ライフサイエンスコーパス: epidemic model

1 ease and behavior parameters in a behavioral epidemic model.

2 ortality reports, and a spatially structured epidemic model.

3 ial distancing measures in an age structured epidemic model.

4 ectories of both RSV and influenza, using an epidemic model.

5 onsider the Susceptible-Infectious-Recovered epidemic model.

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

7 etween pathogens can be updated using simple epidemic models.

8 the dynamics of social stress with classical epidemic models.

9 read utilizing mathematical results from PDE epidemic models.

10 and calibration of stochastic compartmental epidemic models.

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

12 quilibrium is equivalent to that of standard epidemic models.

13 nce of including changing mixing patterns in epidemic models.

14 tochastic models were developed: patient and epidemic models.

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

16 question has been studied extensively using epidemic models.

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

18 is similar to standard results for diffusive epidemic models.

19 to address socioeconomic vulnerabilities in epidemic models.

20 behaviour has rarely been included in plant epidemic models.

21 ting socioeconomic and other dimensions into epidemic modeling.

22 inter-disciplinary field to improve applied epidemic modeling.

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

24 performance of three mechanistic behavioral epidemic models across nine geographies and two modeling

25 done with the Optima HIV model and the AIDS Epidemic Model (AEM) for selected countries.

26 Our epidemic model agrees well with the observed epidemic da

27 ies from sparse observations within the SIRS epidemic model and the computation of both typical obser

28 twork relates to the incidence rate in a SIS epidemic model and the economic insights we can gain thr

29 The best fit sub-epidemic model and three ensemble models constructed usi

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

31 m deep learning and graph neural networks to epidemic modeling and social networks.

32 Its substantial contributions to epidemic modelling and public health planning are invalu

33 essential for the development of predictive epidemic models and can inform their use for public heal

34 ioeconomic strata are typically neglected by epidemic models and considered, if at all, only at poste

35 uch as network efficiency, path enumeration, epidemic models and standard graph centrality measures.

36 on framework that expects a Python function (epidemic model) and epidemic data and performs parameter

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

38 y the Susceptible-Infected-Susceptible (SIS) epidemic model -appropriate for STDs- over a two-layer n

39 Epidemic models are being increasingly used for generati

40 apable of fitting parameters for any type of epidemic model, as long as it is provided as a Python fu

41 he importance of integrating SES traits into epidemic models, as neglecting them might lead to substa

42 used susceptible-infected-recovered (S-I-R) epidemic model assumes a uniform, well-mixed population,

43 disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public p

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

45 These arguments are applicable to any epidemic modeled by SIR equations.

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

47 We integrate these data into a mechanistic epidemic model calibrated on surveillance data.

48 We present an epidemic model called SIQ with an isolation protocol, fo

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

50 e and validate a landscape-scale, stochastic epidemic model capturing the spread of the disease throu

51 ate how generalized contact matrices enhance epidemic models, capturing variations in behaviors such

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

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

54 models constructed using the top-ranking sub-epidemic models consistently outperformed the ARIMA mode

55 ether epidemiological parameters that define epidemic models could be determined from the epidemic tr

56 This work combines the power of analytical epidemic modeling, data analysis and agent-based simulat

57 ntributes to the retrospective validation of epidemic models developed amid the COVID-19 Pandemic and

58 erbia, Tajikistan, and Uzbekistan) using HIV epidemic models developed with Optima HIV.

59 eted directly or used to inform more complex epidemic models (e.g. for forecasting) on the value of [

60 model incorporating the top four ranking sub-epidemic models (Ensemble(4)) outperformed the (log) ARI

61 A predictive epidemic model estimated a reproduction number of 2.2; c

62 Spatially explicit epidemic models explore population-level consequences of

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

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

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

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

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

68 We analyze an ensemble of n-sub-epidemic modeling for forecasting the trajectory of epid

69 ilable, open-source modeling platform (Local Epidemic Modeling for Management & Action, version 2.1)

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

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

72 obility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distingu

73 Previous work on epidemic modeling has focused on developing mechanistic

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

75 Substantial progress on global stability of epidemic models has been made in the last 20 y, primaril

76 Numerous epidemic models have been developed to capture aspects o

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

78 Epidemic models have explored this phenomenon, but they

79 ations are further enhancing data analytics, epidemic modelling, hotspot prediction, and differentiat

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

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

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

83 Here, we show that any compartmental epidemic model in which susceptible individuals cannot b

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

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

86 The vast majority of epidemic models in the literature are parametric, meanin

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

88 The deterministic epidemic model includes five compartments: colonized and

89 of the Susceptible-Infectious-Removed (SIR) epidemic model including host demography (births and dea

90 ed frameworks to examining various networked epidemic models, including a model that was recently int

91 m evolution of a wide range of compartmental epidemic models, including group and networked processes

92 Therefore, many epidemic models incorporate contact patterns through con

93 Behavioral epidemic models incorporating endogenous societal risk-r

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

95 A stochastic metapopulation epidemic model is developed to evaluate and rank the con

96 The compartmental epidemic model is programmed in R and Stan, uses bayesia

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

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

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

100 nal and inference approaches, Epydemix makes epidemic modeling more accessible to a wider range of us

101 Current COVID-19 epidemic models need to be expanded to account for the c

102 hand, the continuous-time Kermack-McKendrick epidemic model of 1927 (KM27) allows an arbitrary genera

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

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

105 Existing behavioral epidemic models often lack real-world data calibration a

106 r of traders, and a compartmental stochastic epidemic model on an Erdos-Renyi network.

107 Here, we include these features in an epidemic model on weighted hypergraphs to capture group-

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

109 We estimate two unknown epidemic model parameters (basic reproductive number [Fo

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

111 lation with penalized splines and stochastic epidemic models, respectively.

112 of probabilistic models on networks such as epidemic models, spin models, and Bayesian graphical mod

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

114 Metapopulation epidemic modeling studies in the susceptible-exposed-inf

115 For simple seasonal epidemic models, such as the stochastic Susceptible-Infe

116 Finally, epidemic modeling suggests that historical mobility data

117 Detailed epidemic models support differences in age of infection,

118 aluate vaccination strategies, we propose an epidemic model that explicitly accounts for both demogra

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

120 We investigated a fractional-order epidemic model that incorporates imitation and aspiratio

121 s, we develop a parsimonious behavior-driven epidemic model that incorporates population mixing, indo

122 ematical "hybrid" approach, we propose a new epidemic model that is able to predict the future number

123 Here, we show that an epidemic model that is set as an organized system on net

124 We used an agent-based epidemic model that was calibrated to HIV-1 trends in So

125 We introduce a modified epidemic model that we name the controlled-SIR model, in

126 Epidemic models that determine which interventions can s

127 should be benchmarked against more realistic epidemic models that take endogenous social distancing i

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

129 re, we develop a data-driven, age-stratified epidemic model to evaluate the effects of COVID-19 vacci

130 Here we build a stochastic epidemic model to examine the effects of COVID-19 clinic

131 ble-Vaccinated-Infected-Susceptible) disease epidemic model to investigate the impacts of hazard rate

132 Here we use a global metapopulation epidemic model to provide a mechanistic understanding of

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

134 Here, we use a climate-dependent epidemic model to simulate the SARS-CoV-2 pandemic by pr

135 We introduce an epidemic model to study the contagion of scholarly produ

136 In our work, we adapt a traditional SEIR epidemic model to the specific dynamic compartments and

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

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

139 es, regions, and communities develop various epidemic models to evaluate spread and guide mitigation

140 egrating SES dimensions, alongside age, into epidemic models to inform more equitable and effective p

141 Here we construct seven mechanistic epidemic models to test the effect of two major climate

142 and transmissions risks and integrated with epidemics models to further assess the public health out

143 ective social contact rate using traditional epidemic modeling tools and a utility function with dela

144 ndition estimation enhances the precision of epidemic modeling, ultimately supporting more effective

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

146 vidual-level viral load trajectories into an epidemic model, we further studied the impacts of testin

147 he genomic epidemiology of MPXV and HIV with epidemic modeling, we demonstrate that the transmission

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

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

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

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

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

153 e WIS ranged from 377.6 to 421.3 for the sub-epidemic models, whereas it ranged from 439.29 to 767.05

154 SARS-CoV-2 in Italy by using a computational epidemic model which takes into account demographic, mob

155 We implement five realistic epidemic models which include age structure, multiple vi

156 wever, these factors are often overlooked in epidemic models, which typically stratify social contact

157 g algorithms are interleaved with the actual epidemic models, which yields combinations of model-para

158 c Time-series Susceptible-Infected-Recovered epidemic model with assumptions about reporting biases i

159 A stochastic epidemic model with stochastic simulations is also prese

160 eptible-Infected-Removed-Vaccinated (A-SIRV) epidemic model with time-dependent transmission and remo

161 ure evolution of the outbreak, and to inform epidemic models with crucial data.

162 Compartmental epidemic models with dynamics that evolve over a graph n

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

164 Using a combination of nonlinear epidemic models with statistical techniques, we find con

165 ng methods, we show that a simple stochastic epidemic model, with minimal historical specifications,

166 fitting that can (fairly) compare different epidemic models without jeopardizing the quality of the