexity and scaling up the model. Although adding complexity allows us to produce robust results, it increases run time so modelling anything beyond a small population is not feasible. Thus we focus on scaling up the model (from a town to a county) and determining what trade-offs need to be made to keep the model computationally tractable. With our scaled up model we look at characteristics of a town that come from its place in a network of towns, looking at how the centrality of a town affects how an outbreak spreads from a town and enters a town. We determine when a town has a high in degree centrality the i centrality of the other towns are not as important with respect to whether the outbreak will spread to the other towns. The additional agents in the scaled up model lead to an extended run time. In order to reduce run time we make an assumption about the importance of heterogeneous mixing when there is a large number of agents infected and create a hybrid agent-based and equation based model that switches between an agent based disease component and an equation based disease component based on a threshold of the number of agents infected. The hybrid model is able to save time compared to a fully agent-based model without losing a significant level of fidelity. This allows for the model to be scaled up to larger geographies and populations. Scaling the model to larger populations is essential in studying and testing the efficacy of interventions that would not be applicable at a smaller scale. To show this we use the hybrid model to analyse the effects of school closure policies across a network of towns, showing that closing both the town where an outbreak starts in and the town in the region with the highest in degree centrality can help mitigate an outbreak

Oropouche virus: clinical, epidemiological, and molecular aspects of a neglected orthobunyavirus.

Oropouche virus (OROV) is an important cause of arboviral illness in Latin American countries, more specifically in the Amazon region of Brazil, Venezuela and Peru, as well as in other countries such as Panama. In the past decades, the clinical, epidemiological, pathological, and molecular aspects of OROV have been published and provide the basis for a better understanding of this important human pathogen. Here, we describe the milestones in a comprehensive review of OROV epidemiology, pathogenesis, and molecular biology, including a description of the first isolation of the virus, the outbreaks during the past six decades, clinical aspects of OROV infection, diagnostic methods, genome and genetic traits, evolution, and viral dispersal

Some Remarks about the Complexity of Epidemics Management

Recent outbreaks of Ebola, H1N1 and other infectious diseases have shown that the assumptions underlying the established theory of epidemics management are too idealistic. For an improvement of procedures and organizations involved in fighting epidemics, extended models of epidemics management are required. The necessary extensions consist in a representation of the management loop and the potential frictions influencing the loop. The effects of the non-deterministic frictions can be taken into account by including the measures of robustness and risk in the assessment of management options. Thus, besides of the increased structural complexity resulting from the model extensions, the computational complexity of the task of epidemics management - interpreted as an optimization problem - is increased as well. This is a serious obstacle for analyzing the model and may require an additional pre-processing enabling a simplification of the analysis process. The paper closes with an outlook discussing some forthcoming problems

Nephropathia epidemica and Puumala virus occurrence in relation to bank vole (Clethrionomys glareolus) dynamics and environmental factors in northern Sweden

The objectives of the thesis were to investigate the spatio-temporal patterns of nephropathia epidemica (NE) in humans and Puumala virus (PUU) occurrence in relation to bank vole (Clethrionomys glareolus) dynamics and environmental factors in a region of high incidence of NE in northern Sweden. Nephropathia epidemica is a mild form of hemorrhagic fever with renal syndrome, and in northern Sweden the most prevailing serious febrile viral infection, second to influenza. All serologically confirmed NE cases during 1991-2001 in the four northernmost counties (n = 2,468) were used to establish spatio-temporal patterns of the occurrence of the human disease. Within the study region, the bank voles show marked population fluctuations with 3-4 yr cycles and the incidence of NE has a temporal component strongly correlated to annual numbers of bank voles in autumn. People living in rural dwellings near coastal areas were abundant among notified cases and middle-aged males were over-represented. The patients were often infected in autumn when engaged in activities such as handling of fire wood, gardening or hay-handling near man-made rodent refugia or cleaning/redecorating within one. A proportion of these patients, confident about site of PUU exposure, were used to establish field sites in two separate studies. Firstly a five year study (1995-1999) at six sites spanning a bank vole population cycle, and secondly a spatially extensive study at 32 sites was conducted in autumn 1998. Densities, fluctuations and demography of vole populations differ between sites of known occurrence of NE were compared to random forest sites. Five years of repeated biannual sampling revealed that case sites harbored more bank voles than random forest sites, in particular during population peaks. For the individual bank voles, the probability of PUU infection was significantly higher in population peak year, increased with age and was higher for males than for females. In the spatially extended study, it was found that in particular environmental characteristics associated with old-growth moist forests (i.e. Alectoria spp., Picea abies, fallen wood and Vaccinium myrtillus) were associated with high bank vole numbers and numbers of PUU infected bank voles. This implies that success in circulation and persistence of PUU within local bank vole populations is strongly influenced by the local environments. In future modeling of PUU transmission, influence of bank vole demography and environmental factors should be useful on establishing risk assessments and identifying areas of particular risk of PUU exposure

Molecular epidemiology of African sleeping sickness

Human sleeping sickness in Africa, caused by Trypanosoma brucei spp. raises a number of questions. Despite the widespread distribution of the tsetse vectors and animal trypanosomiasis, human disease is only found in discrete foci which periodically give rise to epidemics followed by periods of endemicity A key to unravelling this puzzle is a detailed knowledge of the aetiological agents responsible for different patterns of disease--knowledge that is difficult to achieve using traditional microscopy. The science of molecular epidemiology has developed a range of tools which have enabled us to accurately identify taxonomic groups at all levels (species, subspecies, populations, strains and isolates). Using these tools, we can now investigate the genetic interactions within and between populations of Trypanosoma brucei and gain an understanding of the distinction between human- and nonhuman-infective subspecies. In this review, we discuss the development of these tools, their advantages and disadvantages and describe how they have been used to understand parasite genetic diversity, the origin of epidemics, the role of reservoir hosts and the population structure. Using the specific case of T.b. rhodesiense in Uganda, we illustrate how molecular epidemiology has enabled us to construct a more detailed understanding of the origins, generation and dynamics of sleeping sickness epidemics

A new formulation of compartmental epidemic modelling for arbitrary distributions of incubation and removal times

The paradigm for compartment models in epidemiology assumes exponentially distributed incubation and removal times, which is not realistic in actual populations. Commonly used variations with multiple exponentially distributed variables are more flexible, yet do not allow for arbitrary distributions. We present a new formulation, focussing on the SEIR concept that allows to include general distributions of incubation and removal times. We compare the solution to two types of agent-based model simulations, a spatially homogeneous one where infection occurs by proximity, and a model on a scale-free network with varying clustering properties, where the infection between any two agents occurs via their link if it exists. We find good agreement in both cases. Furthermore a family of asymptotic solutions of the equations is found in terms of a logistic curve, which after a non-universal time shift, fits extremely well all the microdynamical simulations. The formulation allows for a simple numerical approach; software in Julia and Python is provided.Comment: 21 pages, 11 figures. v2 matches published version: improved presentation (including title, abstract and references), results and conclusions unchange