Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics

Infectious diseases remain among the top contributors to human illness and death worldwide, among which many diseases produce epidemic waves of infection. The unavailability of specific drugs and ready-to-use vaccines to prevent most of these epidemics makes the situation worse. These force public health officials and policymakers to rely on early warning systems generated by reliable and accurate forecasts of epidemics. Accurate forecasts of epidemics can assist stakeholders in tailoring countermeasures, such as vaccination campaigns, staff scheduling, and resource allocation, to the situation at hand, which could translate to reductions in the impact of a disease. Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics. We analyse a wide variety of epidemic time series datasets using a maximal overlap discrete wavelet transform (MODWT) based autoregressive neural network and call it EWNet model. MODWT techniques effectively characterize non-stationary behavior and seasonal dependencies in the epidemic time series and improve the nonlinear forecasting scheme of the autoregressive neural network in the proposed ensemble wavelet network framework. From a nonlinear time series viewpoint, we explore the asymptotic stationarity of the proposed EWNet model to show the asymptotic behavior of the associated Markov Chain. We also theoretically investigate the effect of learning stability and the choice of hidden neurons in the proposal. From a practical perspective, we compare our proposed EWNet framework with several statistical, machine learning, and deep learning models. Experimental results show that the proposed EWNet is highly competitive compared to the state-of-the-art epidemic forecasting methods

Modelling drug use: methods to quantify and understand hidden processes

Dynamic modelling is a quantitative technique used by scientists when data are scarce to generate estimates, test hypotheses and forecast trends. This publication explores the potential role of dynamic modelling in helping to interpret data on drug use and its consequences in the European Union. The monograph contains over a dozen expert reviews on modelling techniques and their use in estimating drug use and related health consequences. The publication presents dynamic modelling as a valuable analytical tool, not only in improving insight into drug use, but also in contributing to the development of evidence-based drug policies and interventions

Chronic HCV Infection: Clinical Advances and Eradication Perspectives

Hepatitis C virus (HCV) chronic infection can determine liver fibrosis, cirrhosis and hepatocellular carcinoma, as well as several extra-hepatic manifestations (i.e., mixed cryoglobulinemia, metabolic syndrome, kidney disease, etc.). HCV infection is asymptomatic until severe stages of disease, thus screening policy in the general population and in specific risk categories is necessary to allow for timely intervention. Despite a high sustained virological response by direct-acting antiviral drugs, a limited percentage of treated subject failed therapy according to resistance associated substitution carried on viral isolates and comorbidities in infected patients. Therefore, tailored therapy is required to cure HCV infection. Failure to comply with these conditions may impair success of HCV eradication expected by 2030. This Special Issue aims to discuss eradication perspectives related to therapy efficacy in patients with chronic diseases, developments in diagnostic procedures and improvements in screening policy

The detection of meningococcal disease through identification of antimicrobial peptides using an in silico model creation

Philosophiae Doctor - PhDNeisseria meningitidis (the meningococcus), the causative agent of meningococcal disease (MD) was identified in 1887 and despite effective antibiotics and partially effective vaccines, Neisseria meningitidis (N. meningitidis) is the leading cause worldwide of meningitis and rapidly fatal sepsis usually in otherwise healthy individuals. Over 500 000 meningococcal cases occur every year. These numbers have made bacterial meningitis a top ten infectious cause of death worldwide. MD primarily affects children under 5 years of age, although in epidemic outbreaks there is a shift in disease to older children, adolescents and adults. MD is also associated with marked morbidity including limb loss, hearing loss, cognitive dysfunction, visual impairment, educational difficulties, developmental delays, motor nerve deficits, seizure disorders and behavioural problems. Antimicrobial peptides (AMPs) are molecules that provide protection against environmental pathogens, acting against a large number of microorganisms, including bacteria, fungi, yeast and virus. AMPs production is a major component of innate immunity against infection. The chemical properties of AMPs allow them to insert into the anionic cell wall and phospholipid membranes of microorganisms or bind to the bacteria making it easily detectable for diagnostic purposes. AMPs can be exploited for the generation of novel antibiotics, as biomarkers in the diagnosis of inflammatory conditions, for the manipulation of the inflammatory process, wound healing, autoimmunity and in the combat of tumour cells. Due to the severity of meningitis, early detection and identification of the strain of N. meningitidis is vital. Rapid and accurate diagnosis is essential for optimal management of patients and a major problem for MD is its diagnostic difficulties and experts conclude that with an early intervention the patient’ prognosis will be much improved. It is becoming increasingly difficult to confirm the diagnosis of meningococcal infection by conventional methods. Although polymerase chain reaction (PCR) has the potential advantage of providing more rapid confirmation of the presence of the bacterium than culturing, it is still time consuming as well as costly. Introduction of AMPs to bind to N. meningitidis receptors could provide a less costly and time consuming solution to the current diagnostic problems. World Health Organization (WHO) meningococcal meningitis program activities encourage laboratory strengthening to ensure prompt and accurate diagnosis to rapidly confirm the presence of MD. This study aimed to identify a list of putative AMPs showing antibacterial activity to N. meningitidis to be used as ligands against receptors uniquely expressed by the bacterium and for the identified AMPs to be used in a Lateral Flow Device (LFD) for the rapid and accurate diagnosis of MD

Rapid incidence estimation from SARS-CoV-2 genomes reveals decreased case detection in Europe during summer 2020

By October 2021, 230 million SARS-CoV-2 diagnoses have been reported. Yet, a considerable proportion of cases remains undetected. Here, we propose GInPipe, a method that rapidly reconstructs SARS-CoV-2 incidence profiles solely from publicly available, time-stamped viral genomes. We validate GInPipe against simulated outbreaks and elaborate phylodynamic analyses. Using available sequence data, we reconstruct incidence histories for Denmark, Scotland, Switzerland, and Victoria (Australia) and demonstrate, how to use the method to investigate the effects of changing testing policies on case ascertainment. Specifically, we find that under-reporting was highest during summer 2020 in Europe, coinciding with more liberal testing policies at times of low testing capacities. Due to the increased use of real-time sequencing, it is envisaged that GInPipe can complement established surveillance tools to monitor the SARS-CoV-2 pandemic. In post-pandemic times, when diagnostic efforts are decreasing, GInPipe may facilitate the detection of hidden infection dynamics.Results - Method validation: in silico experiment. - Method validation: phylodynamics. - Reconstructed incidence histories. - Relative case detection rate. Discussion Method

Modelling the hepatitis C virus disease burden among injecting drug users in Scotland

A forward projection model was used to estimate the numbers of, both current and former, IDUs who acquired HCV infection and progressed to mild, moderate and severe HCV disease in Glasgow and Scotland between 1960 and 2030. The model was developed initially for Glasgow because more epidemiological information exists for this region, than elsewhere in Scotland, to calibrate model outcomes with local data relating to HCV and its consequence. Insights gained from the model fitting process in Glasgow were then used to extend the model to the rest of Scotland. First, the incidence and cessation of injecting drug use in Glasgow during 1960-2000 were derived through the use of a modified Delphi approach. Instead of the usual iterative process to refine experts’ estimates, the elicitation of IDU incidence and cessation provided an opportunity to combine these data and examine coherence with capture-recapture IDU prevalence estimates. Coherent estimates indicated that incidence (median: 28 to 49) and cessation (1 to 24%) remained low and stable during 1960-1975, rose steeply between 1975-1985 (incidence from 49 to 1,335; cessation from 2% to 6%), and by 2000 there had been a decline in incidence (1,195) but a further rise in cessation (15%). Secondly, stochastic simulation was used to model the transmission of HCV among current IDUs in Glasgow, according to their injecting risk behaviours, and estimate the past incidence of HCV infection. The model that considered higher infectivity during acute viraemia following infection produced seroprevalences (median: 62-72%) and incidences (18-30 per 100 susceptible injector-years) consistent with observed data during the 1990s. The annual number of new HCV infections among current IDUs in Glasgow was estimated to be low during 1960-1976 (median: 10-60), rise steeply during 1960-1976 (median: 10-60), rise steeply during the early 1980s to peak in 1985 (1,120), stabilise during 1991-1997 (510-610) and rise again during 1998-2000 (710-780). Scenario analyses indicated that potentially as many as 4,500 HCV infections (10th and 90th percentiles: 2,400-7,700) had been prevented in Glasgow during 1988-2000 as a result of harm-reduction measures. Scenario analyses also permitted the gauging of changes in risk behaviours required to effect appreciable reductions in the incidence of HCV infection. Incidence can be successfully reduced if IDUs who, unavoidably, share needles/syringes confine their borrowing to one person; with this strategy alone, an estimated 5,300 HCV infections (10th and 90th percentiles: 4,100-6,700) could have been averted in Glasgow during 1988-2000. Such insights will inform those responsible for developing new ways to prevent HCV transmission among IDU populations. Thirdly, linkage of laboratory data on diagnosed HCV antibody positive persons in Scotland to clinical data from hospital and death records provided a unique national epidemiological dataset to estimate the number who had progressed to severe HCV disease

Modelling the genomic structure, and antiviral susceptibility of Human Cytomegalovirus

Human Cytomegalovirus (HCMV) is found ubiquitously in humans worldwide, and once acquired, the infection persists within the host throughout their life. Although Immunocompetent people rarely are affected by HCMV infections, their related diseases pose a major health problem worldwide for those with compromised or suppressed immune systems such as transplant recipients. Additionally, congenital transmission of HCMV is the most common infectious cause of birth defects globally and is associated with a substantial economic burden. This thesis explores the application of statistical modelling and genomics to unpick three key areas of interest in HCMV research. First, a comparative genomics analysis of global HCMV strains was undertaken to delineate the molecular population structure of this highly variable virus. By including in-house sequenced viruses of African origin and by developing a statistical framework to deconvolute highly variable regions of the genome, novel and important insights into the co-evolution of HCMV with its host were uncovered. Second, a rich database relating mutations to drug sensitivity was curated for all the antiviral treated herpesviruses. This structured information along with the development of a mutation annotation pipeline, allowed the further development of statistical models that predict the phenotype of a virus from its sequence. The predictive power of these models was validated for HSV1 by using external unseen mutation data provided in collaboration with the UK Health Security Agency. Finally, a nonlinear mixed effects model, expanded to account for Ganciclovir pharmacokinetics and pharmacodynamics, was developed by making use of rich temporal HCMV viral load data. This model allowed the estimation of the impact of immune-clearance versus antiviral inhibition in controlling HCMV lytic replication in already established infections post-haematopoietic stem cell transplant

2013 IMSAloquium, Student Investigation Showcase

This year, we are proudly celebrating the twenty-fifth anniversary of IMSA’s Student Inquiry and Research (SIR) Program. Our first IMSAloquium, then called Presentation Day, was held in 1989 with only ten presentations; this year we are nearing two hundred.https://digitalcommons.imsa.edu/archives_sir/1005/thumbnail.jp