James M. Hyman - US grants

The goal of the project is to improve the mathematical models used to understand the spread of vector-borne diseases such as the recent Rift Valley Fever (RVF) epidemics in Africa, the dengue fever (DENV) outbreaks in South America (and Florida), and the continued spread of West Nile virus (WNV) in the United States. Because of the importance of the environment and weather on mosquito-borne epidemics, existing models will be extended to be reactive to environmental changes. For mosquito-borne diseases spread by viruses, there is the possibility that an infected mother can transmit the virus to her eggs and her offspring be born infected. Therefore, vertical transmission will also be included in the modeling and a range of parameters will be identified to determine when vertical transmission is important. These models are analyzed, validated on field data, and undergo a full uncertainty quantification analysis.

Mathematical models based on the underlying transmission mechanisms of the disease can help the public health community understand and anticipate the spread of an epidemic and evaluate the potential effectiveness of different approaches for bringing an epidemic under control. The goal is to develop mathematical models to improve our understanding of the essential relationships between the biological, environmental, and mitigation mechanisms that influence the spread of vector-borne diseases. Such models can be used to assess the relative impact of different mitigation efforts to control the spread of an epidemic; saving time, resources, and lives.

This research will create and analyze mathematical models for predicting and mitigating the spread of Ebola Virus Disease. The investigators propose to combine the strengths of three common modeling approaches by developing a new, integrative model. The result will be a more robust model that can be used to provide valuable and timely information to help control the current epidemic and inform future decision making.

Standard modeling approaches such as ordinary differential equation models, network models and individual based models, have common limitations. Alone, none of these models are sufficient to capture the complexity of the 2014 Ebola epidemic. The investigators will build on existing mathematical analysis, data, and software to combine these approaches on data from the World Health Organization (WHO) as well as the Centers for Disease Control (CDC). This agent-based model will be integrated in a city and county-based network model for the migration, health care, response, and migration efforts. Specific aims for the model include forecasting the incidence of Ebola virus disease, quantifying the uncertainty in the early stages of the epidemic, estimating the spread to new regions, quantifying the impact of interventions and behavior changes, utilizing statistical analysis to estimate the degree of underreporting, quantifying the model?s uncertainty, and determining how uncertainty will impact the different mitigation approaches.

This research project aims to create improved mathematical models for predicting the spread of mosquito-borne diseases. These models can be used to help guide public health workers in improving the effectiveness of intervention strategies for mitigating the impact of these diseases. The project focuses on analyzing approaches that have the potential for optimizing mitigation strategies for controlling chikungunya, dengue fever, and Zika virus.

The partial differential equation mosquito-borne disease transmission models under development in this project will account for spatial heterogeneity in the population density of the mosquito and host populations. The research aims to create a new two-sex model that can account for both vertical and horizontal transmission of bacterial control measures, such as using Wolbachia to mitigate the disease spread. The mathematical analysis of these models will include quantifying the uncertainty in the model forecasts for multiple species of mosquitoes.

Project Summary/Abstract Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a progressive loss of memory. Pathological hallmarks of AD include amyloid-beta (A?) plaque deposition, neurofibrillary tangle (NFT) formation and the progressive loss of synapses and neurons. A growing literature has demonstrated immune activation in the brain, in particular activated microglia contribute to the onset and progression of AD by facilitating A? deposition and NFTs. There is no known cause for AD, however, several risk factors have been identified in the development of AD, including diabetes mellitus (DM) and advanced age. Patients with DM have a 1.5-4 fold increased risk of developing AD. The precise mechanisms by which DM increases the risk of AD is not known, but DM is associated with immune activation. As DM is more prevalent in aging populations it is likely these 2 risk factors combine in the pathogenesis of AD. We hypothesize that elevated inflammatory activation in DM, that is exacerbated by age is the primary driver of increased risk for AD, given inflammation exacerbates AD pathology. Separate data indicate the endogenous neurotransmitter GABA is capable of modulating activation of microglia and immune function and could serve a therapeutic role in DM patients at risk for AD, as well as AD patients comorbid with DM. In preliminary investigations we have demonstrated administration of an already FDA approved GABA receptor agonist in a DM animal model rescues learning and memory deficits, tau phosphorylation (NFT), and immune activation consistent with AD clinical populations and AD animal model systems. The research proposed will provide an opportunity to determine if the elevated inflammation associated with DM is a major contributor to AD pathology, in both males and females. Further, the experiments proposed will expand on our preliminary data to include evaluation of network function disrupted in AD, as well as determine if the rescue is mediated via direct modulation of microglia. Lastly, the data from the proposed research will elucidate the mechanisms underlying the rescue of AD related pathology and provide the necessary data to support the repurposing of an already FDA approved drug for use in a large subset of the AD population.