782 research outputs found
Recommended from our members
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'
Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019.
BackgroundAs of February 25, 2019, 875 cases of Ebola virus disease (EVD) were reported in North Kivu and Ituri Provinces, Democratic Republic of Congo. Since the beginning of October 2018, the outbreak has largely shifted into regions in which active armed conflict has occurred, and in which EVD cases and their contacts have been difficult for health workers to reach. We used available data on the current outbreak, with case-count time series from prior outbreaks, to project the short-term and long-term course of the outbreak.MethodsFor short- and long-term projections, we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission rates estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used two regression models to estimate similar projection periods. Short- and long-term projections were estimated using negative binomial autoregression and Theil-Sen regression, respectively. We also used Gott's rule to estimate a baseline minimum-information projection. We then constructed an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes. From August 20, 2018 to February 25, 2019, short-term model projections were validated against known case counts.ResultsDuring validation of short-term projections, from one week to four weeks, we found models consistently scored higher on shorter-term forecasts. Based on case counts as of February 25, the stochastic model projected a median case count of 933 cases by February 18 (95% prediction interval: 872-1054) and 955 cases by March 4 (95% prediction interval: 874-1105), while the auto-regression model projects median case counts of 889 (95% prediction interval: 876-933) and 898 (95% prediction interval: 877-983) cases for those dates, respectively. Projected median final counts range from 953 to 1,749. Although the outbreak is already larger than all past Ebola outbreaks other than the 2013-2016 outbreak of over 26,000 cases, our models do not project that it is likely to grow to that scale. The stochastic model estimates that vaccination coverage in this outbreak is lower than reported in its trial setting in Sierra Leone.ConclusionsOur projections are concentrated in a range up to about 300 cases beyond those already reported. While a catastrophic outbreak is not projected, it is not ruled out, and prevention and vigilance are warranted. Prospective validation of our models in real time allowed us to generate more accurate short-term forecasts, and this process may prove useful for future real-time short-term forecasting. We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination, and this model estimate may offer a surrogate indicator for the outbreak response challenges
Predicting the extinction of Ebola spreading in Liberia due to mitigation strategies
The Ebola virus is spreading throughout West Africa and is causing thousands of deaths. In order to quantify the effectiveness of different strategies for controlling the spread, we develop a mathematical model in which the propagation of the Ebola virus through Liberia is caused by travel between counties. For the initial months in which the Ebola virus spreads, we find that the arrival times of the disease into the counties predicted by our model are compatible with World Health Organization data, but we also find that reducing mobility is insufficient to contain the epidemic because it delays the arrival of Ebola virus in each county by only a few weeks. We study the effect of a strategy in which safe burials are increased and effective hospitalisation instituted under two scenarios: (i) one implemented in mid-July 2014 and (ii) one in mid-August—which was the actual time that strong interventions began in Liberia. We find that if scenario (i) had been pursued the lifetime of the epidemic would have been three months shorter and the total number of infected individuals 80% less than in scenario (ii). Our projection under scenario (ii) is that the spreading will stop by mid-spring 2015.H.E.S. thanks the NSF (grants CMMI 1125290 and CHE-1213217) and the Keck Foundation for financial support. L.D.V. and L.A.B. wish to thank to UNMdP and FONCyT (Pict 0429/2013) for financial support. (CMMI 1125290 - NSF; CHE-1213217 - NSF; Keck Foundation; UNMdP; Pict 0429/2013 - FONCyT)Published versio
A generalized-growth model to characterize the early ascending phase of infectious disease outbreaks
A better characterization of the early growth dynamics of an epidemic is
needed to dissect the important drivers of disease transmission. We introduce a
2-parameter generalized-growth model to characterize the ascending phase of an
outbreak and capture epidemic profiles ranging from sub-exponential to
exponential growth. We test the model against empirical outbreak data
representing a variety of viral pathogens and provide simulations highlighting
the importance of sub-exponential growth for forecasting purposes. We applied
the generalized-growth model to 20 infectious disease outbreaks representing a
range of transmission routes. We uncovered epidemic profiles ranging from very
slow growth (p=0.14 for the Ebola outbreak in Bomi, Liberia (2014)) to near
exponential (p>0.9 for the smallpox outbreak in Khulna (1972), and the 1918
pandemic influenza in San Francisco). The foot-and-mouth disease outbreak in
Uruguay displayed a profile of slower growth while the growth pattern of the
HIV/AIDS epidemic in Japan was approximately linear. The West African Ebola
epidemic provided a unique opportunity to explore how growth profiles vary by
geography; analysis of the largest district-level outbreaks revealed
substantial growth variations (mean p=0.59, range: 0.14-0.97). Our findings
reveal significant variation in epidemic growth patterns across different
infectious disease outbreaks and highlights that sub-exponential growth is a
common phenomenon. Sub-exponential growth profiles may result from
heterogeneity in contact structures or risk groups, reactive behavior changes,
or the early onset of interventions strategies, and consideration of
"deceleration parameters" may be useful to refine existing mathematical
transmission models and improve disease forecasts.Comment: 31 pages, 9 Figures, 1 Supp. Figure, 1 Table, final accepted version
(in press), Epidemics - The Journal on Infectious Disease Dynamics, 201
A Simulation Study on Hypothetical Ebola Virus Transmission in India Using Spatiotemporal Epidemiological Modeler (STEM): A Way towards Precision Public Health
Background. Precision public health is a state-of-the-art concept in public health research and its application in health care. Application of information technology in field of epidemiology paves the way to its transformation to digital epidemiology. A geospatial epidemiological model was simulated to estimate the spread of Ebola virus disease after a hypothetical outbreak in India. Methods. It was a simulation study based on SEIR (Susceptible-Exposed-Infectious-Recovered) compartmental model. Simulation was done in Spatiotemporal Epidemiological Modeler (STEM). Epidemiological profile of Ebola virus, that transmitted throughout the Sierra Leon in 2014–2016, was fitted into the SEIR deterministic compartment model designed for India. Result. Spatiotemporal distribution of EVD exposed, infectious, and recovered population at 4-month interval represented by different figures. It is estimated that if no intervention is taken to stop the spread, within 2 years, almost half of the country will be effected by EVD and cumulative number of exposed individuals, infectious persons, and deaths will be 106947760, 30651674, and 18391005, respectively. Conclusion. Precision public health may play the key role to achieve the health related targets in the Sustainable Development Goals. Policy makers, public health specialists, and data scientists need to put their hands together to make precision public health a reality
Predicting the extinction of Ebola spreading in Liberia due to mitigation strategies
The Ebola virus is spreading throughout West Africa and is causing thousands
of deaths. In order to quantify the effectiveness of different strategies for
controlling the spread, we develop a mathematical model in which the
propagation of the Ebola virus through Liberia is caused by travel between
counties. For the initial months in which the Ebola virus spreads, we find that
the arrival times of the disease into the counties predicted by our model are
compatible with World Health Organization data, but we also find that reducing
mobility is insufficient to contain the epidemic because it delays the arrival
of Ebola virus in each county by only a few weeks. We study the effect of a
strategy in which safe burials are increased and effective hospitalisation
instituted under two scenarios: (i) one implemented in mid-July 2014 and (ii)
one in mid-August---which was the actual time that strong interventions began
in Liberia. We find that if scenario (i) had been pursued the lifetime of the
epidemic would have been three months shorter and the total number of infected
individuals 80\% less than in scenario (ii). Our projection under scenario (ii)
is that the spreading will stop by mid-spring 2015
Testing Modeling Assumptions in the West Africa Ebola Outbreak
The Ebola virus in West Africa has infected almost 30,000 and killed over
11,000 people. Recent models of Ebola Virus Disease (EVD) have often made
assumptions about how the disease spreads, such as uniform transmissibility and
homogeneous mixing within a population. In this paper, we test whether these
assumptions are necessarily correct, and offer simple solutions that may
improve disease model accuracy. First, we use data and models of West African
migration to show that EVD does not homogeneously mix, but spreads in a
predictable manner. Next, we estimate the initial growth rate of EVD within
country administrative divisions and find that it significantly decreases with
population density. Finally, we test whether EVD strains have uniform
transmissibility through a novel statistical test, and find that certain
strains appear more often than expected by chance.Comment: 16 pages, 14 figure
- …