Natural language processing to evaluate texting conversations between patients and healthcare providers during COVID-19 Home-Based Care in Rwanda at scale.

Community isolation of patients with communicable infectious diseases limits spread of pathogens but our understanding of isolated patients' needs and challenges is incomplete. Rwanda deployed a digital health service nationally to assist public health clinicians to remotely monitor and support SARS-CoV-2 cases via their mobile phones using daily interactive short message service (SMS) check-ins. We aimed to assess the texting patterns and communicated topics to better understand patient experiences.

Assessing factors associated with compliance to preventive measures of COVID-19 in Rwanda: a cross-sectional community survey.

Objective: To assess the level of compliance with COVID-19 preventive measures and compliance-associated factors in the Rwanda community.

Design: Cross-sectional study.

Settings: Country-wide community survey in Rwanda.

Exploring the drinking water supply in Rwanda with panel data analysis.

This study in Rwanda offers a comprehensive analysis of water quality, reliability, and cost-effectiveness, departing from previous research by utilizing panel data analysis for a nuanced understanding of spatiotemporal dynamics. Unlike earlier studies focusing on specific aspects, this research adopts a holistic approach, examining factors crucial for water supply, quality, and cost, thus providing an integrated view of Rwanda's water sector. By analyzing data from various sources, including the Water and Sanitation Corporation (WASAC), the study evaluates the reliability, quality, and cost-effectiveness of drinking water.

Child stunting prevalence determination at sector level in Rwanda using small area estimation.

Background: Stunting among children under 5 years of age remains a worldwide concern, with 148.1 million (22.3%) stunted in 2022.

Leveraging artificial intelligence and data science techniques in harmonizing, sharing, accessing and analyzing SARS-COV-2/COVID-19 data in Rwanda (LAISDAR Project): study design and rationale.

Background: Since the outbreak of COVID-19 pandemic in Rwanda, a vast amount of SARS-COV-2/COVID-19-related data have been collected including COVID-19 testing and hospital routine care data. Unfortunately, those data are fragmented in silos with different data structures or formats and cannot be used to improve understanding of the disease, monitor its progress, and generate evidence to guide prevention measures. The objective of this project is to leverage the artificial intelligence (AI) and data science techniques in harmonizing datasets to support Rwandan government needs in monitoring and predicting the COVID-19 burden, including the hospital admissions and overall infection rates.