Malaria time series in the extra-Amazon region of Brazil: epidemiological scenario and a two-year prediction model

Malaria Mortality in Brazil: Age-Period-Cohort Effects, Sociodemographic Factors, and Sustainable Development Indicators

INTRODUCTION

Human malaria is a zoonosis considered a serious global public health problem caused by five species of protozoa of the genus Plasmodium spp., which are transmitted by mosquito vectors belonging to the genus Anopheles spp.

OBJECTIVE

To verify whether there is a relationship between the age-standardized malaria mortality rate in Brazil and age-period-cohort effect variables, sociodemographic differences, and indicators of sustainable development.

METHODS

Data on malaria mortality in Brazil from 2000 to 2022 were analyzed using sociodemographic factors such as ethnicity, region of residence, and sustainable development indicators.

RESULTS

Statistical data demonstrated that from 2000 to 2022, there was a reduction in malaria mortality; the 0-4 years age group was more susceptible to death, and the infection affected more men, Indigenous people, and residents of the North Region. Environmental factors such as CO2 emissions and sanitation predict mortality in specific regions. The North and Northeast Regions had higher mortality rates. In the North, low CO2 emissions, deforestation, weak urban sanitation, a lower GDP, and a higher Gini index were related to high mortality; the latter was also a factor in high rates of deforestation and solid waste collection in the Central West and Northeast Regions. The number of consultations and professionals was a predictive factor for high rates in the three regions mentioned. The Southeast Region had the lowest mortality rate and lowest health expenditure, while the Northeast and Midwest Regions had the highest expenditure in this sector.

CONCLUSIONS

This study can contribute to the direction of public policy due to the specificities of each region in Brazil.

Towards malaria elimination: a case-control study to assess associated factors to malaria relapses in the extra-Amazon Region of Brazil from 2008 to 2019

BACKGROUND

Malaria is an infectious disease caused by the Plasmodium species and is a global burden. When not treated correctly, it can reemerge as a relapse or recrudescence. Malaria relapse cases can contribute to maintaining active transmission chains and can influence the patient to develop severe malaria, potentially leading to hospitalization or death. The objective of this study is to estimate the number of malaria relapse cases in the extra-Amazon region of Brazil and to investigate the associated factors.

METHODS

This is a case-control study that analyses malaria infections caused by Plasmodium vivax, as reported in Notifiable Diseases Information System (Sinan) for the Brazilian extra-Amazon region (an area not endemic for the disease) from 2008 to 2019. For the identification of relapse cases, deduplication record linkage processes in R software were used. Malaria relapses were defined as the case group, and new malaria infections were defined as the control group. Logistic regression models were used to assess associated factors.

RESULTS

Of the 711 malaria relapses, 589 (82.8%) were first relapses. Most relapses (71.6%) occurred between 30 and 120 days after the previous infection. Malaria relapses are spread throughout the extra-Amazon region, with a higher concentration near big cities. Driver occupation was found to be a common risk factor compared to other occupations, along with asymptomatic individuals. Other associated factors were: being infected in the Brazilian Amazon region, having follow-ups for malaria relapses, and having parasite density of the previous infection higher than 10,000 parasites per mm3.

CONCLUSIONS

This study provides evidence that allows malaria health surveillance services to direct their efforts to monitor cases of malaria in the highest risk segments identified in this study, particularly in the period between 30 and 120 days after being infected and treated. Relapses were associated to driver occupation, absence of symptoms, infection in endemic areas of Brazil, being detected through active surveillance or routine follow-up actions, and with parasitaemia greater than 10,000 parasites per mm3 in the previous infection. Improving cases follow-up is essential for preventing relapses.

Exploring the influence of environmental indicators and forecasting influenza incidence using ARIMAX models

Background

Influenza is a respiratory infection that poses a significant health burden worldwide. Environmental indicators, such as air pollutants and meteorological factors, play a role in the onset and propagation of influenza. Accurate predictions of influenza incidence and understanding the factors influencing it are crucial for public health interventions. Our study aims to investigate the impact of various environmental indicators on influenza incidence and apply the ARIMAX model to integrate these exogenous variables to enhance the accuracy of influenza incidence predictions.

Method

Descriptive statistics and time series analysis were employed to illustrate changes in influenza incidence, air pollutants, and meteorological indicators. Cross correlation function (CCF) was used to evaluate the correlation between environmental indicators and the influenza incidence. We used ARIMA and ARIMAX models to perform predictive analysis of influenza incidence.

Results

From January 2014 to September 2023, a total of 21,573 cases of influenza were reported in Fuzhou, with a noticeable year-by-year increase in incidence. The peak of influenza typically occurred around January each year. The results of CCF analysis showed that all 10 environmental indicators had a significant impact on the incidence of influenza. The ARIMAX(0, 0, 1) (1, 0, 0)12 with PM10(lag5) model exhibited the best prediction performance, as indicated by the lowest AIC, AICc, and BIC values, which were 529.740, 530.360, and 542.910, respectively. The model achieved a fitting RMSE of 2.999 and a predicting RMSE of 12.033.

Conclusion

This study provides insights into the impact of environmental indicators on influenza incidence in Fuzhou. The ARIMAX(0, 0, 1) (1, 0, 0)12 with PM10(lag5) model could provide a scientific basis for formulating influenza control policies and public health interventions. Timely prediction of influenza incidence is essential for effective epidemic control strategies and minimizing disease transmission risks.

Towards development of new antimalarial compounds through in silico and in vitro assays

Malaria is one of most widespread infectious disease in world. The antimalarial therapy presents a series of limitations, such as toxicity and the emergence of resistance, which makes the search for new drugs urgent. Thus, it becomes necessary to explore essential and exclusive therapeutic targets of the parasite to achieve selective inhibition. Enoyl-ACP reductase is an enzyme of the type II fatty acid biosynthetic pathway and is responsible for the rate-limiting step in the fatty acid elongation cycle. In this work, we use hierarchical virtual screening and drug repositioning strategies to prioritize compounds for phenotypic assays and molecular dynamics studies. The molecules were tested against chloroquine-resistant W2 strain of Plasmodium falciparum (EC50 between 330.05 and 13.92 µM). Nitrofurantoin was the best antimalarial activity at low micromolar range (EC50 = 13.92 µM). However, a hit compound against malaria must have a biological activity value below 1 µM. A large number of molecules present problems with permeability in biological membranes and reaching an effective concentration in their target's microenvironment. Nitrofurantoin derivatives with inclusions of groups which confer increased lipid solubility (methyl groups, halogens and substituted and unsubstituted aromatic rings) have been proposed. These derivatives were pulled through the lipid bilayer in molecular dynamics simulations. Molecules 14, 18 and 21 presented lower free energy values than nitrofurantoin when crossing the lipid bilayer.

Malaria in areas under mining activity in the Amazon: A review

Deforestation and high human mobility due to mining activities have been key to the increase in malaria cases in the Americas. Here, we review the epidemiological and control aspects of malaria in the Amazon mining areas. Epidemiological evidence shows: 1) a positive correlation between illegal mining activity and malaria incidence, mostly in the Amazon region; 2) most Brazilian miners are males aged 15-29 years who move between states and even countries; 3) miners do not fear the disease and rely on medical care, diagnosis, and medication when they become ill; 4) illegal mining has emerged as the most reported anthropogenic activity within indigenous lands and is identified as a major cause of malaria outbreaks among indigenous people in the Amazon; and 5) because mining is largely illegal, most areas are not covered by any healthcare facilities or activities, leading to little assistance in the diagnosis and treatment of malaria. Our review identified five strategies for reducing the malaria incidence in areas with mining activities: 1) reviewing legislation to control deforestation and mining expansion, particularly in indigenous lands; 2) strengthening malaria surveillance by expanding the network of community health agents to support rapid diagnosis and treatment; 3) reinforcing vector control strategies, such as the use of insecticide-treated nets; 4) integrating deforestation alerts into the national malaria control program; and 5) implementing multi-sectoral activities and providing prompt assistance to indigenous populations. With this roadmap, we can expect a decrease in malaria incidence in the Amazonian mining areas in the future.

Brazilian plants with antimalarial activity: A review of the period from 2011 to 2022

ETHNOPHARMACOLOGICAL RELEVANCE

Malaria continues to be a serious global public health problem in subtropical and tropical countries of the world. The main drugs used in the treatment of human malaria, quinine and artemisinin, are isolates of medicinal plants, making the use of plants a widespread practice in countries where malaria is endemic. Over the years, due to the increased resistance of the parasite to chloroquine and artemisinin in certain regions, new strategies for combating malaria have been employed, including research with medicinal plants.

AIM

This review focuses on the scientific production regarding medicinal plants from Brazil whose antimalarial activity was evaluated during the period from 2011 to 2022. 2.

METHODOLOGY

For this review, four electronic databases were selected for research: Pubmed, ScienceDirect, Scielo and Periódicos CAPES. Searches were made for full texts published in the form of scientific articles written in Portuguese or English and in a digital format. In addition, prospects for new treatments as well as future research that encourages the search for natural products and antimalarial derivatives are also presented.

RESULTS

A total of 61 publications were encountered, which cited 36 botanical families and 92 species using different Plasmodium strains in in vitro and in vivo assays. The botanical families with the most expressive number of species found were Rubiaceae, Apocynaceae, Fabaceae and Asteraceae (14, 14, 9 and 6 species, respectively), and the most frequently cited species were of the genera Psychotria L. (8) and Aspidosperma Mart. (12), which belong to the families Rubiaceae and Apocynaceae. Altogether, 75 compounds were identified or isolated from 28 different species, 31 of which are alkaloids. In addition, the extracts of the analyzed species, including the isolated compounds, showed a significant reduction of parasitemia in P. falciparum and P. berghei, especially in the clones W2 CQ-R (in vitro) and ANKA (in vivo), respectively. The Brazilian regions with the highest number of species analyzed were those of the north, especially the states of Pará and Amazonas, and the southeast, especially the state of Minas Gerais.

CONCLUSION

Although many plant species with antimalarial potential have been identified in Brazil, studies of new antimalarial molecules are slow and have not evolved to the production of a phytotherapeutic medicine. Given this, investigations of plants of traditional use and biotechnological approaches are necessary for the discovery of natural antimalarial products that contribute to the treatment of the disease in the country and in other endemic regions.

Record Linkage for Malaria Deaths Data Recovery and Surveillance in Brazil

OBJECTIVE

The objective is to describe the results and the methodological processes of record linkage for matching deaths and malaria cases.

METHODS

A descriptive cross-sectional study was conducted with probabilistic record linkage of death and malaria cases data in Brazil from 2011 to 2020 using death records from the Mortality Information System (SIM) and epidemiological data from the Notifiable Diseases Information System (Sinan) and Epidemiological Surveillance Information Systems for malaria (Sivep-Malaria). Three matching keys were used: patient's name, date of birth, and mother's name, with an analysis of cosine and Levenshtein dissimilarity measures.

RESULTS

A total of 490 malaria deaths were recorded in Brazil between 2011 and 2020. The record linkage resulted in the pairing of 216 deaths (44.0%). Pairings where all three matching keys were identical accounted for 30.1% of the total matched deaths, 39.4% of the matched deaths had two identical variables, and 30.5% had only one of the three key variables identical. The distribution of the variables of the matched deaths (216) was similar to the distribution of all recorded deaths (490). Out of the 216 matched deaths, 80 (37.0%) had poorly specified causes of death in the SIM.

CONCLUSIONS

The record linkage allowed for the detailing of the data with additional information from other epidemiological systems. Record linkage enables data linkage between information systems that lack interoperability and is an extremely useful tool for refining health situation analyses and improving malaria death surveillance in Brazil.

Polymorphisms in Toll-Like receptors genes and their associations with immunological parameters in Plasmodium vivax malaria in the Brazil-French Guiana Border

BACKGROUND

The innate immune response plays an important role during malaria. Toll-like receptors (TLR) are capable of recognizing pathogen molecules. We aimed to evaluate five polymorphisms in TLR-4, TLR-6, and TLR-9 genes and their association with cytokine levels and clinical parameters in malaria from the Brazil-French Guiana border.

METHODS

A case-control study was conducted in Amapá, Brazil. P. vivax patients and individuals not infected were evaluated. Genotyping of five SNPs was carried out by qPCR. Circulating cytokines were measured by CBA. The MSP-119 IgG antibodies were performed by ELISA.

RESULTS

An association between TLR4 A299G with parasitemia was observed. There was an increase for IFN-ɤ, TNF-ɑ, IL-6, and IL-10 in the TLR-4 A299G and T3911, TLR-6 S249P, and TLR-9 1486C/T, SNPs for the studied malarial groups. There were significant findings for the TLR-4 variants A299G and T3911, TLR-9 1237C/T, and 1486C/T. For the reactivity of MSP-119 antibodies levels, no significant results were found in malaria, and control groups.

CONCLUSIONS

The profile of the immune response observed by polymorphisms in TLRs genes does not seem to be standard for all types of malaria infection around the world. This can depend on the human population and the species of Plasmodium.

Optimizing Forecasted Activity Notifications with Reinforcement Learning

In this paper, we propose the notification optimization method by providing multiple alternative times as a reminder for a forecasted activity with and without probabilistic considerations for the activity that needs to be completed and needs notification. It is important to consider various factors when sending notifications to people after obtaining the results of the forecasted activity. We should not send notifications only when we have forecasted results because future daily activities are unpredictable. Therefore, it is important to strike a balance between providing useful reminders and avoiding excessive interruptions, especially for low probabilities of forecasted activity. Our study investigates the impact of the low probability of forecasted activity and optimizes the notification time with reinforcement learning. We also show the gaps between forecasted activities that are useful for self-improvement by people for the balance of important tasks, such as tasks completed as planned and additional tasks to be completed. For evaluation, we utilize two datasets: the existing dataset and data we collected in the field with the technology we have developed. In the data collection, we have 23 activities from six participants. To evaluate the effectiveness of these approaches, we assess the percentage of positive responses, user response rate, and response duration as performance criteria. Our proposed method provides a more effective way to optimize notifications. By incorporating the probability level of activity that needs to be done and needs notification into the state, we achieve a better response rate than the baseline, with the advantage of reaching 27.15%, as well as than the other criteria, which are also improved by using probability.