Spatial Risk Distribution of Dengue Based on the Ecological Niche Model of Aedes aegypti (Diptera: Culicidae) in the Central Mexican Highlands

Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review

BACKGROUND

Creating a spatial model of dengue fever risk is challenging duet to many interrelated factors that could affect dengue. Therefore, it is crucial to understand how these critical factors interact and to create reliable predictive models that can be used to mitigate and control the spread of dengue.

METHODS

This scoping review aims to provide a comprehensive overview of the important predictors, and spatial modelling tools capable of producing Dengue Haemorrhagic Fever (DHF) risk maps. We conducted a methodical exploration utilizing diverse sources, i.e., PubMed, Scopus, Science Direct, and Google Scholar. The following data were extracted from articles published between January 2011 to August 2022: country, region, administrative level, type of scale, spatial model, dengue data use, and categories of predictors. Applying the eligibility criteria, 45 out of 1,349 articles were selected.

RESULTS

A variety of models and techniques were used to identify DHF risk areas with an arrangement of various multiple-criteria decision-making, statistical, and machine learning technique. We found that there was no pattern of predictor use associated with particular approaches. Instead, a wide range of predictors was used to create the DHF risk maps. These predictors may include climatology factors (e.g., temperature, rainfall, humidity), epidemiological factors (population, demographics, socio-economic, previous DHF cases), environmental factors (land-use, elevation), and relevant factors.

CONCLUSIONS

DHF risk spatial models are useful tools for detecting high-risk locations and driving proactive public health initiatives. Relying on geographical and environmental elements, these models ignored the impact of human behaviour and social dynamics. To improve the prediction accuracy, there is a need for a more comprehensive approach to understand DHF transmission dynamics.

Spatio-temporal variability in macroinvertebrate community structure and ecological health status of a tropical dynamic river-estuarine system, India: An integrated approach of multivariate analysis

River-estuarine ecosystems are under severe anthropogenic threat due to resource exploitation, transportation, sewage/industrial discharges, and pollutants from surrounding areas. Monitoring the water quality and biological communities is essential for assessing ecosystem health and sustainability. Present study integrated the ecological community data along with water quality analysis to understand the impact of anthropogenic pressures on benthic macroinvertebrates. Samples were collected from 10 locations (comprising of both rural and urban areas) for Benthic macroinvertebrates, physico-chemical and microbiological parameters along the lower stretch of the Bhagirathi-Hooghly river-estuarine (BHE) system during the post-monsoon seasons of 2020, 2021, and 2022. During the entire study period, a total of 5730 individuals from 54 families in 19 orders of 3 phylum of macroinvertebrate were recorded. Among them Thiaridae (27.1%) and Chironomidae (22.8%) were found to be the most abundant families. Based on the water quality data Cluster analysis and nMDS indicated two distinct groups of locations: Group-I with rural settings and Group-II with urban settings. Alpha diversity metrics showed higher diversity (2.817) and evenness (0.744) in rural locations (Group-I) compared to urban locations (Group-II). The overall saprobic score of the macroinvebrate data revealed Group-I (5.09) to be in good condition, while Group-II (4.95) showed moderately polluted conditions. Redundancy analysis (RDA) highlighted the correlation of pollution-tolerant species (Chironomidae, Culicidae) with high organic loads i.e., biochemical oxygen demand (BOD), chemical oxygen demand (COD) in Group-II. In contrast, Group-I locations exhibited positive correlations with Dissolved Oxygen (DO) and supported less pollution-tolerant organisms (Coenagrionidae, Dytiscidae). The study emphasizes the importance of integrated analysis of ecological community data and water quality parameters to assess the health status of river-estuarine ecosystems.

Trends in mosquito species distribution modeling: insights for vector surveillance and disease control

Species distribution modeling (SDM) has become an increasingly common approach to explore questions about ecology, geography, outbreak risk, and global change as they relate to infectious disease vectors. Here, we conducted a systematic review of the scientific literature, screening 563 abstracts and identifying 204 studies that used SDMs to produce distribution estimates for mosquito species. While the number of studies employing SDM methods has increased markedly over the past decade, the overwhelming majority used a single method (maximum entropy modeling; MaxEnt) and focused on human infectious disease vectors or their close relatives. The majority of regional models were developed for areas in Africa and Asia, while more localized modeling efforts were most common for North America and Europe. Findings from this study highlight gaps in taxonomic, geographic, and methodological foci of current SDM literature for mosquitoes that can guide future efforts to study the geography of mosquito-borne disease risk.

Promoting Effects of the Exercise Behavioral Ecological Model on Physical Activity Behaviors of Students

Objectives: In this study, we explored the promoting effects of the Exercise Behavioral Ecological Model on the physical activity behaviors of middle school students, and relevant risk factors for physical inactivity. Methods: A total of 291 middle school students (junior and senior high school students) were enrolled as the research participants. The sedentary behavior of middle school students was assessed using the Adolescent Sedentary Behavior Scale. We used the Exercise Behavioral Ecological Model to influence the exercise behaviors of middle school students from the perspectives of environment, physiology, and psychology. Then the independent risk factors for physical inactivity behaviors of middle school students were analyzed by means of multivariate logistic regression analysis. Results: The change strategy, positive effect on decision-making balance, and self- efficacy scores were higher and the negative effect on decision-making balance score was lower than those before intervention (p<.05). Multivariate logistic regression analysis denoted that grade (senior high school students), sedentary time (> 4 hours), daily TV watching time (>2 hours) and change stage (pre-contemplation stage) were associated risk factors for physical inactivity among middle school students (p <.05). Conclusions: The Exercise Behavioral Ecological Model can facilitate physical activity and reduce sedentary behavior.

A Systematic Review on Modeling Methods and Influential Factors for Mapping Dengue-Related Risk in Urban Settings

Dengue fever is an acute mosquito-borne disease that mostly spreads within urban or semi-urban areas in warm climate zones. The dengue-related risk map is one of the most practical tools for executing effective control policies, breaking the transmission chain, and preventing disease outbreaks. Mapping risk at a small scale, such as at an urban level, can demonstrate the spatial heterogeneities in complicated built environments. This review aims to summarize state-of-the-art modeling methods and influential factors in mapping dengue fever risk in urban settings. Data were manually extracted from five major academic search databases following a set of querying and selection criteria, and a total of 28 studies were analyzed. Twenty of the selected papers investigated the spatial pattern of dengue risk by epidemic data, whereas the remaining eight papers developed an entomological risk map as a proxy for potential dengue burden in cities or agglomerated urban regions. The key findings included: (1) Big data sources and emerging data-mining techniques are innovatively employed for detecting hot spots of dengue-related burden in the urban context; (2) Bayesian approaches and machine learning algorithms have become more popular as spatial modeling tools for predicting the distribution of dengue incidence and mosquito presence; (3) Climatic and built environmental variables are the most common factors in making predictions, though the effects of these factors vary with the mosquito species; (4) Socio-economic data may be a better representation of the huge heterogeneity of risk or vulnerability spatial distribution on an urban scale. In conclusion, for spatially assessing dengue-related risk in an urban context, data availability and the purpose for mapping determine the analytical approaches and modeling methods used. To enhance the reliabilities of predictive models, sufficient data about dengue serotyping, socio-economic status, and spatial connectivity may be more important for mapping dengue-related risk in urban settings for future studies.

Understanding the role of temporal variation of environmental variables in predicting Aedes aegypti oviposition activity in a temperate region of Argentina

Environmental variables related to vegetation and weather are some of the most influential factors that impacting Aedes (Stegomya) aegypti, a mosquito vector of dengue, chikungunya and Zika viruses. In this paper, we aim to develop temporal predictive models for Ae. aegypti oviposition activity utilizing vegetation and meteorological variables as predictors in Córdoba city (Argentina). Eggs were collected using ovitraps placed throughout the city from 2009 to 2012 that were replaced weekly. Temporal generalized linear mixed models were developed with negative binomial distributions of errors that model average number of eggs collected weekly as a function of vegetation and meteorological variables with time lags. The best model included a vegetation index, vapor pressure of water, precipitation and photoperiod. With each unit of increment in vegetation index per week the average number of eggs increased by 1.71 in the third week. Furthermore, each millimeter increase of accumulated rain during 4 weeks was associated with a decrease of 0.668 in the average number of eggs found in the following week. This negative effect of precipitation could occur during abundant rainfalls that fill containers completely, thereby depriving females of oviposition sites and leading them to search for other suitable breeding sites. Furthermore, the average number of eggs increased with the photoperiod at low values of mean vapor pressure; however the average number of eggs decreased at high values of mean vapor pressure, and the positive relationship between the response variable and mean vapor pressure was stronger at low values of photoperiod. Additionally, minimum temperature was associated positively with oviposition activity and that low minimum temperatures could be a limiting factor in Ae. aegypti oviposition activity. Our results emphasize the important role that climatic variables such as temperature, precipitation, and vapor pressure play in Ae. aegypti oviposition activity and how these variables along with vegetation indices can be used to inform predictive temporal models of Ae. aegypti population dynamics that can be used for informing mosquito population control and arbovirus mitigation strategies.