A latent process model approach to improve the utility of indicator species

Oikos ◽  
2020 ◽  
Vol 129 (12) ◽  
pp. 1753-1762
Author(s):  
Jill Fleming ◽  
Chris Sutherland ◽  
Sean C. Sterrett ◽  
Evan H. Campbell Grant
Keyword(s):  
Indicator Species ◽  
Process Model ◽  
Latent Process ◽  
Model Approach

scholarly journals Attribute Fusion in a Latent Process Model for Time Series of Graphs

2013 ◽  
Vol 61 (7) ◽  
pp. 1721-1732 ◽  
Author(s):  
Minh Tang ◽  
Youngser Park ◽  
Nam H. Lee ◽  
Carey E. Priebe
Keyword(s):  
Time Series ◽  
Process Model ◽  
Latent Process

Business Process Model Approach for Management of Plant Alarm System

2015 ◽  
Vol 48 (8) ◽  
pp. 641-645 ◽  
Author(s):  
Kazuhiro Takeda ◽  
Takashi Hamaguchi ◽  
Naoki Kimura ◽  
Masaru Noda
Keyword(s):  
Business Process ◽  
Process Model ◽  
Alarm System ◽  
Business Process Model ◽  
Model Approach

scholarly journals Integrating serological and genetic data to quantify cross-species transmission: brucellosis as a case study

Parasitology ◽  
2016 ◽  
Vol 143 (7) ◽  
pp. 821-834 ◽  
Author(s):  
MAFALDA VIANA ◽  
GABRIEL M. SHIRIMA ◽  
KUNDA S. JOHN ◽  
JULIE FITZPATRICK ◽  
RUDOVICK R. KAZWALA ◽  
...  
Keyword(s):  
Process Model ◽  
Epidemiological Data ◽  
Human Infection ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Northern Tanzania ◽  
Animal Infection ◽  
Latent Process ◽  
Level Of Understanding

SUMMARYEpidemiological data are often fragmented, partial, and/or ambiguous and unable to yield the desired level of understanding of infectious disease dynamics to adequately inform control measures. Here, we show how the information contained in widely available serology data can be enhanced by integration with less common type-specific data, to improve the understanding of the transmission dynamics of complex multi-species pathogens and host communities. Using brucellosis in northern Tanzania as a case study, we developed a latent process model based on serology data obtained from the field, to reconstruct Brucella transmission dynamics. We were able to identify sheep and goats as a more likely source of human and animal infection than cattle; however, the highly cross-reactive nature of Brucella spp. meant that it was not possible to determine which Brucella species (B. abortus or B. melitensis) is responsible for human infection. We extended our model to integrate simulated serology and typing data, and show that although serology alone can identify the host source of human infection under certain restrictive conditions, the integration of even small amounts (5%) of typing data can improve understanding of complex epidemiological dynamics. We show that data integration will often be essential when more than one pathogen is present and when the distinction between exposed and infectious individuals is not clear from serology data. With increasing epidemiological complexity, serology data become less informative. However, we show how this weakness can be mitigated by integrating such data with typing data, thereby enhancing the inference from these data and improving understanding of the underlying dynamics.

A Latent Process Model for Temporal Extremes

2013 ◽  
Vol 41 (3) ◽  
pp. 606-621 ◽  
Author(s):  
Paola Bortot ◽  
Carlo Gaetan
Keyword(s):  
Process Model ◽  
Latent Process

A latent process model for the pricing of corporate securities

2008 ◽  
Vol 69 (3) ◽  
pp. 439-455 ◽  
Author(s):  
Masaaki Kijima ◽  
Teruyoshi Suzuki ◽  
Keiichi Tanaka
Keyword(s):  
Process Model ◽  
Corporate Securities ◽  
Latent Process
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