Pandemic | EPFL Graph Search

A pandemic (paen'dEmIk ) is an epidemic of an infectious disease that has spread across a large region, for instance multiple continents or worldwide, affecting a substantial number of individuals. Widespread endemic diseases with a stable number of infected individuals such as recurrences of seasonal influenza are generally excluded as they occur simultaneously in large regions of the globe rather than being spread worldwide. Throughout human history, there have been a number of pandemics of diseases such as smallpox. The most fatal pandemic in recorded history was the Black Death—also known as The Plague—which killed an estimated 75–200 million people in the 14th century. The term had not been used then but was used for later epidemics, including the 1918 influenza pandemic—more commonly known as the Spanish flu. The most recent pandemics include the HIV/AIDS pandemic, the 2009 H1N1 pandemic and the COVID-19 pandemic. Almost all these diseases still circulate consistently among hu

The molecular treasure box of environmental Vibrio cholerae strains

Natalia Carolina Drebes Dörr

Cholera pandemics have been affecting humankind for centuries and are still considered a major public health problem, especially in regions around the world with poor access to clean water and sanitation. Cholera pandemics are caused by a specific lineage of the bacterial pathogen Vibrio cholerae, while the vast majority of the species' diversity is found in innocuous environmental strains. The complete set of factors that allowed the emergence and success of the pandemic lineage are still unknown. Importantly, most V. cholerae strains are adapted to aquatic habitats and trigger their behavior accordingly. The aim of this thesis was therefore to gain a better understanding of what is special about the pandemic lineage when contrasted to its environmental counterparts. Specifically, we investigated environmentally important bacterial behaviors in a comparative framework. Firstly, considering the relevance of eukaryotic predation in shaping bacterioplankton structure, we investigated possible strategies used by V. cholerae to avoid grazing by the predatory amoeba Dictyostelium discoideum. We observed that V. cholerae was able to rapidly intoxicate amoebae and that the intoxication dynamics differed when caused by pandemic or environmental strains. Secondly, we compared a well-conserved set of mobile genetic elements (MGEs) of the pandemic lineage, many of which are involved in key aspects of cholera pathogenesis, with the respective counterparts of environmental strains. To do so, we sequenced and de novo assembled the genomes of fifteen environmental isolates and thoroughly described their mobilome. Moreover, transcription profiling demonstrated that most genes located on these MGEs are expressed. Thirdly, we compared environmental and pandemic strains regarding their potential to kill bacterial competitors and to defend themselves against protozoan predation. Specifically, we assessed the role of two molecular weapons: the pore-forming toxin hemolysin and the type six secretion system (T6SS), a molecular killing device that delivers effector toxins into target cells. While environmental strains keep both of these weapons constitutively active, pandemic strains employ regulatory mechanisms to control their expression. We observed that all environmental V. cholerae isolates used their T6SS to efficiently outcompete prey bacteria, while only two clades of the environmental isolates also intoxicated eukaryotic amoebae. Furthermore, we performed a meticulous in silico characterization of the effector and immunity proteins carried by the environmental strains and showed in pairwise killing experiments that a high degree of immunity protein identity was required to allow the strains' coexistence. Finally, we addressed the phenomenon of T6SS constitutive activity in non-pandemic V. cholerae strains, which has puzzled the field since the T6SS' discovery in 2006. Using a transformation-based strain library, we uncovered a genomic region that controls T6SS activity. In summary, work developed in this thesis contributes to a better understanding of V. cholerae's evolution from an innocuous inhabitant of aquatic environments to a pandemic-causing human pathogen.

EPFL2021

Dynamic Model Predictions of Covid-19 Pandemic Mitigation Scenarios

David Jenny

Relevant pandemic-spread scenario simulations provide guiding principles for containment and mitigation policy developments. In this report we discuss model predictions for infected, recovered and dead people during a pandemic. The model consists of a simple reaction mechanism, which considers the population size, reported and unreported infections, reported and unreported recoveries and number of deaths. It is assumed that recovered persons are immune (e.g. mutations are not considered), that the virus can only be passed on by unreported infected persons and that no vaccine will be found within the considered periods. Moreover, the model does not take into account age dependency, but considers higher mortality rates due to temporary shortage of emergency rooms. Some of the model parameters have been fitted to the reported cases outside of China1 from January 22 to March 12 of the 2020 Covid-19 pandemic. The other parameters were chosen in a plausible range and sensitivity studies were performed; among all tested parameter values, those leading to the lowest number of deaths were chosen for the presented case studies, i.e., the predictions are rather optimistic. Scenarios in which the infection rate per person is reduced for periods of 680 and 183 days (”social distancing phase”) and scenarios in which the testing frequency is increased for 680 days (”frequent testing phase”) are studied. While the predicted number of deaths can dramatically be reduced during the ”social distancing” and ”frequent testing” phases, the fatality numbers increase again some time after these phases end; for all these scenarios the predicted final number of deaths lies between 3% and 5% of the initial population (not much different from the base case). It is interesting, however, that more frequent testing seems to be much more effective than social distancing and may cause less economic damage. The currently low detection rate is obviously linked to the long incubation period typical for Covid-19. Incubation for typical flu is much shorter, and as soon people fall ill and stay at home, they are effectively detected and do not further spread the virus.

On the use of applied machine learning and digital infrastructure to leverage social media data in health and epidemiology

Martin Mathias Müller

The quantification of population-level health behaviors is crucial for guiding public health policy. However, traditional methods for measuring such health behaviors have several short- comings. In recent years social media data has been successfully used to measure health behaviors and may be used as a low-cost and real-time addition to traditional data sources. Methods from the field of natural language processing are increasingly used to automatically process, filter and categorize the rapidly growing amount of publicly available social media data. However, a number of methodological challenges limit the rate at which we can generate insight from such data. In this work I will argue that long-term investment into digital infrastructure and open source tooling is required in order to overcome these challenges. In chapter 2 we introduce the Crowd- breaks platform which is the basis of this thesis. Crowdbreaks is an open source framework for real-time data collection, continuous crowdsourced annotation, and continuous re-training of machine learning classifiers. In contrast to traditional research workflows, projects on Crowdbreaks run over an extended period of time, allowing for the observation of health trends over multiple years while keeping algorithms up-to-date. In chapter 3 we quantify the occurrence of concept drift in vaccine-related Twitter data, which further validates the need for the Crowdbreaks platform. In chapter 4 we use the Crowdbreaks platform to trace sentiment towards the novel gene-editing technology CRISPR/Cas9 back to its first application in 2013 and investigate how public opinion may have been affected in context of recent scandals sur- rounding the technology. In chapter 5 we turn our attention to the COVID-19 pandemic and analyze who was speaking and who was heard in the early months of the pandemic. Chapter 6 builds on this work and explores the dynamics of Twitter communities during the COVID-19 pandemic. Lastly, in chapter 7 we introduce COVID-Twitter-BERT, a domain-specific language model which has been used in various downstream natural language processing applications on COVID-19-related Twitter data.

EPFL2021

On the use of applied machine learning and digital infrastructure to leverage social media data in health and epidemiology

Martin Mathias Müller

EPFL2021

The molecular treasure box of environmental Vibrio cholerae strains

Natalia Carolina Drebes Dörr

EPFL2021

Dynamic Model Predictions of Covid-19 Pandemic Mitigation Scenarios

David Jenny