Adequacy of SEIR models when epidemics have spatial structure: Ebola in Sierra Leone.

Dynamic SEIR (Susceptible, Exposed, Infectious, Removed) compartmental models provide a tool for predicting the size and duration of both unfettered and managed outbreaks-the latter in the context of interventions such as case detection, patient isolation, vaccination and treatment. The reliability of this tool depends on the validity of key assumptions that include homogeneity of individuals and spatio-temporal homogeneity. Although the SEIR compartmental framework can easily be extended to include demographic (e.g. age) and additional disease (e.g. healthcare workers) classes, dependence of transmission rates on time, and metapopulation structure, fitting such extended models is hampered by both a proliferation of free parameters and insufficient or inappropriate data. This raises the question of how effective a tool the basic SEIR framework may actually be. We go some way here to answering this question in the context of the 2014-2015 outbreak of Ebola in West Africa by comparing fits of an SEIR time-dependent transmission model to both country- and district-level weekly incidence data. Our novel approach in estimating the effective-size-of-the-populations-at-risk ( Neff) and initial number of exposed individuals ( E0) at both district and country levels, as well as the transmission function parameters, including a time-to-halving-the-force-of-infection ( tf/2) parameter, provides new insights into this Ebola outbreak. It reveals that the estimate R0 ≈ 1.7 from country-level data appears to seriously underestimate R0 ≈ 3.3 - 4.3 obtained from more spatially homogeneous district-level data. Country-level data also overestimate tf/2 ≈ 22 weeks, compared with 8-10 weeks from district-level data. Additionally, estimates for the duration of individual infectiousness is around two weeks from spatially inhomogeneous country-level data compared with 2.4-4.5 weeks from spatially more homogeneous district-level data, which estimates are rather high compared with most values reported in the literature. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'

Tests for neglected heterogeneity in moment condition models

The central concern of the paper is with the formulation of tests of neglected parameter heterogeneity appropriate for model environments specified by a number of unconditional or conditional moment conditions. We initially consider the unconditional moment restrictions framework. Optimal m-tests against moment condition parameter heterogeneity are derived with the relevant Jacobian matrix obtained as the second order derivative of the moment indicator in a leading case. GMM and GEL tests of specification based on generalized information matrix equalities appropriate for moment-based models are described and their relation to the optimal m-tests against moment condition parameter heterogeneity examined. A fundamental and important difference is noted between GMM and GEL constructions. The paper is concluded by a generalization of these tests to the conditional moment context.

A symptotic Bias for GMM and GEL Estimators with Estimated Nuisance Parameter

This papers studies and compares the asymptotic bias of GMM and generalized empirical likelihood (GEL) estimators in the presence of estimated nuisance parameters. We consider cases in which the nuisance parameter is estimated from independent and identical samples. A simulation experiment is conducted for covariance structure models. Empirical likelihood offers much reduced mean and median bias, root mean squared error and mean absolute error, as compared with two-step GMM and other GEL methods. Both analytical and bootstrap bias-adjusted two-step GMM estima-tors are compared. Analytical bias-adjustment appears to be a serious competitor to bootstrap methods in terms of finite sample bias, root mean squared error and mean absolute error. Finite sample variance seems to be little affected

Asymptotic bias for GMM and GEL estimators with estimated nuisance parameters

This papers studies and compares the asymptotic bias of GMM and generalized empirical likelihood (GEL) estimators in the presence of estimated nuisance parameters. We consider cases in which the nuisance parameter is estimated from independent and identical samples. A simulation experiment is conducted for covariance structure models. Empirical likelihood offers much reduced mean and median bias, root mean squared error and mean absolute error, as compared with two-step GMM and other GEL methods. Both analytical and bootstrap bias-adjusted two-step GMM estimators are compared. Analytical bias-adjustment appears to be a serious competitor to bootstrap methods in terms of finite sample bias, root mean squared error and mean absolute error. Finite sample variance seems to be little affected.

An estuarine box model of freshwater delivery to the coastal ocean for use in climate models

Present day climate models employ a coarse horizontal grid that is unable to fully resolve estuaries or continental shelves. The importation of fresh water from rivers is critical to the state of deep ocean stratification, but currently the processing of that fresh water as it passes from the river through the estuary and adjacent shelf is not represented in the coastal boundary conditions of climate models. An efficient way to represent this input of fresh water to the deep ocean would be to treat the estuary and shelf domains as two coupled box models with river water input to the estuarine box and mixed fresh water and coastal water output from the shelf box to the deep ocean.We develop and test the estuary box model here. The potential energy anomaly ϕ is found from the five competing rates of change induced by freshwater inflow, mixed water outflow to the shelf, tidal mixing, surface heat flux, and wind-induced mixing. When application of the box model is made to the Delaware estuary, the wind mixing term contributes little. A 15-year time series of ϕ compares surprisingly well with the calculations of a three-dimensional numerical model applied to the Delaware estuary. The results encourage the future development of a shelf box model as the next step in constructing needed boundary conditions for input of fresh water to the deep ocean component of coupled climate models

Filling Knowledge Gaps in a Broad-Coverage Machine Translation System

Knowledge-based machine translation (KBMT) techniques yield high quality in domains with detailed semantic models, limited vocabulary, and controlled input grammar. Scaling up along these dimensions means acquiring large knowledge resources. It also means behaving reasonably when definitive knowledge is not yet available. This paper describes how we can fill various KBMT knowledge gaps, often using robust statistical techniques. We describe quantitative and qualitative results from JAPANGLOSS, a broad-coverage Japanese-English MT system.Comment: 7 pages, Compressed and uuencoded postscript. To appear: IJCAI-9

Understanding Practical Limits to Heavy Truck Drag Reduction

A heavy truck wind tunnel test program is currently underway at the Langley Full Scale Tunnel (LFST). Seven passive drag reducing device configurations have been evaluated on a heavy truck model with the objective of understanding the practical limits to drag reduction achievable on a modern tractor trailer through add-on devices. The configurations tested include side skirts of varying length, a full gap seal, and tapered rear panels. All configurations were evaluated over a nominal 15 degree yaw sweep to establish wind averaged drag coefficients over a broad speed range using SAE J1252. The tests were conducted by first quantifying the benefit of each individual treatment and finally looking at the combined benefit of an ideal fully treated vehicle. Results show a maximum achievable gain in wind averaged drag coefficient (65 mph) of about 31 percent for the modern conventional-cab tractor-trailer. © 2009 SAE International