Simulating an invasion: unsealed water storage (rainwater tanks) and urban block design facilitate the spread of the dengue fever mosquito, Aedes aegypti, in Brisbane, Australia

Urban Aedes aegypti suitability indicators: a study in Rio de Janeiro, Brazil

BACKGROUND

Controlling Aedes aegypti stands as the primary strategy in curtailing the global threat of vector-borne viral infections such as dengue fever, which is responsible for around 400 million infections and 40 000 fatalities annually. Effective interventions require a precise understanding of Ae aegypti spatiotemporal distribution and behaviour, particularly in urban settings where most infections occur. However, conventionally applied sample-based entomological surveillance systems often fail to capture the high spatial variability of Ae aegypti that can arise from heterogeneous urban landscapes and restricted Aedes flight range.

METHODS

In this study, we aimed to address the challenge of capturing the spatial variability of Ae aegypti by leveraging emerging geospatial big data, including openly available satellite and street view imagery, to locate common Ae aegypti breeding habitats. These data enabled us to infer the seasonal suitability for Ae aegypti eggs and larvae at a spatial resolution of 200 m within the municipality of Rio de Janeiro, Brazil.

FINDINGS

The proposed microhabitat and macrohabitat indicators for immature Ae aegypti explained the distribution of Ae aegypti ovitrap egg counts by up to 72% (95% CI 70-74) and larval counts by up to 74% (72-76). Spatiotemporal interpolations of ovitrap counts, using suitability indicators, provided high-resolution insights into the spatial variability of urban immature Ae aegypti that could not be captured with sample-based surveillance techniques alone.

INTERPRETATION

The potential of the proposed method lies in synergising entomological field measurements with digital indicators on urban landscape to guide vector control and address the prevailing spread of Ae aegypti-transmitted viruses. Estimating Ae aegypti distributions considering habitat size is particularly important for targeting novel vector control interventions such as Wolbachia.

FUNDING

German Research Foundation and Austrian Science Fund.

Modeling Intraday Aedes-human exposure dynamics enhances dengue risk prediction

Cities are the hot spots for global dengue transmission. The increasing availability of human movement data obtained from mobile devices presents a substantial opportunity to address this prevailing public health challenge. Leveraging mobile phone data to guide vector control can be relevant for numerous mosquito-borne diseases, where the influence of human commuting patterns impacts not only the dissemination of pathogens but also the daytime exposure to vectors. This study utilizes hourly mobile phone records of approximately 3 million urban residents and daily dengue case counts at the address level, spanning 8 years (2015-2022), to evaluate the importance of modeling human-mosquito interactions at an hourly resolution in elucidating sub-neighborhood dengue occurrence in the municipality of Rio de Janeiro. The findings of this urban study demonstrate that integrating knowledge of Aedes biting behavior with human movement patterns can significantly improve inferences on urban dengue occurrence. The inclusion of spatial eigenvectors and vulnerability indicators such as healthcare access, urban centrality measures, and estimates for immunity as predictors, allowed a further fine-tuning of the spatial model. The proposed concept enabled the explanation of 77% of the deviance in sub-neighborhood DENV infections. The transfer of these results to optimize vector control in urban settings bears significant epidemiological implications, presumably leading to lower infection rates of Aedes-borne diseases in the future. It highlights how increasingly collected human movement patterns can be utilized to locate zones of potential DENV transmission, identified not only by mosquito abundance but also connectivity to high incidence areas considering Aedes peak biting hours. These findings hold particular significance given the ongoing projection of global dengue incidence and urban sprawl.

Climate-based modelling and forecasting of dengue in three endemic departments of Peru

Amid profound climate change, incidence of dengue continues to rise and expand in distribution across the world. Here, we analysed dengue in three coastal departments of Peru which have recently experienced public health emergencies during the worst dengue crises in Latin American history. We developed a climate-based spatiotemporal modelling framework to model monthly incidence of new dengue cases in Piura, Tumbes, and Lambayeque over 140 months from 2010 to 2021. The framework enabled accurate description of in-sample and out-of-sample dengue incidence trends across the departments, as well as the characterisation of the timing, structure, and intensity of climatic relationships with human dengue incidence. In terms of dengue incidence rate (DIR) risk factors, we inferred non-linear and delayed effects of greater monthly mean maximum temperatures, extreme precipitation, sustained drought conditions, and extremes of a Peruvian-specific indicator of the El Niño Southern Oscillation. Building on our model-based understanding of climatic influences, we performed climate-model-based forecasting of dengue incidence across 2018 to 2021 with a forecast horizon of one month. Our framework enabled representative, reliable forecasts of future dengue outbreaks, including correct classification of 100% of all future outbreaks with DIR ≥ 50 (or 150) per 100,000, whilst retaining relatively low probability of 0.12 (0.05) for false alarms. Therefore, our model framework and analysis may be used by public health authorities to i) understand climatic drivers of dengue incidence, and ii) alongside our forecasts, to mitigate impacts of dengue outbreaks and potential public health emergencies by informing early warning systems and deployment of vector control resources.

Modelling the risk of Japanese encephalitis virus in Victoria, Australia, using an expert-systems approach

Predictive models for vector-borne diseases (VBDs) are instrumental to understanding the potential geographic spread of VBDs and therefore serve as useful tools for public health decision-making. However, predicting the emergence of VBDs at the micro-, local, and regional levels presents challenges, as the importance of risk factors can vary spatially and temporally depending on climatic factors and vector and host abundance and preferences. We propose an expert-systems-based approach that uses an analytical hierarchy process (AHP) deployed within a geographic information system (GIS), to predict areas susceptible to the risk of Japanese encephalitis virus (JEV) emergence. This modelling approach produces risk maps, identifying micro-level risk areas with the potential for disease emergence. The results revealed that climatic conditions, especially the minimum temperature and precipitation required for JEV transmission, contributed to high-risk conditions developed during January and March of 2022 in Victora. Compared to historical climate records, the risk of JEV emergence was increased in most parts of the state due to climate. Importantly, the model accurately predicted 7 out of the 8 local government areas that reported JEV-positive cases during the outbreak of 2022 in Victorian piggeries. This underscores the model's potential as a reliable tool for supporting local risk assessments in the face of evolving climate change.

Invasive hematophagous arthropods and associated diseases in a changing world

Biological invasions have increased significantly with the tremendous growth of international trade and transport. Hematophagous arthropods can be vectors of infectious and potentially lethal pathogens and parasites, thus constituting a growing threat to humans-especially when associated with biological invasions. Today, several major vector-borne diseases, currently described as emerging or re-emerging, are expanding in a world dominated by climate change, land-use change and intensive transportation of humans and goods. In this review, we retrace the historical trajectory of these invasions to better understand their ecological, physiological and genetic drivers and their impacts on ecosystems and human health. We also discuss arthropod management strategies to mitigate future risks by harnessing ecology, public health, economics and social-ethnological considerations. Trade and transport of goods and materials, including vertebrate introductions and worn tires, have historically been important introduction pathways for the most prominent invasive hematophagous arthropods, but sources and pathways are likely to diversify with future globalization. Burgeoning urbanization, climate change and the urban heat island effect are likely to interact to favor invasive hematophagous arthropods and the diseases they can vector. To mitigate future invasions of hematophagous arthropods and novel disease outbreaks, stronger preventative monitoring and transboundary surveillance measures are urgently required. Proactive approaches, such as the use of monitoring and increased engagement in citizen science, would reduce epidemiological and ecological risks and could save millions of lives and billions of dollars spent on arthropod control and disease management. Last, our capacities to manage invasive hematophagous arthropods in a sustainable way for worldwide ecosystems can be improved by promoting interactions among experts of the health sector, stakeholders in environmental issues and policymakers (e.g. the One Health approach) while considering wider social perceptions.

Association Between Hydrological Conditions and Dengue Fever Incidence in Coastal Southeastern China From 2013 to 2019

IMPORTANCE

Dengue fever is a climate-sensitive infectious disease. However, its association with local hydrological conditions and the role of city development remain unclear.

OBJECTIVE

To quantify the association between hydrological conditions and dengue fever incidence in China and to explore the modification role of city development in this association.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study collected data between January 1, 2013, and December 31, 2019, from 54 cities in 4 coastal provinces in southeast China. The Standardized Precipitation Evapotranspiration Index (SPEI) was calculated from ambient temperature and precipitation, with SPEI thresholds of 2 for extreme wet conditions and -2 for extreme dry conditions. The SPEI-dengue fever incidence association was examined over a 6-month lag, and the modification roles of 5 city development dimensions were assessed. Data were analyzed in May 2022.

EXPOSURES

City-level monthly temperature, precipitation, SPEI, and annual city development indicators from 2013 to 2019.

MAIN OUTCOMES AND MEASURES

The primary outcome was city-level monthly dengue fever incidence. Spatiotemporal bayesian hierarchal models were used to examine the SPEI-dengue fever incidence association over a 6-month lag period. An interaction term between SPEI and each city development indicator was added into the model to assess the modification role of city development.

RESULTS

Included in the analysis were 70 006 dengue fever cases reported in 54 cities in 4 provinces in China from 2013 to 2019. Overall, a U-shaped cumulative curve was observed, with wet and dry conditions both associated with increased dengue fever risk. The relative risk [RR] peaked at a 1-month lag for extreme wet conditions (1.27; 95% credible interval [CrI], 1.05-1.53) and at a 6-month lag for extreme dry conditions (1.63; 95% CrI, 1.29-2.05). The RRs of extreme wet and dry conditions were greater in areas with limited economic development, health care resources, and income per capita. Extreme dry conditions were higher and prolonged in areas with more green space per capita (RR, 1.84; 95% CrI, 1.37-2.46). Highly urbanized areas had a higher risk of dengue fever after extreme wet conditions (RR, 1.80; 95% CrI, 1.26-2.56), while less urbanized areas had the highest risk of dengue fever in extreme dry conditions (RR, 1.70; 95% CrI, 1.11-2.60).

CONCLUSIONS AND RELEVANCE

Results of this study showed that extreme hydrological conditions were associated with increased dengue fever incidence within a 6-month lag period, with different dimensions of city development playing various modification roles in this association. These findings may help in developing climate change adaptation strategies and public health interventions against dengue fever.

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.