An Early Warning System for Detecting H1N1 Disease Outbreak - A Spatio-temporal Approach

The outbreaks of new and emerging infectious diseases in recent decades have caused widespread social and economic disruptions in the global economy. Various guidelines for pandemic influenza planning are based upon traditional infection control, best practice and evidence. This article describes the development of an early warning system for detecting disease outbreaks in the urban setting of Hong Kong, using 216 confirmed cases of H1N1 influenza from 1 May 2009 to 20 June 2009. The prediction model uses two variables – daily influenza cases and population numbers – as input to the spatio-temporal and stochastic SEIR model to forecast impending disease cases. The fairly encouraging forecast accuracy metrics for the 1- and 2-day advance prediction suggest that the number of impending cases could be estimated with some degree of certainty. Much like a weather forecast system, the procedure combines technical and scientific skills using empirical data but the interpretation requires experience and intuitive reasoning.postprin

Impact of the shedding level on transmission of persistent infections in Mycobacterium avium subspecies paratuberculosis (MAP)

Super-shedders are infectious individuals that contribute a disproportionate amount of infectious pathogen load to the environment. A super-shedder host may produce up to 10 000 times more pathogens than other infectious hosts. Super-shedders have been reported for multiple human and animal diseases. If their contribution to infection dynamics was linear to the pathogen load, they would dominate infection dynamics. We here focus on quantifying the effect of super-shedders on the spread of infection in natural environments to test if such an effect actually occurs in Mycobacterium avium subspecies paratuberculosis (MAP). We study a case where the infection dynamics and the bacterial load shed by each host at every point in time are known. Using a maximum likelihood approach, we estimate the parameters of a model with multiple transmission routes, including direct contact, indirect contact and a background infection risk. We use longitudinal data from persistent infections (MAP), where infectious individuals have a wide distribution of infectious loads, ranging upward of three orders of magnitude. We show based on these parameters that the effect of super-shedders for MAP is limited and that the effect of the individual bacterial load is limited and the relationship between bacterial load and the infectiousness is highly concave. A 1000-fold increase in the bacterial contribution is equivalent to up to a 2–3 fold increase in infectiousness.https://doi.org/10.1186/s13567-016-0323-

Privacy-Preserving Epidemiological Modeling on Mobile Graphs

Since 2020, governments all over the world have used a variety of containment measures to control the spread of COVID-19, such as contact tracing, social distance regulations, and curfews. Epidemiological simulations are commonly used to assess the impact of those policies before they are implemented. Unfortunately, their predictive accuracy is hampered by the scarcity of relevant empirical data, specifically detailed social contact graphs. As this data is inherently privacy-critical, there is an urgent need for a method to perform powerful epidemiological simulations on real-world contact graphs without disclosing sensitive~information. In this work, we present RIPPLE, a privacy-preserving epidemiological modeling framework that enables the execution of standard epidemiological models for infectious disease on a population\u27s most recent real contact graph while keeping all contact information privately and locally on the participants\u27 devices. As underlying building block, we present PIR-SUM, a novel extension to private information retrieval that allows users to securely download the sum of a set of elements from a database rather than individual elements. We provide a proof-of-concept implementation of our protocols demonstrating that a 2-week simulation over a population of half a million can be finished in 7 minutes, with each participant communicating less than 50 KB of data