Developing a Decision Support Testing Algorithm to Detect Severity Level of Dengue

Dengue is a vector borne disease that has become a global threat. In order to reduce the mortality rate early detection of dengue severity level is crucial. This study is an extension of the decision models developed individually for inflammatory mediators and immune parameters. The objective of this study is to improve the individual models by considering their combined effect and to improve the decision making at 96 hours from onset of illness. In order to combine these, three approaches are attempted including, combining together the individual full models on inflammatory mediators and immune parameters, combining the immune parameters based model with decision tree informed cytokines and implementing a decision tree informed model with immune parameters and inflammatory mediators. The decision tree algorithm that is used in model development is Improved ID3 algorithm. The decision tree based model is a two-step decision system with the initial decision being made using the parameters TNF-?, IL-10, dengue NS1 antigen and dengue IgG antibody and, the operator values above 0.4413, are then subjected to the second test including platelet and Platelet Activating Factor. The decision tree based model performed well with an accuracy of 76.19% and 82.3% of DHF patients were correctly classified. Sensitivity analysis indicated the model to be robust

Mathematical Modelling in Engineering & Human Behaviour 2018

This book includes papers in cross-disciplinary applications of mathematical modelling: from medicine to linguistics, social problems, and more. Based on cutting-edge research, each chapter is focused on a different problem of modelling human behaviour or engineering problems at different levels. The reader would find this book to be a useful reference in identifying problems of interest in social, medicine and engineering sciences, and in developing mathematical models that could be used to successfully predict behaviours and obtain practical information for specialised practitioners. This book is a must-read for anyone interested in the new developments of applied mathematics in connection with epidemics, medical modelling, social issues, random differential equations and numerical methods

Molecular Communications in Viral Infections Research: Modelling, Experimental Data and Future Directions

Hundreds of millions of people worldwide are affected by viral infections each year, and yet, several of them neither have vaccines nor effective treatment during and post-infection. This challenge has been highlighted by the COVID-19 pandemic, showing how viruses can quickly spread and impact society as a whole. Novel interdisciplinary techniques must emerge to provide forward-looking strategies to combat viral infections, as well as possible future pandemics. In the past decade, an interdisciplinary area involving bioengineering, nanotechnology and information and communication technology (ICT) has been developed, known as Molecular Communications. This new emerging area uses elements of classical communication systems to molecular signalling and communication found inside and outside biological systems, characterizing the signalling processes between cells and viruses. In this paper, we provide an extensive and detailed discussion on how molecular communications can be integrated into the viral infectious diseases research, and how possible treatment and vaccines can be developed considering molecules as information carriers. We provide a literature review on molecular communications models for viral infection (intra-body and extra-body), a deep analysis on their effects on immune response, how experimental can be used by the molecular communications community, as well as open issues and future directions

Understanding the Impact of Social Factors on the Transmission Dynamics of Infectious Diseases Across Highly Heterogeneous Risk Environments.

abstract: This dissertation explores the impact of environmental dependent risk on disease dynamics within a Lagrangian modeling perspective; where the identity (defined by place of residency) of individuals is preserved throughout the epidemic process. In Chapter Three, the impact of individuals who refuse to be vaccinated is explored. MMR vaccination and birth rate data from the State of California are used to determine the impact of the anti-vaccine movement on the dynamics of growth of the anti-vaccine sub-population. Dissertation results suggest that under realistic California social dynamics scenarios, it is not possible to revert the influence of anti-vaccine contagion. In Chapter Four, the dynamics of Zika virus are explored in two highly distinct idealized environments defined by a parameter that models highly distinctive levels of risk, the result of vector and host density and vector control measures. The underlying assumption is that these two communities are intimately connected due to economics with the impact of various patterns of mobility being incorporated via the use of residency times. In short, a highly heterogeneous community is defined by its risk of acquiring a Zika infection within one of two "spaces," one lacking access to health services or effective vector control policies (lack of resources or ignored due to high levels of crime, or poverty, or both). Low risk regions are defined as those with access to solid health facilities and where vector control measures are implemented routinely. It was found that the better connected these communities are, the existence of communities where mobility between risk regions is not hampered, lower the overall, two patch Zika prevalence. Chapter Five focuses on the dynamics of tuberculosis (TB), a communicable disease, also on an idealized high-low risk set up. The impact of mobility within these two highly distinct TB-risk environments on the dynamics and control of this disease is systematically explored. It is found that collaboration and mobility, under some circumstances, can reduce the overall TB burden.Dissertation/ThesisDoctoral Dissertation Applied Mathematics for the Life and Social Sciences 201

Prognostic Models in Dengue

Reliable prediction models in dengue would facilitate early identification of patients likely to progress to more severe disease, potentially improving patient management. However, most published studies have limitations with respect to their modelling strategy, sample size, and Chosen clinical outcomes, and to date none have exploited longitudinal data. Moreover, only a few studies have examined outcomes in patients presenting with dengue shock syndrome (DSS), the most severe form of the disease. This thesis aims to overcome these limitations by using two large prospective datasets describing a) 1719 children with established DSS and b) 2598 children hospitalized with dengue. First, the population of children with DSS was characterized, and profound DSS, a composite outcome reflecting the need for intensive supportive care, was established as a suitable outcome for prognostic research in this population. Second, risk factors for profound DSS were identified and included in a robust prediction model. Based on this model, a simple score chart for use in clinical practice was derived. Third, risk factors for progression to DSS among children hospitalized with dengue were identified, and a prognostic model for progression to DSS was carefully developed. However, this model displayed only moderate performance and had limited clinical utility. Lastly, differences between acute and chronic diseases, and the implications for dynamic prediction modeling based on longitudinal data, are discussed. A case study of dynamic prediction modeling for development of DSS suggested that (1) the current platelet count can be used to improve baseline models that rely on enrolment values only, and (2) simple conditional dynamic models displayed similar performance to more complex joint models in this situation

Zika virus: causality, open science and risk of emerging infectious diseases

The Zika virus (ZIKV) outbreak in the Americas between 2015 and 2017 took the world by surprise. Within two years, over 1.5 million suspected or confirmed cases were reported. However, the true incidence is likely much higher, due to under-reporting and asymptomatic infections that are undetected. As of July 2019, 87 countries had reported ongoing or past circulation of ZIKV. ZIKV infection results generally in mild and transient symptoms. The disease caused by ZIKV is often asymptomatic or mild. However, infection during pregnancy can result in severe adverse congenital outcomes with microcephaly as most prominent. This was first noted in clusters of infants born with disabilities linked to ZIKV infection in Brazil in 2015, making ZIKV a disease with a serious public health impact. In this thesis, I explore different aspects of the ZIKV epidemic. I use different epidemiological methods to provide insight in the Zika virus as a cause of adverse outcomes, ZIKV as a sexually transmitted disease and the risk of future ZIKV outbreaks. In Chapter 1, I provide an introduction to the history of emerging infections and the emergence of ZIKV specifically. I describe the investigation of causality, the use and accumulation of evidence during disease outbreaks, and how disease transmission can be investigated using mathematical models. In Chapter 2, I provide insight in how evidence accumulates during an outbreak and more in general during new causal questions. Case reports and case series were the first studies to appear, followed by basic research (in vivo and in vitro studies). It took more than a year after the onset of the ZIKV outbreak for robust epidemiological studies to be published. Establishing early public health guidance thus requires a broad approach taking into account all evidence available. We have to make do with the low quality evidence. To minimize further delays, evidence should be accessible as soon as it becomes available through rapid and open access dissemination. In Chapter 3, I extend a systematic review that was conducted earlier, and turn it into a living systematic review. I introduce the concept and implementation of living systematic reviews in the context of an emerging disease. I assess the evidence on the causal relation between ZIKV infection and adverse congenital and auto-immune neurological outcomes, published between May 30, 2016 and January 18, 2017, using a framework based on the causality dimensions of Bradford Hill. During this period, the evidence expanded that ZIKV was indeed a cause of congenital abnormalities and Guillain-Barré syndrome (GBS). I provide a proof of concept for the use of living systematic reviews to synthesize evidence about an emerging pathogen such as ZIKV. In Chapter 4, I assess the evidence published between January 18, 2017 and July 1, 2019. I quantify the strength of association of the relation between maternal ZIKV infection and congenital adverse outcomes and between ZIKV infection and GBS. I found that the strength of association between ZIKV infection and adverse outcomes from case-control studies differs according to whether exposure to ZIKV is assessed in the mother (odds ratio (OR) 3.8, 95% CI: 1.7–8.7, I2=19.8%) or the foetus/infant (OR 37.4, 95% CI: 11.0–127.1, I2=0%). In cohort studies, the risk of congenital abnormalities was 3.5 times higher after ZIKV infection (95% CI: 0.9–13.5, I2=0%). The strength of association between ZIKV infection and GBS was higher in studies that enrolled controls from hospital (OR: 55.8, 95% CI: 17.2-181.7, I2=0%) than in studies that enrolled controls at random from the same community or household (OR: 2.0, 95% CI: 0.8–5.4, I2=74.6%). The heterogeneity between the studies could be partly explained by the heterogeneity in methods and sampled populations. Studies suffered from bias and uncontrolled residual confounding. In Chapter 5, I present a framework to systematically assess the evidence for ZIKV as a sexually transmitted disease. I reviewed all available literature and concluded that the risk of sexual transmission of ZIKV is likely small, but relevant for certain risk groups. I found that in semen viral RNA could be detected for a median period of 34 days (95% CI: 28–41 days) and 35 days (no CI given) based on two cohort studies. Aggregated data about detection of ZIKV RNA from 37 case reports and case series indicate a median duration of 40 days (95% CI: 30–49 days) and a maximum duration of 370 days in semen. In human vaginal fluid, the median duration was 14 days (95% CI: 7–20 days) and the maximum duration was 37 days. Infectious virus in human semen was detected for a median duration of 12 days (95% CI: 1–21 days) and a maximum of 69 days. I highlight the poor quality of the evidence and the need for systematic observational studies that evaluate the risk of sexual transmission of ZIKV. In Chapter 6, I present predictions on the future risk of ZIKV, based on data from Managua, Nicaragua, using mathematical modelling. The risk of a new outbreak in the next decades is low due to herd immunity. However, a next outbreak will disproportionally hit people in the young reproductive age hardest (age 15–29 years). Vaccination could curb this risk: Early introduction of vaccination in 15-year-old girls has the capacity to extend the herd immunity and be of benefit to the whole population. Introduction of a vaccine needs to happen within a decade after the 2016 outbreak to achieve this protection. The duration of immunity following ZIKV infection has impact on the speed at which outbreaks will reoccur. In Chapter 7, I present an overview of the main findings and I discuss the interpretation and implications of these results. I discuss the strengths and limitations of the work, and outline follow-up questions emerging from the work. In this thesis, I establish and use different frameworks and methods that help to make sense of the limited evidence that is available during disease outbreaks. ZIKV has been introduced on the American continent, and it is likely there to stay, thus we have to accept that ZIKV will continue to re-emerge. At the same time, due to the climate change, the European temperate region also becomes more suitable for vector-borne disease such as ZIKV. With the ZIKV epidemic on the wane, we now have time to consolidate findings and implement the lessons learnt. We need to be prepared for the re-emergence of ZIKV but also for the emergence of new diseases. The tools and methods I present in this thesis, will help us to be more prepared for a next outbreak

Integrated computational and experimental analysis of host-virus interaction systems

Host-virus systems biology seeks to elucidate the complex interactions between a virus and its host, and to determine the downstream consequences of these interactions for the host. Traditional studies of host-virus interactions, conducted one-at-a-time, yield high-quality results, but these have limited scope. By contrast, systems biology uses a holistic approach to examine many interactions simultaneously, thereby increasing the breadth of interactions revealed. However, these studies have largely focused on common human pathogens (e.g., influenza or HIV), and their results may not apply to unrelated viruses, such as those that cause hemorrhagic fevers. Combining experimental and computational techniques can yield novel information about host-virus interactions that traditional virological or purely computational systems-biology methods cannot uncover. In this thesis, I demonstrate the utility of combined experimental and computational approaches by: (1) revealing general principles of host-virus interactions, broadly applicable to a wide range of viruses; and (2) probing a specific host-virus interaction system to identify transcriptional signatures which elucidate host response to Ebola virus. I identify general mechanisms governing host-virus protein-protein interactions (PPIs) using domain-resolved PPI networks. This method identifies mechanistic differences between virus-human and within-human interactions, such as the preference viral proteins exhibit for binding human proteins containing linear motif-binding domains. Using domain-resolved PPIs reveals novel signatures of pleiotropy, economy, and convergent evolution in the viral-host interactome not previously identified in other PPI networks. I further identify transcriptional signatures of host response to Ebola virus (EBOV) infection by pairing high-throughput microarray data with advanced pathway analyses. I compare EBOV-infected, non-human primates with and without anticoagulant treatment, to identify transcriptional signatures associated with survival following infection. Having found that CCAAT-enhancer binding proteins (CEBPs) are associated with survival, I determine the role CEBPs have in EBOV infection by using comparative microarray analysis of multiple viral infections of hemorrhagic and non-hemorrhagic origin. I also identify unique transcriptional changes in the host that distinguish EBOV infection from other viral infections, such as Influenza. Integrating these two areas of research provides information about universally applicable patterns of viral infection, while simultaneously examining the consequences of specific host-pathogen interactions

From Functional Genomics to Functional Immunomics: New Challenges, Old Problems, Big Rewards

The development of DNA microarray technology a decade ago led to the establishment of functional genomics as one of the most active and successful scientific disciplines today. With the ongoing development of immunomic microarray technology—a spatially addressable, large-scale technology for measurement of specific immunological response—the new challenge of functional immunomics is emerging, which bears similarities to but is also significantly different from functional genomics. Immunonic data has been successfully used to identify biological markers involved in autoimmune diseases, allergies, viral infections such as human immunodeficiency virus (HIV), influenza, diabetes, and responses to cancer vaccines. This review intends to provide a coherent vision of this nascent scientific field, and speculate on future research directions. We discuss at some length issues such as epitope prediction, immunomic microarray technology and its applications, and computation and statistical challenges related to functional immunomics. Based on the recent discovery of regulation mechanisms in T cell responses, we envision the use of immunomic microarrays as a tool for advances in systems biology of cellular immune responses, by means of immunomic regulatory network models

Progress, pitfalls, and path forward of drug repurposing for COVID-19 treatment

On 30 January 2020, the World Health Organization (WHO) declared the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic a public health emergency of international concern. The viral outbreak led in turn to an exponential growth of coronavirus disease 2019 (COVID-19) cases, that is, a multiorgan disease that has led to more than 6.3 million deaths worldwide, as of June 2022. There are currently few effective drugs approved for treatment of SARS-CoV-2/COVID-19 patients. Many of the compounds tested so far have been selected through a drug repurposing approach, that is, by identifying novel indications for drugs already approved for other conditions. We here present an up-to-date review of the main Food and Drug Administration (FDA)–approved drugs repurposed against SARS-CoV-2 infection, discussing their mechanism of action and their most important preclinical and clinical results. Reviewed compounds were chosen to privilege those that have been approved for use in SARS-CoV-2 patients or that have completed phase III clinical trials. Moreover, we also summarize the evidence on some novel and promising repurposed drugs in the pipeline. Finally, we discuss the current stage and possible steps toward the development of broadly effective drug combinations to suppress the onset or progression of COVID-19

Making ecological models adequate

Critical evaluation of the adequacy of ecological models is urgently needed to enhance their utility in developing theory and enabling environmental managers and policymakers to make informed decisions. Poorly supported management can have detrimental, costly or irreversible impacts on the environment and society. Here, we examine common issues in ecological modelling and suggest criteria for improving modelling frameworks. An appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures. Model details unsupported by data typically lead to over parameterisation and poor model performance. Conversely, a lack of mechanistic details may limit a model's ability to predict ecological systems' responses to management. Ecological studies that employ models should follow a set of model adequacy assessment protocols that include: asking a series of critical questions regarding state and control variable selection, the determinacy of data, and the sensitivity and validity of analyses. We also need to improve model elaboration, refinement and coarse graining procedures to better understand the relevancy and adequacy of our models and the role they play in advancing theory, improving hind and forecasting, and enabling problem solving and management