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Early warning signal for dengue outbreaks and identification of high risk areas for dengue fever in Colombia using climate and non-climate datasets.

Abstract

BACKGROUND

Dengue has been prevalent in Colombia with high risk of outbreaks in various locations. While the prediction of dengue epidemics will bring significant benefits to the society, accurate forecasts have been a challenge. Given competing health demands in Colombia, it is critical to consider the effective use of the limited healthcare resources by identifying high risk areas for dengue fever.

METHODS

The Climate Risk Factor (CRF) index was constructed based upon temperature, precipitation, and humidity. Considering the conditions necessary for vector survival and transmission behavior, elevation and population density were taken into account. An Early Warning Signal (EWS) model was developed by estimating the elasticity of the climate risk factor function to detect dengue epidemics. The climate risk factor index was further estimated at the smaller geographical unit (5 km by 5 km resolution) to identify populations at high risk.

RESULTS

From January 2007 to December 2015, the Early Warning Signal model successfully detected 75% of the total number of outbreaks 1 ~ 5 months ahead of time, 12.5% in the same month, and missed 12.5% of all outbreaks. The climate risk factors showed that populations at high risk are concentrated in the Western part of Colombia where more suitable climate conditions for vector mosquitoes and the high population level were observed compared to the East.

CONCLUSIONS

This study concludes that it is possible to detect dengue outbreaks ahead of time and identify populations at high risk for various disease prevention activities based upon observed climate and non-climate information. The study outcomes can be used to minimize potential societal losses by prioritizing limited healthcare services and resources, as well as by conducting vector control activities prior to experiencing epidemics.

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  • Authors+Show Affiliations

    ,

    Department of Zoology, The University of Oxford, The Tinbergen Building, South Parks Road, Oxford, OX1 3PS, UK. jungseok.lee@linacre.ox.ac.uk.

    ,

    Department of Epidemiology, McGill University, Biostatistics and Occupational Health, Purvis Hall, 1020 Pine Avenue West, Quebec, Montreal, H3A1A2, Canada. International Vaccine Institute, SNU Research Park, San 4-8, Seoul, Nakseongdae-dong, Gwanak-gu, 151-919, South Korea.

    ,

    International Vaccine Institute, SNU Research Park, San 4-8, Seoul, Nakseongdae-dong, Gwanak-gu, 151-919, South Korea.

    ,

    Clinical Epidemiology Unit, School of Medicine, Universidad Industrial de Santander, Cra 32 # 29 - 31 Office, 304, Bucaramanga, Santander, Colombia.

    ,

    International Vaccine Institute, SNU Research Park, San 4-8, Seoul, Nakseongdae-dong, Gwanak-gu, 151-919, South Korea.

    ,

    Clinical Epidemiology Unit, School of Medicine, Universidad Industrial de Santander, Cra 32 # 29 - 31 Office, 304, Bucaramanga, Santander, Colombia.

    Department of Zoology, The University of Oxford, The Tinbergen Building, South Parks Road, Oxford, OX1 3PS, UK.

    Source

    BMC infectious diseases 17:1 2017 07 10 pg 480

    MeSH

    Animals
    Climate
    Colombia
    Culicidae
    Dengue
    Disease Outbreaks
    Humans
    Humidity
    Population Density
    Risk Factors
    Temperature
    Weather

    Pub Type(s)

    Journal Article

    Language

    eng

    PubMed ID

    28693483

    Citation

    Lee, Jung-Seok, et al. "Early Warning Signal for Dengue Outbreaks and Identification of High Risk Areas for Dengue Fever in Colombia Using Climate and Non-climate Datasets." BMC Infectious Diseases, vol. 17, no. 1, 2017, p. 480.
    Lee JS, Carabali M, Lim JK, et al. Early warning signal for dengue outbreaks and identification of high risk areas for dengue fever in Colombia using climate and non-climate datasets. BMC Infect Dis. 2017;17(1):480.
    Lee, J. S., Carabali, M., Lim, J. K., Herrera, V. M., Park, I. Y., Villar, L., & Farlow, A. (2017). Early warning signal for dengue outbreaks and identification of high risk areas for dengue fever in Colombia using climate and non-climate datasets. BMC Infectious Diseases, 17(1), p. 480. doi:10.1186/s12879-017-2577-4.
    Lee JS, et al. Early Warning Signal for Dengue Outbreaks and Identification of High Risk Areas for Dengue Fever in Colombia Using Climate and Non-climate Datasets. BMC Infect Dis. 2017 07 10;17(1):480. PubMed PMID: 28693483.
    * Article titles in AMA citation format should be in sentence-case
    TY - JOUR T1 - Early warning signal for dengue outbreaks and identification of high risk areas for dengue fever in Colombia using climate and non-climate datasets. AU - Lee,Jung-Seok, AU - Carabali,Mabel, AU - Lim,Jacqueline K, AU - Herrera,Victor M, AU - Park,Il-Yeon, AU - Villar,Luis, AU - Farlow,Andrew, Y1 - 2017/07/10/ PY - 2017/01/11/received PY - 2017/06/29/accepted PY - 2017/7/12/entrez PY - 2017/7/12/pubmed PY - 2017/10/11/medline KW - Dengue KW - Dengue epidemic KW - Early warning system KW - Population at risk for dengue fever SP - 480 EP - 480 JF - BMC infectious diseases JO - BMC Infect. Dis. VL - 17 IS - 1 N2 - BACKGROUND: Dengue has been prevalent in Colombia with high risk of outbreaks in various locations. While the prediction of dengue epidemics will bring significant benefits to the society, accurate forecasts have been a challenge. Given competing health demands in Colombia, it is critical to consider the effective use of the limited healthcare resources by identifying high risk areas for dengue fever. METHODS: The Climate Risk Factor (CRF) index was constructed based upon temperature, precipitation, and humidity. Considering the conditions necessary for vector survival and transmission behavior, elevation and population density were taken into account. An Early Warning Signal (EWS) model was developed by estimating the elasticity of the climate risk factor function to detect dengue epidemics. The climate risk factor index was further estimated at the smaller geographical unit (5 km by 5 km resolution) to identify populations at high risk. RESULTS: From January 2007 to December 2015, the Early Warning Signal model successfully detected 75% of the total number of outbreaks 1 ~ 5 months ahead of time, 12.5% in the same month, and missed 12.5% of all outbreaks. The climate risk factors showed that populations at high risk are concentrated in the Western part of Colombia where more suitable climate conditions for vector mosquitoes and the high population level were observed compared to the East. CONCLUSIONS: This study concludes that it is possible to detect dengue outbreaks ahead of time and identify populations at high risk for various disease prevention activities based upon observed climate and non-climate information. The study outcomes can be used to minimize potential societal losses by prioritizing limited healthcare services and resources, as well as by conducting vector control activities prior to experiencing epidemics. SN - 1471-2334 UR - https://www.unboundmedicine.com/medline/citation/28693483/Early_warning_signal_for_dengue_outbreaks_and_identification_of_high_risk_areas_for_dengue_fever_in_Colombia_using_climate_and_non_climate_datasets_ L2 - https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-017-2577-4 DB - PRIME DP - Unbound Medicine ER -