A probabilistic spatial dengue fever risk assessment by a threshold-based-quantile regression method

Fitting the return period of dengue fever epidemic in Guangdong province of China

Objectives

The prevention and control of dengue fever (DF) has been a major public health issue in Guangdong (GD) province, China. This study aims to analyze the return period (RP) and the return level (RL) of DF epidemic in GD, to help the formulation of prevention and control plan.

Methods

Three models, namely Lognormal distribution (Lognor D.), normal distribution (Norm D.), and generalized logistic distribution (GLD) were selected to fit the annual number of indigenous DF cases in GD from 1978 to 2021. The coefficient of determination (R2), the root mean squared error (RMSE), and the Akaike information criterion (AIC) were used to evaluate the goodness of fit. We predicted the RP of 45130 historical maximum cases that occurred in 2014 and the RP of 4884 peak cases that occurred in 2019 over the 5 years up to 2021.

Results

Fitting through the three models, the R2 was 0.98, 0.98, and 0.96, respectively. The predicted RLs of the annual DF case number were between 297 and 43234, 297 and 43233, 362 and 41868 for the RPs of 2-45 years. The predicted RPs of DF outbreaks exceeding the historical maximum were 43, 43, and 44 years, and the RPs of DF epidemic exceeding the peak in 2019 were 7, 7, and 8 years, respectively. Therefore, we predicted that GD would experience a DF outbreak beyond the historical maximum in the next 35 or 36 years from 2022. And in the next 4 or 5 years from 2022, there would be a DF epidemic exceeding the peak in 2019.

Conclusions

The study discloses a temporal periodicity inherent to the DF epidemic in GD. The three models are applicable for forecasting and evaluating the RP and RL of DF epidemic in GD, separately.

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.

Land use and land cover change and its impacts on dengue dynamics in China: A systematic review

Background

Dengue is a prioritized public health concern in China. Because of the larger scale, more frequent and wider spatial distribution, the challenge for dengue prevention and control has increased in recent years. While land use and land cover (LULC) change was suggested to be associated with dengue, relevant research has been quite limited. The “Open Door” policy introduced in 1978 led to significant LULC change in China. This systematic review is the first to review the studies on the impacts of LULC change on dengue dynamics in China. This review aims at identifying the research evidence, research gaps and provide insights for future research.

Methods

A systematic literature review was conducted following the PRISMA protocol. The combinations of search terms on LULC, dengue and its vectors were searched in the databases PubMed, Web of Science, and Baidu Scholar. Research conducted on China published from 1978 to December 2019 and written in English or Chinese was selected for further screening. References listed in articles meeting the inclusion criteria were also reviewed and included if again inclusion criteria were met to minimize the probability of missing relevant research.

Results

28 studies published between 1978 and 2017 were included for the full review. Guangdong Province and southern Taiwan were the major regional foci in the literature. The majority of the reviewed studies observed associations between LULC change factors and dengue incidence and distribution. Conflictive evidence was shown in the studies about the impacts of green space and blue space on dengue in China. Transportation infrastructure and urbanization were repeatedly suggested to be positively associated with dengue incidence and spread. The majority of the studies reviewed considered meteorological and sociodemographic factors when they analyzed the effects of LULC change on dengue. Primary and secondary remote sensing (RS) data were the primary source for LULC variables. In 21 of 28 studies, a geographic information system (GIS) was used to process data of environmental variables and dengue cases and to perform spatial analysis of dengue.

Conclusions

The effects of LULC change on the dynamics of dengue in China varied in different periods and regions. The application of RS and GIS enriches the means and dimensions to explore the relations between LULC change and dengue. Further comprehensive regional research is necessary to assess the influence of LULC change on local dengue transmission to provide practical advice for dengue prevention and control.

Dengue is a major public health concern in China. The rapid development of urbanization along with climate change increases the challenge for dengue prevention and control. Previous research has mainly focused on the meteorological variables whereas land use and land cover (LULC) change received comparatively less attention. Our review identified that the regional research hotspots of dengue epidemics in China were Guangdong Province and southern Taiwan. Though inconsistent, most included studies somehow observed associations between at least one of the LULC change factors and dengue. A geographical information system (GIS) was widely used to perform spatial analysis in the selected literature. Its application provided a novel view to describe the relationships between environmental factors and the situation of dengue, which enabled scholars to explore more characteristics of dengue transmission. Meanwhile, the use of remote sensing (RS) enriched the means of environmental monitoring. However, there are research gaps in the area of dengue and LULC change, such as the less consideration of dengue vector study, the lack of interplays between factors, and the lack of considering interventions and policies. Furthermore, because of different research settings, results from these studies were difficult to compare. Thus, further comprehensive and comparable investigations are necessary to better understand the effects of LULC change on dengue in China. This review is the first to expound the studies on the associations between LULC change and dengue dynamics in China. It demonstrates the findings and methodologies and provided insights for future research.

Semi-Supervised Text Classification Framework: An Overview of Dengue Landscape Factors and Satellite Earth Observation

In recent years there has been an increasing use of satellite Earth observation (EO) data in dengue research, in particular the identification of landscape factors affecting dengue transmission. Summarizing landscape factors and satellite EO data sources, and making the information public are helpful for guiding future research and improving health decision-making. In this case, a review of the literature would appear to be an appropriate tool. However, this is not an easy-to-use tool. The review process mainly includes defining the topic, searching, screening at both title/abstract and full-text levels and data extraction that needs consistent knowledge from experts and is time-consuming and labor intensive. In this context, this study integrates the review process, text scoring, active learning (AL) mechanism, and bidirectional long short-term memory (BiLSTM) networks, and proposes a semi-supervised text classification framework that enables the efficient and accurate selection of the relevant articles. Specifically, text scoring and BiLSTM-based active learning were used to replace the title/abstract screening and full-text screening, respectively, which greatly reduces the human workload. In this study, 101 relevant articles were selected from 4 bibliographic databases, and a catalogue of essential dengue landscape factors was identified and divided into four categories: land use (LU), land cover (LC), topography and continuous land surface features. Moreover, various satellite EO sensors and products used for identifying landscape factors were tabulated. Finally, possible future directions of applying satellite EO data in dengue research in terms of landscape patterns, satellite sensors and deep learning were proposed. The proposed semi-supervised text classification framework was successfully applied in research evidence synthesis that could be easily applied to other topics, particularly in an interdisciplinary context.

A Mapping Review on Urban Landscape Factors of Dengue Retrieved from Earth Observation Data, GIS Techniques, and Survey Questionnaires

To date, there is no effective treatment to cure dengue fever, a mosquito-borne disease which has a major impact on human populations in tropical and sub-tropical regions. Although the characteristics of dengue infection are well known, factors associated with landscape are highly scale dependent in time and space, and therefore difficult to monitor. We propose here a mapping review based on 78 articles that study the relationships between landscape factors and urban dengue cases considering household, neighborhood and administrative levels. Landscape factors were retrieved from survey questionnaires, Geographic Information Systems (GIS), and remote sensing (RS) techniques. We structured these into groups composed of land cover, land use, and housing type and characteristics, as well as subgroups referring to construction material, urban typology, and infrastructure level. We mapped the co-occurrence networks associated with these factors, and analyzed their relevance according to a three-valued interpretation (positive, negative, non significant). From a methodological perspective, coupling RS and GIS techniques with field surveys including entomological observations should be systematically considered, as none digital land use or land cover variables appears to be an univocal determinant of dengue occurrences. Remote sensing urban mapping is however of interest to provide a geographical frame to distribute human population and movement in relation to their activities in the city, and as spatialized input variables for epidemiological and entomological models.

A spatial model of socioeconomic and environmental determinants of dengue fever in Cali, Colombia

Dengue fever has gradually re-emerged across the global South, particularly affecting urban areas of the tropics and sub-tropics. The dynamics of dengue fever transmission are sensitive to changes in environmental conditions, as well as local demographic and socioeconomic factors. In 2010, the municipality of Cali, Colombia, experienced one of its worst outbreaks, however the outbreak was not spatially homogeneous across the city. In this paper, we evaluate the role of socioeconomic and environmental factors associated with this outbreak at the neighborhood level, using a Geographically Weighted Regression model. Key socioeconomic factors include population density and socioeconomic stratum, whereas environmental factors are proximity to both tire shops and plant nurseries and the presence of a sewage system (R²=0.64). The strength of the association between these factors and the incidence of dengue fever is spatially heterogeneous at the neighborhood level. The findings provide evidence to support public health strategies in allocating resources locally, which will enable a better detection of high risk areas, a reduction of the risk of infection and to strengthen the resilience of the population.

Re-assess Vector Indices Threshold as an Early Warning Tool for Predicting Dengue Epidemic in a Dengue Non-endemic Country

Background

Despite dengue dynamics being driven by complex interactions between human hosts, mosquito vectors and viruses that are influenced by climate factors, an operational model that will enable health authorities to anticipate the outbreak risk in a dengue non-endemic area has not been developed. The objectives of this study were to evaluate the temporal relationship between meteorological variables, entomological surveillance indices and confirmed dengue cases; and to establish the threshold for entomological surveillance indices including three mosquito larval indices [Breteau (BI), Container (CI) and House indices (HI)] and one adult index (AI) as an early warning tool for dengue epidemic.

Methodology/Principal Findings

Epidemiological, entomological and meteorological data were analyzed from 2005 to 2012 in Kaohsiung City, Taiwan. The successive waves of dengue outbreaks with different magnitudes were recorded in Kaohsiung City, and involved a dominant serotype during each epidemic. The annual indigenous dengue cases usually started from May to June and reached a peak in October to November. Vector data from 2005–2012 showed that the peak of the adult mosquito population was followed by a peak in the corresponding dengue activity with a lag period of 1–2 months. Therefore, we focused the analysis on the data from May to December and the high risk district, where the inspection of the immature and mature mosquitoes was carried out on a weekly basis and about 97.9% dengue cases occurred. The two-stage model was utilized here to estimate the risk and time-lag effect of annual dengue outbreaks in Taiwan. First, Poisson regression was used to select the optimal subset of variables and time-lags for predicting the number of dengue cases, and the final results of the multivariate analysis were selected based on the smallest AIC value. Next, each vector index models with selected variables were subjected to multiple logistic regression models to examine the accuracy of predicting the occurrence of dengue cases. The results suggested that Model-AI, BI, CI and HI predicted the occurrence of dengue cases with 83.8, 87.8, 88.3 and 88.4% accuracy, respectively. The predicting threshold based on individual Model-AI, BI, CI and HI was 0.97, 1.16, 1.79 and 0.997, respectively.

Conclusion/Significance

There was little evidence of quantifiable association among vector indices, meteorological factors and dengue transmission that could reliably be used for outbreak prediction. Our study here provided the proof-of-concept of how to search for the optimal model and determine the threshold for dengue epidemics. Since those factors used for prediction varied, depending on the ecology and herd immunity level under different geological areas, different thresholds may be developed for different countries using a similar structure of the two-stage model.

With the continuously high levels of worldwide dengue transmission, predicting dengue outbreaks in advance of their occurrence or identifying specific locations where outbreak risks are highest is of critical importance. However, only few studies have been conducted in dengue non-endemic countries to evaluate the association of vector index with the occurrence of dengue cases; and the establishment of an early warning signal would significantly enhance the public health intervention. Our study here provided the proof-of-concept results, utilizing a two-stage model to identify the best set of lag effects of meteorological and entomological variables, explaining dengue epidemics based on the data obtained from Taiwan, which is a dengue-non-endemic country. Each of the vector indices when combined with the meteorological factors has better performance compared to the prediction using AI, BI, CI and HI alone, with 83.8, 87.8, 88.3 and 88.4% accuracy, respectively. Because of the complex interplays between the size of human hosts and movement, environmental factors and dynamic changes of mosquito population and density, each country should consider its own individual data and situation and apply this two-stage model to find the optimal predictive models for allocating public health resources and prevention strategies.