Spatio-temporal detection for dengue outbreaks in the Central Region of Malaysia using climatic drivers at mesoscale and synoptic scale

Yip, Stan; Him, Norziha Che; Jamil, Nur Izzah; He, Daihai; Sahu, Sujit K.

doi:10.1016/j.crm.2022.100429

Cited by 11 publications

(9 citation statements)

References 89 publications

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“…Lee et al ( Lee et al., 2018 ) used a sensitivity analysis of the temperature-dependent parameters to explore the effects of climate change on dengue transmission dynamics. Yap et al ( Yip et al., 2022 ) used a rigorous Bayesian modeling framework to construct an early warning system for the central region of Malaysia and found 46.87% increase in dengue cases due to an increase of 1°C in the central equatorial Pacific sea surface temperature with a lag time of 6 weeks.…”

Section: Discussionmentioning

confidence: 99%

Collaboration between meteorology and public health: Predicting the dengue epidemic in Guangzhou, China, by meteorological parameters

Chen

Ding

Liu

et al. 2022

Front. Cell. Infect. Microbiol.

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BackgroundDengue has become an increasing public health threat around the world, and climate conditions have been identified as important factors affecting the transmission of dengue, so this study was aimed to establish a prediction model of dengue epidemic by meteorological methods.MethodsThe dengue case information and meteorological data were collected from Guangdong Provincial Center for Disease Prevention and Control and Guangdong Meteorological Bureau, respectively. We used spatio-temporal analysis to characterize dengue epidemics. Spearman correlation analysis was used to analyze the correlation between lagged meteorological factors and dengue fever cases and determine the maximum lagged correlation coefficient of different meteorological factors. Then, Generalized Additive Models were used to analyze the non-linear influence of lagged meteorological factors on local dengue cases and to predict the number of local dengue cases under different weather conditions.ResultsWe described the temporal and spatial distribution characteristics of dengue fever cases and found that sporadic single or a small number of imported cases had a very slight influence on the dengue epidemic around. We further created a forecast model based on the comprehensive consideration of influence of lagged 42-day meteorological factors on local dengue cases, and the results showed that the forecast model has a forecast effect of 98.8%, which was verified by the actual incidence of dengue from 2005 to 2016 in Guangzhou.ConclusionA forecast model for dengue epidemic was established with good forecast effects and may have a potential application in global dengue endemic areas after modification according to local meteorological conditions. High attention should be paid on sites with concentrated patients for the control of a dengue epidemic.

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Section: Discussionmentioning

confidence: 99%

Collaboration between meteorology and public health: Predicting the dengue epidemic in Guangzhou, China, by meteorological parameters

Chen

Ding

Liu

et al. 2022

Front. Cell. Infect. Microbiol.

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“…The main groups of purposes or goals of risk maps vary depending on the specific disease and context, but can generally be grouped into four categories: 1) providing a broad overview of the spatial distribution of risk over long-term averages and suggesting how it might change under different scenarios of global changes in climate, economics, and demographics (e.g., [28,29]); 2) predicting the spread of outbreaks and gaining a better understanding of major drivers of geographical spread (e.g, [30,31]); 3) evaluating and planning vaccination programs by estimating disease burden and identifying high-risk areas at the continental or country-level scale (e.g., [32,33]); and 4) informing planning and outbreak response by increasing the precision of risk estimates and mapping sub-national risk using surveillance data (e.g., [34,35]).…”

Section: Purpose Of Mapsmentioning

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

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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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“…Aegypti and Ae. Albopictus mosquitoes, showing high fever with acute onset, joint and muscle pain, skin rashes, myalgias, hemorrhagic episodes, and circulatory shock [ 4 , 5 ]. Studies show that Ae.…”

Section: Introductionmentioning

confidence: 99%

The Risk of Emerging of Dengue Fever in Romania, in the Context of Global Warming

Ivanescu

Bodale

Grigore-Hristodorescu

et al. 2023

TropicalMed

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(1) Background: Few studies to date have assessed the influences induced by climate change on the spatial distribution and population abundance of Aedes albopictus using the latest climate scenarios. In this study, we updated the current distribution of Ae. albopictus mosquitoes and evaluated the changes in their distribution under future climate conditions, as well as the risk of dengue virus emergence in Romania. (2) Methods: Under the two scenarios: High scenario (HS) when no drastic measures to reduce the effects of global warming will be taken, or they are not effective and low scenario (LS) when very stringent greenhouse control measures will be implemented. (3) Results: The results estimate an increase in temperatures in Romania of up to 2.6 °C in HS and up to 0.4 °C in LS, with an increase in the period of virus replication within the vector from June to October in HS and from May to September in LS. Moreover, in 2022, Ae. albopictus was reported in a new county, where it was not identified at the last monitoring in 2020. (4) Conclusions: The rapid spread of this invasive species and the need to implement monitoring and control programs for the Aedes population in Romania are emphasized.

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Spatio-temporal detection for dengue outbreaks in the Central Region of Malaysia using climatic drivers at mesoscale and synoptic scale

Cited by 11 publications

References 89 publications

Collaboration between meteorology and public health: Predicting the dengue epidemic in Guangzhou, China, by meteorological parameters

Collaboration between meteorology and public health: Predicting the dengue epidemic in Guangzhou, China, by meteorological parameters

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

The Risk of Emerging of Dengue Fever in Romania, in the Context of Global Warming

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