Abstract:Dengue Hemorrhagic Fever (DHF) is a tropical disease that always attacks densely populated urban communities. Some factors, such as environment, climate and mobility, have contributed to the spread of the disease. The Aedes aegypti mosquito is an agent of dengue virus in humans, and by inhibiting its life cycle it can reduce the spread of the dengue disease. Therefore, it is necessary to involve the dynamics of mosquito's life cycle in a model in order to obtain a reliable risk map for intervention. The aim of… Show more
“…1 is a SIR-SI model. It is commonly used in studies of the spread of DHF disease in human populations [ 5 , 10 ]. Fig.…”
Section: Related Workmentioning
confidence: 99%
“…The SIR-SI model explained in Fig. 1 can be transformed into the differential equation system [ 5 , 25 ]. The systems are used to provide a link to stochastic process (see Eq.…”
Section: Related Workmentioning
confidence: 99%
“…Even though the DHF has been investigated for years, it remains a significant public health problem, especially in tropical countries [1] , [2] , [3] . The DHF dynamics have also gained prominence recently since climate change or global warming is predicted to have unexpected effects on its incidence [ 4 , 5 ]. The complexities in the life cycle of the mosquito, highly complex environmental and social interactions, unforeseen consequences of climate change, and population migration between endemic and non-endemic areas continued to contribute to the massive burden of morbidity and mortality accompanying the DHF [ 6 , 7 ].…”
Section: Introductionmentioning
confidence: 99%
“…But in these models, the dynamics of cross-infection between humans and mosquitoes are not considered. Some authors perform a stochastic process to assess the dynamics of cross-infection between humans and mosquitoes, such as [ 4 , 5 ]. Recent research by [ 4 , 5 ] provided a stochastic process for cross-infection such as susceptible host, infected host, re-covered host and susceptible vector, infective vector, or SIR-SI model.…”
“…1 is a SIR-SI model. It is commonly used in studies of the spread of DHF disease in human populations [ 5 , 10 ]. Fig.…”
Section: Related Workmentioning
confidence: 99%
“…The SIR-SI model explained in Fig. 1 can be transformed into the differential equation system [ 5 , 25 ]. The systems are used to provide a link to stochastic process (see Eq.…”
Section: Related Workmentioning
confidence: 99%
“…Even though the DHF has been investigated for years, it remains a significant public health problem, especially in tropical countries [1] , [2] , [3] . The DHF dynamics have also gained prominence recently since climate change or global warming is predicted to have unexpected effects on its incidence [ 4 , 5 ]. The complexities in the life cycle of the mosquito, highly complex environmental and social interactions, unforeseen consequences of climate change, and population migration between endemic and non-endemic areas continued to contribute to the massive burden of morbidity and mortality accompanying the DHF [ 6 , 7 ].…”
Section: Introductionmentioning
confidence: 99%
“…But in these models, the dynamics of cross-infection between humans and mosquitoes are not considered. Some authors perform a stochastic process to assess the dynamics of cross-infection between humans and mosquitoes, such as [ 4 , 5 ]. Recent research by [ 4 , 5 ] provided a stochastic process for cross-infection such as susceptible host, infected host, re-covered host and susceptible vector, infective vector, or SIR-SI model.…”
“…human SEIR -mosquito SIR models were used in six studies to explain the impact of different factors on the transmission dynamics, especially for smaller scales e.g. country or sub-national scale [75,88,[122][123][124][125]. Eight studies used metapopulation or network models, all of which considered the connectivity between areas or regions by including the patterns of daily human mobility or air travel data [31,67,70,71,73,82,83,126].…”
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.
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 176 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%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% 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 identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping.
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