Modelling dengue fever risk in the State of Yucatan, Mexico using regional-scale satellite-derived sea surface temperature

Laureano-Rosario, Abdiel E.; Garcia-Rejon, Julián E.; Gómez-Carro, Salvador; Farfán-Ale, José A.; Muller‐Karger, Frank E.

doi:10.1016/j.actatropica.2017.04.017

Cited by 20 publications

(35 citation statements)

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“…Dengue fever outbreaks are known to be strongly influenced by imported cases (Sang et al, , 2015, mosquito density (Sang et al, , 2015Lai, 2011), meteorological factors (Wang et al, 2013a) (such as air temperature (Sang et al, , 2015Eastin et al, 2014;Xu et al, 2016;Goto et al, 2013), rainfall (Sang et al, , 2015Xu et al, 2016;Goto et al, 2013;Castro et al, 2018), relative humidity , vapor pressure , air pressure , and sea surface temperature (Lai, 2011;Laureano-Rosario et al, 2017)), socio-economic factors (Qi et al, 2015;Hagenlocher et al, 2013;Wu et al, 2009), and environmental factors (such as water (Fullerton et al, 2014;Tian et al, 2016), vegetation (Qi et al, 2015), river levels (Hashizume et al, 2012), access to paved roads, and housing conditions (Lippi et al, 2018)). Moreover, in dengue fever field monitoring, the present authors have found that dengue fever is closely related to environmental and socio-economic conditions, such as sanitation status, population density, ventilation conditions, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Land surface temperature derived from remote sensing images at a moderate spatial resolution has a smaller spatial scale and shows the true environmental condition more directly. Some researchers have studied risk factors of dengue fever using remote sensing data on the coarse scale of a city or neighborhood (Laureano-Rosario et al, 2017;Tian et al, 2016;Khormi and Kumar, 2011), such as sea surface temperature (Laureano-Rosario et al, 2017) and surface water areas (Tian et al, 2016). Others have studied dengue fever on a neighborhood scale but with the existing field investigation data (Delmelle et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Spatial analysis of dengue fever and exploration of its environmental and socio-economic risk factors using ordinary least squares: A case study in five districts of Guangzhou City, China, 2014

Yue

Sun

Liu

et al. 2018

International Journal of Infectious Diseases

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial analysis of dengue fever and exploration of its environmental and socio-economic risk factors using ordinary least squares: A case study in five districts of Guangzhou City, China, 2014

Yue

Sun

Liu

et al. 2018

International Journal of Infectious Diseases

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“…For instance, the time was approximately one month in Malaysia [15], two months in Sri Lanka [34], and three months in Brazil [39]. This lag time is justifiable with time necessary for the growth and development of vector; from egg-larva, pupa-adult mosquito lead time [40]. The current research demonstrated remotely sensed precipitation estimates can be used to model the temporal pattern of dengue cases.…”

Section: Discussionmentioning

confidence: 92%

Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal

Acharya

Cao

et al. 2018

IJGI

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Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever.

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“…Specifically, we calculated humidity, mean temperature, and temperature range (difference between the maximum and minimum temperatures observed) over a three-week period, lagged by six weeks from the week of case reporting (i.e., nine to seven weeks prior, following previous work) [20,45,46]. Similarly, we calculated the cumulative rainfall over a six-week period, lagged by three weeks from the week of case reporting (i.e., nine to four weeks prior), extending the window applied to lagged temperature period by three weeks in order to better capture the effects of water accumulation over time [47,48]. To compare differences in overall epidemic characteristics (e.g., total number of cases, mean force of infection Table 1; Figure 1) among provinces, we also calculated province-level mean humidity, mean temperature, temperature range, and cumulative rainfall over the biologically relevant time lag described above.…”

Section: Methodsmentioning

confidence: 99%

Climate drives spatial variation in Zika epidemics in Latin America

Harris

Caldwell

Mordecai

2018

Preprint

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24Between 2015 and 2017, Zika virus spread rapidly through populations in the Americas with no 25 prior exposure to the disease. Although climate is a known determinant of many Aedes- 26 transmitted diseases, it is currently unclear whether climate was a major driver the of Zika 27 epidemic and how climate might have differentially impacted outbreak intensity across locations 28 within Latin America. Here, we estimated force of infection for Zika over time and across 29 provinces in Latin America using a time-varying Susceptible Infectious Recovered model. 30 Climate factors explained less than 5% of the variation in weekly transmission intensity in a 31 spatiotemporal model of force of infection by province over time, suggesting that week to week 32 transmission within provinces may be too stochastic to predict. By contrast, climate and 33 population factors were highly predictive of spatial variation in the presence and intensity of 34 Zika transmission among provinces, with pseudo R 2 values between 0.33 and 0.60. Temperature, 35 temperature range, rainfall, and population size were the most important predictors of where 36 Zika transmission occurred, while rainfall, relative humidity, and a nonlinear effect of 37 temperature were the best predictors of Zika intensity and burden. Surprisingly, force of 38 infection was greatest in locations with temperatures near 24°C, much lower than previous 39 estimates from mechanistic models, potentially suggesting that existing vector control programs 40 and/or prior exposure to other mosquito-borne diseases may have limited transmission in 41 locations most suitable for Aedes aegypti, the main vector of Zika, dengue, and chikungunya 42 viruses in Latin America. 43 44 45 46 48health concern, yet trends in mosquito-borne disease transmission are hard to predict because 49 they are influenced by the underlying immunity in the population, which is usually unknown [1-50 3]. The emergence and spread of Zika virus through Latin America in a population with no prior 51 exposure or immunity to the disease provides an opportunity to characterize relationships among 52 the environment, populations, and disease transmission without the confounding effects of pre-53 existing immunity (although cross-reactivity between dengue antibodies and Zika virus may 54 provide some protection) [4]. Between 2015 and 2017, Zika rapidly spread to 51 countries and 55 territories in the Americas, with over 800,000 cases reported [5]. Here, we quantified the force of 56 infection for Zika and studied factors that contributed to variation in transmission across time 57 and space as Zika emerged. 58 Force of infection, or the per capita rate at which susceptible individuals contract an 59 infection, can be used to compare disease transmission over the course of an outbreak and across 60 geographic regions [2,6]. Force of infection estimates can account for variation in the number of 61 immune individuals and entomological factors that influence the time it takes for mosquito-borne 62dise...

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Modelling dengue fever risk in the State of Yucatan, Mexico using regional-scale satellite-derived sea surface temperature

Cited by 20 publications

References 54 publications

Spatial analysis of dengue fever and exploration of its environmental and socio-economic risk factors using ordinary least squares: A case study in five districts of Guangzhou City, China, 2014

Spatial analysis of dengue fever and exploration of its environmental and socio-economic risk factors using ordinary least squares: A case study in five districts of Guangzhou City, China, 2014

Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal

Climate drives spatial variation in Zika epidemics in Latin America

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