@article{fdi:010061865, title = {{D}isentangling the complexity of infectious diseases : time is ripe to improve the first-line statistical toolbox for epidemiologists}, author = {{H}anf, {M}. and {G}u{\'e}gan, {J}ean-{F}ran{\c{c}}ois and {A}hmed, {I}. and {N}acher, {M}.}, editor = {}, language = {{ENG}}, abstract = {{B}ecause 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. {M}ost epidemiologists now acknowledge that most human infectious diseases are likely to have complex dynamics. {H}owever, this knowledge still percolates with difficulty in their statistical "modus operandi'. {I}ndeed, for the study of complex systems, the traditional first-line statistical toolbox of epidemiologists (mainly built around the {G}eneralized {L}inear {M}odel family), despite its undeniable practicality and robustness, has structural limitations deprecating its usefulness. {T}hree 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. {T}hree 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. {T}he aforementioned methodologies have the advantage to be generalizations of {GLM} models and are relatively easy to implement. {T}heir assimilation and implementation would thus be greatly facilitated for epidemiologists. {M}ore 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. {T}his is particularly true in the field of infectious diseases for which major public health challenges will have to be addressed in the coming decades.}, keywords = {{I}nfectious diseases ; {C}omplexity ; {S}tatistics ; {M}ulti-level organization ; {N}onlinearity ; {I}nteractions}, booktitle = {}, journal = {{I}nfection {G}enetics and {E}volution}, volume = {21}, numero = {}, pages = {497--505}, ISSN = {1567-1348}, year = {2014}, DOI = {10.1016/j.meegid.2013.09.006}, URL = {https://www.documentation.ird.fr/hor/fdi:010061865}, }