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The Socio-Environmental Determinants Underlying the Spatial Epidemiology of Urban Vector-Borne Diseases: Incorporating Human Mobility

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Infectious Disease Epidemiology".

Deadline for manuscript submissions: closed (22 March 2023) | Viewed by 11292

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Special Issue Editor

Dr. Richard Paul

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Guest Editor

Functional Genetics of Infectious Diseases Unit, Institut Pasteur, 25 Rue du Dr. Roux, CEDEX 15, 75724 Paris, France
Interests: dengue; malaria; transmission; reservoir of infection; urban ecology; vector control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cities and their peri-urban suburbs are expected to house 70% of the world’s population by 2050, and developing resilient health systems is a significant challenge. Whilst considerable emphasis has been placed on strategies to mitigate against the negative health effects of heat and pollution in cities, diseases associated with vector-borne pathogens (VBDs) have largely been ignored. However, the World Health Organization estimates that one of the main consequences of global warming will be an increased burden of such diseases. Indeed the very latest latest modelling predictions emphasise the increased burden of many such VBDs in urban areas.

Several of the mosquito-borne diseases are of particular concern, as the species in question have adapted to the urban environment. This is the case for Aedes aegypti, the vector of dengue, Zika, chikungunya and yellow fever viruses, Anopheles stephensi, the vector of urban malaria and Culex spp., vectors of West Nile virus. These pathogens are inflicting a huge health burden globally. In addition to these major tropical mosquito vector spp., at least six novel invasive mosquito spp. competent for arbovirus transmission have spread geographically and all are container-breeding and thus will exploit urban habitats.

Urbanization has been frequently linked with the endemicity of such diseases, where high population density coupled with poor environmental hygiene provides a conducive environment for mosquito vector breeding and an increased probability of transmission. Whilst the importance of socioeconomic factors that alter risk of exposure to infectious mosquitoes is known, it is also recognized that it is humans and not mosquitoes that ferry the pathogens around cities. Incorporating human mobility patterns at intra- and inter-urban scales can significantly alter epidemiological patterns. Disentangling the relative contributions of where you live versus who moves where is key to understand observed epidemiological patterns and thereby derive appropriate mitigation and intervention strategies.

This Special Issue aims to bring together articles that place socio-economic risk factors within specific defined urban contexts, articulated with human mobility networks at local and regional scales.

Dr. Richard Paul
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

dengue and other arboviruses
socio-economic risk factors
human mobility networks
disease mitigation

Published Papers (4 papers)

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Research

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22 pages, 4452 KiB

Open AccessArticle

A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention

by Mokhalad A. Majeed, Helmi Zulhaidi Mohd Shafri, Zed Zulkafli and Aimrun Wayayok

Int. J. Environ. Res. Public Health 2023, 20(5), 4130; https://doi.org/10.3390/ijerph20054130 - 25 Feb 2023

Cited by 5 | Viewed by 2870

Abstract

This research aims to predict dengue fever cases in Malaysia using machine learning techniques. A dataset consisting of weekly dengue cases at the state level in Malaysia from 2010 to 2016 was obtained from the Malaysia Open Data website and includes variables such as climate, geography, and demographics. Six different long short-term memory (LSTM) models were developed and compared for dengue prediction in Malaysia: LSTM, stacked LSTM (S-LSTM), LSTM with temporal attention (TA-LSTM), S-LSTM with temporal attention (STA-LSTM), LSTM with spatial attention (SA-LSTM), and S-LSTM with spatial attention (SSA-LSTM). The models were trained and evaluated on a dataset of monthly dengue cases in Malaysia from 2010 to 2016, with the task of predicting the number of dengue cases based on various climate, topographic, demographic, and land-use variables. The SSA-LSTM model, which used both stacked LSTM layers and spatial attention, performed the best, with an average root mean squared error (RMSE) of 3.17 across all lookback periods. When compared to three benchmark models (SVM, DT, ANN), the SSA-LSTM model had a significantly lower average RMSE. The SSA-LSTM model also performed well in different states in Malaysia, with RMSE values ranging from 2.91 to 4.55. When comparing temporal and spatial attention models, the spatial models generally performed better at predicting dengue cases. The SSA-LSTM model was also found to perform well at different prediction horizons, with the lowest RMSE at 4- and 5-month lookback periods. Overall, the results suggest that the SSA-LSTM model is effective at predicting dengue cases in Malaysia. Full article

(This article belongs to the Special Issue The Socio-Environmental Determinants Underlying the Spatial Epidemiology of Urban Vector-Borne Diseases: Incorporating Human Mobility)

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13 pages, 948 KiB

Open AccessArticle

Knowledge, Attitudes and Perception of Mosquito Control in Different Citizenship Regimes within and Surrounding the Malakasa Open Accommodation Refugee Camp in Athens, Greece

by Antonios Kolimenakis, Demetrios Tsesmelis, Clive Richardson, Georgios Balatsos, Panagiotis G. Milonas, Angeliki Stefopoulou, Olaf Horstick, Laith Yakob, Dimitrios P. Papachristos and Antonios Michaelakis

Int. J. Environ. Res. Public Health 2022, 19(24), 16900; https://doi.org/10.3390/ijerph192416900 - 16 Dec 2022

Cited by 1 | Viewed by 2930

Abstract

The study aims to evaluate the Knowledge, Attitude and Perception (KAP) of different societal groups concerning the implementation of targeted community-based mosquito surveillance and control interventions in different citizenship regimes. Targeted surveys were carried out within Malakasa camp for migrants and refugees, neighboring residential areas and urban areas in the wider Athens metropolitan area to investigate different knowledge levels and the role that both local and migrant communities can play in the implementation of community-based interventions based on their attitudes and perceptions. A scoring system was used to rate the collected responses. Results indicate different levels of KAP among the various groups of respondents and different priorities that should be considered in the design and execution of community interventions. Findings indicate a lower level of Knowledge Attitudes and Perceptions for the migrants, while the rate of correct answers for Perception significantly improved for migrants following a small-scale information session. The study highlights disparities in the levels of knowledge for certain public health issues and the feasibility of certain approaches for alleviating health-related challenges such as mosquito-borne diseases. Findings suggest that essential preparedness is needed by public authorities to respond to public health challenges related to migration and the spread of vector-borne diseases. Full article

(This article belongs to the Special Issue The Socio-Environmental Determinants Underlying the Spatial Epidemiology of Urban Vector-Borne Diseases: Incorporating Human Mobility)

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23 pages, 4182 KiB

Open AccessArticle

Importance of Public Transport Networks for Reconciling the Spatial Distribution of Dengue and the Association of Socio-Economic Factors with Dengue Risk in Bangkok, Thailand

by Bertrand Lefebvre, Rojina Karki, Renaud Misslin, Kanchana Nakhapakorn, Eric Daudé and Richard E. Paul

Int. J. Environ. Res. Public Health 2022, 19(16), 10123; https://doi.org/10.3390/ijerph191610123 - 16 Aug 2022

Cited by 6 | Viewed by 2278

Abstract

Dengue is the most widespread mosquito-borne viral disease of man and spreading at an alarming rate. Socio-economic inequality has long been thought to contribute to providing an environment for viral propagation. However, identifying socio-economic (SE) risk factors is confounded by intra-urban daily human mobility, with virus being ferried across cities. This study aimed to identify SE variables associated with dengue at a subdistrict level in Bangkok, analyse how they explain observed dengue hotspots and assess the impact of mobility networks on such associations. Using meteorological, dengue case, national statistics, and transport databases from the Bangkok authorities, we applied statistical association and spatial analyses to identify SE variables associated with dengue and spatial hotspots and the extent to which incorporating transport data impacts the observed associations. We identified three SE risk factors at the subdistrict level: lack of education, % of houses being cement/brick, and number of houses as being associated with increased risk of dengue. Spatial hotspots of dengue were found to occur consistently in the centre of the city, but which did not entirely have the socio-economic risk factor characteristics. Incorporation of the intra-urban transport network, however, much improved the overall statistical association of the socio-economic variables with dengue incidence and reconciled the incongruous difference between the spatial hotspots and the SE risk factors. Our study suggests that incorporating transport networks enables a more real-world analysis within urban areas and should enable improvements in the identification of risk factors. Full article

(This article belongs to the Special Issue The Socio-Environmental Determinants Underlying the Spatial Epidemiology of Urban Vector-Borne Diseases: Incorporating Human Mobility)

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20 pages, 1686 KiB

Open AccessSystematic Review

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

by Shi Yin, Chao Ren, Yuan Shi, Junyi Hua, Hsiang-Yu Yuan and Lin-Wei Tian

Int. J. Environ. Res. Public Health 2022, 19(22), 15265; https://doi.org/10.3390/ijerph192215265 - 18 Nov 2022

Cited by 10 | Viewed by 2473

Abstract

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. Full article

(This article belongs to the Special Issue The Socio-Environmental Determinants Underlying the Spatial Epidemiology of Urban Vector-Borne Diseases: Incorporating Human Mobility)

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