2,911 research outputs found
Extracting the time-dependent transmission rate from infection data via solution of an inverse ODE problem
The transmission rate of many acute infectious diseases varies significantly in time, but the underlying mechanisms are usually uncertain. They may include seasonal changes in the environment, contact rate, immune system response, etc. The transmission rate has been thought difficult to measure directly. We present a new algorithm to compute the time-dependent transmission rate directly from prevalence data, which makes no assumptions about the number of susceptible or vital rates. The algorithm follows our complete and explicit solution of a mathematical inverse problem for SIR-type transmission models. We prove that almost any infection profile can be perfectly fitted by an SIR model with variable transmission rate. This clearly shows a serious danger of overfitting such transmission models. We illustrate the algorithm with historic UK measles data and our observations support the common belief that measles transmission was predominantly driven by school contacts
Propagation on networks: an exact alternative perspective
By generating the specifics of a network structure only when needed
(on-the-fly), we derive a simple stochastic process that exactly models the
time evolution of susceptible-infectious dynamics on finite-size networks. The
small number of dynamical variables of this birth-death Markov process greatly
simplifies analytical calculations. We show how a dual analytical description,
treating large scale epidemics with a Gaussian approximations and small
outbreaks with a branching process, provides an accurate approximation of the
distribution even for rather small networks. The approach also offers important
computational advantages and generalizes to a vast class of systems.Comment: 8 pages, 4 figure
The Social and Political Dimensions of the Ebola Response: Global Inequality, Climate Change, and Infectious Disease
The 2014 Ebola crisis has highlighted public-health vulnerabilities in Liberia, Sierra
Leone, and Guinea – countries ravaged by extreme poverty, deforestation and
mining-related disruption of livelihoods and ecosystems, and bloody civil wars in
the cases of Liberia and Sierra Leone. Ebola’s emergence and impact are grounded
in the legacy of colonialism and its creation of enduring inequalities within African
nations and globally, via neoliberalism and the Washington Consensus. Recent
experiences with new and emerging diseases such as SARS and various strains of
HN influenzas have demonstrated the effectiveness of a coordinated local and
global public health and education-oriented response to contain epidemics. To what
extent is international assistance to fight Ebola strengthening local public health and
medical capacity in a sustainable way, so that other emerging disease threats, which
are accelerating with climate change, may be met successfully? This chapter
considers the wide-ranging socio-political, medical, legal and environmental factors
that have contributed to the rapid spread of Ebola, with particular emphasis on the
politics of the global and public health response and the role of gender, social
inequality, colonialism and racism as they relate to the mobilization and
establishment of the public health infrastructure required to combat Ebola and other
emerging diseases in times of climate change
Can co-location be used as a proxy for face-to-face contacts?
Technological advances have led to a strong increase in the number of data
collection efforts aimed at measuring co-presence of individuals at different
spatial resolutions. It is however unclear how much co-presence data can inform
us on actual face-to-face contacts, of particular interest to study the
structure of a population in social groups or for use in data-driven models of
information or epidemic spreading processes. Here, we address this issue by
leveraging data sets containing high resolution face-to-face contacts as well
as a coarser spatial localisation of individuals, both temporally resolved, in
various contexts. The co-presence and the face-to-face contact temporal
networks share a number of structural and statistical features, but the former
is (by definition) much denser than the latter. We thus consider several
down-sampling methods that generate surrogate contact networks from the
co-presence signal and compare them with the real face-to-face data. We show
that these surrogate networks reproduce some features of the real data but are
only partially able to identify the most central nodes of the face-to-face
network. We then address the issue of using such down-sampled co-presence data
in data-driven simulations of epidemic processes, and in identifying efficient
containment strategies. We show that the performance of the various sampling
methods strongly varies depending on context. We discuss the consequences of
our results with respect to data collection strategies and methodologies
Rumors of disease in the global village: outbreak verification.
Emerging infectious diseases and the growth of information technology have produced new demands and possibilities for disease surveillance and response. Increasing numbers of outbreak reports must be assessed rapidly so that control efforts can be initiated and unsubstantiated reports can be identified to protect countries from unnecessary economic damage. The World Health Organization has set up a process for timely outbreak verification to convert large amounts of data into accurate information for suitable action. We describe the context and processes of outbreak verification and information dissemination
Epidemic outbreak prediction using machine learning models
In today's world,the risk of emerging and re-emerging epidemics have
increased.The recent advancement in healthcare technology has made it possible
to predict an epidemic outbreak in a region.Early prediction of an epidemic
outbreak greatly helps the authorities to be prepared with the necessary
medications and logistics required to keep things in control. In this article,
we try to predict the epidemic outbreak (influenza, hepatitis and malaria) for
the state of New York, USA using machine and deep learning algorithms, and a
portal has been created for the same which can alert the authorities and health
care organizations of the region in case of an outbreak. The algorithm takes
historical data to predict the possible number of cases for 5 weeks into the
future. Non-clinical factors like google search trends,social media data and
weather data have also been used to predict the probability of an outbreak.Comment: 16 pages, 5 tables, 4 figure
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A mechanistic spatio-temporal framework for modelling individual-to-individual transmission—With an application to the 2014-2015 West Africa Ebola outbreak
In recent years there has been growing availability of individual-level spatio-temporal disease data, particularly due to the use of modern communicating devices with GPS tracking functionality. These detailed data have been proven useful for inferring disease transmission to a more refined level than previously. However, there remains a lack of statistically sound frameworks to model the underlying transmission dynamic in a mechanistic manner. Such a development is particularly crucial for enabling a general epidemic predictive framework at the individual level. In this paper we propose a new statistical framework for mechanistically modelling individual-to-individual disease transmission in a landscape with heterogeneous population density. Our methodology is first tested using simulated datasets, validating our inferential machinery. The methodology is subsequently applied to data that describes a regional Ebola outbreak in Western Africa (2014-2015). Our results show that the methods are able to obtain estimates of key epidemiological parameters that are broadly consistent with the literature, while revealing a significantly shorter distance of transmission. More importantly, in contrast to existing approaches, we are able to perform a more general model prediction that takes into account the susceptible population. Finally, our results show that, given reasonable scenarios, the framework can be an effective surrogate for susceptible-explicit individual models which are often computationally challenging
Reconstructing the recent West Nile virus lineage 2 epidemic in Europe and Italy using discrete and continuous phylogeography
West Nile virus lineage 2 (WNV-2) was mainly confined to sub-Saharan Africa until the early 2000s, when it was identified for the first time in Central Europe causing outbreaks of human and animal infection. The aim of this study was to reconstruct the origin and dispersion of WNV-2 in Central Europe and Italy on a phylodynamic and phylogeographical basis. To this aim, discrete and continuous space phylogeographical models were applied to a total of 33 newly characterised full-length viral genomes obtained from mosquitoes, birds and humans in Northern Italy in the years 2013-2015 aligned with 64 complete sequences isolated mainly in Europe. The European isolates segregated into two highly significant clades: a small one including three sequences and a large clade including the majority of isolates obtained in Central Europe since 2004. Discrete phylogeographical analysis showed that the most probable location of the root of the largest European clade was in Hungary a mean 12.78 years ago. The European clade bifurcated into two highly supported subclades: one including most of the Central/East European isolates and the other encompassing all of the isolates obtained in Greece. The continuous space phylogeographical analysis of the Italian clade showed that WNV-2 entered Italy in about 2008, probably by crossing the Adriatic sea and reaching a central area of the Po Valley. The epidemic then spread simultaneously eastward, to reach the region of the Po delta in 2013, and westward to the border area between Lombardy and Piedmont in 2014; later, the western strain changed direction southward, and reached the central area of the Po valley once again in 2015. Over a period of about seven years, the virus spread all over an area of northern Italy by following the Po river and its main tributaries
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