Modeling spatial pattern of dengue in North Central Mexico using survey data and logistic regression

Sánchez-Hernández, Daniel; Aguirre-Salado, Carlos Arturo; Sánchez-Díaz, Guillermo; Aguirre-Salado, Alejandro Ivan; Soubervielle‐Montalvo, Carlos; Reyes-Cárdenas, Oscar; Hernández, Humberto Reyes; Santana-Juárez, Marcela Virginia

doi:10.1080/09603123.2019.1700938

Cited by 5 publications

(4 citation statements)

References 36 publications

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“…For the 33 studies that both checked the multicollinearity of covariates and performed variable selection, we summarised the retention rate of different groups of covariates in the final models (Fig 6A) [27,32,[38][39][40][41][42][43][44][46][47][48]54,59,60,72,78,79,86,93,[99][100][101][102][103][104][105][106][107][108][109][110][111][112]. Of 33 studies, 25 studies (96.2%) retained climatic variables when tested.…”

Section: Spatiotemporal Incidencementioning

confidence: 99%

“…Ecological data (7 tested and 2 rejected) and spatiotemporal incidence (5 tested and 1 rejected) were the least tested and included (Fig 6A). The most common combinations of retained categories were climatic, environmental, demography, and socio-economic (n = 4) [41,44,103,108]. For climatic variables, different measures of temperature and rainfall were tested in reviewed studies.…”

Section: Spatiotemporal Incidencementioning

confidence: 99%

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A systematic review of the data, methods and environmental covariates used to mapAedes-borne arbovirus transmission risk

Lim

Jafari

Caldwell

et al. 2023

Preprint

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Background Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. Methods We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc). Results We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 183 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, ii) regional models used to predict the spread of major epidemics between countries and iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 33/148) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc) and only 48% of studies assessed predictive performance via out-of-sample validation procedures. Conclusions Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We outline specific recommendations for future studies regarding aims and data choice, covariate selection, model formulation and evaluation.

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Section: Spatiotemporal Incidencementioning

confidence: 99%

Section: Spatiotemporal Incidencementioning

confidence: 99%

A systematic review of the data, methods and environmental covariates used to mapAedes-borne arbovirus transmission risk

Lim

Jafari

Caldwell

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Different studies highlight the problems that incorrectly georeferenced cases pose to adequately addressing the infection process of dengue fever (Oliveira et al 2013;Stewart-Ibarra et al 2014). By contrast, a study carried out in Mexico also used a casecontrol approach, but it did not consider the distance to previous cases as an indicator of the virus presence (Sánchez-Hernández et al 2021). We acknowledge that our approach could present some drawbacks.…”

Section: ₈₁₆ De C�� mentioning

confidence: 99%

Socioeconomic and environmental conditions influence the risk of dengue infection in a subtropical city of Argentina

Costas,

Barrenechea,

Sánchez

et al. 2023

Ecol Austral

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The increasing connectivity and urbanization of human population favor the reemergence and spread of dengue fever in subtropical and temperate regions, which poses onerous challenges to health systems. Analyzing the contribution of socioenvironmental conditions to the contagion risk is essential to design preventive strategies. In this retrospective analysis, we aim to assess the effects of social, demographic and environmental factors on the likelihood of becoming infected with dengue virus in Tucumán, the main subtropical city of Argentina. We implemented a case-control study to analyze the 2016 dengue outbreak. The control group included all persons reported with nonspecific febrile syndrome, and the cases were those confirmed with dengue virus after laboratory analyses. We recorded the age, sex and diagnostic date for every patient, and we georeferenced their house. This georeferentiation allowed us to estimate the distance to the nearest previous positive case (to control autocorrelation), population density, proportion of households with insufficient material constructive quality, overcrowding, educational level, NDVI, surface temperature and distance to nearest canal and cemetery. We constructed and compared logistic regression models to identify the combination of variables that best predicted dengue cases. Proximity to previous cases, cemeteries and canals, scarcity of surrounding vegetation, age and educational deficit were associated with an increased probability of being positive for dengue. In Argentina, socioeconomic differences are reflected in environmental inequalities, which reinforce the differential odds of suffering from dengue fever. Our results suggest that improving urban environmental quality may constitute an efficient way of preventing individual contagion of dengue and subsequent outbreaks.

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“…In the study carried out by Daniel Sánchez-Hernández et al carried out correlation analysis to inspect they built the relationships of proximity, environmental, social factors and location about the occurrence of dengue, a multivariable logistic regression model. A logistic curve may be produced by graphing the connection between the explanatory and predicted variables, which will help us evaluate the correlation strength ( 39 ). Various correlation techniques have been according to the review Pearson, and spearman's correlation test were widely used and effective in most of the studies.…”

Section: Analytical Review Of Predicting Dengue Outbreakmentioning

confidence: 99%

A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions

Bhatia

Bansal

Patil

et al. 2022

Front. Public Health

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Climate change is unexpected weather patterns that can create an alarming situation. Due to climate change, various sectors are affected, and one of the sectors is healthcare. As a result of climate change, the geographic range of several vector-borne human infectious diseases will expand. Currently, dengue is taking its toll, and climate change is one of the key reasons contributing to the intensification of dengue disease transmission. The most important climatic factors linked to dengue transmission are temperature, rainfall, and relative humidity. The present study carries out a systematic literature review on the surveillance system to predict dengue outbreaks based on Machine Learning modeling techniques. The systematic literature review discusses the methodology and objectives, the number of studies carried out in different regions and periods, the association between climatic factors and the increase in positive dengue cases. This study also includes a detailed investigation of meteorological data, the dengue positive patient data, and the pre-processing techniques used for data cleaning. Furthermore, correlation techniques in several studies to determine the relationship between dengue incidence and meteorological parameters and machine learning models for predictive analysis are discussed. In the future direction for creating a dengue surveillance system, several research challenges and limitations of current work are discussed.

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Modeling spatial pattern of dengue in North Central Mexico using survey data and logistic regression

Cited by 5 publications

References 36 publications

A systematic review of the data, methods and environmental covariates used to mapAedes-borne arbovirus transmission risk

A systematic review of the data, methods and environmental covariates used to mapAedes-borne arbovirus transmission risk

Socioeconomic and environmental conditions influence the risk of dengue infection in a subtropical city of Argentina

A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions

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