ライフサイエンスコーパス: epidemic model

1 onsider the Susceptible-Infectious-Recovered epidemic model.

2 , analogous to the reproduction number of an epidemic model.

3 ial distancing measures in an age structured epidemic model.

4 quilibrium is equivalent to that of standard epidemic models.

5 nce of including changing mixing patterns in epidemic models.

6 tochastic models were developed: patient and epidemic models.

7 ted for the unforced and forced SIR and SEIR epidemic models.

8 question has been studied extensively using epidemic models.

9 ns differ strongly from the ones provided by epidemic models.

10 is similar to standard results for diffusive epidemic models.

11 series susceptible-infected-recovered (TSIR) epidemic models.

12 ate in a worldwide-structured metapopulation epidemic model a timescale-separation technique for eval

13 Our epidemic model agrees well with the observed epidemic da

14 Using epidemic modeling and data from two well-documented Ebol

15 ation for the susceptible-infected-recovered epidemic model applicable to arbitrary dynamic networks.

16 Epidemic models are being increasingly used for generati

17 We develop an agent-based epidemic model based on HIV viral load dynamics.

18 udy the spread of social phenomena relies on epidemic models by establishing analogies between the tr

19 re to include differential susceptibility to epidemic models can lead to a systematic over/under esti

20 We show that a basic Galton-Watson epidemic model combined with the selection bias of obser

21 fferences between these 2 fields and how the epidemic modeling community is rising to the challenges

22 Spatially explicit epidemic models explore population-level consequences of

23 is stochastic model is based on an influenza epidemic model, expressed in terms of a system of ordina

24 mate the uncertainty in the parameters of an epidemic model, focusing on smallpox bioterrorism.

25 We investigate this hypothesis using an epidemic model for dengue in which immunological distanc

26 s in natural populations, thus supporting an epidemic model for the evolution of selfish genes, where

27 -exposed-infectious-susceptible human-vector epidemic model for the spread of the disease.

28 g diseases have led to the widespread use of epidemic models for evaluating public health strategies.

29 dlife surveillance in assessing and refining epidemic models for wildlife diseases.

30 Previous work on epidemic modeling has focused on developing mechanistic

31 ssible mechanisms or interactions assumed by epidemic models has been limited: either independent of

32 In recent years, epidemic models have been used to guide public health in

33 phone commuting network is considered in the epidemic model, however preserving to a high degree the

34 We address two basic issues in global epidemic modeling: (i) we study the role of the large sc

35 Hence we focus our study in an epidemic model in a two-layer network, and we use an iso

36 d final size of an epidemic for a variety of epidemic models in homogeneous and heterogeneous populat

37 as simulated using stochastic age-structured epidemic models in settings conducive to high or low mea

38 wer questions that are often addressed using epidemic models, in particular: on the basis of potentia

39 The deterministic epidemic model includes five compartments: colonized and

40 The epidemic model is a susceptible-infected-recovered syste

41 A 1-n-n-1 type differential epidemic model is proposed, where the differentiality al

42 One of the main uses of an epidemic model is to predict the scale of an outbreak fr

43 early on in well-mixed populations mean that epidemic models may be linearised and we can calculate o

44 We created a mathematical epidemic model of TB, calibrated to global incidence.

45 Partly because epidemic models often capture the dynamics of prior epid

46 chastic susceptible-infectious-removed (SIR) epidemic models on undirected contact networks.

47 data from satellites, and weather stations, epidemic models rely on human interactions, multiple dat

48 y on epidemiological dynamics, we propose an epidemic model structured according to immunity level th

49 Detailed epidemic models support differences in age of infection,

50 EIR (susceptible-exposed-infectious-removed) epidemic model that includes a smooth transition in the

51 Epidemic models that determine which interventions can s

52 We use a data-driven global stochastic epidemic model to analyze the spread of the Zika virus (

53 stochastic simulations with a compartmental epidemic model to quantify the impact of genetic diversi

54 Here we use epidemic modelling to show a more consistent derivation

55 se detailed geographic surveillance data and epidemic models to estimate the critical community size

56 Using a simple epidemic model we demonstrate a method to calculate the

57 Using epidemic modeling, we show that using the average of soc

58 In analogy with epidemic models, we define basic and absolute recruitmen

59 With dynamic epidemic models, we demonstrate that measures of populat

60 By coupling game theory and epidemic models, we examine vaccination choice among pop

61 model coupled with the individual-based SIS epidemic model where susceptible individuals adopt a pre

62 A stochastic epidemic model with stochastic simulations is also prese

63 uced as a means of optimally melding dynamic epidemic models with epidemiological observations and da