Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
49,327
result(s) for
"SIR"
Sort by:
A Life of Sir Harry Parkes
Harry Parkes was at the heart of Britain's relations with the Far East from fourteen, to his death at fifty-seven. In his day, he was seen as both a hero and a monster and is still bitterly resented in China for his part in the country's humiliations at Western hands, but largely esteemed in Japan for helping it to industrialise.
eBook
The other Norfolk admirals : Myngs, Narbrough and Shovell
The tale of three Norfolk admirals: Christopher Myngs, a buccaneering sailor; John Narbrough, the consummate explorer and navigator, and Cloudesley Shovell, Queen Anne's finest seaman.
Book
PART II: OBITUARY OF EMINENT PERSONS DECEASED IN 1915
JANUARY (pg. 131-135). FEBRUARY (pg. 135-139). MARCH (pg. 139-144). APRIL (pg. 144-149). MAY (pg. 149-153). JUNE (pg. 153-156). JULY (pg. 156-159). AUGUST (pg. 159-162). SEPTEMBER (pg. 162-167). OCTOBER (pg. 167-172). NOVEMBER (pg. 172-174). DECEMBER (pg. 174-178).
Book Chapter
FORECASTING SEASONAL INFLUENZA WITH A STATE-SPACE SIR MODEL
Seasonal influenza is a serious public health and societal problem due to its consequences resulting from absenteeism, hospitalizations, and deaths. The overall burden of influenza is captured by the Centers for Disease Control and Prevention's influenza-like illness network, which provides invaluable information about the current incidence. This information is used to provide decision support regarding prevention and response efforts. Despite the relatively rich surveillance data and the recurrent nature of seasonal influenza, forecasting the timing and intensity of seasonal influenza in the U.S. remains challenging because the form of the disease transmission process is uncertain, the disease dynamics are only partially observed, and the public health observations are noisy. Fitting a probabilistic state-space model motivated by a deterministic mathematical model [a susceptible-infectious-recovered (SIR) model] is a promising approach for forecasting seasonal influenza while simultaneously accounting for multiple sources of uncertainty. A significant finding of this work is the importance of thoughtfully specifying the prior, as results critically depend on its specification. Our conditionally specified prior allows us to exploit known relationships between latent SIR initial conditions and parameters and functions of surveillance data. We demonstrate advantages of our approach relative to alternatives via a forecasting comparison using several forecast accuracy metrics.
Journal Article
The Macroeconomics of Epidemics
We extend the canonical epidemiology model to study the interaction between economic decisions and epidemics. Our model implies that people cut back on consumption and work to reduce the chances of being infected. These decisions reduce the severity of the epidemic but exacerbate the size of the associated recession. The competitive equilibrium is not socially optimal because infected people do not fully internalize the effect of their economic decisions on the spread of the virus. In our benchmark model, the best simple containment policy increases the severity of the recession but saves roughly half a million lives in the United States.
Journal Article
Influential Nodes Identification in Complex Networks via Information Entropy
Identifying a set of influential nodes is an important topic in complex networks which plays a crucial role in many applications, such as market advertising, rumor controlling, and predicting valuable scientific publications. In regard to this, researchers have developed algorithms from simple degree methods to all kinds of sophisticated approaches. However, a more robust and practical algorithm is required for the task. In this paper, we propose the EnRenew algorithm aimed to identify a set of influential nodes via information entropy. Firstly, the information entropy of each node is calculated as initial spreading ability. Then, select the node with the largest information entropy and renovate its l-length reachable nodes’ spreading ability by an attenuation factor, repeat this process until specific number of influential nodes are selected. Compared with the best state-of-the-art benchmark methods, the performance of proposed algorithm improved by 21.1%, 7.0%, 30.0%, 5.0%, 2.5%, and 9.0% in final affected scale on CEnew, Email, Hamster, Router, Condmat, and Amazon network, respectively, under the Susceptible-Infected-Recovered (SIR) simulation model. The proposed algorithm measures the importance of nodes based on information entropy and selects a group of important nodes through dynamic update strategy. The impressive results on the SIR simulation model shed light on new method of node mining in complex networks for information spreading and epidemic prevention.
Journal Article