Background: Malaria has a considerable impact on the health of the populations of developing countries; indeed, the entire population of Rwanda is at risk of contracting the disease. Although various interventions to control malaria have been implemented in Rwanda, the incidence of malaria has increased since 2012. There is an interest in understanding factors driving its persistence in Rwanda. This study aims at evaluating the effect of socioeconomic and environmental factors, seasonality and the use of insecticide-treated mosquito nets (ITNs) on malaria persistence in Rwanda. Methods: This study analysed data from the 2014-2015 Rwanda Demographic and Health Survey of 11,202 household's members composed of children under the age of 5 and women aged between 15 and 49. Bivariate analysis was performed between the outcome and each covariate including wealth, altitude, education level, place of residence, and use of ITNs generating percentages. Chi square test was performed to compare malaria negatives and positives on each covariate. Significant variables were subjected to logistic regression analysis to evaluate factors that are significantly associated with malaria at P < 0.05. The analysis was performed in R x64 3.6 and QGIS3.6 was used to map geographical distribution of malaria cases. Results: The lowest wealth category was associated with the incidence of malaria [AOR] = 1.54, 95% CI (1.78-2.03). Having a place of residence < 1700 m above sea level (asl) and non-use of ITNs were significantly associated with the incidence of malaria (adjusted odds ratio [AOR] = 2.93, 95% confidence interval [95% CI] 1.94-4.42 and [AOR] = 1.29, 95% C.I (1.03-1.60), respectively). Season and type of residence were not significantly associated with malaria prevalence while women had lower risk of contracting malaria than children. Conclusion: Increased malaria prevalence was associated with lower income, non-compliance with bed-net usage and living below 1700 m of altitude. In addition to current malaria control strategies, potential interventions in individuals with lower income and areas at low altitudes should be taken into consideration when formulating malariacontrol strategies, Also use of ITNs to control the spread of malaria should be emphasized.

Particulate matters less than 2.5 micrometers in diameter (PM2.5), whose concentration has increased in Korea, has a considerable impact on health. From a risk management point of view, there has been interest in understanding the variations in real-time PM2.5 concentrations per activity in different microenvironments. We analyzed personal monitoring data collected from 15 children aged 6 to 11 years engaged in different activities such as commuting in a car, visiting a commercial building, attending an education institute, and resting inside home from October 2018 to March 2019. The fraction of daily mean exposure duration per activity was 72.7 ± 18.7% for resting inside home, 27.2 ± 14.4% for attending an education institute, and 11.5 ± 9.6% and 5.3 ± 5.9% for visiting a commercial building, commuting in a car, respectively. Daily median (interquartile range) PM2.5 exposure amount was 88.9 (55.9–159.7) μg in houses and that in education buildings was 43.3 (22.9–55.6) μg. Real-time PM2.5 exposure levels varied by person and time of day (p-value < 0.05). This study demonstrated that our real-time personal monitoring and data analysis methodologies were effective in detecting polluted microenvironments and provided a potential person-specific management strategy to reduce a person’s exposure level to PM2.5.

This study aimed to compare Korean smokers’ smoking-related biomarker levels by tobacco product type, including heat-not-burn cigarettes (HNBC), liquid e-cigarettes (EC), and traditional cigarettes (TC). Nicotine dependence levels were evaluated in Korean adult study participants including TC-, EC-, HNBC-only users and nonsmokers (n = 1586) from March 2019 to July 2019 in Seoul and Cheonan/Asan South Korea using the Fagerström Test Score. Additionally, urine samples (n = 832) were collected for the measurement of urinary nicotine, cotinine, OH-cotinine, NNAL(4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol), CYMA(N-acetyl-S-(2-cyanoehtyl)-L-cysteine), or CEMA (2-cyanoethylmercapturic acid) using LC–MS/MS. The median(interquartile range) nicotine dependence level was not different among the three types of smokers, being 3.0 (2.0–5.0) for TC- (n = 726), 3.0 (1.0–4.0) for EC- (n = 316), and 3.0 (2.0–4.0) for HNBC- (n = 377) only users. HNBC-only users presented similar biomarker levels compared to TC-only users, except for NNAL (HNBC: 14.5 (4.0–58.8) pg/mL, TC: 32.0 (4.0–69.6) pg/mL; p = 0.0106) and CEMA (HNBC: 60.4 (10.0–232.0) ng/mL, TC: 166.1 (25.3–532.1) ng/mL; p = 0.0007). TC and HNBC users showed increased urinary cotinine levels as early as the time after the first smoke of the day. EC users’ biomarker levels were possibly lower than TC or HNBC users’ but higher than those of non-smokers.

Various studies have indicated that particulate matter <2.5 μm (PM2.5) could cause adverse health effects on pulmonary functions in susceptible groups, especially asthmatic children. Although the impact of ambient PM2.5 on children’s lower respiratory health has been well-established, information regarding the associations between indoor PM2.5 levels and respiratory symptoms in asthmatic children is relatively limited. This randomized, crossover intervention study was conducted among 26 asthmatic children’s homes located in Incheon metropolitan city, Korea. We aimed to evaluate the effects of indoor PM2.5 on children’s peak expiratory flow rate (PEFR), with a daily intervention of air purifiers with filter on, compared with those groups with filter off. Children aged between 6–12 years diagnosed with asthma were enrolled and randomly allocated into two groups. During a crossover intervention period of seven weeks, we observed that, in the filter-on group, indoor PM2.5 levels significantly decreased by up to 43%. (p < 0.001). We also found that the daily or weekly unit (1 μg/m3) increase in indoor PM2.5 levels could significantly decrease PEFR by 0.2% (95% confidence interval (CI) = 0.1 to 0.5) or PEFR by 1.2% (95% CI = 0.1 to 2.7) in asthmatic children, respectively. The use of in-home air filtration could be considered as an intervention strategy for indoor air quality control in asthmatic children’s homes.

Although voluminous studies addressed the link between indoor air pollution and the incidence, prevalence, and morbidity of allergic diseases such as asthma, there is a scarcity in public health and environmental policies in forecasting evidence to develop preventive guidelines for vulnerable individuals due to the limitation in predictability models. Recently, machine learning techniques may provide evidence-based interventions that enable technologies to analyze and accurately predict allergic diseases such as asthma, but studies are still limited. This study adopted a deep learning algorithm to predict the deterioration of health symptoms among asthmatic children between 8-12 years of age. It is based on Peak Expiratory Flow Rates (PEFR) and indoor air pollution data, as well as meteorological data collected at their indoor residences every 2 minutes using portable monitoring devices with a low-cost sensor between November 2018 and March 2019. The PEFR results collected twice a day were matched with daily PM2.5.A personalized model has been developed to predict the peak expiratory flow rate of the next day, taking into account indoor air quality data including PM2.5, humidity, temperature, and CO2 level in previous days. Two models were developed that incorporate Indoor Air Quality (IAQ) with the PEFR-only model. The IAQ are of 2 types; one uses the daily IAQ, and the other uses 10-minute basis IAQ in predicting the future PEFR. Recurrent Neural Networks (RNN) and Deep Neural Networks (DNN) models were trained using 4 months of linked data to predict PEFR for the next days during the study period. The 10-minute RNN model was found to predict better PEFR with a Root Mean Square Error (RMSE) of 42.5 and a Mean Absolute Percentage Error (MAPE) of 14.0, as it consolidates the cumulative effects of PM2.5 concentrations over time. The highly accurate estimation showed that indoor air quality significantly affects PEFR. This model and larger sample size may be important in making scientific and medical data-driven decisions.INDEX TERMS asthma, big data, machine learning, recurrent neural network, peak expiratory flow rates (PEFR)