We previously discussed the construction of an arbitrary order SIR model with physical meaning. We believe that arbitrary order derivatives can be obtained through Mittag-Leffler based laws in the ...infectivity and removal functions. Here we are interested in obtaining information about the influence of the parameters in the studied model, with the aim of promoting its use and strengthening the biological interpretation.
The world has faced many epidemics during its evolution of human civilization from the influenza epidemic in 1200 BC, to severe acute respiratory syndrome SARSCOV-2 in 2019, each epidemic has become ...fatal and people have died from thousands to millions. The epidemic pattern of COVID-19 in Nepal is analyzed and predicted using the Exponential, Logistic, SIR (Susceptible Infectious Recovered), and SIRD (Susceptible Infectious Recovered Deceased) models. The cumulative instances of an outbreak rise exponentially at first, described by the exponential model but there is a point of inflection after some time where the curve nearly turns linear which is predicted by the logistic model. The SIR and SIRD models are used to anticipate the number of cumulative cases of COVID-19 for the 400 days based on information supplied by the Ministry of Health and Population. Based on real-time data and data from our simulation, we can conclude that by strengthening the efficiency of social isolation and lockdown, we could significantly reduce the spread of COVID-19 in our country. BIBECHANA 19 (2022) 102-110
•Robust inferences of COVID-19 pandemic models are essential for pandemic control.•Global sensitivity methods are used for identifying key drivers and interactions.•For all countries ...intervention-related parameters are the most important.•The analysis is performed with correlated and uncorrelated inputs.•The methodology is applicable to other types COVID-19 pandemics models.
Operations researchers worldwide rely extensively on quantitative simulations to model alternative aspects of the COVID-19 pandemic. Proper uncertainty quantification and sensitivity analysis are fundamental to enrich the modeling process and communicate correctly informed insights to decision-makers. We develop a methodology to obtain insights on key uncertainty drivers, trend analysis and interaction quantification through an innovative combination of probabilistic sensitivity techniques and machine learning tools. We illustrate the approach by applying it to a representative of the family of susceptible-infectious-recovered (SIR) models recently used in the context of the COVID-19 pandemic. We focus on data of the early pandemic progression in Italy and the United States (the U.S.). We perform the analysis for both cases of correlated and uncorrelated inputs. Results show that quarantine rate and intervention time are the key uncertainty drivers, have opposite effects on the number of total infected individuals and are involved in the most relevant interactions.
Many of the models used nowadays in mathematical epidemiology, in particular in COVID-19 research, belong to a certain subclass of compartmental models whose classes may be divided into three ...“(x,y,z)” groups, which we will call respectively “susceptible/entrance, diseased, and output” (in the classic SIR case, there is only one class of each type). Roughly, the ODE dynamics of these models contains only linear terms, with the exception of products between x and y terms. It has long been noticed that the reproduction number R has a very simple Formula in terms of the matrices which define the model, and an explicit first integral Formula is also available. These results can be traced back at least to Arino, Brauer, van den Driessche, Watmough, and Wu (2007) and to Feng (2007), respectively, and may be viewed as the “basic laws of SIR-type epidemics”. However, many papers continue to reprove them in particular instances. This motivated us to redraw attention to these basic laws and provide a self-contained reference of related formulas for (x,y,z) models. For the case of one susceptible class, we propose to use the name SIR-PH, due to a simple probabilistic interpretation as SIR models where the exponential infection time has been replaced by a PH-type distribution. Note that to each SIR-PH model, one may associate a scalar quantity Y(t) which satisfies “classic SIR relations”,which may be useful to obtain approximate control policies.
A novel design concept to enhance the bandwidth of a differential-fed patch antenna using the dual-resonant radiation of a stepped-impedance resonator (SIR) is proposed. The SIR is composed of two ...distinctive portions: the radiating patch and a pair of open stubs. Initially, based on the transmission line model, the first and second odd-order radiative resonant modes, i.e., TM10 and TM30, of this SIR-typed patch antenna are extensively investigated. It is demonstrated that the frequency ratio between the dual-resonant modes can be fully controlled by the electrical length and the impedance ratios between the open stub and radiating patch. After that, the SIR-typed patch antenna is reshaped with stepped ground plane in order to increase the impedance ratio as highly required for wideband radiation. With this arrangement, these two radiative modes are merged with each other, resulting in a wide impedance bandwidth with a stable radiation pattern under dual-resonant radiation. Finally, the proposed antenna is designed, fabricated, and measured. It is verified in experiment that the impedance bandwidth (|S dd11 | <; -10 dB) of the proposed antenna has gained tremendous increment up to 10% (0.85-0.94 GHz) with two attenuation poles. Most importantly, the antenna has achieved a stable gain varying from 7.4 to 8.5 dB within the whole operating band, while keeping low-cross polarization.
Identifying influential spreaders plays a crucial role in understanding and controlling the spreading processes on complex networks. Previous works mainly focus on the direct spread via edge eij from ...node i to node j. However, the indirect spread through an intermediate node k, i.e. the infection is spread successively via edge eik and edge ekj, is a ubiquitous phenomenon in a spreading process. Considering the spreading influence of a node, an asymmetric connection strength cij is proposed, which combines the indirect infections in i’s neighborhood with the traditional direct infections. Then node i’s spreading influence sic, is defined as the sum of cij among i’s neighbors. We investigate the Susceptible–Infected–Removed (SIR) model on nine real-world networks to evaluate the accuracy of sc in ranking the spreading influence of nodes. The results show that sc is more accurate and more robust ranking of nodes’ spreading influence in general compared with the node strength s and s-shell index ss. Our research sheds light on the mechanism that dominates the spreading strength of nodes. The indirect spread among the neighborhood effectively catches more details for ranking the node influence in the spreading process.
•We concentrate on how to improve the ranking of the spreading influence of nodes.•A topological measure for ranking the spreading influence of a node is proposed.•The proposed measure relies on the indirect spread in the neighborhood of a node.•The proposed measure outperforms the previous measures in accuracy.
Although the Poisson point process (PPP) has been widely used to model base station (BS) locations in cellular networks, it is an idealized model that neglects the spatial correlation among BSs. This ...paper proposes the use of the determinantal point process (DPP) to take into account these correlations, in particular the repulsiveness among macro BS locations. DPPs are demonstrated to be analytically tractable by leveraging several unique computational properties. Specifically, we show that the empty space function, the nearest neighbor function, the mean interference, and the signal-to-interference ratio (SIR) distribution have explicit analytical representations and can be numerically evaluated for cellular networks with DPP-configured BSs. In addition, the modeling accuracy of DPPs is investigated by fitting three DPP models to real BS location data sets from two major U.S. cities. Using hypothesis testing for various performance metrics of interest, we show that these fitted DPPs are significantly more accurate than popular choices such as the PPP and the perturbed hexagonal grid model.
The well-publicized disinformation campaigns surrounding recent elections, pandemic vaccination adoption, as well as supply-chain disruptions and shortages have made historical problems of ...disinformation more apparent. When disinformation targets transportation infrastructure, supply chains can be disrupted, resulting in commodities shortages such as food and fuel, consequently jeopardizing the health and safety of communities. This research proposes an integrated epidemiological-optimization model that quantifies the impacts of weaponized disinformation on infrastructure networks that transport multiple commodities. The model aims to minimize the overall weighted shortage of commodities caused by different disinformation spread rates. Results show that disinformation weaponized against transportation infrastructure, potentially targeting a particular geographical region or a particular commodity, can have wide-ranging impacts across different commodities. The proposed model is applied to the multi-commodity Swedish railway network carrying 14 different categories of commodities over 1360 supply and demand nodes.
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