Evaluation of the Spatiotemporal Epidemiological Modeler (STEM) during the recent COVID-19 pandemic

In early December 2019, some people in China were diagnosed with an unknown pneumonia in Wuhan, in the Hubei province. The responsible of the outbreak was identified in a novel human-infecting coronavirus which differs both from severe acute respiratory syndrome coronavirus and from Middle East respiratory syndrome coronavirus. The new coronavirus, officially named severe acute respiratory syndrome coronavirus 2 by the International Committee on Taxonomy of Viruses, has spread worldwide within few weeks. Only two vaccines have been approved by regulatory agencies and some others are under development. Moreover, effective treatments have not been yet identified or developed even if some potential molecules are under investigation. In a pandemic outbreak, when treatments are not available, the only method that contribute to reduce the virus spreading is the adoption of social distancing measures, like quarantine and isolation. With the intention of better managing emergencies like this, which are a great public health threat, it is important to dispose of predictive epidemiological tools that can help to understand both the virus spreading in terms of people infected, hospitalized, dead and recovered and the effectiveness of containment measures

A Sensitivity Analysis of Model Structure in Stochastic Differential Equation and Agent-Based Epidemiological Models

The dynamics of infectious diseases have been modelled by several universally recognised procedures. The most common two modelling methods are differential equation models (DEM) and agent based models (ABM). These models have both been used through the late 20th and early 21st century to gain an understanding of prevalence levels and behaviour of infectious diseases; and subsequently to forecast potential impacts of a treatment. In the case of a life-threatening disease such as Malaria, it is problematic to be working with incorrect predictions and an epidemic may result from a misinformed judgement on the required treatment program. DEM and ABM have been documented to provide juxtapositioned results (and conclusions) in several cases, even whilst fitting identical data sets [Figueredo, et al. 2014]. Under the correct model, one would expect a fair representation of an infectious disease and hence an insightful conclusion. It is hence detrimental for the choice of treatment tactics to be dependent on the choice of model structure. This honours thesis has identified the necessity for caution on the model methodology and performs a sensitivity analysis on the incidence and prevalence of an infectious disease under varying levels of treatment. This thesis hones in on modelling methodology under various structures: the procedure is applicable to any infectious disease, and this thesis provides a case study on Malaria modelling with a later extension into Ebola. Beginning with a simple Susceptible-Infected-Recovered-Susceptible (SIRS) model: immediately obvious differences are examined to give an indication of the point at which the models lose integrity in direct comparability. The SIRS models are built up to include varying levels of exposure, treatment and movement dynamics and examining the nature of the differences in conclusions drawn from separate models

Emergence of infectious diseases

From SARS to avian influenza, Ebola virus and MERS-CoV, infectious diseases have received increasing attention in recent decades from scientists, risk managers, the media and the general public. What explains the constant emergence of infectious diseases? What are the related challenges? In five chapters, experts from different scientific fields analyse the ecological, social, institutional and political dynamics associated with emerging infectious diseases. This book discusses how the concepts, scientific results and action plans of international or governmental organizations are constructed and coordinated. In clear straightforward language, this book explores the continuities and discontinuities that occur with emerging infectious diseases, both in terms of collective action and in our relationship to the biological world

A Survey of the Individual-Based Model applied in Biomedical and Epidemiology

Individual-based model (IBM) has been used to simulate and to design control strategies for dynamic systems that are subject to stochasticity and heterogeneity, such as infectious diseases. In the IBM, an individual is represented by a set of specific characteristics that may change dynamically over time. This feature allows a more realistic analysis of the spread of an epidemic. This paper presents a literature survey of IBM applied to biomedical and epidemiology research. The main goal is to present existing techniques, advantages and future perspectives in the development of the model. We evaluated 89 articles, which mostly analyze interventions aimed at endemic infections. In addition to the review, an overview of IBM is presented as an alternative to complement or replace compartmental models, such as the SIR (Susceptible-Infected-Recovered) model. Numerical simulations also illustrate the capabilities of IBM, as well as some limitations regarding the effects of discretization. We show that similar side-effects of discretization scheme for compartmental models may also occur in IBM, which requires careful attention

Catching the flu: syndromic surveillance, algorithmic governmentality and global health security

This thesis offers a critical analysis of the rise of syndromic surveillance systems for the advanced detection of pandemic threats within contemporary global health security frameworks. The thesis traces the iterative evolution and ascendancy of three such novel syndromic surveillance systems for the strengthening of health security initiatives over the past two decades: 1) The Program for Monitoring Emerging Diseases (ProMED-mail); 2) The Global Public Health Intelligence Network (GPHIN); and 3) HealthMap. This thesis demonstrates how each newly introduced syndromic surveillance system has become increasingly oriented towards the integration of digital algorithms into core surveillance capacities to continually harness and forecast upon infinitely generating sets of digital, open-source data, potentially indicative of forthcoming pandemic threats. This thesis argues that the increased centrality of the algorithm within these next-generation syndromic surveillance systems produces a new and distinct form of infectious disease surveillance for the governing of emergent pathogenic contingencies. Conceptually, the thesis also shows how the rise of this algorithmic mode of infectious disease surveillance produces divergences in the governmental rationalities of global health security, leading to the rise of an algorithmic governmentality within contemporary contexts of Big Data and these surveillance systems. Empirically, this thesis demonstrates how this new form of algorithmic infectious disease surveillance has been rapidly integrated into diplomatic, legal, and political frameworks to strengthen the practice global health security – producing subtle, yet distinct shifts in the outbreak notification and reporting transparency of states, increasingly scrutinized by the algorithmic gaze of syndromic surveillance