An analysis of malaria in the Brazilian Legal Amazon using divergent association rules

Temporal patterns, spatial risks, and characteristics of tegumentary leishmaniasis in Brazil in the first twenty years of the 21st Century

BACKGROUND

Tegumentary leishmaniasis (TL) is a significant public health issue in Brazil. The present ecological study describes the clinical and epidemiological characteristics of TL cases reported in the country, and analyzes the spatial and temporal patterns of the incidences and risks of occurrence across the five geopolitical regions and 27 federative units.

METHODOLOGY/PRINCIPAL FINDINGS

Data regarding new cases of TL notified between 2001 and 2020 were obtained from the Information System for Notifiable Diseases of the Brazilian Ministry of Health. Joinpoint and spatial and temporal generalized additive models were used to establish trends in the evolution of TL during the target period. The incidence rate for the entire period was 226.41 cases/100,000 inhabitants. All regions of Brazil showed trends of decreasing incidence rates, albeit with fluctuations at specific times, with the exception of the Southeast where rates have increased since 2014, most particularly in Minas Gerais state. The disease was concentrated predominantly in the North region, with Acre state leading the incidence rank in the whole country, followed by Mato Grosso (Midwest), Maranhão and Bahia (Northeast) states. The spatial distribution of the risk of TL occurrence in relation to the annual averages was relatively stable throughout the period. The cutaneous form of TL was predominant and cases most frequently occurred in rural areas and among men of working age. The ages of individuals contracting TL tended to increase during the time series. Finally, the proportion of confirmations by laboratory tests was lower in the Northeast.

CONCLUSION/SIGNIFICANCE

TL shows a declining trend in Brazil, but its widespread occurrence and the presence of areas with increasing incidence rates demonstrate the persistent relevance of this disease and the need for constant monitoring. Our findings reinforce the importance of temporal and spatial tools in epidemiologic surveillance routines and are valuable for targeting preventive and control actions.

Systematic Mapping Study of AI/Machine Learning in Healthcare and Future Directions

This study attempts to categorise research conducted in the area of: use of machine learning in healthcare, using a systematic mapping study methodology. In our attempt, we reviewed literature from top journals, articles, and conference papers by using the keywords use of machine learning in healthcare. We queried Google Scholar, resulted in 1400 papers, and then categorised the results on the basis of the objective of the study, the methodology adopted, type of problem attempted and disease studied. As a result we were able to categorize study in five different categories namely, interpretable ML, evaluation of medical images, processing of EHR, security/privacy framework, and transfer learning. In the study we also found that most of the authors have studied cancer, and one of the least studied disease was epilepsy, evaluation of medical images is the most researched and a new field of research, Interpretable ML/Explainable AI, is gaining momentum. Our basic intent is to provide a fair idea to future researchers about the field and future directions.