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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.

Visceral leishmaniasis (VL), a vector-borne disease strongly influenced by environmental factors, has (re)-emerged in Ethiopia during the last two decades and is currently of increasing public health concern. Based on VL incidence in each locality (kebele) documented from federal or regional health bureaus and/or hospital records in the country, geographical information systems (GIS), coupled with binary and multivariate logistic regression methods, were employed to develop a risk map for Ethiopia with respect to VL based on soil type, altitude, rainfall, slope and temperature. The risk model was subsequently validated in selected sites. This environmental VL risk model provided an overall prediction accuracy of 86% with mean land surface temperature and soil type found to be the best predictors of VL. The total population at risk was estimated at 3.2 million according to the national population census in 2007. The approach presented here should facilitate the identification of priority areas for intervention and the monitoring of trends as well as providing input for further epidemiological and applied research with regard to this disease in Ethiopia.

Objectives We developed a prototype technique that expresses the need for intervention and the effectiveness of the treatment when “not being at risk of injury to the oral cavity or to general health” due to the presence of teeth or prostheses is taken as the desired outcome of dental treatment for older people near the end of life. The objective of this study was to use the prototype risk assessment matrix to identify the risk for each patient according to their course and show the effectiveness of treatment. Material and Methods We produced a prototype Dental Risk Map (Dental R‐map) based on the risk map method of risk management. Risk is classified into three levels according to the level of tolerability: (A) Risk for which watchful waiting should be included among measures to be considered; (B) risk for which intervention should be considered; or (C) risk requiring urgent intervention. Results We report the application of this technique to two men in their 80s. Both were assessed as risk tolerability Level C, requiring immediate intervention. Dental treatment eliminated this risk in one and reduced it to Level B in the other. Conclusions We developed the prototype Dental R‐map to identify oral risks and indicate the need for intervention to address these risks and the effectiveness of treatment for older people near the end of life. We used the Dental R‐map for two patients and successfully avoided oral risks that might cause physical injury in both cases until their deaths.

Background: Cutaneous leishmaniasis due to Leishmania major is an important public health problem in the world. Khuzestan Province is one of the main foci of zoonotic cutaneous leishmaniasis (ZCL) in the southwest of Iran. We aimed to predict the spatial distribution of the vector and reservoir(s) of ZCL using decision-making tool and to pre­pare risk map of the disease using integrative GIS, RS and AHP methods in endemic foci in Shush (plain area) and Khorramshahr (coastal area) counties of Khuzestan Province, southern Iran from Mar 2012 to Jan 2013. Methods: Thirteen criteria including temperature, relative humidity, rainfall, soil texture, soil organic matter, soil pH, soil moisture, altitude, land cover, land use, underground water depth, distance from river, slope and distance from human dwelling with the highest chance of the presence of the main vector and reservoir of the disease were chosen for this study. Weights of the criteria classes were determined using the Expert choice 11 software. The pres­ence proba­bility maps of the vector and reservoir of the disease were prepared with the combination of AHP method and Arc GIS 9.3. Results: Based on the maps derived from the AHP model, in Khorramshahr study area, the highest probability of ZCL is predicted in Gharb Karoon rural district. The presence probability of ZCL was high in Hossein Abad and Benmoala rural districts in the northeast of Shush. Conclusion: Prediction maps of ZCL distribution pattern provide valuable information which can guide policy mak­ers and health authorities to be precise in making appropriate decisions before occurrence of a possible disease out­break.

The Semeru Volcano eruption on December 4, 2021 caused damage to social, economic and environmental aspects. The Rejali Watershed (DAS) is one of the areas severely affected due to the eruption. The eruption resulted in 51 deaths, 10,565 displaced people, 1,027 houses damaged, two connecting routes and 43 public facilities damaged. This study mapped the disaster risk areas due to the eruption of Semeru Volcano. This research used Laharz to analyze the lava flow hazard map and weighting for social, economic, physical, environmental, and capacity vulnerability parameters. The results showed that the risk level of Semeru Volcano eruption is divided into three classes: high,  medium, and low risk. The high-risk area is 8915.09 Ha (14 %), the medium-risk area is 2174.74 Ha (17 %), and the low-risk area is 1885.60 Ha (69%). The high and medium risks were located on the upper and middle slopes of the Rejali watershed because the upstream area experiences a narrowing of the river flow (bottleneck) due to direct borders with structural land. The Semeru Volcano disaster risk map results can be used as a reference in sustainable risk management efforts in the Rejali watershed to reduce the impact and damage caused by the eruption.

The large diversity of unmanned aircraft requires a suitable and proper risk assessment. In this paper, we propose the use of risk maps to define the risk associated to accidents with unmanned aircraft. It is a two-dimensional location-based map that quantifies the risk to the population on ground of flight operations over a specified area. The risk map is generated through a probabilistic approach and combines several layers, including population density, sheltering factor, no-fly zones, and obstacles. Each element of the risk map has associated a risk value that quantifies the risk of flying over a specific location. Risk values are defined by a risk assessment process using different uncontrolled descent events, drone parameters, environmental characteristics, as well as uncertainties on parameters. The risk map is able to quantify the risk of large areas, such as urban environments, and allows for easy identification of high and low-risk locations. The map is a tool for informed decision making, and our results report some examples of risk map with different aircraft in a realistic urban environment.

Haemonchus contortus is a gastrointestinal nematode parasite of small ruminants, which feeds on blood and causes significant disease and production loss in sheep and goats, especially in warmer parts of the world. The life cycle includes free-living immature stages, which are subject to climatic influences on development, survival and availability, and this species therefore exhibits spatio-temporal heterogeneity in its infection pressure based on the prevailing climate. Models that better explain this heterogeneity could predict future epidemiological changes. The basic reproduction quotient (Q0) was used as a simple process-based model to predict climate-driven changes in the potential transmission of H. contortus across widely different geo-climatic zones, and showed good agreement with the observed frequency of this species in the gastrointestinal nematode fauna of sheep (r = 0.81, P <0.01). Averaged monthly Q0 output was further used within a geographical information system (GIS) to produce preliminary haemonchosis risk maps for the United Kingdom (UK) over a four-year historical span and under future climate change scenarios. Prolonged transmission seasons throughout the UK are predicted, especially in the south although with restricted transmission in peak summer due to rainfall limitation. Additional predictive ability might be achieved if information such as host density and distribution, grazing pattern and edaphic conditions were included as risk layers in the GIS-based risk map. However, validation of such risk maps presents a significant challenge, with georeferenced observed data of sufficient spatial and temporal resolution rarely available and difficult to obtain.

Novel Coronavirus (2019-nCoV [SARS-COV-2]) was detected in humans during the last week of December 2019 at Wuhan city in China, and caused 24 554 cases in 27 countries and territories as of 5 February 2020. The objective of this study was to estimate the risk of transmission of 2019-nCoV through human passenger air flight from four major cities of China (Wuhan, Beijing, Shanghai and Guangzhou) to the passengers' destination countries. We extracted the weekly simulated passengers' end destination data for the period of 1–31 January 2020 from FLIRT, an online air travel dataset that uses information from 800 airlines to show the direct flight and passengers' end destination. We estimated a risk index of 2019-nCoV transmission based on the number of travellers to destination countries, weighted by the number of confirmed cases of the departed city reported by the World Health Organization (WHO). We ranked each country based on the risk index in four quantiles (4ᵗʰ quantile being the highest risk and 1ˢᵗ quantile being the lowest risk). During the period, 388 287 passengers were destined for 1297 airports in 168 countries or territories across the world. The risk index of 2019-nCoV among the countries had a very high correlation with the WHO-reported confirmed cases (0.97). According to our risk score classification, of the countries that reported at least one Coronavirus-infected pneumonia (COVID-19) case as of 5 February 2020, 24 countries were in the 4ᵗʰ quantile of the risk index, two in the 3ʳᵈ quantile, one in the 2ⁿᵈ quantile and none in the 1ˢᵗ quantile. Outside China, countries with a higher risk of 2019-nCoV transmission are Thailand, Cambodia, Malaysia, Canada and the USA, all of which reported at least one case. In pan-Europe, UK, France, Russia, Germany and Italy; in North America, USA and Canada; in Oceania, Australia had high risk, all of them reported at least one case. In Africa and South America, the risk of transmission is very low with Ethiopia, South Africa, Egypt, Mauritius and Brazil showing a similar risk of transmission compared to the risk of any of the countries where at least one case is detected. The risk of transmission on 31 January 2020 was very high in neighbouring Asian countries, followed by Europe (UK, France, Russia and Germany), Oceania (Australia) and North America (USA and Canada). Increased public health response including early case recognition, isolation of identified case, contract tracing and targeted airport screening, public awareness and vigilance of health workers will help mitigate the force of further spread to naïve countries.

This paper presents a risk-aware path planning strategy for Unmanned Aerial Vehicles in urban environments. The aim is to compute an effective path that minimizes the risk to the population, thus enforcing safety of flight operations over inhabited areas. To quantify the risk, the proposed approach uses a risk-map that associates discretized locations of the space with a suitable risk-cost. Path planning is performed in two phases: first, a tentative path is computed off-line on the basis on the information related to static risk factors; then, using a dynamic risk-map, an on-line path planning adjusts and adapts the off-line path to dynamically arising conditions. Off-line path planning is performed using riskA*, an ad-hoc variant of the A* algorithm, which aims at minimizing the risk. While off-line path planning has no stringent time constraints for its execution, this is not the case for the on-line phase, where a fast response constitutes a critical design parameter. We propose a novel algorithm called Borderland , which uses the check and repair approach to rapidly identify and adjust only the portion of path involved by the inception of relevant dynamical changes in the risk factor. After the path planning, a smoothing process is performed using Dubins curves. Simulation results confirm the suitability of the proposed approach.

Fire risk prediction is significant for fire prevention and fire resource allocation. Fire risk maps are effective methods for quantifying regional fire risk. Laoshan National Forest Park has many precious natural resources and tourist attractions, but there is no fire risk assessment model. This paper aims to construct the forest fire risk map for Nanjing Laoshan National Forest Park. The forest fire risk model is constructed by factors (altitude, aspect, topographic wetness index, slope, distance to roads and populated areas, normalized difference vegetation index, and temperature) which have a great influence on the probability of inducing fire in Laoshan. Since the importance of factors in different study areas is inconsistent, it is necessary to calculate the significance of each factor of Laoshan. After the significance calculation is completed, the fire risk model of Laoshan can be obtained. Then, the fire risk map can be plotted based on the model. This fire risk map can clarify the fire risk level of each part of the study area, with 16.97% extremely low risk, 48.32% low risk, 17.35% moderate risk, 12.74% high risk and 4.62% extremely high risk, and it is compared with the data of MODIS fire anomaly point. The result shows that the accuracy of the risk map is 76.65%.