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The Tufts Initiative for the Forecasting and Modeling of Infectious Diseases (InForMID) was established to conduct research and provide a venue for training in the fields of computational epidemiology, conservation medicine, biostatistics, and bioinformatics with the emphasis on public health applications. The mission of the Initiative is to improve the quality of biomedical research and health care by developing innovative analytical and computational tools and systems to life-science researchers, public health professionals, and policy makers.


At InForMID we use advanced computational and analytical techniques that expands on information provided by traditional data visualization and analysis methods, and open doors for researchers working to better understand an infectious disease outbreak.

Spatial Spread:
Geographic Information Systems and Dynamic Mapping

Understanding the geographic spread of a disease is an important first step to identifying causal and contributing factors, and preventing further disease transmission. Geographic Information Systems (GIS) and Dynamic Maps create easy to interpret visuals of disease incidence rates across space. We have used maps in our research of how infectious disease and weather events affect the elderly and will make extensive use of these tools in our continued studies of factors affecting elderly health and outbreaks of waterborne illness.

Temporal Patterns:
Seasonality Assessment & Distributed Time Lags

Periodic patterns in infectious disease are among the best-known and worst understood phenomena in the study of disease dynamics. InForMID researchers are on the forefront of research regarding these seasonal behaviors. Our researchers use new regression techniques to retain and quantify seasonal fluctuations, in order to more accurately examine the influence of these variations on severity and timing of infectious outbreaks over the years. We have proposed an analytical and conceptual framework for the assessment of disease seasonality demonstrated by seasonal patterns of enterically transmitted and respiratory diseases. These methods are combined with a new time-series analysis tool-Temporal Exposure Response Surface (TERS) Data Visualization. This new technique offers a three-dimensional picture of disease spread revealing magnitude, duration and shape of the epidemic curve of an infectious outbreak in association with the level of exposure. We demonstrated the use of TERS-plots as an advanced visualization tool for syndromic surveillance systems. It also allowed us to detect a secondary spread of cryptosporidiosis infection, as well as shorter average incubation time, in the elderly compared to general population.

We introduced the Distributed Lag Model, an approach for modeling time-distributed effects with respect to an incubation period of infection through an analysis of the association between ambient temperatures and enterically-transmitted infections.

Improved Surveillance:
Outbreak and Disease Signature

We've used mathematical modeling to describe the concept of ‘outbreak signature', as an improved tool for surveillance and early detection of infectious disease. Building on this work, we used mathematical models to create a hypothetical outbreak scenario and defined the idea of a population-specific ‘disease signature' combining all three elements of disease dynamics-the temporal and spatial spread of outbreaks in and across populations. A disease signature allows for a higher level of differentiation between disease-spread scenarios in different populations, leading to a new practical definition for ‘outbreak'. This model can be used to understand, estimate, and, in some cases, correct for, reporting error inherent in traditional disease surveillance.