Testing Modeling Assumptions in the West Africa Ebola Outbreak

Burghardt, Keith; Verzijl, Christopher; Huang, Junming; Ingram, Matthew C.; Song, Binyang; Hasne, Marie Pierre

doi:10.1038/srep34598

Cited by 17 publications

(20 citation statements)

References 28 publications

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“…To illustrate these ideas, consider the logistic model, where ( ) = − , ( and are positive constants), with the equilibrium point = as = − , the equilibrium is stable. The cumulative number of cases in some epidemics approximately agree with the logistic model [4][5][6][7] . Our analysis shows that COVID-19 cumulative numbers deviate from the logistic variation.…”

Section: Methods and Discussionsupporting

confidence: 62%

Evolution of the COVID Pandemic: A Technique for Mathematical Analysis of Data

Tennakone

DeSilva

Wimalasena

et al. 2020

Preprint

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The analysis of COVID-19 statistics in different regions of the world with the intention of understanding the trend of progression of the pandemic is a task of paramount importance.Publicly available data includes cumulative number of cases, new cases each day, and the mortality. Extracting information from this data necessitates mathematical modeling. In this note a simple technique is adopted to determine the trend towards stabilized elimination of the infection, as implicated by saturation of the cumulative number of cases. Results pertaining to several representative regions of the world are presented. In several regions, evidence there to the effect that the pandemic will come to an end. The estimated saturation values of the cumulative numbers are indicated.

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Section: Methods and Discussionsupporting

confidence: 62%

Evolution of the COVID Pandemic: A Technique for Mathematical Analysis of Data

Tennakone

DeSilva

Wimalasena

et al. 2020

Preprint

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“…The cumulative data for infected people has a sigmoid form in general and has been shown to have a form given by eq. (5) in a recent study as well 27 . To establish that indeed the Euclidean network is an appropriate model responsible for the epidemic spreading, one should be able to reproduce from the model the consistent results and trends using suitable values of the parameters, at least qualitatively.…”

Section: Resultsmentioning

confidence: 67%

“…Our assumption that agents are immobile is supported by ref. 27 in which it is argued that migration did not play a role in the spreading. Even so, this simple model is able to yield data which is consistent with real data and we conclude that it captures the basic mechanism of the epidemic spread.…”

Section: Discussionmentioning

confidence: 99%

“…Ebola virus is transmitted through body fluids and it is also believed that a person can contract the disease only once. A few attempts have been made to analyse the data so far 22 23 24 25 26 27 . Different factors like demographic effect, hospitalization, vaccination and treatment plans have been incorporated in the traditional and well-known SIR model to understand the dynamics of Ebola disease 25 26 27 .…”

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confidence: 99%

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An empirical analysis of the Ebola outbreak in West Africa

Khaleque

Sen

2017

Sci Rep

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The data for the Ebola outbreak that occurred in 2014–2016 in three countries of West Africa are analysed within a common framework. The analysis is made using the results of an agent based Susceptible-Infected-Removed (SIR) model on a Euclidean network, where nodes at a distance l are connected with probability P(l) ∝ l−δ, δ determining the range of the interaction, in addition to nearest neighbors. The cumulative (total) density of infected population here has the form , where the parameters depend on δ and the infection probability q. This form is seen to fit well with the data. Using the best fitting parameters, the time at which the peak is reached is estimated and is shown to be consistent with the data. We also show that in the Euclidean model, one can choose δ and q values which reproduce the data for the three countries qualitatively. These choices are correlated with population density, control schemes and other factors. Comparing the real data and the results from the model one can also estimate the size of the actual population susceptible to the disease. Rescaling the real data a reasonably good quantitative agreement with the simulation results is obtained.

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“…There is a large body of research showing how in many settings homogeneous mixing is inadequate for accurately modeling disease dynamics, such as [43][44][45][46][47][48][49] (see [46] and [48] for further reference listings on this). As a recent concrete example [50], evaluated model assumptions in the West African Ebola outbreak and stated, "we see that alternative hypotheses for how [Ebola Virus Disease] spreads, such as homogeneous mixing and nearest neighbor interactions, provide quantitatively poorer agreement with data" (p.3). While not a focus of this paper, one important aspect is preferential mixing based on age [51].…”

Section: Introductionmentioning

confidence: 99%

Simulation-free estimation of an individual-based SEIR model for evaluating nonpharmaceutical interventions with an application to COVID-19 in the District of Columbia

Sewell

2020

PLoS ONE

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The ongoing COVID-19 pandemic has overwhelmingly demonstrated the need to accurately evaluate the effects of implementing new or altering existing nonpharmaceutical interventions. Since these interventions applied at the societal level cannot be evaluated through traditional experimental means, public health officials and other decision makers must rely on statistical and mathematical epidemiological models. Nonpharmaceutical interventions are typically focused on contacts between members of a population, and yet most epidemiological models rely on homogeneous mixing which has repeatedly been shown to be an unrealistic representation of contact patterns. An alternative approach is individual based models (IBMs), but these are often time intensive and computationally expensive to implement, requiring a high degree of expertise and computational resources. More often, decision makers need to know the effects of potential public policy decisions in a very short time window using limited resources. This paper presents a computation algorithm for an IBM designed to evaluate nonpharmaceutical interventions. By utilizing recursive relationships, our method can quickly compute the expected epidemiological outcomes even for large populations based on any arbitrary contact network. We utilize our methods to evaluate the effects of various mitigation measures in the District of Columbia, USA, at various times and to various degrees. Rcode for our method is provided in the supplementry material, thereby allowing others to utilize our approach for other regions.

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Testing Modeling Assumptions in the West Africa Ebola Outbreak

Cited by 17 publications

References 28 publications

Evolution of the COVID Pandemic: A Technique for Mathematical Analysis of Data

Evolution of the COVID Pandemic: A Technique for Mathematical Analysis of Data

An empirical analysis of the Ebola outbreak in West Africa

Simulation-free estimation of an individual-based SEIR model for evaluating nonpharmaceutical interventions with an application to COVID-19 in the District of Columbia

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