scholarly journals Improving early epidemiological assessment of emerging Aedes-transmitted epidemics using historical data

2018 ◽  
Author(s):  
Julien Riou ◽  
Chiara Poletto ◽  
Pierre-Yves Boëlle
Keyword(s):  
Historical Data ◽  
A Priori ◽  
Total Duration ◽  
Reproduction Ratio ◽  
Peak Incidence ◽  
Incidence Data ◽  
Epidemic Size ◽  
Data Points ◽  
Final Epidemic Size ◽  
Reporting Rates

AbstractModel-based epidemiological assessment is useful to support decision-making at the beginning of an emerging Aedes-transmitted outbreak. However, early forecasts are generally unreliable as little information is available in the first few incidence data points. Here, we show how past Aedes-transmitted epidemics help improve these predictions. The approach was applied to the 2015-2017 Zika virus epidemics in three islands of the French West Indies, with historical data including other Aedes-transmitted diseases (Chikungunya and Zika) in the same and other locations. Hierarchical models were used to build informative a priori distributions on the reproduction ratio and the reporting rates. The accuracy and sharpness of forecasts improved substantially when these a priori distributions were used in models for prediction. For example, early forecasts of final epidemic size obtained without historical information were 3.3 times too high on average (range: 0.2 to 5.8) with respect to the eventual size, but were far closer (1.1 times the real value on average, range: 0.4 to 1.5) using information on past CHIKV epidemics in the same places. Likewise, the 97.5% upper bound for maximal incidence was 15.3 times (range: 2.0 to 63.1) the actual peak incidence, and became much sharper at 2.4 times (range: 1.3 to 3.9) the actual peak incidence with informative a priori distributions. Improvements were more limited for the date of peak incidence and the total duration of the epidemic. The framework can adapt to all forecasting models at the early stages of emerging Aedes-transmitted outbreaks.

Heterogeneity in epidemic models and its effect on the spread of infection

1998 ◽  
Vol 35 (3) ◽  
pp. 651-661 ◽  
Author(s):  
Håkan Andersson ◽  
Tom Britton
Keyword(s):  
Heterogeneous Population ◽  
Epidemic Models ◽  
Homogeneous Population ◽  
Epidemic Size ◽  
Infection Pressure ◽  
Group A ◽  
Final Epidemic Size ◽  
Spread Of Infection ◽  
Very High ◽  
High Infectivity

We first study an epidemic amongst a population consisting of individuals with the same infectivity but with varying susceptibilities to the disease. The asymptotic final epidemic size is compared with the corresponding size for a homogeneous population. Then we group a heterogeneous population into households, assuming very high infectivity within households, and investigate how the global infection pressure is affected by rearranging individuals between the households. In both situations considered, it turns out that whether or not homogenizing the individuals or households will result in an increased spread of infection actually depends on the infectiousness of the disease.

scholarly journals Asymptomatic individuals can increase the final epidemic size under adaptive human behavior

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Baltazar Espinoza ◽  
Madhav Marathe ◽  
Samarth Swarup ◽  
Mugdha Thakur
Keyword(s):  
Planning Horizon ◽  
Infection Risk ◽  
Epidemiological Models ◽  
Epidemic Size ◽  
Behavioral Adaptations ◽  
Future State ◽  
Final Epidemic Size ◽  
Asymptomatic Individuals ◽  
The Impact ◽  
Finite Planning Horizon

AbstractInfections produced by non-symptomatic (pre-symptomatic and asymptomatic) individuals have been identified as major drivers of COVID-19 transmission. Non-symptomatic individuals, unaware of the infection risk they pose to others, may perceive themselves—and be perceived by others—as not presenting a risk of infection. Yet, many epidemiological models currently in use do not include a behavioral component, and do not address the potential consequences of risk misperception. To study the impact of behavioral adaptations to the perceived infection risk, we use a mathematical model that incorporates the behavioral decisions of individuals, based on a projection of the system’s future state over a finite planning horizon. We found that individuals’ risk misperception in the presence of non-symptomatic individuals may increase or reduce the final epidemic size. Moreover, under behavioral response the impact of non-symptomatic infections is modulated by symptomatic individuals’ behavior. Finally, we found that there is an optimal planning horizon that minimizes the final epidemic size.

scholarly journals Areal parameter estimates from multiple datasets

2019 ◽  
Vol 475 (2231) ◽  
pp. 20190352 ◽  
Author(s):  
B. L. N. Kennett
Keyword(s):  
Significant Contribution ◽  
A Priori ◽  
Relative Weight ◽  
Parameter Estimates ◽  
Multiple Datasets ◽  
Wide Range ◽  
Spatially Distributed ◽  
Data Points ◽  
Spatial Terms ◽  
Weighted Combination

A wide range of methods exist for interpolation between spatially distributed points drawn from a single population. Yet often multiple datasets are available with differing distribution, character and reliability. A simple scheme is introduced to allow the fusion of multiple datasets. Each dataset is assigned an a priori spatial influence zone around each point and a relative weight based on its physical character. The composite result at a specific location is a weighted combination of the spatial terms for all the available data points that make a significant contribution. The combination of multiple datasets is illustrated with the construction of a unified Moho surface in part of southern Australia from results exploiting a variety of different styles of analysis.

scholarly journals Antiviral treatment for the control of pandemic influenza: some logistical constraints

2007 ◽  
Vol 5 (22) ◽  
pp. 545-553 ◽  
Author(s):  
N Arinaminpathy ◽  
A.R McLean
Keyword(s):  
Influenza Pandemic ◽  
Antiviral Treatment ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Epidemic Size ◽  
Conservative Policy ◽  
Control Programmes ◽  
Final Epidemic Size ◽  
Number Of Treatment ◽  
Modelling Approach

Disease control programmes for an influenza pandemic will rely initially on the deployment of antiviral drugs such as Tamiflu, until a vaccine becomes available. However, such control programmes may be severely hampered by logistical constraints such as a finite stockpile of drugs and a limit on the distribution rate. We study the effects of such constraints using a compartmental modelling approach. We find that the most aggressive possible antiviral programme minimizes the final epidemic size, even if this should lead to premature stockpile run-out. Moreover, if the basic reproductive number R 0 is not too high, such a policy can avoid run-out altogether. However, where run-out would occur, such benefits must be weighed against the possibility of a higher epidemic peak than if a more conservative policy were followed. Where there is a maximum number of treatment courses that can be dispensed per day, reflecting a manpower limit on antiviral distribution, our results suggest that such a constraint is unlikely to have a significant impact (i.e. increasing the final epidemic size by more than 10%), as long as drug courses sufficient to treat at least 6% of the population can be dispensed per day.

EXACT ANALYTICAL EXPRESSIONS FOR THE FINAL EPIDEMIC SIZE OF AN SIR MODEL ON SMALL NETWORKS

2016 ◽  
Vol 57 (4) ◽  
pp. 429-444 ◽  
Author(s):  
K. MCCULLOCH ◽  
M. G. ROBERTS ◽  
C. R. LAING
Keyword(s):  
Initial Conditions ◽  
Transmission Rate ◽  
Sir Model ◽  
Contact Network ◽  
Sir Epidemic Model ◽  
Node Number ◽  
Sir Epidemic ◽  
Epidemic Size ◽  
Final Epidemic Size ◽  
Analytical Expressions

We investigate the dynamics of a susceptible infected recovered (SIR) epidemic model on small networks with different topologies, as a stepping stone to determining how the structure of a contact network impacts the transmission of infection through a population. For an SIR model on a network of$N$nodes, there are$3^{N}$configurations that the network can be in. To simplify the analysis, we group the states together based on the number of nodes in each infection state and the symmetries of the network. We derive analytical expressions for the final epidemic size of an SIR model on small networks composed of three or four nodes with different topological structures. Differential equations which describe the transition of the network between states are also derived and solved numerically to confirm our analysis. A stochastic SIR model is numerically simulated on each of the small networks with the same initial conditions and infection parameters to confirm our results independently. We show that the structure of the network, degree of the initial infectious node, number of initial infectious nodes and the transmission rate all significantly impact the final epidemic size of an SIR model on small networks.

Heterogeneity in epidemic models and its effect on the spread of infection

1998 ◽  
Vol 35 (03) ◽  
pp. 651-661 ◽  
Author(s):  
Håkan Andersson ◽  
Tom Britton
Keyword(s):  
Heterogeneous Population ◽  
Epidemic Models ◽  
Homogeneous Population ◽  
Epidemic Size ◽  
Infection Pressure ◽  
Group A ◽  
Final Epidemic Size ◽  
Spread Of Infection ◽  
Very High ◽  
High Infectivity

We first study an epidemic amongst a population consisting of individuals with the same infectivity but with varying susceptibilities to the disease. The asymptotic final epidemic size is compared with the corresponding size for a homogeneous population. Then we group a heterogeneous population into households, assuming very high infectivity within households, and investigate how the global infection pressure is affected by rearranging individuals between the households. In both situations considered, it turns out that whether or not homogenizing the individuals or households will result in an increased spread of infection actually depends on the infectiousness of the disease.

Reverse Engineering of Mechanical Parts: A Brief Overview of Existing Approaches and Possible New Strategies

2016 ◽  
Author(s):  
Francesco Buonamici ◽  
Monica Carfagni
Keyword(s):  
Reverse Engineering ◽  
A Priori ◽  
Reconstruction Process ◽  
Mechanical Parts ◽  
Cad System ◽  
Software Packages ◽  
Geometrical Constraints ◽  
Cad Models ◽  
Data Points ◽  
New Strategies

Reverse Engineering (RE), also known as “CAD reconstruction”, aims at the reconstruction of 3D geometric models of objects/mechanical parts, starting from 3D measured data (points/mesh). In recent years, considerable developments in RE were achieved thanks to both academic and industrial research (e.g. RE software packages). The aim of this work is to provide an overview of state of the art techniques and approaches presented in recent years (considering at the same time tools and methods provided by commercial CAD software and RE systems). In particular, this article focuses on the “constrained fitting” approach, which considers geometrical constraints between the generated surfaces, improving the reconstruction result. On the basis of the overview, possible theoretical principles are drafted with the aim of suggest new strategies to make the CAD reconstruction process more effective in order to obtain more ready/usable CAD models. Finally, a new RE framework is briefly outlined: the proposed approach hypothesizes a tool built within the environment of an existing CAD system and considers the fitting of a custom-built archetypal model, defined with all the a-priori known dimensions and constraints, to the scanned data.

Improving epidemic size prediction through stable reconstruction of disease parameters by reduced iteratively regularized Gauss–Newton algorithm

2017 ◽  
Vol 25 (5) ◽  
pp. 653-667 ◽  
Author(s):  
Alexandra Smirnova ◽  
Gerardo Chowell-Puente ◽  
Linda deCamp ◽  
Seyed Moghadas ◽  
Michael Jameson Sheppard
Keyword(s):  
Phenomenological Models ◽  
Public Health Intervention ◽  
Epidemiological Data ◽  
Risk Groups ◽  
Stable Algorithm ◽  
Incidence Data ◽  
Epidemic Size ◽  
Optimal Resource ◽  
Non Linear Operator ◽  
Stable Reconstruction

AbstractClassical compartmental epidemic models of infectious diseases track the dynamic transition of individuals between different epidemiological states or risk groups. Reliable quantification of various transmission pathways in these models is paramount for optimal resource allocation and successful design of public health intervention programs. However, with limited epidemiological data available in the case of an emerging disease, simple phenomenological models based on a smaller number of parameters can play an important role in our quest to make forward projections of possible outbreak scenarios. In this paper, we employ the generalized Richards model for stable numerical estimation of the epidemic size (defined as the total number of infections throughout the epidemic) and its turning point using case incidence data of the early epidemic growth phase. The minimization is carried out by what we call the Reduced Iteratively Regularized Gauss–Newton (RIRGN) algorithm, a problem-oriented numerical scheme that takes full advantage of the specific structure of the non-linear operator at hand. The convergence analysis of the RIRGN method is suggested and numerical simulations are conducted with real case incidence data for the 2014–15 Ebola epidemic in West Africa. We show that the proposed RIRGN provides a stable algorithm for early estimation of turning points using simple phenomenological models with limited data.

scholarly journals A Novel Measurement-Based Model for Calculating Diffusive Fluxes Across Substrate-Water Interfaces of Marine Aggregates, Sediments and Biofilms

2021 ◽  
Vol 8 ◽  
Author(s):  
Nasrollah Moradi ◽  
Isabell Klawonn ◽  
Morten H. Iversen ◽  
Frank Wenzhöfer ◽  
Hans-Peter Grossart ◽  
...  
Keyword(s):  
A Priori ◽  
Biologically Active ◽  
Small Scale ◽  
Interface Position ◽  
Marine Aggregates ◽  
Data Points ◽  
Global Biogeochemical Cycles ◽  
Diffusive Fluxes ◽  
Earth’S Climate

Our understanding of the small-scale processes that drive global biogeochemical cycles and the Earth’s climate is dependent on accurate estimations of interfacial diffusive fluxes to and from biologically-active substrates in aquatic environments. In this study, we present a novel model approach for accurate calculations of diffusive fluxes of dissolved gases, nutrients, and solutes from concentration profiles measured across the substrate-water interfaces using microsensors. The model offers a robust computational scheme for automatized determination of the interface position and enables precise calculations of the interfacial diffusive fluxes simultaneously. In contrast to other methods, the new approach is not restricted to any particular substrate geometry, does not require a priori determination of the interface position for the flux calculation, and, thus, reduces the uncertainties in calculated fluxes arising from partly subjective identification of the interface position. In addition, it is robust when applied to measured profiles containing scattered data points and insensitive to reasonable decreases of the spatial resolution of the data points. The latter feature allows for significantly reducing measurement time which is a crucial factor for in situ experiments.

scholarly journals Ensemble Stock Market Prediction using SVM,LSTM, and Linear Regression

2021 ◽  
Author(s):  
Amila Indika ◽  
Nethmal Warusamana ◽  
Erantha Welikala ◽  
Sampath Deegalla
Keyword(s):  
Linear Regression ◽  
Stock Market ◽  
Historical Data ◽  
Stock Exchange ◽  
Stock Market Prediction ◽  
Data Points

Data for this research was gathered from online available sources from the NASDAQ American stock exchange.<div>We gathered data for most active 20 companies and 10 years of historical data from 21st September 2019 backwards. We used 40044 data points in total.</div>

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