The overall goal of the software development and core facilities section is to make the new methodologies, algorithms, and models developed in the MIDAS Center's research projects more accessible to both the research and public health communities. To do so, we will integrate some of the independently developed tools to create a coherent computational infrastructure that is more than the sum of its methodological parts. In particular, we propose to develop and to maintain a suite of computational tools for infectious disease modeling and analysis centered around a multi-scale modeling computational platform (MCP), optimized for cloud deployment. The major aims of the software developments are: i) Develop an MCP capable of integrating large scale stochastic modeling approaches working at different scales of resolution. We will start by creating a multiscale hybrid model based on combining the global individual-based metapopulation model GLEAM with the US scale agent-based influenza model FluTE. The MCP architecture will be designed around the existing GLEAMviz client-server architecture. It will be expanded with the development of Application Programming Interfaces (APIs) for the plug-in of models developed by the MIDAS research community. The MCP will consist of computationally intensive tools, so we plan to design and to deploy a cloud computing environment with elastic capabilities, ii) Develop new features and graphical user interfaces (GUIs) for statistical tools developed in the context of the research activities of the Center such as extensions of pomp, TranStat, among others. We will develop integrated workflows aimed at allowing those programs to exchange data and parameter estimates with the MCP in an automated way. iii) Develop a website that will act as a sharing repository for the software developed in this MIDAS Center, with training materials, manuals, and workflow examples.
We aim to provide the best accessibility and to foster adoption of the developed software by MIDAS, the research community, and public health agencies by using best practices and public repositories
To extend the results of the MIDAS Center's research projects beyond academia, the Software Development team will help translate the research into products that can be used by external researchers and public health officials. The scope of work includes dissemination of the Center's research tools, updating computational tools to suit the needs of users, and integrating multiple tools into coherent, usable platforms.
| Liu, Quan-Hui; Ajelli, Marco; Aleta, Alberto et al. (2018) Measurability of the epidemic reproduction number in data-driven contact networks. Proc Natl Acad Sci U S A 115:12680-12685 |
| Gallagher, Molly E; Brooke, Christopher B; Ke, Ruian et al. (2018) Causes and Consequences of Spatial Within-Host Viral Spread. Viruses 10: |
| Dean, Natalie E; Halloran, M Elizabeth; Longini, Ira M (2018) DESIGN OF VACCINE TRIALS DURING OUTBREAKS WITH AND WITHOUT A DELAYED VACCINATION COMPARATOR. Ann Appl Stat 12:330-347 |
| Bento, Ana I; Riolo, Maria A; Choi, Yoon H et al. (2018) Core pertussis transmission groups in England and Wales: A tale of two eras. Vaccine 36:1160-1166 |
| Moore, James; Ahmed, Hasan; Jia, Jonathan et al. (2018) What Controls the Acute Viral Infection Following Yellow Fever Vaccination? Bull Math Biol 80:46-63 |
| Ajelli, Marco; Zhang, Qian; Sun, Kaiyuan et al. (2018) The RAPIDD Ebola forecasting challenge: Model description and synthetic data generation. Epidemics 22:3-12 |
| Domenech de Cellès, Matthieu; Magpantay, Felicia M G; King, Aaron A et al. (2018) The impact of past vaccination coverage and immunity on pertussis resurgence. Sci Transl Med 10: |
| Dinh, Vu; Tung Ho, Lam Si; Suchard, Marc A et al. (2018) Consistency and convergence rate of phylogenetic inference via regularization. Ann Stat 46:1481-1512 |
| Yang, Yang; Meng, Ya; Halloran, M Elizabeth et al. (2018) Reply to Aguiar and Stollenwerk. Clin Infect Dis 66:642 |
| Bretó, Carles (2018) Modeling and inference for infectious disease dynamics: a likelihood-based approach. Stat Sci 33:57-69 |
Showing the most recent 10 out of 134 publications
