کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5910300 1570182 2014 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Disentangling the complexity of infectious diseases: Time is ripe to improve the first-line statistical toolbox for epidemiologists
ترجمه فارسی عنوان
پراکندگی پیچیدگی بیماری های عفونی: زمان رسیده است تا مجموعه ابزار آماری خط اول برای اپیدمیولوژیست ها را بهبود بخشد
کلمات کلیدی
بیماری های عفونی، پیچیدگی، آمار، سازمان چند سطحی، غیر خطی، فعل و انفعالات،
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک بوم شناسی، تکامل، رفتار و سامانه شناسی
چکیده انگلیسی


- Most epidemiologists now acknowledge that infectious diseases have complex dynamics.
- Multilevel organization of data, non-linear behaviors and interactions/feedbacks/loopings are three major sources of complexity.
- The epidemiologist first-line statistical toolbox has structural limitations limiting its ability to capture complexity.
- 3 models more able to deal with these sources of complexity are the GLMM, the GAM and the SEM models.
- An improved use of this kind of methods has to be performed to elucidate the complexity of infectious diseases.

Because many biological processes related to the dynamics of infectious diseases are caused by complex interactions between the environment, the host(s) and the agent(s), the necessity to address the methodological implications of this inherent complexity has recently emerged in epidemiology. Most epidemiologists now acknowledge that most human infectious diseases are likely to have complex dynamics. However, this knowledge still percolates with difficulty in their statistical “modus operandi”. Indeed, for the study of complex systems, the traditional first-line statistical toolbox of epidemiologists (mainly built around the Generalized Linear Model family), despite its undeniable practicality and robustness, has structural limitations deprecating its usefulness. Three major sources of complexity neglected or not taken into account by this first-line statistical toolbox and having deep statistical implications are the multi-level organization of data, the non-linear relationships between variables and the complex interactions between variables. Three promising candidates to incorporate along with traditional tools for a new first-line statistical toolbox more suitable to apprehend these sources of complexity are the generalized linear mixed models, the generalized additive models, and the structural equation models. The aforementioned methodologies have the advantage to be generalizations of GLM models and are relatively easy to implement. Their assimilation and implementation would thus be greatly facilitated for epidemiologists. More globally, this text underlines that an improved use of other methods as such described here compared to traditional ones has to be performed to better understand the complexity challenging epidemiologists every day. This is particularly true in the field of infectious diseases for which major public health challenges will have to be addressed in the coming decades.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Infection, Genetics and Evolution - Volume 21, January 2014, Pages 497-505
نویسندگان
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