Spatial and temporal analysis of hospitalized dengue patients in Bandung: demographics and risk

Understanding the spread of infectious diseases in edge areas of hotspots: dengue epidemics in tropical metropolitan regions

Identifying clusters or hotspots from disease maps is critical in research and practice. Hotspots have been shown to have a higher potential for transmission risk and may be the source of infections, making them a priority for controlling epidemics. However, the role of edge areas of hotspots in disease transmission remains unclear. This study aims to investigate the role of edge areas in disease transmission by examining whether disease incidence rate growth is higher in the edges of disease hotspots during outbreaks. Our data is based on the three most severe dengue epidemic years in Kaohsiung city, Taiwan, from 1998 to 2020. We employed conditional autoregressive (CAR) models and Bayesian areal Wombling methods to identify significant edge areas of hotspots based on the extent of risk difference between adjacent areas. The difference-in-difference (DID) estimator in spatial panel models measures the growth rate of risk by comparing the incidence rate between two groups (hotspots and edge areas) over two time periods. Our results show that in years characterized by exceptionally large-scale outbreaks, the edge areas of hotspots have a more significant increase in disease risk than hotspots, leading to a higher risk of disease transmission and potential disease foci. This finding explains the geographic diffusion mechanism of epidemics, a pattern mixed with expansion and relocation, indicating that the edge areas play an essential role. The study highlights the importance of considering edge areas of hotspots in disease transmission. Furthermore, it provides valuable insights for policymakers and health authorities in designing effective interventions to control large-scale disease outbreaks.

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.

Optimal Validated Multi-Factorial Climate Change Risk Assessment for Adaptation Planning and Evaluation of Infectious Disease: A Case Study of Dengue Hemorrhagic Fever in Indonesia

(1) Background: This paper will present an elaboration of the risk assessment methodology by Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH (GIZ), Eurac Research and United Nations University Institute for Environment and Human Security (UNU-EHS) for the assessment of dengue. (2) Methods: We validate the risk assessment model by best-fitting it with the number of dengue cases per province using the least-square fitting method. Seven out of thirty-four provinces in Indonesia were chosen (North Sumatra, Jakarta Capital, West Java, Central Java, East Java, Bali and East Kalimantan). (3) Results: A risk assessment based on the number of dengue cases showed an increased risk in 2010, 2015 and 2016 in which the effects of El Nino and La Nina extreme climates occurred. North Sumatra, Bali, and West Java were more influenced by the vulnerability component, in line with their risk analysis that tends to be lower than the other provinces in 2010, 2015 and 2016 when El Nino and La Nina occurred. (4) Conclusion: Based on data from the last ten years, in Jakarta Capital, Central Java, East Java and East Kalimantan, dengue risks were mainly influenced by the climatic hazard component while North Sumatra, Bali and West Java were more influenced by the vulnerability component.

The application of geographic information system for dengue epidemic in Southeast Asia: A review on trends and opportunity

The infectious disease dengue hemorrhagic fever remains an unresolved global problem, with climatic conditions and the location of areas located at the equator more often infected with dengue fever. Various modeling approaches have been employed for the development of a dengue risk map. The geographic information system approach was used as an instrument in applying mathematical algorithms to process field vector data into a preventive objective which is studied, then the application of remote sensing provides spatial-temporal data related to land use/land cover data sources as other variable categories. Map of hotspots for dengue fever cases is used to identify the risk of dengue fever areas by applying various complex methodologies, analysis, and visualization of advanced data are needed for its application in public health. In the last 10 years, the increase in the publication of dengue hemorrhagic fever in Southeast Asia in reputable international journals has increased significantly.