Exploring the spatial and temporal relationships between mosquito population dynamics and dengue outbreaks based on climatic factors

Tsai, Ching-Tsan; Fung-Chang, Sung; Chen, Patrick S.; Lin, Shu‐Chiung

doi:10.1007/s00477-011-0527-z

Cited by 18 publications

(10 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although various models have been proposed for the prediction of infectious disease spread, they mostly focus on long-term predictions that take into account explanatory variables, such as meteorological factors, to characterize the potential trend of the epidemic (Yu et al, 2011;Tu et al, 2012;Tsai et al, 2012). On the other hand, stochastic SIR models have been applied to simulate and predict the spatiotemporal diffusion of infectious diseases (Hufnagel et al, 2004;Cressie and Wikle, 2011;Ball and Sirl, 2012;Ji et al, 2012;de Souza et al, 2013;Zhang et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

An online spatiotemporal prediction model for dengue fever epidemic inKaohsiung (Taiwan)

Angulo

Cheng

et al. 2014

Biometrical J

View full text Add to dashboard Cite

The emergence and re-emergence of disease epidemics is a complex question that may be influenced by diverse factors, including the space-time dynamics of human populations, environmental conditions, and associated uncertainties. This study proposes a stochastic framework to integrate space-time dynamics in the form of a Susceptible-Infected-Recovered (SIR) model, together with uncertain disease observations, into a Bayesian maximum entropy (BME) framework. The resulting model (BME-SIR) can be used to predict space-time disease spread. Specifically, it was applied to obtain a space-time prediction of the dengue fever (DF) epidemic that took place in Kaohsiung City (Taiwan) during 2002. In implementing the model, the SIR parameters were continually updated and information on new cases of infection was incorporated. The results obtained show that the proposed model is rigorous to user-specified initial values of unknown model parameters, that is, transmission and recovery rates. In general, this model provides a good characterization of the spatial diffusion of the DF epidemic, especially in the city districts proximal to the location of the outbreak. Prediction performance may be affected by various factors, such as virus serotypes and human intervention, which can change the space-time dynamics of disease diffusion. The proposed BME-SIR disease prediction model can provide government agencies with a valuable reference for the timely identification, control, and prevention of DF spread in space and time.

show abstract

Section: Discussionmentioning

confidence: 99%

An online spatiotemporal prediction model for dengue fever epidemic inKaohsiung (Taiwan)

Angulo

Cheng

et al. 2014

Biometrical J

View full text Add to dashboard Cite

show abstract

“…We have only considered Aedes aegypti here as they have very different ecological footprint compared to Aedes albopictus , hence they require different models. Furthermore, it has been shown that Aedes aegypti are the most competent vector for transmitting dengue, particularly in Taiwan [42]. For our study, we use the observed numbers of Aedes aegypti larvae and the number of observed containers (both inside and outside).…”

Section: Methodsmentioning

confidence: 99%

“…Tsai et al . [42] conclude that there may be a sharp inflation in the mosquito population, seven days after a period of intense rain, if the weather remains warm and humid. They also mention that this may not be an immediate impact of the rainfall, but its contribution to maintain humidity is preferred for larvae to survive.…”

Section: Introductionmentioning

confidence: 99%

Risk prediction system for dengue transmission based on high resolution weather data

et al. 2018

View full text Add to dashboard Cite

BackgroundDengue is the fastest spreading vector-borne viral disease, resulting in an estimated 390 million infections annually. Precise prediction of many attributes related to dengue is still a challenge due to the complex dynamics of the disease. Important attributes to predict include: the risk of and risk factors for an infection; infection severity; and the timing and magnitude of outbreaks. In this work, we build a model for predicting the risk of dengue transmission using high-resolution weather data. The level of dengue transmission risk depends on the vector density, hence we predict risk via vector prediction.Methods and findingsWe make use of surveillance data on Aedes aegypti larvae collected by the Taiwan Centers for Disease Control as part of the national routine entomological surveillance of dengue, and weather data simulated using the IBM’s Containerized Forecasting Workflow, a high spatial- and temporal-resolution forecasting system. We propose a two stage risk prediction system for assessing dengue transmission via Aedes aegypti mosquitoes. In stage one, we perform a logistic regression to determine whether larvae are present or absent at the locations of interest using weather attributes as the explanatory variables. The results are then aggregated to an administrative division, with presence in the division determined by a threshold percentage of larvae positive locations resulting from a bootstrap approach. In stage two, larvae counts are estimated for the predicted larvae positive divisions from stage one, using a zero-inflated negative binomial model. This model identifies the larvae positive locations with 71% accuracy and predicts the larvae numbers producing a coverage probability of 98% over 95% nominal prediction intervals. This two-stage model improves the overall accuracy of identifying larvae positive locations by 29%, and the mean squared error of predicted larvae numbers by 9.6%, against a single-stage approach which uses a zero-inflated binomial regression approach.ConclusionsWe demonstrate a risk prediction system using high resolution weather data can provide valuable insight to the distribution of risk over a geographical region. The work also shows that a two-stage approach is beneficial in predicting risk in non-homogeneous regions, where the risk is localised.

show abstract

“…The agent factor in the form of the dengue virus is transmitted through Aedes aegypti. Another factor had a big part in the environment, for example, geographical location, climatic and weather conditions in an area, which affects the increase of the spread pattern of DHF cases [9][10][11]. The trend of DHF cases generally occurs in the tropics and appears in the rainy season then decreases after the rainy season ends.…”

Section: Introductionmentioning

confidence: 99%

Early Identification Model for Dengue Haemorrhagic Fever (DHF) Outbreak Areas Using Rule-Based Stratification Approach

Munir¹,

Hartati²,

Musdholifah³

2019

IJIES

View full text Add to dashboard Cite

The spread of dengue hemorrhagic fever (DHF) globally with a frequency level that tends to be high in the past 50 years raises a systematic idea of prevention. One of the efforts to prevent DHF is the need for early identification of areas that are potentially epidemic. Early identification is carried out by getting an overview of the incident one step ahead by data forecasting. The focus of the study was the development of area stratification algorithms as an early identification of DHF outbreak areas by using data forecasting methods with surveillance data variables. Surveillance data which became the references for system modeling were DHF case data, rainfall, humidity, air temperature, wind speed and Larva-free Number (ABJ) for the span of 2010-2016 in 17 districts in Sleman Regency, Special Region of Yogyakarta. There were four steps during the study, i.e., 1) Forecasting of DHF case for the period of 12 months, 2) Forecasting of Larva-free Number (ABJ), 3) Determination of DHF case pattern for the last three years and the average of ABJ in the third year and 4) Area classification into stratification class. A method used for data forecasting of DHF case was seasonal autoregressive moving average (SARIMA), and the determination of area class pattern was conducted by using a neural network, meanwhile to obtain area stratification class used rule-based approach referring to guidelines controlling DHF outbreaks of the Ministry of Health of the Republic of Indonesia. Early identification was carried out by dividing into 4 area classes. Area class target included endemic (K1), sporadic (K2), a potential (K3) and free (K4). The testing of accuracy forecasting used relative mean absolute error (RMAE) for 12 months period. The results of the forecasting accuracy test on 17 districts in Sleman Regency showed RMAE average of 1.46 was considered low for it was still below 10%. Furthermore, the results of the early identification of area stratification classes in 2014 and 2015 from 17 districts showed that 3 of the four regions were endemic areas while in 2016 almost all districts were endemic areas and only one area was classified as sporadic.

show abstract

Exploring the spatial and temporal relationships between mosquito population dynamics and dengue outbreaks based on climatic factors

Cited by 18 publications

References 38 publications

An online spatiotemporal prediction model for dengue fever epidemic inKaohsiung (Taiwan)

An online spatiotemporal prediction model for dengue fever epidemic inKaohsiung (Taiwan)

Risk prediction system for dengue transmission based on high resolution weather data

Early Identification Model for Dengue Haemorrhagic Fever (DHF) Outbreak Areas Using Rule-Based Stratification Approach

Contact Info

Product

Resources

About