Identification of Risk Determinants of Dengue Transmission Through Landscape Analysis

Dengue Clinical Trial

— IRDDENGUELA  

Official title:

Identification of Risk Determinants of Dengue Transmission Through Landscape Analysis in the Neighborhood "El Vergel", Tapachula, Chiapas

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05893134
• Other study ID #: F-CNIC-2023-060
• Status: Completed
• Start date: June 1, 2023
• Est. completion date: January 30, 2024

Study information

• Verified date: March 2024
• Source: Instituto Mexicano del Seguro Social
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

This retrospective observational study aims to determine the probability of the risk of dengue transmission through a model based on epidemiological, entomological, socioeconomic, demographic, and landscape variables in the El Vergel neighborhood in the municipality of Tapachula, Chiapas, Mexico. The main question it aims to answer is: 1. Is it possible to identify the risk determinants of dengue transmission by developing a probabilistic model based on the landscape analysis of epidemiological, entomological, sociodemographic, and landscape variables in an endemic urban area of the municipality of Tapachula, Chiapas, Mexico? Participants will be selected from a registry obtained from the Secretary of Health of cases of dengue fever, which will be contrasted with the entomological, socioeconomic, demographic, and landscape variables in the El Vergel neighborhood in Tapachula, Chiapas, Mexico. They will be not contacted or sampled for biologic testing in any shape or form, only the data already collected from the health services will be used.

Description:

Identification of the risk determinants of dengue transmission through landscape analysis in the "El Vergel" neighborhood, Tapachula, Chiapas, Mexico Dengue is a disease transmitted mainly by the Ae. aegypti present in our region, despite vector surveillance and control activities, the circulation of the virus is constant and new strategies are required that contribute to reducing the incidence of the disease, which can be fatal. On the other hand, drones are tools already used in precision and security agriculture, among others; by means of them, it is possible to obtain high-resolution images of large areas of land. This work will use these images in combination with epidemiological, entomological, socioeconomic, and demographic data to identify the risk factors for dengue transmission in an urban area of the city of Tapachula and will generate a model that will allow defining the risk areas in the area. study. Objective: To determine the probability of the risk of dengue transmission through a model based on epidemiological, entomological, socioeconomic, demographic, and landscape variables in the El Vergel neighborhood in the municipality of Tapachula, Chiapas. Material and methods: Information from entomological, housing condition, and sociodemographic surveys of the El Vergel neighborhood, Tapachula, Chiapas, obtained during the period from November to December 2019, will be used. In addition to epidemiological information on the incidence of dengue and the placement of ovitraps in the study area during the sampling period, six months before and six months after. Specialized cartography will be used, made from fine-scale aerial photographs taken at a height of 100m by a multirotor drone with six DJI Matrice 600 model rotors with two types of cameras, a Zenmuse X5 model that captures images in the visible spectrum at 16 MP and a multispectral camera with five spectral bands MicaSense RedEdge -MX with RGB sensor with a spatial resolution of 5 cm per pixel. The images were taken simultaneously with the entomological, socioeconomic, and demographic surveys. Georeferenced orthophoto cartographic maps, digital surface models, digital terrain models, and specialized cartography of vegetation indices will be used: Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index Green Normalized (GNDVI), RedEdge Normalized Difference Vegetation Index (NDVIRe) and Chlorophyll Index (CIGreen), height and diameter of the trees present in the study area, to take various variables related to the landscape (environmental variables). The data analysis will be based on a mathematical model based on the principle of partial least squares, to determine the spatial association between the epidemiological indicators (number and georeferencing of cases), entomological (immature and adult stages of Ae. aegypti), condition index housing, sociodemographic and landscape data. Period: 6 months Type of study: Cross-sectional, retrospective, observational. Selection criteria: The construction of databases will consider the houses of Colonia El Vergel, Tapachula, Chiapas, where its inhabitants of legal age, accepted through informed consent to participate in the surveys and collection of entomological and sociodemographic data in situ and aerial photographs at a height of 100m away. Homes that do not have residents will be grounds for exclusion, and those in which the participants do not allow the collection of complete information will be eliminated. Sample size and sampling: A multi-stage stratified sampling will be used to select dwellings. The sample size will be obtained according to the sample formula for proportions, which was calculated in n=196 dwellings. Results: A probabilistic risk model will be generated based on the variables of different natures used and maps will be built to identify the areas of greatest risk for dengue transmission in the study area. Conclusion: Generate scientific evidence that allows maximum use of these advances for the benefit of populations. The determination of risk areas using specialized cartography carried out using high-resolution aerial photography using drones, has already been demonstrated and recently published.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 196
• Est. completion date: January 30, 2024
• Est. primary completion date: July 31, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

• The epidemiological information of all suspected cases of dengue with the onset of symptoms in the period from June 2019 to May 2020 that have a record on the platform of the National System for Epidemiological Surveillance will be included.

Exclusion Criteria:

• Records that do not have sufficient information for their georeferencing will be excluded.

Related Conditions & MeSH terms

• Dengue

Intervention

Other:
• Risk Assessment
A probabilistic risk model will be generated based on the variables of different natures used and maps will be built to identify the areas of greatest risk for dengue transmission in the study area

Locations

Country	Name	City	State
Mexico	Hospital General de Zona No. 1	Tapachula	Chiapas

Sponsors (3)

Lead Sponsor	Collaborator
Instituto Mexicano del Seguro Social	Centro de Investigación en Matemáticas A.C. (CIMAT), Instituto Nacional de Salud Publica, Mexico

Country where clinical trial is conducted

Mexico, 

References & Publications (34)

Abdullah NAMH, Dom NC, Salleh SA, Salim H, Precha N. The association between dengue case and climate: A systematic review and meta-analysis. One Health. 2022 Oct 31;15:100452. doi: 10.1016/j.onehlt.2022.100452. eCollection 2022 Dec. — View Citation

Arredondo-Jimenez JI, Valdez-Delgado KM. Aedes aegypti pupal/demographic surveys in southern Mexico: consistency and practicality. Ann Trop Med Parasitol. 2006 Apr;100 Suppl 1:S17-S32. doi: 10.1179/136485906X105480. — View Citation

Aswi A, Cramb SM, Moraga P, Mengersen K. Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review. Epidemiol Infect. 2018 Oct 29;147:e33. doi: 10.1017/S0950268818002807. — View Citation

Avirutnan P, Matangkasombut P. Unmasking the role of mast cells in dengue. Elife. 2013 Apr 30;2:e00767. doi: 10.7554/eLife.00767. — View Citation

Bennett JE, Dolin R, Blaser MJ, editores. Mandell, Douglas, and Bennett's principles and practice of infectious diseases. Ninth edition. Philadelphia, PA: Elsevier; 2020. 1 p.

Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, Drake JM, Brownstein JS, Hoen AG, Sankoh O, Myers MF, George DB, Jaenisch T, Wint GR, Simmons CP, Scott TW, Farrar JJ, Hay SI. The global distribution and burden of dengue. Nature. 2013 Apr 25;496(7446):504-7. doi: 10.1038/nature12060. Epub 2013 Apr 7. — View Citation

Brady OJ, Gething PW, Bhatt S, Messina JP, Brownstein JS, Hoen AG, Moyes CL, Farlow AW, Scott TW, Hay SI. Refining the global spatial limits of dengue virus transmission by evidence-based consensus. PLoS Negl Trop Dis. 2012;6(8):e1760. doi: 10.1371/journal.pntd.0001760. Epub 2012 Aug 7. — View Citation

Carrasco-Escobar G, Moreno M, Fornace K, Herrera-Varela M, Manrique E, Conn JE. The use of drones for mosquito surveillance and control. Parasit Vectors. 2022 Dec 16;15(1):473. doi: 10.1186/s13071-022-05580-5. — View Citation

Case E, Shragai T, Harrington L, Ren Y, Morreale S, Erickson D. Evaluation of Unmanned Aerial Vehicles and Neural Networks for Integrated Mosquito Management of Aedes albopictus (Diptera: Culicidae). J Med Entomol. 2020 Sep 7;57(5):1588-1595. doi: 10.1093/jme/tjaa078. — View Citation

Ferraguti M, Martinez-de la Puente J, Roiz D, Ruiz S, Soriguer R, Figuerola J. Effects of landscape anthropization on mosquito community composition and abundance. Sci Rep. 2016 Jul 4;6:29002. doi: 10.1038/srep29002. — View Citation

Gubler DJ. Dengue and dengue hemorrhagic fever. Clin Microbiol Rev. 1998 Jul;11(3):480-96. doi: 10.1128/CMR.11.3.480. — View Citation

Guzman MG, Harris E. Dengue. Lancet. 2015 Jan 31;385(9966):453-65. doi: 10.1016/S0140-6736(14)60572-9. Epub 2014 Sep 14. — View Citation

Hossain, M.S.; Raihan, M.E.; Hossain, M.S.; Syeed, M.M.M.; Rashid, H.; Reza, M.S. Aedes Larva Detection Using Ensemble Learning to Prevent Dengue Endemic. BioMedInformatics 2022, 2, 405-423. https://doi.org/10.3390/biomedinformatics2030026

Kuhn RJ, Zhang W, Rossmann MG, Pletnev SV, Corver J, Lenches E, Jones CT, Mukhopadhyay S, Chipman PR, Strauss EG, Baker TS, Strauss JH. Structure of dengue virus: implications for flavivirus organization, maturation, and fusion. Cell. 2002 Mar 8;108(5):717-25. doi: 10.1016/s0092-8674(02)00660-8. — View Citation

Leandro AS, Ayala MJC, Lopes RD, Martins CA, Maciel-de-Freitas R, Villela DAM. Entomo-Virological Aedes aegypti Surveillance Applied for Prediction of Dengue Transmission: A Spatio-Temporal Modeling Study. Pathogens. 2022 Dec 20;12(1):4. doi: 10.3390/pathogens12010004. — View Citation

Lee GO, Vasco L, Marquez S, Zuniga-Moya JC, Van Engen A, Uruchima J, Ponce P, Cevallos W, Trueba G, Trostle J, Berrocal VJ, Morrison AC, Cevallos V, Mena C, Coloma J, Eisenberg JNS. A dengue outbreak in a rural community in Northern Coastal Ecuador: An analysis using unmanned aerial vehicle mapping. PLoS Negl Trop Dis. 2021 Sep 27;15(9):e0009679. doi: 10.1371/journal.pntd.0009679. eCollection 2021 Sep. — View Citation

Lorenz C, Castro MC, Trindade PMP, Nogueira ML, de Oliveira Lage M, Quintanilha JA, Parra MC, Dibo MR, Favaro EA, Guirado MM, Chiaravalloti-Neto F. Predicting Aedes aegypti infestation using landscape and thermal features. Sci Rep. 2020 Dec 10;10(1):21688. doi: 10.1038/s41598-020-78755-8. — View Citation

Mechan F, Bartonicek Z, Malone D, Lees RS. Unmanned aerial vehicles for surveillance and control of vectors of malaria and other vector-borne diseases. Malar J. 2023 Jan 20;22(1):23. doi: 10.1186/s12936-022-04414-0. — View Citation

Moloney JM, Skelly C, Weinstein P, Maguire M, Ritchie S. Domestic Aedes aegypti breeding site surveillance: limitations of remote sensing as a predictive surveillance tool. Am J Trop Med Hyg. 1998 Aug;59(2):261-4. doi: 10.4269/ajtmh.1998.59.261. — View Citation

Muñiz-Sánchez, V.; Valdez-Delgado, K.M.; Hernandez-Lopez, F.J.; Moo-Llanes, D.A.; González-Farías, G.; Danis-Lozano, R. Use of Unmanned Aerial Vehicles for Building a House Risk Index of Mosquito-Borne Viral Diseases. Machines 2022, 10, 1161. https://doi.org/10.3390/machines10121161

Orta-Pineda G, Abella-Medrano CA, Suzan G, Serrano-Villagrana A, Ojeda-Flores R. Effects of landscape anthropization on sylvatic mosquito assemblages in a rainforest in Chiapas, Mexico. Acta Trop. 2021 Apr;216:105849. doi: 10.1016/j.actatropica.2021.105849. Epub 2021 Jan 30. — View Citation

Pardo Martínez D, Ojeda Martínez B, Alonso Remedios A. Dinámica de la respuesta inmune en la infección por virus del dengue. MediSur. febrero de 2018;16:76-84.

Rahman MS, Pientong C, Zafar S, Ekalaksananan T, Paul RE, Haque U, Rocklov J, Overgaard HJ. Mapping the spatial distribution of the dengue vector Aedes aegypti and predicting its abundance in northeastern Thailand using machine-learning approach. One Health. 2021 Dec 4;13:100358. doi: 10.1016/j.onehlt.2021.100358. eCollection 2021 Dec. — View Citation

Reinhold JM, Lazzari CR, Lahondere C. Effects of the Environmental Temperature on Aedes aegypti and Aedes albopictus Mosquitoes: A Review. Insects. 2018 Nov 6;9(4):158. doi: 10.3390/insects9040158. — View Citation

Sallam MF, Fizer C, Pilant AN, Whung PY. Systematic Review: Land Cover, Meteorological, and Socioeconomic Determinants of Aedes Mosquito Habitat for Risk Mapping. Int J Environ Res Public Health. 2017 Oct 16;14(10):1230. doi: 10.3390/ijerph14101230. — View Citation

Scott TW, Morrison AC. Vector dynamics and transmission of dengue virus: implications for dengue surveillance and prevention strategies: vector dynamics and dengue prevention. Curr Top Microbiol Immunol. 2010;338:115-28. doi: 10.1007/978-3-642-02215-9_9. — View Citation

Silver JB. Mosquito ecology: field sampling methods. springer science & business media; 2007.

Stanton MC, Kalonde P, Zembere K, Hoek Spaans R, Jones CM. The application of drones for mosquito larval habitat identification in rural environments: a practical approach for malaria control? Malar J. 2021 May 31;20(1):244. doi: 10.1186/s12936-021-03759-2. — View Citation

Talavera JO, Rivas-Ruiz R, Bernal-Rosales LP. [Clinical research V. Sample size]. Rev Med Inst Mex Seguro Soc. 2011 Sep-Oct;49(5):517-22. Spanish. — View Citation

Tun-Lin W, Kay BH, Barnes A. Understanding productivity, a key to Aedes aegypti surveillance. Am J Trop Med Hyg. 1995 Dec;53(6):595-601. doi: 10.4269/ajtmh.1995.53.595. — View Citation

Valdez-Delgado KM, Moo-Llanes DA, Danis-Lozano R, Cisneros-Vazquez LA, Flores-Suarez AE, Ponce-Garcia G, Medina-De la Garza CE, Diaz-Gonzalez EE, Fernandez-Salas I. Field Effectiveness of Drones to Identify Potential Aedes aegypti Breeding Sites in Househ — View Citation

Valdez-Delgado KM. Aplicación del uso de drones a fina escala para la asociación de factores demográficos, socio-económicos y ambientales con la abundancia de mosquitos Aedes aegypti (Linnaeus) y Aedes albopictus (Skuse) Diptera: Culicidae, en áreas persi

World Health Organization. (2012). Global strategy for dengue prevention and control 2012-2020. World Health Organization. https://apps.who.int/iris/handle/10665/75303

Yin S, Ren C, Shi Y, Hua J, Yuan HY, Tian LW. A Systematic Review on Modeling Methods and Influential Factors for Mapping Dengue-Related Risk in Urban Settings. Int J Environ Res Public Health. 2022 Nov 18;19(22):15265. doi: 10.3390/ijerph192215265. — View Citation

* Note: There are 34 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Risk	A probabilistic risk model will be generated based on the variables of different natures used and maps will be built to identify the areas of greatest risk for dengue transmission in the study area	One year, six months previous to the survey application (November-December 2019) and six months after