Infectious Disease

Justin Cohen of the Clinton Health Access Initiative Inc in the U.S. will develop a user-friendly electronic data collection tool using an open source platform and spatial intelligence to better monitor mass drug administration campaigns in schools and in the community. Their platform integrates population mapping with field data collection processes to accurately calculate intervention coverage, such as the distribution of bed-nets or administered drugs, in near real-time.

This study intends to evaluate the use of long- lasting insecticide-impregnated nets (LLINs) and their implications in a municipality of five Brazilian Amazonian states. Brazil has been using LLINs as a supplementary control tool for over 10 years and during this period many questions regarding its effectiveness were raised.

The goal of this project is to establish an ICU-based sentinel surveillance network in Africa, describe prevalent and incident patterns of colonization in representative ICU’s and evaluate the correlation between AMR patterns in clinical isolates and in surveillance cultures of the ICU microbiome. Sentinel surveillance through ICU AMR monitoring and data sharing could be used identify modifiable factors in the persistence and spread of AMR.

The main aim of the project is to investigate use of molecular methods for detection of resistance genes without culturing organisms and the study proposes a new innovative approach to combine genomic data analysis with epidemiological evidence to explain patterns of AMR transmission between food animals and humans.

The aim of the project is to determine whether the antibiotic profile found WWTPS may be used as a proxy of community antibiotic usage. The antibiotic resistant bacteria (ARB) and antibiotic resistant gene (ARG) profiles in WWTPs and receiving environments in South Africa and other African countries will also be determined with the intention of providing mitigating strategies to prevent their release into the receiving environment.

The proposed project aims to investigate the prevalence of AMR in the human population and will work from the hypothesis that metagenomic sequencing of human sewage can be used to detect, and in combination with epidemiological / ecological modeling, explain and predict emergence and trends of AMR.

The aim of the project is to use advanced statistical and machine learning techniques to interrogate the vast amount of existing data relating to antimicrobial resistance (AMR). It is of importance to identify key risk factors driving clinical cases of resistant infections in a One Health framework. The project relies on a network of researchers across Africa generating relevant data, which implies that the data will come in many different forms and wildly varying quality. This is seen as part of the key research question: instead of assessing the data from a binary perspective (i.e.

To identify and understand drivers of antimicrobial resistance responsible for emergence of extended spectrum beta-lactamase (ESBL) Escherichia coli and Klebsiella pneumoniae in pediatric and maternal populations within Uganda.

Antibiotic resistance (ABR) is a public health threat and largely attributed to heavy selective pressures resulting from widespread of antibiotic use coupled with the exchange of genetic resistance genes between microorganisms through plasmids. These plasmids can be specific to a type of host(s) limiting their spread or may be broad range with capabilities of spreading across species.

To capture data on AMR indicators- antibiotic prescriptions, drug sensitivities and resistance patterns from the private sector (human and animal clinics and laboratories) to generate evidence that will contribute to improving antibiotic prescription practices, epidemiological surveillance and effective control of AMR in Uganda.