Infectious Disease Modeling Contributions to the American Journal of Epidemiology

M. Elizabeth Halloran1 and Marc Lipsitch2

1 Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, GA 30322
2 Department of Epidemiology, Harvard School of Public Health, Boston, MA 02135

In a recent editorial (1Go), the statistical editors propose guidelines for preparing methodological papers for the American Journal of Epidemiology (AJE) in response to epidemiology's becoming methodologically and computationally more sophisticated. Infectious disease epidemiology is also evolving. More complex and dynamic models are being used to develop novel estimation procedures, to motivate improved study designs, and to explore indirect effects of interventions. All of the concepts and guidelines provided by the previous editorial also apply to methodological papers in infectious disease epidemiology. In this editorial, we suggest additional guidelines for preparing papers on mathematical modeling of infectious diseases. Such models are required to capture the effects of transmission and dependent happenings in infectious diseases (2Go), parameters such as the transmission probability and the basic reproductive number R0, natural history parameters, and indirect effects of interventions. The complex interaction of the infectious agent, the human host population, and interventions makes mathematical dynamic models increasingly important tools.

Priority for publication in the AJE will be given to papers that are 1) highly relevant, either scientifically or for policy considerations, and 2) timely, that is, addressing current important questions. More specifically, priority will be given to substantial contributions on topics such as 1) making quantitative or qualitative predictions about the spread of infectious agents under particular circumstances, 2) clarifying the relative effectiveness of intervention strategy options to prevent or to treat an infectious disease, 3) clarifying the relative benefits and costs of such options, 4) illustrating the sufficiency of a particular biologic or population dynamic mechanism to explain data that are not currently understood, 5) using a dynamic model, along with data, to make an inference about important disease processes, 6) identifying current data gaps that substantially impede our understanding of infectious disease epidemiology, 7) proposing improved study designs that will better serve the stated purpose, or 8) identifying shortcomings or limitations of current study designs.

This list is not exhaustive nor are the parts mutually exclusive. Priority will be given to papers using models with the appropriate level of detail for the question being asked, including dynamics, deterministic or stochastic approach, or spatial structure. Papers can address questions for a specific infectious disease or for a class of related infectious diseases. The natural history of infectiousness should be appropriate and well documented. No distinction will be made by the technical difficulty of the methods involved, as long as they are well explained and referenced. However, less priority will be given to very technical modeling papers that discuss theoretical or computational aspects of modeling per se. Even when the centerpiece of the paper is a dynamic mathematical model, the highest epidemiologic and statistical standards will be expected.

Specific examples of using dynamic models for science and policy include understanding the role of core groups in disease spread and interventions, exploring indirect effects of intervention strategies, such as shift in age distribution of infection, elucidating the role of pre- or asymptomatic transmission in determining the success of intervention strategies, and demonstrating what field studies are required to obtain needed additional data. A model, dynamic or otherwise, could be used to estimate from data important parameters such as the transmission probability, the basic reproductive number R0, and the latent, infectious, and incubation periods. Examples of novel and improved study designs include augmented studies with contact information to estimate the effect of interventions on person-to-person transmission, community studies for estimating indirect effects of interventions, and studies for better evaluation of hospital interventions against nosocomial infections.

The form of these submissions should follow the general author guidelines found in the AJE Instructions to Authors (http://www3.oup.co.uk/jnls/list/aje/instauth/auth1.html), as well as those in the editorial by Dominici et al. (1Go), if applicable. The introductory section should motivate the problem, describe what has been done up to now, with appropriate references to previous analyses, and state what this paper contributes over and above previous work to science, policy, or methodology. The Materials and Methods section should include a clear description of the dynamic model, estimation procedure, or study design, including all the key assumptions. If data from previous studies are used, they should be described with person, time and place, and proper references. All parameters and variables need to be defined clearly. A description of the appropriate natural history of infectiousness and, if relevant, the natural history of disease and behavioral aspects should be included. The assumed social and mixing structure should be described and justified.

In all instances, the appropriate references and sources are required. The parameter ranges considered should be made explicit. Bounds on the parameter ranges should be justified by the appropriate citations whenever possible. Multidimensional parameter space should be sampled in an appropriate way unless analytical results justify only a small number of scenarios. The Results should be presented concisely, with quantitative results accompanied by clear statements aimed at intuitive understanding. Surprising simulation results from very complex models can be replicated where practical by analysis of a much simpler model to provide at least mathematical intuition, and perhaps even nonmathematical intuition, of what elements of the model were responsible for the results.

The Discussion section should include a brief summary of the key findings. The results should be interpreted appropriately, with careful consideration of how simplifying assumptions, model structure, parameter ranges, and other factors limit validity and how generally applicable the results are. It should be clearly stated whether the results should be interpreted qualitatively or quantitatively. The structural stability of the model should be considered. In particular, convincing arguments should be made that simplifying assumptions in the model, if not strongly justified by the data, are not artifactually generating misleading results. Results from the uncertainty analysis should be differentiated from the limitations imposed by the chosen model structure, upon which the uncertainty ranges depend. When the data are analyzed to obtain parameter estimates, the effect of the underlying assumptions about the dynamics on the interpretation of the results should be made explicit. The Appendix should contain any large sets of equations and technical material that would interrupt the flow of the main text or be of interest only to the more technically interested readers. The authors are encouraged, when possible, to provide software and data, so other scientists can have a better understanding of the mathematical modeling.

Obviously we could not cover the broad scope of quantitative infectious disease epidemiology in this brief editorial. However, we hope that these guidelines will help authors to decide whether their research in infectious disease modeling, statistical analysis, or study designs are appropriate for AJE and to prepare their manuscripts for submission.


    References
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 References
 

  1. Dominici F, Spiegelman D, Cole SR. Methodological contributions to the American Journal of Epidemiology. (Editorial). Am J Epidemiol 2004;160:197–8.[Free Full Text]
  2. Ross R. An application of the theory of probabilities to the study of a priori pathometry, part 1. Proc R Soc Lond Ser A 1916;92:204–30.




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