This project addresses the Challenge Area 15: Translational Science. The Challenge Topic is 15-TW- 101: Models to predict health effects of climate change. The slow but steady increase in the global mean temperature as a result of climate change is bringing with it a steadily increasing probability that certain tropical diseases may emerge and explosively expand into formerly temperate zones. Due to climate change combined with the growth of human population, easy long distance travel, and other factors, humanity is now at the brink of emergence of new and possibly devastating infectious epidemic diseases, and the reemergence of resistant strains of old ones. Monitoring and suppressing small outbreaks so that they do not become a global emergency has become the daily routine of public health systems in many parts of the world. The objective of this project is to develop a spatial epidemic simulation system capable of assimilating (i.e., incorporating and adapting to) real-time data, such as incidence, prevalence, and mortality reports from locations in the field, while the model is running. The technology and advanced theory for this spatial data assimilation has only recently been developed in the fields of climate science, meteorology, and wildfire studies, where it has had a major impact. We propose to translate this exciting new technology into the domain of epidemic tracking as rapidly as possible. We anticipate that this technology will constitute a dramatic improvement in the capability to forecast epidemics and to validate spatial epidemic models, and that this will have a beneficial impact on public health, enabling more effective policies and the concentration of resources to save lives. Given the global nature of pandemic disease and the dangers implied by a failure to control an outbreak, it would not be a great exaggeration to suggest that the size of the community impacted by the benefits of this proposal is the entire world's population. The system will be validated against historical as well as artificial test data streams. The project will culminate with an operational exercise to be performed with the Office of Emergency Preparedness of the State of Colorado Department of Public Health, and the University of Colorado Denver's School of Public Health. This exercise will test the ability of the modeling system to provide decision- makers with timely and useful spatial information on a dynamic digital map.
This project will adapt new methods currently in use in wildfire tracking to develop a spatial computer model for forecasting the spread of major epidemics, and mass population movements that may occur as a result. The model will be able to update itself flexibly and easily from sparse data arriving in real time, thus significantly improving our ability to provide predictions of the geographical spread of the disease in a timely manner. This new capability will make it possible for public health authorities to concentrate their efforts where they matter most, and for the government to adopt effective policies anticipating the response of the population.
| Cobb, Loren; Krishnamurthy, Ashok; Mandel, Jan et al. (2014) Bayesian tracking of emerging epidemics using ensemble optimal statistical interpolation. Spat Spatiotemporal Epidemiol 10:39-48 |
| Mandel, Jan; Cobb, Loren; Beezley, Jonathan D (2011) ON THE CONVERGENCE OF THE ENSEMBLE KALMAN FILTER. Appl Math (Prague) 56:533-541 |
| Mandel, Jan; Beezley, Jonathan D; Cobb, Loren et al. (2010) Data driven computing by the morphing fast Fourier transform ensemble Kalman filter in epidemic spread simulations. Procedia Comput Sci 1:1221-1229 |
