We are currently working on the groundbreaking research on immunity to influenza in humans as a part of NIAID-funded project “Immune Function and Biodefense in Children, Elderly, and Immunocompromised Populations.” NIAID has funded ten regional Centers of Excellence for Biodefense and Emerging Infectious Diseases Research. This nationwide network of multidisciplinary academic centers is conducting wide-ranging research on infectious diseases and developing diagnostics, therapeutics, and vaccines. The elderly and immunocompromised patients have altered immune responses to viral and bacterial pathogens are the most susceptible to infection, and at serious risk for serious consequences of infection. We are working on the development of mathematical models for seasonality and generating computer simulations for spatial and temporal aspects of illnesses, which assist researchers to better understand the nature of influenza.

This project is being supported by the National Institute of Allergy and Infectious Diseases (U19AI062627, HHSN266200500032C)

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 & 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 trends 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 patterns. Our researchers use new regression techniques to retain and quantify these seasonal fluctuations, in order to more accurately examine the influence of these variations on severity and timing of seasonal outbreaks over the years. We have proposed an analytical and conceptual framework for the assessment of disease seasonality demonstrated by seasonal patterns of six enterically transmitted 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.

Population Dynamics: Simulation Modeling
Simulation modeling is a computational experimental technique in which researchers try to isolate and encode the local rules of a system, and then experiment with alterations to these system rules, comparing the resulting outcomes. In a recent simulation modeling study, we demonstrated that seasonal patterns in infectious disease could arise solely from the differences in social interactions among etiologically distinct subpopulations. 

Improved Surveillance: Outbreak & 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.

Within each population, there exists a great amount of variability in an individual's response to infectious disease. Factors such as susceptibility to pathogens, probability of developing symptoms, mortality and ongoing immunity, to name but a few of the possibilities, heavily influence disease outcomes. Our research seeks to elucidate how different subpopulations and socially mediated interactions between them influence infectious disease dynamics in the population as a whole.

Excessive heat events
We have used different classifications of heat waves/ heat spells to explore their potential effect on the change of gastro intestinal infections (GI) in the major cities of the US.

Atlantic storms
We have developed methodology for estimating the number of elderly affected by Atlantic storms in 1998-2002 using Geographical Information System. Attempting to document historical status of gastro intestinal infections (GI) among elderly (≥65yo) in the areas affected by Hurricane Katrina in Louisiana and Mississippi, we gathered information from various sources and described GI rates for the two states from 1998 to 2002.

Climate classification
The Köppen climate classification system is the most widely used and understood classification. We have suggested an adaptation of the Köppen climate classification and have demonstrated its utility by describing the incidence of campylobacteriosis, a disease caused by thermo-sensitive bacteria in which diverse seasonal patters have been observed worldwide. By carefully studying these associations and their underlying mechanisms, we hope to gain insights into the factors that drive the emergence and seasonal/interannual variations in contemporary epidemic diseases and, possibly, to understand the potential future disease impacts of long-term climate change.

These research studies are supported by NIEHS R01ES 013171.

Air pollution
Our ongoing collaborative work in Quito, Ecuador investigated the causes of acute respiratory infections in the region. We described the prevalence of infections, most common in children, and revealed two distinct air pollutants responsible for increasing the risk of acute respiratory infections: exposure to carbon monoxide fumes from vehicular traffic and long-lasting air borne debris from the Guagua Pichincha Volcanic Eruptions in April 2000. We also analyzed data from seven large U.S. cities and demonstrated that hospital admissions for congestive heart failure showed a consistent association with daily variations in ambient carbon monoxide and fine particulate levels with an increased effect at low temperatures. Our future projects in Ecuador will involve hazard exposure related research by conducting a study of the volcano activity and its impact on human health.

Water pollution
Understanding how incubation periods and modes of transmission differ between groups can lead to earlier detection of potential outbreaks. Our work in time-series analysis has produced innovations in surveillance methods for detecting waterborne outbreaks in a variety of global settings. We have recently demonstrated the applicability of passive surveillance data in determining geographic and temporal patterns of waterborne pathogen incidence, but cautioned that surveillance data are sensitive to patterns of diagnosis and reporting in periods of suspected outbreak.

Cryptosporidium oocysts are common and widespread in ambient water and can persist for months in this environment. Studies conducted in various locations have noted an increase in cryptosporidiosis during the warm and rainy season. We are conducting a meta-analysis to examine how an increase in cryptosporidiosis relates to precipitation and ambient temperature worldwide. We are investigating the potential of using remote sensing data as a proxy for exposure to cryptosporidiosis globally. The source and occurrence of Cryptosporidium in watersheds has been characterized, although continued improvements in monitoring methods and analytical techniques would increase our understanding of these issues. We are currently studying such methods that can lead to discovering specific contamination sources that will ultimately contribute to public health protection.