Predictive Model

Vincent Okungu, Researcher at the University of Nairobi in Kenya, will develop sustainable financing models to boost domestic funding for research and development (R&D) in East Africa. R&D is routinely underfunded in Africa, with the continent producing around 2% of the research output yet accounting for 15% of the global population. Dr. Okungu is a senior health economist whose passion is to see the development of resilient health systems in Africa.

Geoffrey Siwo of the University of Notre Dame in the U.S. will use a computational approach to identify broad-spectrum antiviral drugs that trigger an innate immune response and could be used against a range of viruses. Traditional drug discovery approaches target viral proteins, but this requires prior knowledge of the virus and can lead to the development of resistance. In contrast, compounds that trigger the host’s natural biological defense mechanism inside each cell are less likely to cause resistance, and can be used for treating novel viruses as well as for vaccine adjuvants.

The purpose of this proposal is to develop a system for the identification, monitoring and forecasting of maternal near-miss cases. The project will seek to create a classifier that identifies near-miss cases based on historical data and a predictive model to infer the number of annual near misses in a region. In addition, it will also update near-miss historical number data to forecast the annual number by location.

This project will identify clinical, sociodemographic, psychosocial, neurocognitive and epigenomic factors to assist in the identification of the most effective response to the treatment offered by SUS to detoxify the use of crack and cocaine by women. The project will use the Random Forest algorithm in a database developed by the research group itself in order to predict the factors that impact adherence and maintenance of abstinence among users.

Timely, holistic and accurate information on antibiotic resistance is important for guiding public health actions and treatment decisions. Ng'eno's research explores application of ecological niche models in predicting spatial distribution of antibiotic resistance carriage risk, using antibiotic-use and environmental data.

This study will develop a broad methodology to estimate the economic cost of malaria in Brazil, separated by state and type of parasite. Studies that compose the cost, taking into account the agents involved, regional inequalities, losses in productivity and intangible costs are rare in Brazil. This study will use administrative information, domicile surveys in the states of Amazonia and interviews with local government officials. It intends to develop a platform through which it will be possible to calculate economic costs and help shape malaria control policies.

The study proposes on pregnant women (Garbh-ini) cohort, a multidimensional longitudinal dataset purposely designed to study preterm birth. The study will apply data-driven machine learning approaches to develop an accurate and clinically useful model to predict the risk of preterm births. It will use multiple models for classification, with better objective functions and misclassification penalties that will aid in a higher rate of accurate predictions, and resampling of the data to avert biases arising from class imbalance.

The study aims to explore regional trends and variations in vaccine uptake, uncover relationships to other socioeconomic, demographic, and public health indicators, and develop a predictive model of the state of vaccine confidence in different parts of India. This will infer local-level confidence in vaccines by identifying areas with good access to healthcare infrastructure. The main goal of the proposal to develop a prototype coverage monitoring and forecasting system across districts by using Gaussian process methods.

Over its 13-year trajectory, North Star has built and leveraged coalitions of community actors to engage them in efforts to strengthen the provision of comprehensive sexual and reproductive health (SRH) services for FSWs. In 2016, North Star developed a unique and practical multi-sectoral approach to combat violence against FSWs, by setting up Crisis Response Teams (CRTs).

Mohlopheni Marakalala of the Africa Health Research Institute in South Africa and Eric Rubin of the Harvard TH Chan School of Public Health in the U.S. will use a genetic screening tool, Tn-seq, to identify the specific bacterial genes protecting Mycobacterium tuberculosis (MTB) from immune destruction that could be used to develop new therapeutic approaches to fight tuberculosis, which causes over 1.5 million deaths annually. BCG is the only approved tuberculosis vaccine, but its effect is limited, particularly in adults.