Challenges in control of Covid-19: short doubling time and long delay to effect of interventions

Pellis, Lorenzo; Scarabel, Francesca; Stage, Helena B.; Overton, Christopher E.; Chappell, Lauren H. K.; Lythgoe, Katrina A.; Fearon, Elizabeth; Bennett, Emma; Curran-Sebastian, Jacob; Das, Rajenki; Fyles, Martyn; Lewkowicz, Hugo; Pang, Xiaoxi; Vekaria, Bindu; Webb, Luke; House, Thomas; Hall, Ian P.

doi:10.48550/arxiv.2004.00117

Cited by 7 publications

(9 citation statements)

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“…In order to verify how well the approach can be used to model the COVID-19 pandemic we have chosen to use value of the infectivity parameter β that nearly matches the growth patterns of the epidemic in the countries before countermeasures were applied. As was obtained in a detailed study [15], the epidemic doubling time in many countries is approximately three days. For that reason, we have opted to use the value β = 4.85β th that produces such growth.…”

Section: Numerical Experiments and Discussionmentioning

confidence: 53%

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Non-Markovian SIR epidemic spreading model

Basnarkov,

Tomovski,

Sandev

et al. 2021

Preprint

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We introduce non-Markovian SIR epidemic spreading model inspired by the characteristics of the COVID-19, by considering discrete-and continuous-time versions. The incubation period, delayed infectiousness and the distribution of the recovery period are modeled with general functions. By taking corresponding choice of these functions, it is shown that the model reduces to the classical Markovian case. The epidemic threshold is analytically determined for arbitrary functions of infectivity and recovery and verified numerically. The relevance of the model is shown by modeling the first wave of the epidemic in Italy, in the spring, 2020.

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Section: Numerical Experiments and Discussionmentioning

confidence: 53%

“…where it is assumed that in vicinity of the disease-free state S(t) ≈ 1. Now, make Laplace transform of the perturbation of the rate of infection, I (s) = ∞ 0 δ I d (t)e −st dt and use it in the last equation (15). To do that, we will follow the same approach as in the discrete-time version.…”

Section: Continuous-time Versionmentioning

confidence: 99%

Non-Markovian SIR epidemic spreading model

Basnarkov,

Tomovski,

Sandev

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…The following distance metrics are evaluated: Cityblock, Chebyshev, correlation, cosine, Euclidean, Hamming, Jaccard, Mahalanobis, Minkowski, standardized Euclidean, and Spearman metrics. The k-value is varied within the range of [1,4416] with log scale.…”

Section: Competing Methodsmentioning

confidence: 99%

“…It has infected more than 3 million people, caused thousands of causalities and has so far paralyzed the mobility all around the World. The spreading rate of Covid-19 is so high that the number of cases is expected to be doubled every three days if the social distancing is not strictly observed to slow this accretion [1]. Roughly around half of Covid-19 positive patients exhibit also a comorbidity [2], making difficult to differentiate Covid-19 from other lung diseases.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images

Yamaç¹,

Ahishali²,

Degerli³

et al. 2020

Preprint

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Coronavirus disease (Covid-19) has been the main agenda of the whole world since it came in sight in December, 2019. It has already caused thousands of causalities and infected several millions worldwide. Any technological tool that can be provided to healthcare practitioners to save time, effort, and possibly lives has crucial importance. The main tools practitioners currently use to diagnose Covid-19 are Reverse transcriptionpolymerase chain reaction (RT-PCR) and Computed Tomography (CT), which require significant time, resources and acknowledged experts. X-ray imaging is a common and easily accessible tool that has great potential for Covid-19 diagnosis. In this study, we propose a novel approach for Covid-19 recognition from chest Xray images. Despite the importance of the problem recent studies in this domain produced not so satisfactory results due to the limited datasets available for training. Recall that Deep Learning techniques can generally provide state-of-the-art performance in many classification tasks when trained properly over large datasets, such data scarcity can be a crucial obstacle when using them for Covid-19 detection. Alternative approaches such as representation-based classification (collaborative or sparse representation) might provide satisfactory performance with limited size datasets, but they generally fall short in performance or speed compared to Machine Learning methods. To address this deficiency, Convolution Support Estimation Network (CSEN) has recently been proposed as a bridge between model-based and Deep Learning approaches by providing a non-iterative real-time mapping from query sample to ideally sparse representation coefficient' support, which is critical information for class decision in representation based techniques.Main premises of this study can be summarized as follows: (i) a benchmark X-ray dataset, namely QaTa-Cov19, containing over 6200 X-ray images is created. Up to date, this is the largest dataset covering 462 X-ray images from Covid-19 patients along with three other classes; bacterial pneumonia, viral pneumonia, and normal. (ii) In such a scarce and imbalanced dataset the proposed CSEN based classification scheme equipped with feature extraction from a state-of-the-art deep neural network solution for X-ray images, CheXNet, achieves over 98% sensitivity and over 95% specificity for Covid-19 recognition directly from raw X-ray images without any pre-or post-processing. (iii) Having such an elegant Covid-19 assistive diagnosis performance, this study further provides solid evidence that Covid-19 induces a unique pattern in X-rays that can be discriminated with a high accuracy.

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“…Initially, some of those works focused on its calibration in order to estimate typical parameters of the disease, like infection rates, epidemic doubling times among others 3,5,[16][17][18][19]22,23,[26][27][28] . After these preliminary studies, many authors considered the effect of several types of nonpharmaceutical interventions 2,4,[10][11][12][13][14][15]20,21,24,25,31 .…”

Section: Introductionmentioning

confidence: 99%

What is the potential for a second peak in the evolution of SARS-CoV-2 in emerging and developing economies? Insights from a SIRASD model considering the informal economy

Pires,

Crokidakis,

Cajueiro

et al. 2020

Preprint

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We investigate the emerging scenarios from a two-population Susceptible-Infected-Recovered-Asymptomatic-Symptomatic-Dead (SIRASD) model, where populations differ by their degree of compliance with social distancing policies. Considering epidemiological parameters estimated from data of the propagation of SARS-CoV-2 in Brazil -where there is a significant stake of the population making their living in the informal economy and thus prone to not follow self-isolation -we assert that if the confinement measures are lifted too soon, namely as much as one week of consecutive declining numbers of new cases, it is very likely the appearance of a second peak .The ongoing COVID-19 pandemic has turned into the deepest global health crisis of our time and raised several questions on its evolution and how to quell it. In the absence of a vaccine much of the public health policies revolve around self-isolation or lockdown measures. The former depends on the adherence of the population, which is influenced by the capacity of staying home whilst keeping livelihood. In a middle income country like Brazil a large stake of the population makes a living in the informal economy that does not fit home-working. Between the rock of breaching self-isolation to obtain income, and possibly get infected, and the hard place of staying home, and end up starving, the stance of these people is crucial to a successful pandemic control program, namely the emergence of a second peak as individuals either cannot help abiding by self-isolation or assume the worse is over.

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Challenges in control of Covid-19: short doubling time and long delay to effect of interventions

Cited by 7 publications

References 0 publications

Non-Markovian SIR epidemic spreading model

Non-Markovian SIR epidemic spreading model

Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images

What is the potential for a second peak in the evolution of SARS-CoV-2 in emerging and developing economies? Insights from a SIRASD model considering the informal economy

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