Temporal and spatial analysis of over 7,000 measles cases outbreak from 2018 to 2019 in the Brazilian Amazon

ABSTRACT Objective: This study aimed to present a temporal and spatial analysis of the 2018 measles outbreak in Brazil, particularly in the metropolitan city of Manaus in the Amazon region, and further introduce a new tool for spatial analysis. Methods: We analyzed the geographical data of the residences of over 7,000 individuals with measles in Manaus during 2018 and 2019. Spatial and temporal analyses were conducted to characterize various aspects of the outbreak, including the onset and prevalence of symptoms, demographics, and vaccination status. A visualization tool was also constructed to display the geographical and temporal distribution of the reported measles cases. Results: Approximately 95% of the included participants had not received vaccination within the past decade. Heterogeneity was observed across all facets of the outbreak, including variations in the incubation period and symptom presentation. Age distribution exhibited two peaks, occurring at one year and 18 years of age, and the potential implications of this distribution on predictive analysis were discussed. Additionally, spatial analysis revealed that areas with the highest case densities tended to have the lowest standard of living. Conclusion: Understanding the spatial and temporal spread of measles outbreaks provides insights for decision-making regarding measures to mitigate future epidemics.

Prediction of malaria using deep learning models: A case study on city clusters in the state of Amazonas, Brazil, from 2003 to 2018

ABSTRACT Background: Malaria is curable. Nonetheless, over 229 million cases of malaria were recorded in 2019, along with 409,000 deaths. Although over 42 million Brazilians are at risk of contracting malaria, 99% percent of all malaria cases in Brazil are located in or around the Amazon rainforest. Despite declining cases and deaths, malaria remains a major public health issue in Brazil. Accurate spatiotemporal prediction of malaria propagation may enable improved resource allocation to support efforts to eradicate the disease. Methods: In response to calls for novel research on malaria elimination strategies that suit local conditions, in this study, we propose machine learning (ML) and deep learning (DL) models to predict the probability of malaria cases in the state of Amazonas. Using a dataset of approximately 6 million records (January 2003 to December 2018), we applied k-means clustering to group cities based on their similarity of malaria incidence. We evaluated random forest, long-short term memory (LSTM) and dated recurrent unit (GRU) models and compared their performance. Results: The LSTM architecture achieved better performance in clusters with less variability in the number of cases, whereas the GRU presents better results in clusters with high variability. Although Diebold-Mariano testing suggested that both the LSTM and GRU performed comparably, GRU can be trained significantly faster, which could prove advantageous in practice. Conclusions: All models showed satisfactory accuracy and strong performance in predicting new cases of malaria, and each could serve as a supplemental tool to support regional policies and strategies.

Fatores associados à mortalidade por causas inespecíficas e mal definidas no estado do Amazonas, Brasil, de 2006 a 2012 / Factors associated with unspecified and ill-defined causes of death in the State of Amazonas, Brazil, from 2006 to 2012

Resumo Objetivou-se investigar fatores associados à mortalidade por causas inespecíficas e mal definidas no estado do Amazonas (AM). Desenvolveu-se um estudo seccional incluindo 90.439 registros de óbitos não fetais, com residência e ocorrência no AM entre 2006 e 2012. Foram estimadas razões de chances de causas inespecíficas e mal definidas por meio de regressão logística multinomial hierárquica. A proporção de causas mal definidas e inespecíficas foi, respectivamente, 16,6% e 9,1%. A ocorrência de causas mal definidas diminuiu ao longo dos anos e a de causas inespecíficas somente no último biênio. As causas inespecíficas associaram-se com residência e ocorrência do óbito fora da capital, via pública, sexo feminino, dos 10 aos 49 anos, cor parda e quando atestadas por legistas. As causas mal definidas associaram-se com residência e ocorrência fora da capital, em domicílios, a partir de 40 anos, cor não branca, não ser solteiro, baixa escolaridade, assistência médica e falta de informação sobre o atestante. A mortalidade por causas mal definidas e inespecíficas no AM declinou entre 2006 e 2012, associando-se às dimensões espacial e temporal, fatores demográficos, socioeconômicos e à assistência médica na ocasião do óbito.

Abstract This study aimed to investigate factors associated with unspecified and ill-defined causes of death in the State of Amazonas (AM), Brazil. This is a cross-sectional study on 90,439 non-fetal deaths of residents in AM from 2006 to 2012. The hierarchical multinomial logistic model estimated odds ratios of unspecified and ill-defined causes of death. Ill-defined and unspecified causes of death proportional mortality was, respectively, 16.6% and 9.1%. Ill-defined causes showed a decreasing trend over the years, while unspecified causes only decreased in the last two years. Unspecified causes of death were associated with residence and death outside the capital, public roads, female gender, age group 10-49 years, brown skin color and when certified by forensic doctors. Ill-defined causes of death were associated with residence and occurrence outside capital, at home, ages 40 years and older, non-whites, not being single, low schooling, under medical care and when examiner was unknown. Ill-defined and unspecified cause mortality in the State of Amazonas decreased between 2006 and 2012 in AM and was associated with space and time, demographic and socioeconomic factors and medical care at the moment of death.

Overview of artemisinin effectiveness during outset years of its implementation in the western Brazilian Amazon

BACKGROUND The elimination of malaria depends on the blocking of transmission and of an effective treatment. In Brazil, artemisinin therapy was introduced in 1991, and here we present a performance overview during implementation outset years. METHODS It is a retrospective cohort (1991 to 2002) of patients treated in a tertiary centre of Manaus, with positive microscopic diagnosis of Plasmodium falciparum malaria, under treatment with using injectable or rectal artemisinin derivatives, and followed over 35-days to evaluate parasite clearance, death and recurrence. FINDINGS This cohort outcome resulted 97.6% (1554/1593) of patients who completed the 35-day follow-up, 0.6% (10/1593) of death and 1.8% (29/1593) of follow-up loss. All patients that died and those that presented parasitaemia recurrence had pure P. falciparum infections and received monotherapy. Considering patients who completed 35-day treatment, 98.2% (1527/1554) presented asexual parasitaemia clearance until D4 and 1.8% (27/1554) between D5-D10. It is important to highlight that had no correlation between the five treatment schemes and the sexual parasite clearance. Finally, it is noteworthy that we were able to observe also gametocytes carriage during all follow-up (D0-D35). MAIN CONCLUSIONS Artemisinin derivatives remained effective in the treatment of falciparum malaria during first 12-years of use in north area of Brazil.

Do climate changes alter the distribution and transmission of malaria? Evidence assessment and recommendations for future studies

Abstract Malaria, a mosquito-borne infectious disease, is considered a significant global health burden. Climate changes or different weather conditions may impact infectious diseases, specifically those transmitted by insect vectors and contaminated water. Based on the current predictions for climate change associated with the increase in carbon dioxide concentrations in the atmosphere and the increase in atmospheric temperature, the Intergovernmental Panel on Climate Change predicts that in 2050, malaria may threaten some previously unexposed areas worldwide and cause a 50% higher probability of malaria cases. Climate-based distribution models of malaria depict an increase in the geographic distribution of the disease as global environmental temperatures and conditions worsen. Researchers have studied the influence of changes in climate on the prevalence of malaria using different mathematical models that consider different variables and predict the conditions for malaria distribution. In this context, we conducted a mini-review to elucidate the important aspects described in the literature on the influence of climate change in the distribution and transmission of malaria. It is important to develop possible risk management strategies and enhance the surveillance system enhanced even in currently malaria-free areas predicted to experience malaria in the future.

Current vector control challenges in the fight against malaria in Brazil

Abstract In Brazil, malaria is an important public health problem first reported in 1560. Historically, fluctuations in malaria cases in Brazil are attributed to waves of economic development; construction of railroads, highways, and hydroelectric dams; and population displacement and land occupation policies. Vector control measures have been widely used with an important role in reducing malaria cases. In this review article, we reviewed the vector control measures established in the Brazilian territory and aspects associated with such measures for malaria. Although some vector control measures are routinely used in Brazil, many entomological and effectiveness information still need better evidence in endemic areas where Plasmodium vivax predominates. Herein, we outlined some of the needs and priorities for future research: a) update of the cartography of malaria vectors in Brazil, adding molecular techniques for the correct identification of species and complexes of species; b) evaluation of vector competence of anophelines in Brazil; c) strengthening of local entomology teams to perform vector control measures and interpret results; d) evaluation of vector control measures, especially use of insecticide-treated nets and long-lasting insecticidal nets, estimating their effectiveness, cost-benefit, and population acceptance; e) establishment of colonies of malaria vectors in Brazil, i.e., Anopheles darlingi, to understand parasite-vector interactions better; f) study of new vector control strategies with impacts on non-endophilic vectors; g) estimation of the impact of insecticide resistance in different geographical areas; and h) identification of the relative contribution of natural and artificial breeding sites in different epidemiological contexts for transmission.