Embedded model discrepancy: A case study of Zika modeling

Mathematical models of epidemiological systems enable investigation of and predictions about potential disease outbreaks. However, commonly used models are often highly simplified representations of incredibly complex systems. Because of these simplifications, the model output, of say new cases of a disease over time, or when an epidemic will occur, may be inconsistent with available data. In this case, we must improve the model, especially if we plan to make decisions based on it that could affect human health and safety, but direct improvements are often beyond our reach. In this work, we explore this problem through a case study of the Zika outbreak in Brazil in 2016. We propose an embedded discrepancy operator---a modification to the model equations that requires modest information about the system and is calibrated by all relevant data. We show that the new enriched model demonstrates greatly increased consistency with real data. Moreover, the method is general enough to easily apply to many other mathematical models in epidemiology.Comment: 9 pages, 7 figure

How do medical researchers make causal inferences?

Bradford Hill (1965) highlighted nine aspects of the complex evidential situation a medical researcher faces when determining whether a causal relation exists between a disease and various conditions associated with it. These aspects are widely cited in the literature on epidemiological inference as justifying an inference to a causal claim, but the epistemological basis of the Hill aspects is not understood. We offer an explanatory coherentist interpretation, explicated by Thagard's ECHO model of explanatory coherence. The ECHO model captures the complexity of epidemiological inference and provides a tractable model for inferring disease causation. We apply this model to three cases: the inference of a causal connection between the Zika virus and birth defects, the classic inference that smoking causes cancer, and John Snow’s inference about the cause of cholera

Taking forward a 'One Health' approach for turning the tide against the Middle East respiratory syndrome coronavirus and other zoonotic pathogens with epidemic potential

The appearance of novel pathogens of humans with epidemic potential and high mortality rates have threatened global health security for centuries. Over the past few decades new zoonotic infectious diseases of humans caused by pathogens arising from animal reservoirs have included West Nile virus, Yellow fever virus, Ebola virus, Nipah virus, Lassa Fever virus, Hanta virus, Dengue fever virus, Rift Valley fever virus, Crimean-Congo haemorrhagic fever virus, severe acute respiratory syndrome coronavirus, highly pathogenic avian influenza viruses, Middle East Respiratory Syndrome Coronavirus, and Zika virus. The recent Ebola Virus Disease epidemic in West Africa and the ongoing Zika Virus outbreak in South America highlight the urgent need for local, regional and international public health systems to be be more coordinated and better prepared. The One Health concept focuses on the relationship and interconnectedness between Humans, Animals and the Environment, and recognizes that the health and wellbeing of humans is intimately connected to the health of animals and their environment (and vice versa). Critical to the establishment of a One Health platform is the creation of a multidisciplinary team with a range of expertise including public health officers, physicians, veterinarians, animal husbandry specialists, agriculturalists, ecologists, vector biologists, viral phylogeneticists, and researchers to co-operate, collaborate to learn more about zoonotic spread between animals, humans and the environment and to monitor, respond to and prevent major outbreaks. We discuss the unique opportunities for Middle Eastern and African stakeholders to take leadership in building equitable and effective partnerships with all stakeholders involved in human and health systems to take forward a 'One Health' approach to control such zoonotic pathogens with epidemic potential

The Impact of Heterogeneity and Awareness in Modeling Epidemic Spreading on Multiplex Networks.

In the real world, dynamic processes involving human beings are not disjoint. To capture the real complexity of such dynamics, we propose a novel model of the coevolution of epidemic and awareness spreading processes on a multiplex network, also introducing a preventive isolation strategy. Our aim is to evaluate and quantify the joint impact of heterogeneity and awareness, under different socioeconomic conditions. Considering, as case study, an emerging public health threat, Zika virus, we introduce a data-driven analysis by exploiting multiple sources and different types of data, ranging from Big Five personality traits to Google Trends, related to different world countries where there is an ongoing epidemic outbreak. Our findings demonstrate how the proposed model allows delaying the epidemic outbreak and increasing the resilience of nodes, especially under critical economic conditions. Simulation results, using data-driven approach on Zika virus, which has a growing scientific research interest, are coherent with the proposed analytic model.This work was partially supported by the following Research Grant: Italian Ministry of University and Research - MIUR “Programma Operativo Nazionale Ricerca e Competitività 2007–2013” within the project “PON-03PE-00132-1” - Servify

Understanding the survival of Zika virus in a vector interconnected sexual contact network

Citation: Ferdousi, T., Cohnstaedt, L. W., McVey, D. S., & Scoglio, C. M. (2019). Understanding the survival of Zika virus in a vector interconnected sexual contact network. Scientific Reports, 9(1), 7253. https://doi.org/10.1038/s41598-019-43651-3The recent outbreaks of the insect-vectored Zika virus have demonstrated its potential to be sexually transmitted, which complicates modeling and our understanding of disease dynamics. Autochthonous outbreaks in the US mainland may be a consequence of both modes of transmission, which affect the outbreak size, duration, and virus persistence. We propose a novel individual-based interconnected network model that incorporates both insect-vectored and sexual transmission of this pathogen. This model interconnects a homogeneous mosquito vector population with a heterogeneous human host contact network. The model incorporates the seasonal variation of mosquito abundance and characterizes host dynamics based on age group and gender in order to produce realistic projections. We use a sexual contact network which is generated on the basis of real world sexual behavior data. Our findings suggest that for a high relative transmissibility of asymptomatic hosts, Zika virus shows a high probability of sustaining in the human population for up to 3 months without the presence of mosquito vectors. Zika outbreaks are strongly affected by the large proportion of asymptomatic individuals and their relative transmissibility. The outbreak size is also affected by the time of the year when the pathogen is introduced. Although sexual transmission has a relatively low contribution in determining the epidemic size, it plays a role in sustaining the epidemic and creating potential endemic scenarios

Understanding the role of sexual transmission in the spread of ZIKA virus using an individual-based interconnected population model

Citation: Tanver, F., Lee, C., Mcvey, D., & Scoglio, S. (2018). Understanding the role of sexual transmission in the spread of ZIKA virus using an individual-based interconnected population model. Pre-Print, Unpublished Manuscript.Zika virus has affected the world as a long-term threat. Modeling its transmission is important in order to facilitate forecasts and control measures. We propose a novel node-based interconnected population model to simulate both vectored and sexual transmission of Zika virus. Using a sexual contact network, we incorporate heterogeneous mixing in the host population with stochastic transmission for realistic predictions. We also incorporate climatic variations in our model, which affect the mosquito vector population and consequently the arbovirus transmission. We perform extensive simulations to understand the effects of sexual transmission rate and network topology on the spreading of infections. Sexual transmission contributes to the epidemic spread and under certain conditions, can sustain it up to several months without vectors. This can potentially lead to recurrences once the mosquitoes overwinter. We also find that sexual transmission can have a stronger effect when vectored transmission is relatively weaker due to climatic conditions. Our results show that vectored and sexual transmission affect the disease dynamics differently