This project will focus on the analysis of data on the transmission and risk factors of virus infections. A major source of data is the Tecumseh study, which was designed to determine the occurrence and etiology of diseases caused by influenzavirus, rhinovirus and rotavirus infections. These infections, which result in respiratory and gastrointestinal symptoms, are among the most frequent reasons for seeking medical care by the U.S. population. Data on recent outbreaks of dengue in several cities in Western Mexico will also be included in this project, as the dengue virus may spread to the Southern U.S. Longini and his co-workers developed and successfully fitted stochastic models describing the transmission of infectious diseases. These models assume different rates of transmission in a household and throughout the community. The proposed research will extend these models by assuming that the transmission probabilities depend upon the levels of demographic and environmental risk factors. A statistical analysis based on these models will enable epidemiologists to assess the net effect of each factor as well as the interactions among the various factors. Knowledge of the associations between risk factors and transmission rates is essential for the effective planning and implementation of disease control strategies such as immunization, education and reduction of mosquito densities (in the case of dengue). Special statistical methods will be developed to analyze infectious disease data, such as those resulting from the Tecumseh and Mexican studies. These methods must take the underlying sampling design into account, as individuals from the same household or neighborhood cannot assumed to be independent with regard to the dependent variable (infected or not) or to the risk factors. Recently published methods for the analysis of categorical data from complex sampling surveys will be used. The statistical analysis will be supplemented by simulations of the spread of an infectious agent under various control strategies. In this way, it will be possible to identify the most effective strategy to control the spread of a specific virus in a given community.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
Project #
Application #
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Emory University
Graduate Schools
United States
Zip Code
Rampey Jr, A H; Longini Jr, I M; Haber, M et al. (1992) A discrete-time model for the statistical analysis of infectious disease incidence data. Biometrics 48:117-28
Addy, C L; Longini Jr, I M; Haber, M (1991) A generalized stochastic model for the analysis of infectious disease final size data. Biometrics 47:961-74
Haber, M; Longini Jr, I M; Halloran, M E (1991) Measures of the effects of vaccination in a randomly mixing population. Int J Epidemiol 20:300-10
Ackerman, E; Longini Jr, I M; Seaholm, S K et al. (1990) Simulation of mechanisms of viral interference in influenza. Int J Epidemiol 19:444-54
Koopman, J S; Monto, A S; Longini Jr, I M (1989) The Tecumseh Study. XVI: Family and community sources of rotavirus infection. Am J Epidemiol 130:760-8
Longini Jr, I M; Clark, W S; Byers, R H et al. (1989) Statistical analysis of the stages of HIV infection using a Markov model. Stat Med 8:831-43
Haber, M; Longini Jr, I M; Cotsonis, G A (1988) Models for the statistical analysis of infectious disease data. Biometrics 44:163-73
Longini Jr, I M; Monto, A S (1988) Efficacy of virucidal nasal tissues in interrupting familial transmission of respiratory agents. A field trial in Tecumseh, Michigan. Am J Epidemiol 128:639-44
Longini Jr, I M; Koopman, J S; Haber, M et al. (1988) Statistical inference for infectious diseases. Risk-specific household and community transmission parameters. Am J Epidemiol 128:845-59