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Fine-scale spatial mapping of urban malaria prevalence for microstratification in an urban area of Ghana

Malaria is a focal disease and more localized in low endemic areas. The disease is increasingly becoming a concern in urban areas in most sub-Saharan African countries. The growing threats of Anopheles stephensi and insecticide resistance magnify this concern and hamper elimination efforts. It is, therefore, imperative to identify areas, within urban settings, of high-risk of malaria to help better target interventions.

Prioritizing high-risk populations for soil-transmitted helminth control in the Western Pacific Region

To achieve targets set within the 2030 Sustainable Development Agenda and the 2021–2030 Neglected Tropical Diseases (NTD) Roadmap, the World Health Organization identify the need for improved data analytics to inform NTD control programs. 

Spatiotemporal patterns of influenza in Western Australia

Understanding the geospatial distribution of influenza infection and the risk factors associated with infection clustering can inform targeted preventive interventions. We conducted a geospatial analysis to investigate the spatial patterns and identify drivers of medically attended influenza infection across all age groups in Western Australia.

Clostridioides (Clostridium) difficile in children and adolescents in the community in Cambodia

Clostridioides (Clostridium) difficile transmission between community and healthcare settings has been increasingly reported. We aimed to identify the prevalence and molecular epidemiology of C. difficile colonising adolescents and non-hospitalised children in Cambodia.

Geospatial mapping of drug-resistant tuberculosis prevalence in Africa at national and sub-national levels

o map subnational and local prevalence of drug-resistant tuberculosis (DR-TB) across Africa. We assembled a geolocated dataset from 173 sources across 31 African countries, comprising drug susceptibility test results and covariate data from publicly available databases. We used Bayesian model-based geostatistical framework with multivariate Bayesian logistic regression model to estimate DR-TB prevalence at lower administrative levels.

Spatial co-distribution of tuberculosis prevalence and low BCG vaccination coverage in Ethiopia

While bacille-calmette-guerin (BCG) vaccination is one of the recommended strategies for preventing tuberculosis, its coverage is low in several countries, including Ethiopia. This study investigated the spatial co-distribution and drivers of TB prevalence and low BCG coverage in Ethiopia.

Mapping Drug-Resistant Tuberculosis Treatment Outcomes in Hunan Province, China

Drug-resistant tuberculosis (DR-TB) remains a major public health challenge in China, with varying treatment outcomes across different regions. Understanding the spatial distribution of DR-TB treatment outcomes is crucial for targeted interventions to improve treatment success in high-burden areas such as Hunan Province. This study aimed to map the spatial distribution of DR-TB treatment outcomes at a local level and identify sociodemographic and environmental factors associated with poor treatment outcomes in Hunan Province, China.

Health system and environmental factors affecting global progress towards achieving End TB targets between 2015 and 2020

Health system and environmental factors play a significant role in achieving the World Health Organization End Tuberculosis (TB) targets. However, quantitative measures are scarce or non-existent at a global level. We aimed to measure the progress made towards meeting the global End TB milestones from 2015 to 2020 and identify health system and environmental factors contributing to the success.

Malaria in Nepal: A Spatiotemporal Study of the Disease Distribution and Challenges on the Path to Elimination

Malaria incidence (MI) has significantly declined in Nepal, and this study aimed to investigate the spatiotemporal distribution and drivers of MI at the ward level. Data for malaria cases were obtained from the National Surveillance System from 2013 to 2021. Data for covariates, including annual mean temperature, annual mean precipitation, and distance to the nearest city, were obtained from publicly available sources. A Bayesian spatial model was used to identify factors associated with the spatial distribution of MI.