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      Influence of climate variables on dengue fever occurrence in the southern region of Thailand

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          Abstract

          The 3-5year epidemic cycle of dengue fever in Thailand makes it a major re-emerging public health problem resulting in being a burden in endemic areas. Although the Thai Ministry of Public Health adopted the WHO dengue control strategy, all dengue virus serotypes continue to circulate. Health officers and village health volunteers implement some intervention options but there is a need to ascertain most appropriate (or a combination of) interventions regarding the environment and contextual factors that may undermine the effectiveness of such interventions. This study aims to understand the dengue-climate relationship patterns at the district level in the southern region of Thailand from 2002 to 2018 by examining the statistical association between dengue incidence rate and eight environmental patterns, testing the hypothesis of equal incidence of these. Data on environmental variables and dengue reported cases in Nakhon Si Thammarat province situated in the south of Thailand from 2002 to 2018 were analysed to (1) detect the environmental factors that affect the risk of dengue infectious disease; to (2) determine if disease risk is increasing or decreasing over time; and to (3) identify the high-risk district areas for dengue cases that need to be targeted for interventions. To identify the predictors that have a high and significant impact on reported dengue infection, three steps of analysis were used. First, we used Partial Least Squares (PLS) Regression and Poisson Regression, a variant of the Generalized Linear Model (GLM). Negative co-efficient in correspondence with the PLS components suggests that sea-level pressure, wind speed, and pan evaporation are associated with dengue occurrence rate, while other variables were positively associated. Using the Akaike information criterion in the stepwise GLM, the filtered predictors were temperature, precipitation, cloudiness, and sea level pressure with the standardized coefficients showing that the most influential variable is cloud cover (three times more than temperature and precipitation). Also, dengue occurrence showed a constant negative response to the average increase in sea-level pressure values. In southern Thailand, the predictors that have been locally determined to drive dengue occurrence are temperature, rainfall, cloud cover, and sea-level pressure. These explanatory variables should have important future implications for epidemiological studies of mosquito-borne diseases, particularly at the district level. Predictive indicators guide effective and dynamic risk assessments, targeting pre-emptive interventions.

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          Most cited references56

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              Variable selection with stepwise and best subset approaches.

              While purposeful selection is performed partly by software and partly by hand, the stepwise and best subset approaches are automatically performed by software. Two R functions stepAIC() and bestglm() are well designed for stepwise and best subset regression, respectively. The stepAIC() function begins with a full or null model, and methods for stepwise regression can be specified in the direction argument with character values "forward", "backward" and "both". The bestglm() function begins with a data frame containing explanatory variables and response variables. The response variable should be in the last column. Varieties of goodness-of-fit criteria can be specified in the IC argument. The Bayesian information criterion (BIC) usually results in more parsimonious model than the Akaike information criterion.

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: InvestigationRole: Writing – original draft
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – original draft
                Role: Data curationRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLOS Glob Public Health
                PLOS Glob Public Health
                plos
                PLOS Global Public Health
                Public Library of Science (San Francisco, CA USA )
                2767-3375
                20 April 2022
                2022
                : 2
                : 4
                : e0000188
                Affiliations
                [1 ] Environmental, Safety Technology and Health Program, School of Public Health, Walailak University, Thasala, Nakhon Si Thammarat, Thailand
                [2 ] División de Estudios de Postgrado, Universidad de la Sierra Juárez, Ixtlán de Juárez, Oaxaca, México
                KEMRI-Wellcome Trust Research Programme Nairobi, KENYA
                Author notes

                The authors declare that they have no competing interests.

                ‡ SY and WJ also contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-3226-3246
                https://orcid.org/0000-0002-5492-0836
                https://orcid.org/0000-0002-1638-4227
                https://orcid.org/0000-0002-3436-3745
                Article
                PGPH-D-21-00997
                10.1371/journal.pgph.0000188
                10022128
                36962156
                0f6a6a04-6c40-4bd4-b844-a3dc75a47cf9
                © 2022 Abdulsalam et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 19 November 2021
                : 6 March 2022
                Page count
                Figures: 5, Tables: 3, Pages: 17
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100010034, Walailak University;
                Award ID: CGS-RF-2020/09
                Award Recipient :
                This research work was partly funded by Walailak University Graduate Research Fund (contract number CGS-RF-2020/09). FIA received funding from Walailak University Graduate Research Fund. The funders had a role in study design and data collection.
                Categories
                Research Article
                Medicine and Health Sciences
                Medical Conditions
                Tropical Diseases
                Neglected Tropical Diseases
                Dengue Fever
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Viral Diseases
                Dengue Fever
                Earth Sciences
                Atmospheric Science
                Meteorology
                Clouds
                Medicine and Health Sciences
                Epidemiology
                People and Places
                Geographical Locations
                Asia
                Thailand
                Earth Sciences
                Atmospheric Science
                Meteorology
                Rain
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Disease Vectors
                Insect Vectors
                Mosquitoes
                Aedes Aegypti
                Biology and Life Sciences
                Species Interactions
                Disease Vectors
                Insect Vectors
                Mosquitoes
                Aedes Aegypti
                Biology and Life Sciences
                Zoology
                Entomology
                Insects
                Mosquitoes
                Aedes Aegypti
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Insects
                Mosquitoes
                Aedes Aegypti
                Biology and Life Sciences
                Zoology
                Animals
                Invertebrates
                Arthropoda
                Insects
                Mosquitoes
                Aedes Aegypti
                Medicine and Health Sciences
                Epidemiology
                Infectious Disease Epidemiology
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Infectious Disease Epidemiology
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Custom metadata
                The datasets used and/or analysed during the current study are included in this published article (and its supplementary information files).

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