The Networks Infection Contagious Diseases Positioning System (NICDP-System): The Case of Wuhan-COVID-19

Estrada, Mario Arturo Ruiz; Koutronas, Evangelos

doi:10.2139/ssrn.3548413

Cited by 11 publications

(11 citation statements)

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“…Mathematical modeling has been influential in providing deeper understanding on the transmission mechanisms and burden of the ongoing COVID-19 pandemic, contributing to the development of public health policy and understanding. Most mathematical models of the COVID-19 pandemic can broadly be divided into either population-based, SIR (Kermack-McKendrick)type models, driven by (potentially stochastic) differential equations [38,20,34,22,21,23,31,26,32,24,33], or agent-based models [39,28,25,27,30], in which individuals typically interact on a network structure and exchange infection stochastically. One difficulty of the latter approach is that the network structure is time-varying and can be difficult, if not impossible, to construct with accuracy.…”

Section: Introductionmentioning

confidence: 99%

To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic

Eikenberry

Mancuso

Iboi

et al. 2020

Preprint

202

238

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Face mask use by the general public for limiting the spread of the COVID-19 pandemic is controversial, though increasingly recommended, and the potential of this intervention is not well understood. We develop a compartmental model for assessing the communitywide impact of mask use by the general, asymptomatic public, a portion of which may be asymptomatically infectious. Model simulations, using data relevant to COVID-19 dynamics in the US states of New York and Washington, suggest that broad adoption of even relatively ineffective face masks may meaningfully reduce community transmission of COVID-19 and decrease peak hospitalizations and deaths. Moreover, mask use decreases the effective transmission rate in nearly linear proportion to the product of mask effectiveness (as a fraction of potentially infectious contacts blocked) and coverage rate (as a fraction of the general population), while the impact on epidemiologic outcomes (death, hospitalizations) is highly nonlinear, indicating masks could synergize with other non-pharmaceutical measures. Notably, masks are found to be useful with respect to both preventing illness in healthy persons and preventing asymptomatic transmission. Hypothetical mask adoption scenarios, for Washington and New York state, suggest that immediate near universal (80%) adoption of moderately (50%) effective masks could prevent on the order of 17-45% of projected deaths over two months in New York, while decreasing the peak daily death rate by 34-58%, absent other changes in epidemic dynamics. Even very weak masks (20% effective) can still be useful if the underlying transmission rate is relatively low or decreasing: In Washington, where baseline transmission is much less intense, 80% adoption of such masks could reduce mortality by 24-65% (and peak deaths 15-69%), compared to 2-9% mortality reduction in New York (peak death reduction 9-18%). Our results suggest use of face masks by the general public is potentially of high value in curtailing community transmission and the burden of the pandemic. The community-wide benefits are likely to be greatest when face masks are used in conjunction with other non-pharmaceutical practices (such as social-distancing), and when adoption is nearly universal (nation-wide) and compliance is high.

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Section: Introductionmentioning

confidence: 99%

To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic

Eikenberry

Mancuso

Iboi

et al. 2020

Preprint

202

238

View full text Add to dashboard Cite

show abstract

“…In fact, analysis and prediction could go wrong in the absence of adequate historical real data. On the other hand, various agent-based often stochastic models where individuals interact on a network structure and get infected stochastically, have been treated as useful tools for tracing fine-grained effects of heterogeneous intervention policies in diverse disease outbreaks Ferguson et al, (2020); Chang et al, (2020); Wilder et al, (2020); Ruiz et al, (2020). However, accuracy of this approach can be a vital issue due to the time-varying nature of network-structure.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Covid-19 Pandemic: A Data-Driven Analysis

Nabi

2020

Preprint

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In this paper, a new Susceptible-Exposed-Symptomatic Infectious-Asymptomatic Infectious-Quarantined-Hospitalized-Recovered-Dead (SEI D I U QHRD) deterministic compartmental model has been proposed and calibrated for interpreting the transmission dynamics of the novel coronavirus disease . The purpose of this study is to give a tentative prediction of the epidemic peak for Russia, Brazil, India and Bangladesh which could become the next COVID-19 hotspots in no time by using a Trust-regionreflective (TRR) algorithm which one of the well-known real data fitting techniques. Based on the publicly available epidemiological data from late January until 10 May, it has been estimated that the number of daily new symptomatic infectious cases for the above mentioned countries could reach the peak around the beginning of June with the peak size of ∼ 15, 774 (95% CI, 13,814-17,734) symptomatic infectious cases in Russia, ∼ 26, 449 (95% CI, 23,489-29,409) cases in Brazil, ∼ 9, 504 (95% CI, 8,378-10,630) cases in India and ∼ 2, 209 (95% CI, 1,878-2,540) cases in Bangladesh. As of May 11, 2020, incorporating the infectiousness capability of asymptomatic carriers, our analysis estimates the value of the basic reproduction number (R 0 ) as of May 11, 2020 was found to be ∼ 4.234 (95% CI, 3.764-4.7) in Russia, ∼ 5.347 (95% CI, 4.737-5.95) in Brazil, ∼ 5.218 (95% CI, 4.56-5.81)in India, ∼ 4.649 (95% CI, in the United Kingdom and ∼ 3.53 (95% CI, 3.12-3.94) in Bangladesh. Moreover, Latin hypercube sampling-partial rank correlation coefficient (LHS-PRCC) which is a global sensitivity analysis (GSA) method is applied to quantify the uncertainty of our model mechanisms, which elucidates that for Russia, the recovery rate of undetected asymptomatic carriers, the rate of getting home-quarantined or self-quarantined and the transition rate from quarantined class to susceptible class are the most influential parameters, whereas the rate of getting home-quarantined or self-quarantined and the inverse of the COVID-19 incubation period are highly sensitive parameters in Brazil, India, Bangladesh and the United Kingdom which could significantly affect the transmission dynamics of the novel coronavirus. Our analysis also suggests that relaxing social distancing restrictions too quickly could exacerbate the epidemic outbreak in the above-mentioned countries.

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“…A number of agent-based COVID-19 models have been developed or adapted from influenza pandemic models to simulate the individuals of a population and their interactions [64][65][66][67][68]. This provides a mechanism for modelling interventions that target contacts between individuals and does not assume the population exists in homogeneous compartments as compartmental models generally do, but also requires a number of assumptions regarding the behavior and interactions within a population as well as the infectivity of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model

et al. 2021

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Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the log transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectories. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. We evaluated the model by training it on progressively longer periods of the pandemic and computing its predictive accuracy over 21-day forecasts. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Colorado, and West Virginia. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course.

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The Networks Infection Contagious Diseases Positioning System (NICDP-System): The Case of Wuhan-COVID-19

Cited by 11 publications

References 0 publications

To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic

To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic

Forecasting Covid-19 Pandemic: A Data-Driven Analysis

Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model

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