Deforestation inhibits malaria transmission in Lao PDR: a spatial epidemiology using Earth observation satellites.

BACKGROUND: The present study aimed to analyze the impact of deforestation on the malaria distribution in the Lao People's Democratic Republic (Lao PDR), with consideration of climate change. METHODS: Malaria distribution data from 2002 to 2015 were obtained from the Ministry of Health of Lao PDR and each indicator was calculated. Earth observation satellite data (forested area, land surface temperature, and precipitation) were obtained from the Japan Aerospace Exploration Agency (JAXA). Structured equation modeling (SEM) was conducted to clarify the relationship between the malaria incidence and Earth observation satellite data. RESULTS: As a result, SEM identified two factors that were independently associated with the malaria incidence: area and proportion of forest. Specifically, malaria was found to be more prevalent in the southern region, with the malaria incidence increasing as the percentage of forested land increased (both p < 0.01). With global warming steadily progressing, forested areas are expected to play an important role in the incidence of malaria in Lao PDR. This is believed because malaria in Lao PDR is mainly forest malaria transmitted by Anopheles dirus. CONCLUSION: To accelerate the elimination of malaria in Lao PDR, it is important to identify, prevent, and intervene in places with increased forest coverage (e.g., plantations) and in low-temperature areas adjacent to malaria-endemic areas, where the vegetation is similar to that in malaria-endemic areas.

Impact of precipitation on the prevalence of schistosomiasis mekongi in Lao PDR: Structural equation modelling using Earth observation satellite data.

Increasing attention is being given to the effect of climate change on schistosomiasis, but the impact is currently unknown. As the intermediate snail host (Neotricula aperta) of Schistosoma mekongi inhabits the Mekong River, it is thought that environmental factors affecting the area of water will have an impact on the occurrence of schistosomiasis mekongi. The aim of the present study was to assess the impact of precipitation on the prevalence of human schistosomiasis mekongi using epidemiological data and Earth observation satellite data in Khong district, Champasak province, Lao PDR. Structural equation modelling (SEM) using epidemiological data and Earth observation satellite data was conducted to determine the factors associated with the number of schistosomiasis mekongi patients. As a result, SEM identified 3 significant factors independently associated with schistosomiasis mekongi: (1) a negative association with mass drug administration (MDA); (2) negative association with total precipitation per year; and (3) positive association with precipitation during the dry season. Precisely, regardless of MDA, the increase in total yearly precipitation was suggested to decrease the number of schistosomiasis patients, whereas an increase in precipitation in the dry season increased the number of schistosomiasis patients. This is probably because when total precipitation increases, the water level of the Mekong River rises, thus decreasing the density of infected larvae, cercaria, in the water, and the frequency of humans entering the river would also decrease. In contrast, when precipitation in the dry season is higher, the water level of the Mekong River also rises, which expands the snail habitant, and thus water contact between humans and the snails would also increase. The present study results suggest that increasing precipitation would impact the prevalence of schistosomiasis both positively and negatively, and precipitation should also be considered in the policy to eliminate schistosomiasis mekongi in Lao PDR.

Detection of Schistosoma mekongi DNA in Human Stool and Intermediate Host Snail Neotricula aperta via Loop-Mediated Isothermal Amplification Assay in Lao PDR.

Schistosomiasis mekongi infection represents a public health concern in Laos and Cambodia. While both countries have made significant progress in disease control over the past few decades, eradication has not yet been achieved. Recently, several studies reported the application of loop-mediated isothermal amplification (LAMP) for detecting Schistosoma DNA in low-transmission settings. The objective of this study was to develop a LAMP assay for Schistosoma mekongi using a simple DNA extraction method. In particular, we evaluated the utility of the LAMP assay for detecting S. mekongi DNA in human stool and snail samples in endemic areas in Laos. We then used the LAMP assay results to develop a risk map for monitoring schistosomiasis mekongi and preventing epidemics. A total of 272 stool samples were collected from villagers on Khon Island in the southern part of Laos in 2016. DNA for LAMP assays was extracted via the hot-alkaline method. Following the Kato-Katz method, we determined that 0.4% (1/272) of the stool samples were positive for S. mekongi eggs, as opposed to 2.9% (8/272) for S. mekongi DNA based on the LAMP assays. Snail samples (n = 11,762) were annually collected along the riverside of Khon Island from 2016 to 2018. DNA was extracted from pooled snails as per the hot-alkaline method. The LAMP assay indicated that the prevalence of S. mekongi in snails was 0.26% in 2016, 0.08% in 2017, and less than 0.03% in 2018. Based on the LAMP assay results, a risk map for schistosomiasis with kernel density estimation was created, and the distribution of positive individuals and snails was consistent. In a subsequent survey of residents, schistosomiasis prevalence among villagers with latrines at home was lower than that among villagers without latrines. This is the first study to develop and evaluate a LAMP assay for S. mekongi detection in stools and snails. Our findings indicate that the LAMP assay is an effective method for monitoring pathogen prevalence and creating risk maps for schistosomiasis.

Association between PM10 from vegetation fire events and hospital visits by children in upper northern Thailand.

Few studies have focused on the effects of exposure to air pollutants from vegetation fire events (including forest fire and the burning of crop residues) among children. In this study we aimed to investigate the association between PM10 concentrations and hospital visits by children to address respiratory disease, conjunctivitis, and dermatitis. We examined and compared these associations by the presence of vegetation fire events on a given day (burning, non-burning, and mixed) across the upper northern region of Thailand from 2014 through 2018. A vegetation burning was defined when a fire hotspot (obtained from NASA-MODIS) exceeded the 90th percentile of the entire region and PM10 concentration was over 100 µg/m3. To determine the association between hospital visits among children with PM10 concentrations on burning and non-burning days, we performed a time-stratified case-crossover analysis fitted with conditional logistic regression for each province. A random-effects meta-analysis was applied to pool province-specific effect estimates. The number of burning days ranged from 64 to 139 days across eight provinces. A 10 µg/m3 increase in PM10 concentration on a burning day was associated with a respiratory disease-related hospital visit at lag 0 (OR = 1.01 (95% CIs: 1.00, 1.02)). This association was not observed for hospital visits related to conjunctivitis and dermatitis. A positive association was also observed between PM10 concentration on non-burning days and hospital visits related to respiratory disease at lag 0 (OR = 1.03 (95% CIs: 1.02, 1.04)). Hospital visits for conjunctivitis and dermatitis were significantly associated with PM10 concentration at lag 0 on both non-burning and mixed days.

Spatiotemporal dengue fever hotspots associated with climatic factors in Taiwan including outbreak predictions based on machine-learning.

Early warning systems (EWS) have been proposed as a measure for controlling and preventing dengue fever outbreaks in countries where this infection is endemic. A vaccine is not available and has yet to reach the market due to the economic burden of development, introduction and safety concerns. Understanding how dengue spreads and identifying the risk factors will facilitate the development of a dengue EWS, for which a climate-based model is still needed. An analysis was conducted to examine emerging spatiotemporal hotspots of dengue fever at the township level in Taiwan, associated with climatic factors obtained from remotely sensed data in order to identify the risk factors. Machinelearning was applied to support the search for factors with a spatiotemporal correlation with dengue fever outbreaks. Three dengue fever hotspot categories were found in southwest Taiwan and shown to be spatiotemporally associated with five kinds of sea surface temperatures. Machine-learning, based on the deep AlexNet model trained by transfer learning, yielded an accuracy of 100% on an 8-fold cross-validation test dataset of longitudetime sea surface temperature images.

Japan Aerospace Exploration Agency's public-health monitoring and analysis platform: A satellite-derived environmental information system supporting epidemiological study.

Since the 1970s, Earth-observing satellites collect increasingly detailed environmental information on land cover, meteorological conditions, environmental variables and air pollutants. This information spans the entire globe and its acquisition plays an important role in epidemiological analysis when in situ data are unavailable or spatially and/or temporally sparse. In this paper, we present the development of Japan Aerospace Exploration Agency's (JAXA) Public-health Monitoring and Analysis Platform available from JAXA, a user-friendly, web-based system providing environmental data on shortwave radiation, rainfall, soil moisture, the normalized difference vegetation index, aerosol optical thickness, land surface temperature and altitude. This system has been designed so that users should be able to download and utilize data without the need for additional data processing. The website allows interactive exchange and users can request data for a specific geographic location and time using the information gained for epidemiological analysis.

Japan's efforts to promote global health using satellite remote sensing data from the Japan Aerospace Exploration Agency for prediction of infectious diseases and air quality.

In this paper we review the status of new applications research of the Japanese Aerospace Exploration Agency (JAXA) for global health promotion using information derived from Earth observation data by satellites in cooperation with inter-disciplinary collaborators. Current research effort at JAXA to promote global public health is focused primarily on the use of remote sensing to address two themes: (i) prediction models for malaria and cholera in Kenya, Africa; and (ii) air quality assessment of small, particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3). Respiratory and cardivascular diseases constitute cross-boundary public health risk issues on a global scale. The authors report here on results of current of a collaborative research to call attention to the need to take preventive measures against threats to public health using newly arising remote sensing information from space.