Infectious Disease

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 objective of this project is to develop data visualization tools and malaria outbreaks spreading simulation-based in machine learning methods, using demographic, epidemiological, climatic and clinical data related to malaria in the Brazilian Amazon. Such tools will be provided through a Web Platform with supporting tools to public administrators; the goal is for it to be ready for use in the short term. All the infrastructure for the creation and provision for a Web Platform had been developed.

The study proposes to follow patients with malaria in locations with different transmission scenarios to determine the factors related to infection recurrence. For that, clinical follow-up protocols will be applied, as well as molecular biology techniques, medicine quality assessment, treatment adherence, adverse effect frequency and molecular and pharmacogenetics aspects. The project will improve the knowledge about factors associated with recurrence and classify those episodes, making possible to adjust the recommendation on case management.

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.

The projects aims to reconcile coverage (from ANMOL / CAS) and consumption data (from eVIN) using blockchain technology to create a robust, scalable, real-time tracking and triangulation system on immunization data in India.

The study focuses on the transmission dynamics of resistance in poultry farm workers to estimate the possibility of zoonotic transfer of pathogens. The study will be looking at humans, animals, air and water as well

The study plans to collect stool samples from children admitted to the ICU to identify invasive MDR Enterobacteriaceae. Serial sampling of these children and subsequently their family members in the community will allow for longitudinal study of the microbiome and the presence of carbapenemase bacterial genes in their faecal samples. This will allow assessment of the risk of secondary transmission of hospital acquired resistant strains to household contacts

The project proposes to monitor AMR at metagenomic level by focusing on unique microbial antibiotic resistance genes (ARG) signatures and tracking the resistance from the "source" to the "sink". The approach intends to provide direct information about AMR and its implications on vulnerable populations. This information is lacking in Indian context and a reliable catalogue would help in proper visualization of the network involved in AMR and to develop strategies to mitigate it

The study intends to implement an ecological multi-host surveillance to document the bacterial infections and antibacterial resistance (ABR) among humans, animals, birds and fishes sharing the environment and linkage with antibiotics and disinfectant exposures at individual, household/habitation and community levels from different sources. A multi-host and multi-species approach shall improve understanding on pattern and spread of bacterial infection and resistance considering the "One Health" perspective.

The project intends to create a Raman database by collecting and recording Raman spectra at every step of various bacterial strains that are sensitive, intermediate or resistant to antimicrobial agents. The focus is to understand the progression/emergence of AMR to work as a supportive surveillance technology. The spectral database will also aid in prediction of possible resistance in bacterial strains. we aim to evaluate how bio spectroscopy, in particular Raman spectroscopy, can be utilised to fingerprint microbiomes and study the progression of AMR resistance in bacteria