Article

Developing a Dengue Prediction Model based on Climate in Tawau, Malaysia

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Abstract

Dengue is fast becoming the most urgent health issue in Malaysia, recording close to a 10-fold increase in cases over the last decade. With much uncertainty hovering over the recently introduced tetravalent vaccine and no effective antiviral drugs, vector control remains the most important strategy in combating dengue. This study analyses the relationship between weather predictors including its lagged terms, and dengue incidence in the District of Tawau over a period of 12 years, from 2006 to 2017. A forecasting model purposed to predict future outbreaks in Tawau was then developed using this data. Monthly dengue incidence data, mean temperature, maximum temperature, minimum temperature, mean relative humidity and mean rainfall over a period of 12 years from 2006 to 2017 in Tawau were retrieved from Tawau District Health Office and the Malaysian Meteorological Department. Cross-correlation analysis between weather predictors, lagged terms of weather predictors and dengue incidences established statistically significant cross-correlation between lagged periods of weather predictors-namely maximum temperature, mean relative humidity and mean rainfall with dengue incidence at time lags of 4–6 months. These variables were then employed into 3 different methods: a multivariate Poisson regression model, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and a SARIMA with external regressors for selection. Three models were selected but the SARIMA with external regressors model utilising maximum temperature at a lag of 6 months (p-value:0.001), minimum temperature at a lag of 4 months (p-value:0.01), mean relative humidity at a lag of 2 months (p-value:0.001), and mean rainfall at a lag of 6 months (p-value:0.001) produced an AIC of 841.94, and a log-likelihood score of −413.97 establishing it as the best fitting model of the methodologies utilised. In validating the models, they were utilised to develop forecasts with the model selected with the highest accuracy of predictions being the SARIMA model predicting 1 month in advance (MAE: 7.032, MSE: 83.977). This study establishes the effect of weather on the intensity and magnitude of dengue incidence as has been previously studied. A prediction model remains a novel method of evidence-based forecasting in Tawau, Sabah. The model developed in this study, demonstrated an ability to forecast potential dengue outbreaks 1 to 4 months in advance. These findings are not dissimilar to what has been previously studied in many different countries- with temperature and humidity consistently being established as powerful predictors of dengue incidence magnitude. When used in prognostication, it can enhance- decision making and allow judicious use of resources in public health setting. Nevertheless, the model remains a work in progress- requiring larger and more diverse data.

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... These institutions were responsible for either epidemiological surveillance or meteorological monitoring of the study area. One of the limitations presented in studies that use government data is the underreporting cases (55). Usually, when the individuals do not have the most severe form of the disease, they do not seek health services. ...
... Hence, under these conditions, those individuals are not included in the statistics. Moreover, health data usually have other limitations such as missing values, e.g., (55). However, some works use alternative sources to obtain data. ...
... When we evaluated the studies regarding the types of models used in the predictions, we observed that the vast majority of authors investigated moving average models (27), such as the Autoregressive Integrated Moving Average (ARIMA) (17,23,29,35,41,43,46,56,(61)(62)(63), Seasonal Autoregressive Integrated Moving Average (SARIMA) (55,(63)(64)(65)(66), Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) (67). Several works have also presented a wide variety of models using artificial neural networks, mainly the LSTM (59,(68)(69)(70). ...
Article
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Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.
... , in a given month (Appice et al., 2020;Baquero et al., 2018;Gluskin et al., 2014;Jain et al., 2019;Jayaraj et al., 2019;Karim et al., 2012;Liao et al., 2015;Luz et al., 2008;Phung et al., 2015;Ramadona et al., 2016;Salami et al., 2020;Siregar & Makmur, 2019;Stewart-Ibarra & Lowe, 2013), in dengue-specific seasons (Buczak et al., 2012;Johansson et al., 2016;Lowe et al., 2011Lowe et al., , 2013Stolerman et al., 2019), and in an entire year (Ong et al., 2018;Yuan et al., 2019). ...
... Regression vs. Classification: Several studies approach dengue prediction as a regression problem (Aburas et al., 2010;Baquero et al., 2018;Buczak et al., 2014Buczak et al., , 2018Gluskin et al., 2014;Guo et al., 2017;Hii et al., 2012;Jain et al., 2019;Jayaraj et al., 2019;Johansson et al., 2016;Karim et al., 2012;Li et al., 2017;Liao et al., 2015;Luz et al., 2008;Marques-Toledo et al., 2017;Phung et al., 2015;Reich et al., 2016;Siregar & Makmur, 2019;Stewart-Ibarra & Lowe, 2013;Yuan et al., 2019;Zhao et al., 2020). Others pose dengue prediction as a binary classification problem (Buczak et al., 2012;Ramadona et al., 2016;Stolerman et al., 2019) or a multi-class classification problem (Lowe et al., 2011Ong et al., 2018). ...
... Tools and techniques: Several studies use generalized additive models (GAM) to predict dengue (Baquero et al., 2018;Guo et al., 2017;Jain et al., 2019;Li et al., 2017;Marques-Toledo et al., 2017;Reich et al., 2016). Other works include Autoregressive Integrated Moving Average (ARIMA) and its variants (Baquero et al., 2018;Jayaraj et al., 2019;Johansson et al., 2016;Luz et al., 2008;Phung et al., 2015;Siregar & Makmur, 2019), Neural networks (Aburas et al., 2010;Baquero et al., 2018;Zhao et al., 2020), and Fuzzy logic (Buczak et al., 2014(Buczak et al., , 2012. Recently, Ong et al. (2018) and Zhao et al. (2020) used random forests as a tool to predict dengue cases. ...
Article
Tropical countries face urgent public health challenges regarding epidemic control of Dengue. Since effective vector-control efforts depend on the timing in which public policies take place, there is an enormous demand for accurate prediction tools. In this work, we improve upon a recent approach of coarsely predicting outbreaks in Brazilian urban centers based solely on their yearly climate data. Our methodological advancements encompass a judicious choice of data pre-processing steps and usage of modern computational techniques from signal-processing and manifold learning. Altogether, our results improved earlier prediction accuracy scores from 0.72 to 0.80, solidifying manifold learning on climate data alone as a viable way to make (coarse) dengue outbreak prediction in large urban centers. Ultimately, this approach has the potential of radically simplifying the data required to do outbreak analysis, as municipalities with limited public health funds may not monitor a large number of features needed for more extensive machine learning approaches.
... Locally, it has been observed to cause a cyclical pattern of three to five years in Malaysia, with all four dengue serotypes circulating concurrently. Recent studies in Malaysia have revealed that climate plays an integral role in affecting the magnitude of dengue incidences [3,4]. The increase is secondary to the direct and indirect impact of weather on dengue transmission. ...
... This uniform spatial distribution gives our study an advantage for state-wide analysis by using the Kota Bharu weather station as a proxy. Other studies evaluating the effects of dengue in Malaysia adopted a city-or district-specific approach in consideration of this spatial weather variability [4,6,21]. ...
... Compared to the daily maximum and mean temperature, no obvious temporal pattern is observed between the daily minimum with dengue incidence in Kelantan. This observation contradicts previous studies in Malaysia, where the daily minimum temperature was found to have a significant predictive value for dengue incidences [4,6,22]. For example, an increase in the daily minimum temperature above 24°C increases the relative risk of dengue in Kuala Lumpur, Selangor, and Putrajaya at a 30-, 60-, and 90-day lag [6]. ...
Article
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Introduction: Dengue, a vector-borne viral illness, shows worldwide widening spatial distribution beyond its point of origination, namely, the tropical belt. The persistent hyperendemicity in Malaysia has resulted in the formation of the dengue early warning system. However, weather variables are yet to be fully utilized for prevention and control activities, particularly in east-coast peninsular Malaysia where limited studies have been conducted. We aim to provide a time-based estimate of possible dengue incidence increase following weather-related changes, thereby highlighting potential dengue outbreaks. Method: All serologically confirmed dengue patients in Kelantan, a northeastern state in Malaysia, registered in the eDengue system with an onset of disease from January 2016 to December 2018, were included in the study with the exclusion of duplicate entry. Using a generalized additive model, climate data collected from the Kota Bharu weather station (latitude 6°10'N, longitude 102°18'E) was analysed with dengue data. Result: A cyclical pattern of dengue cases was observed with annual peaks coinciding with the intermonsoon period. Our analysis reveals that maximum temperature, mean temperature, rainfall, and wind speed have a significant nonlinear effect on dengue cases in Kelantan. Our model can explain approximately 8.2% of dengue incidence variabilities. Conclusion: Weather variables affect nearly 10% of the dengue incidences in Northeast Malaysia, thereby making it a relevant variable to be included in a dengue early warning system. Interventions such as vector control activities targeting the intermonsoon period are recommended.
... For forecasting the spread of disease with temperature and humidity, there is a negative association with rainfall. This creates a model using forecasting weather conditions, suitable for use in forecasting migration [31]. Seasonal autoregressive integrated moving averages (SARIMA) models based on annual and combined climate data are independent variables to predict the outbreak of dengue outbreaks [27]. ...
... Seasonal autoregressive integrated moving averages (SARIMA) models based on annual and combined climate data are independent variables to predict the outbreak of dengue outbreaks [27]. Poisson regression method compared with the seasonal autoregressive moving average process (SARIMA) and seasonal automatic autoregressive moving averages (SARIMA) with external regression methods [31]. Predictions using weather produce robust evidence for the use of such models in mitigation [23]. ...
Article
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p class="0abstract">Dengue remains a significant problem that needs to be addressed urgently in Thailand. Although Thailand has spread the dengue fever for more than sixty years, however, it is still found dengue patients in every province and spread to various areas. There is also a variable pattern of disease occurring each year, so it is necessary to have tools to help forecast area to allow the related organization and the people in the area plan to prevent dengue fever that may occur next year. This research aimed to create innovation for predicting dengue fever regions, namely ThaiDengue, by collecting data from dengue patients in Chatuchak District, Bangkok, Thailand, from January 2014 to December 2018. There was a total of 358,524 dengue patients from the Bureau of vector-borne diseases applied to the prediction of patients in the next year with the ARIMA model (1,1,0) (1,1,0). It is predicted that in 2019, Thailand will have dengue patients around 95,000 cases, which has the number of dengue patients close to the year 2018. In the next step, application development and database on fog computing. Fog computing is an evolving technology that brings the benefits achieved by could computing to the periphery of the network devices for faster data analytics. It is better suited than cloud computing for meeting the demands of numerous emerging applications such as self-driving cars, traffic lights, smart homes. While the ThaiDengue consists of the main menu: how to use, forecast, surveillance calendar, notification, disease map, notify patients, contact the Bureau of vector-borne, knowledge information, and scan the QR code. After that, the result of the development, the researcher has the Bureau of vector-borne disease of Thailand used to forecasts, create a GPS map of dengue outbreaks, and create a calendar for dengue monitoring. After that, send a message to alert the people in the area of dengue via a smartphone and send additional emails. The results from using the application found this application can be used as a tool to help the Bureau of vector-borne diseases, to plan dengue fever control and alert the people in the risk areas of dengue outbreak and users are very satisfied with the use of the application.</p
... Therefore, developing predictive models is an essential part of malaria surveillance that enables policymakers and public health staff to predict future incidence of the disease and act proactively [18]. Seasonal Integrated Moving Average (SARIMA) model [19] is widely used to predict different infectious diseases including malaria [20][21][22][23]. Some statistical models have been adopted for malaria in some regions of Iran [14][15][16][18][19][20][21][22][23][24][25][26], but to the best of our knowledge, no study had applied SARIMA time series to predict malaria incidence in Sistan and Baluchistan province. ...
... Seasonal Integrated Moving Average (SARIMA) model [19] is widely used to predict different infectious diseases including malaria [20][21][22][23]. Some statistical models have been adopted for malaria in some regions of Iran [14][15][16][18][19][20][21][22][23][24][25][26], but to the best of our knowledge, no study had applied SARIMA time series to predict malaria incidence in Sistan and Baluchistan province. Therefore, the objective of this study was to provide a SARIMA time series model for the prediction of malaria incidence in the southeast of Iran, and to check if the inclusion of climatic variables enhances the predictive power of the model. ...
Article
Full-text available
Objective: To predict future trends in the incidence of malaria cases in the southeast of Iran as the most important area of malaria using Seasonal Autoregressive Integrated Moving Average (SARIMA) model, and to check the effect of meteorological variables on the disease incidence. Methods: SARIMA method was applied to fit a model on malaria incidence from April 2001 to March 2018 in Sistan and Baluchistan province in southeastern Iran. Climatic variables such as temperature, rainfall, rainy days, humidity, sunny hours and wind speed were also included in the multivariable model as covariates. Then, the best fitted model was adopted to predict the number of malaria cases for the next 12 months. Results: The best-fitted univariate model for the prediction of malaria in the southeast of Iran was SARIMA (1,0,0)(1,1,1)12 [Akaike Information Criterion (AIC)=307.4, validation root mean square error (RMSE)=0.43]. The occurrence of malaria in a given month was mostly related to the number of cases occurring in the previous 1 (p=1) and 12 (P=1) months. The inverse number of rainy days with 8-month lag (β=0.329 2) and temperature with 3-month lag (β=-0.002 6) were the best predictors that could improve the predictive performance of the univariate model. Finally, SARIMA (1,0,0)(1,1,1)12 including mean temperature with a 3-month lag (validation RMSE=0.414) was selected as the final multivariable model. Conclusions: The number of malaria cases in a given month can be predicted by the number of cases in the prior 1 and 12 months. The number of rainy days with an 8-month lag and temperature with a 3-month lag can improve the predictive power of the model.
... Dengue viruses are transmitted mainly by Aedes aegypti and possibly by Ae. albopictus in tropical and subtropical areas [2]. Dengue has become endemic in nearly 100 countries in Africa, the Americas, the Eastern Mediterranean, Southeast Asia, and the Western Pacific [3]. Thailand reported its first experience of dengue fever in 1949 [4] and the first outbreak of dengue hemorrhagic fever (DHF) was reported in Bangkok in 1958 [4]. ...
... Weather parameters have widely been studied as substantial factors that need to be considered for better understanding of dengue transmission worldwide [8,9,11,12]. In recent decades, weather variables such as temperature, rainfall, and relative humidity have been widely studied for their potential to provide an early warning of dengue transmission [13] also in Thailand [11,14], Malaysia [3], and Indonesia [9]. Spatiotemporal modeling is important for predicting the risk of dengue fever in several localities, including Puerto Rico [15], Venezuela [12], and Thailand [11]. ...
Article
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This study aimed to show maps and analyses that display dengue cases and weather-related factors on dengue transmission in the three southernmost provinces of Thailand, namely Pattani, Yala, and Narathiwat provinces. Data on the number of dengue cases and weather variables including rainfall, rainy day, mean temperature, min temperature, max temperature, relative humidity, and air pressure for the period from January 2015 to December 2019 were obtained from the Bureau of Epidemiology, Ministry of Public Health and the Meteorological Department of Southern Thailand, respectively. Spearman rank correlation test was performed at lags from zero to two months and the predictive modeling used time series Poisson regression analysis. The distribution of dengue cases showed that in Pattani and Yala provinces the most dengue cases occurred in June. Narathiwat province had the most dengue cases occurring in August. The air pressure, relative humidity, rainfall, rainy day, and min temperature are the main predictors in Pattani province, while air pressure, rainy day, and max/mean temperature seem to play important roles in the number of dengue cases in Yala and Narathiwat provinces. The goodness-of-fit analyses reveal that the model fits the data reasonably well. The results provide scientific information for creating effective dengue control programs in the community, and the predictive model can support decision making in public health organizations and for management of the environmental risk area.
... The model could predict dengue incidence with adequate time and location precision (Edussuriya et al., 2021). In Malaysia, a study found that the best fitting SARIMA model with external regressors for dengue outbreak forecasting had the following climate variables: 6-month lagged maximum temperature, 4-month lagged minimum temperature, 2-month lagged mean relative humidity, and 6-month lagged mean rainfall (Jayaraj et al., 2019). ...
Article
Dengue is an endemic disease in more than 100 countries, but there are few studies about the effects of hydroclimatic variability on dengue incidence (DI) in tropical dryland areas. This study investigates the association between hydroclimatic variability and DI (2008-2018) in a large tropical dryland area. The area studied comprehends seven municipalities with populations ranging from 32,879 to 2,545,419 inhabitants. First, the precipitation and temperature impacts on interannual and seasonal DI were investigated. Then, the monthly association between DI and hydroclimatic variables was analyzed using generalized least squares (GLS) regression. The model's capability to reproduce DI given the current hydroclimatic conditions and DI seasonality over the entire time period studied were assessed. No association between the interannual variation of precipitation and DI was found. However, seasonal variation of DI was shaped by precipitation and temperature. February-July was the main dengue season period. A precipitation threshold, usually above 100 mm, triggers the rapid DI rising. Precipitation and minimum air temperature were the main explanatory variables. A two-month-lagged predictor was relevant for modeling, occurring in all regressions, followed by a non-lagged predictor. The climate predictors differed among the regression models, revealing the high spatial DI variability driven by hydroclimatic variability. GLS regressions were able to reproduce the beginning, development, and end of the dengue season, although we found underestimation of DI peaks and overestimation of low DI. These model limitations are not an issue for climate change impact assessment on DI at the municipality scale since historical DI seasonality was well simulated. However, they may not allow seasonal DI forecasting for some municipalities. These findings may help not only public health policies in the studied municipalities but also have the potential to be reproducible for other dryland regions with similar data availability.
... Reduced EIP and increased virus dissemination increase transmission potential [23]and could explain the relationship between increasing minimum temperature and decreasing disease incidence observed in this study. Climate variables can be used to build predictive models to anticipate when outbreaks of dengue, chikungunya and Zika are likely to occur [135][136][137][138]. This is useful in the prioritisation of vector-control resources. ...
Article
Full-text available
Dengue, Zika and chikungunya are diseases of global health significance caused by arboviruses and transmitted by the mosquito Aedes aegypti , which is of worldwide circulation. The arrival of the Zika and chikungunya viruses to South America increased the complexity of transmission and morbidity caused by these viruses co-circulating in the same vector mosquito species. Here we present an integrated analysis of the reported arbovirus cases between 2007 and 2017 and local climate and socio-economic profiles of three distinct Colombian municipalities (Bello, Cúcuta and Moniquirá). These locations were confirmed as three different ecosystems given their contrasted geographic, climatic and socio-economic profiles. Correlational analyses were conducted with both generalised linear models and generalised additive models for the geographical data. Average temperature, minimum temperature and wind speed were strongly correlated with disease incidence. The transmission of Zika during the 2016 epidemic appeared to decrease circulation of dengue in Cúcuta, an area of sustained high incidence of dengue. Socio-economic factors such as barriers to health and childhood services, inadequate sanitation and poor water supply suggested an unfavourable impact on the transmission of dengue, Zika and chikungunya in all three ecosystems. Socio-demographic influencers were also discussed including the influx of people to Cúcuta, fleeing political and economic instability from neighbouring Venezuela. Aedes aegypti is expanding its range and increasing the global threat of these diseases. It is therefore vital that we learn from the epidemiology of these arboviruses and translate it into an actionable local knowledge base. This is even more acute given the recent historical high of dengue cases in the Americas in 2019, preceding the COVID-19 pandemic, which is itself hampering mosquito control efforts.
... With the increasing number of cases each year, it is rapidly becoming as the most critical health concern. It is recorded that a huge increment of number of dengue cases was reported between 2000 to 2014 with 91% of increments, from 32 cases per 100,000 population in year 2000 to 361 cases per 100,000 population in 2014 [16]. With such alarming situation, tremendous effort have been done to find the most effective vaccine to combat dengue, which includes the vaccine development, but to no avail [15]. ...
Article
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Meta-heuristic algorithms have been significantly applied in addressing various real-world prediction problem, including in disease prediction. Having a reliable disease prediction model benefits many parties in providing proper preparation for prevention purposes. Hence, the number of cases can be reduced. In this study, a relatively new meta-heuristic algorithm namely Barnacle Mating Optimizer (BMO) is proposed for short term dengue outbreak prediction. The BMO prediction model is realized over real dengue cases data recorded in weekly frequency from Malaysia. In addition, meteorological data sets were also been employed as input. For evaluation purposes, error analysis relative to Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Mean Absolute Deviation (MAD) were employed to validate the performance of the identified algorithms which includes the comparison between BMO against Moth Flame Optimizer (MFO) and Grey Wolf Optimizer (GWO) algorithms. Upon simulation, the superiority is in favour to BMO by producing lower error rates.
... We found the optimum wind speed to be around 2 m/s which is in line with current knowledge of mosquito flight [104,108,109]. Climate variables can be used to build predictive models to anticipate when outbreaks of dengue, chikungunya and Zika are likely to occur [110][111][112][113]. This is useful in the prioritisation of vector-control resources. ...
Preprint
Full-text available
Dengue, Zika and chikungunya are diseases of global health significance caused by arboviruses and transmitted by the mosquito Aedes aegypti of worldwide circulation. The arrival of the Zika and chikungunya viruses to South America increased the complexity of transmission and morbidity caused by these viruses co-circulating in the same vector mosquito species. Here we present an integrated analysis of the reported arbovirus cases between 2007 and 2017 and local climate and socio-economic profiles of three distinct Colombian municipalities (Bello, Cúcuta and Moniquirá). These locations were confirmed as three different ecosystems given their contrasted geographic, climatic and socio-economic profiles. Correlational analyses were conducted with both generalised linear models and generalised additive models for the geographical data. Average temperature and wind speed were strongly correlated with disease incidence. The transmission of Zika during the 2016 epidemic appeared to decrease circulation of dengue in Cúcuta, an area of sustained high incidence of dengue. Socio-economic factors such as barriers to health and childhood services, inadequate sanitation and poor water supply suggested an unfavourable impact on the transmission of dengue, Zika and chikungunya in all three ecosystems. Socio-demographic influencers were also discussed including the influx of people to Cúcuta, fleeing political and economic instability from neighbouring Venezuela. Aedes aegypti is expanding its range and increasing the global threat of these diseases. It is therefore vital that we learn from the epidemiology of these arboviruses and translate it into an actionable knowledge base. This is even more acute given the recent historical high of dengue cases in the Americas in 2019, preceding the COVID-19 pandemic, which is itself hampering mosquito control efforts. Author summary Viruses transmitted by Ae. aegypti mosquitoes (dengue, Zika, chikungunya) are amongst the most significant public health concerns of recent years due to the increase in global cases and the rapid spread of the vectors. The primary method of controlling the spread of these arboviruses is through mosquito control. Understanding factors associated with risk of these viruses is key for informing control programmes and predicting when outbreaks may occur. Climate is an important driver in mosquito development and virus reproduction and hence the association of climate with disease risk. Socio-economic factors contribute to perpetuate disease risk. Areas of high poverty have abundance of suitable habitat for Ae. aegypti (e.g. due to poor housing and sanitation). This study investigated the factors effecting arbovirus incidence in three distinct regions of Colombia: Bello, Cúcuta and Moniquirá. The results show significant relationships between disease incidence and temperature, precipitation and wind speed. A decline in dengue following outbreaks of Zika (2016) is also reported. Measures of poverty, including critical overcrowding and no access to improved water source were also found to be higher in areas of higher disease incidence. The results of this study highlight the importance of using a multifactorial approach when designing vector control programs in order to effectively distribute health care resources.
... As a brief background study on the additional component which is the prediction of the number of dengue patients, this study [8] Analyses the relationship between weather predictors and incidence of dengue in Tawau District over a 12-year period, from 2006 through 2017. A forecast model is been designed to predict future trends, Outbreaks in Tawau using this knowledge. ...
Conference Paper
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This research paper discusses a web-based application that assists Public Health Officers in the dengue identification process. The mosquito classification is done using image processing and machine learning techniques. The training models are developed using Convolutional Neural Networks Algorithm, Support Vector Machine Algorithm, and K-Nearest Neighbors Algorithm to validate the results to determine the most accurate and suitable algorithm. this paper discusses the previous related research work on its significance and drawbacks while highlighting design, methods, and implementation in the solution. We conclude that the CNN algorithm provides the highest accuracy among the machine learning techniques used.
... qualidade das predições dos modelos foi avaliada pelo Erro Médio Absoluto (MAE, do inglês Mean Average Error) e pela Raiz Quadrática do Erro Médio (RMSE, do inglês Root Mean Squared Error). Quanto menores os valores do RMSE e MAE, melhor o desempenho do modelo(Jayaraj et al. 2019).Todas as análises foram realizadas em ambiente estatístico R (CoreTeam 2021). O pacote MASS(Ripley et al. 2013) foi usado para construção dos modelos de regressão binomial negativa. ...
Article
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Resumo-A pluviosidade contribui para o aumento da incidência de doenças infecciosas transmitidas por vetores, como a dengue. Na Região Metropolitana do Cariri (RMC), Ceará, a dengue é um problema recorrente. O objetivo desse artigo é analisar a relação entre incidência de dengue e pluviosidade na RMC. Realizou-se análise de correlação entre séries temporais de precipitação e de casos de dengue em municípios da RMC pela Função de Correlação Cruzada amostral (CCF). Para a modelagem, utilizaram-se Modelos Lineares Generalizados (MLG). Foram aplicadas métricas como a deviance, AIC e BIC, e análise de resíduos para a avaliação da qualidade do ajuste dos modelos. A capacidade de realizar previsões dos modelos foi avaliada pela Raiz do Erro Quadrado Médio (RMSE) e pelo Erro Absoluto Médio (MAE). Como resultados, identificou-se correlação significativa entre séries de pluviosidade e dengue para a maioria dos municípios. O melhor modelo usou defasagens (time lag) de pluviosidade e dengue como variáveis explicativas, obtendo RMSE e MAE iguais a 8,04 casos/100.000 habitantes e 6,52 casos/100.000 habitante, respectivamente. O entendimento da relação entre chuva e a incidência de dengue dá suporte à tomada de decisão em saúde pública e contribui para o desenvolvimento regional sustentável. Palavras-chave: Séries temporais. Estudo de correlação. Distribuição Binomial Negativa. Aedes aegypti. Desenvolvimento sustentável. Abstract-Rainfall contributes to an increase in the incidence of vector-borne infectious diseases, such as dengue. In the Metropolitan Region of Cariri (RMC), Ceará, Brazil, dengue is a recurrent
... Common climatic parameters (rainfall, temperature, and humidity) have been reported to influence the intensity and magnitude of dengue incidence [27][28][29]. These parameters will be requested from the Malaysian Meteorological Department, Ministry of Energy, Science, Technology, Environment and Climate Change for two points: Petaling Jaya and Kuala Lumpur International Airport. ...
Article
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Background In common with many South East Asian countries, Malaysia is endemic for dengue. Dengue control in Malaysia is currently based on reactive vector management within 24 h of a dengue case being reported. Preventive rather than reactive vector control approaches, with combined interventions, are expected to improve the cost-effectiveness of dengue control programs. The principal objective of this cluster randomized controlled trial is to quantify the effectiveness of a preventive integrated vector management (IVM) strategy on the incidence of dengue as compared to routine vector control efforts. Methods The trial is conducted in randomly allocated clusters of low- and medium-cost housing located in the Federal Territory of Kuala Lumpur and Putrajaya. The IVM approach combines: targeted outdoor residual spraying with K-Othrine Polyzone, deployment of mosquito traps as auto-dissemination devices, and community engagement activities. The trial includes 300 clusters randomly allocated in a 1:1 ratio. The clusters receive either the preventive IVM in addition to the routine vector control activities or the routine vector control activities only. Epidemiological data from monthly confirmed dengue cases during the study period will be obtained from the Vector Borne Disease Sector, Malaysian Ministry of Health e-Dengue surveillance system. Entomological surveillance data will be collected in 12 clusters randomly selected from each arm. To measure the effectiveness of the IVM approach on dengue incidence, a negative binomial regression model will be used to compare the incidence between control and intervention clusters. To quantify the effect of the interventions on the main entomological outcome, ovitrap index, a modified ordinary least squares regression model using a robust standard error estimator will be used. Discussion Considering the ongoing expansion of dengue burden in Malaysia, setting up proactive control strategies is critical. Despite some limitations of the trial such as the use of passive surveillance to identify cases, the results will be informative for a better understanding of effectiveness of proactive IVM approach in the control of dengue. Evidence from this trial may help justify investment in preventive IVM approaches as preferred to reactive case management strategies. Trial registration ISRCTN ISRCTN81915073 . Retrospectively registered on 17 April 2020.
... The ultimate goal of the forecast is to identify the catalyst of MBD outbreaks within a short period. Several MBD outbreak prediction systems play a significant role in the intervention program at the targeted hotspot areas within a few days in advance [8]- [11]. However, none of these frameworks manipulates the entomological index as one of the variables. ...
Conference Paper
The control of Mosquito-borne disease (MBD) is not one size fits all as various factors can stimulate vector propagation and increase the infection rate. One cannot simply adopt any MBD outbreak prediction system to their study area due to specific issues such as insufficient data and the arduousness of obtaining particular datasets. Manipulating open-source data available from the Internet might be scarce, unstructured, inadequate, irrelevant, and could merely be noise. Hence, the machine learning predictive power will be affected directly. This paper aims to review the available MBD outbreak prediction framework and propose an enhanced framework with the Entomological Index feature. A new conceptual framework is introduced where machine learning is leveraged to increase the future MBD outbreak predictive.
... Common climatic parameters (rainfall, temperature, and humidity) have been reported to in uence the intensity and magnitude of dengue incidence [27][28][29]. These parameters will be requested from the Malaysian Meteorological Department, Ministry of Energy, Science, Technology, Environment and Climate Change for two points: Petaling Jaya and Kuala Lumpur International Airport. ...
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Background: In common with many South East Asian countries, Malaysia is endemic for dengue. Dengue control in Malaysia is currently based on reactive vector management within 24 hours of a dengue case being reported. Preventive rather than reactive vector control approaches, with combined interventions, are expected to improve the cost-effectiveness of dengue control programs. The principal objective of this cluster randomized controlled trial is to quantify the effectiveness of a preventive integrated vector management (IVM) strategy on the incidence of dengue as compared to routine vector control efforts. Methods: The trial is conducted in randomly allocated low and medium cost clusters located in the Federal Territory of Kuala Lumpur and Putrajaya. The IVM approach combines: targeted outdoor residual spraying with K-Othrine Polyzone, deployment of mosquito traps as auto-dissemination devices, and community engagement activities. The trial includes 280 clusters randomly allocated in a 1:1 ratio. The clusters receive either the preventive IVM in addition to the routine vector control activities, or the routine vector control activities only. Epidemiological data from monthly confirmed dengue cases during the study period will be obtained from the Vector Borne Disease Sector, Malaysian Ministry of Health e-Dengue surveillance system. Entomological surveillance data will be collected in 12 clusters randomly selected from each arm. To measure the effectiveness of the IVM approach on dengue incidence, a negative binomial regression model will be used to compare the incidence between control and intervention clusters. To quantify the effect of the interventions on the main entomological outcome, ovitrap index, a modified ordinary least squares regression model using a robust standard error estimator will be used. Discussion: Considering the ongoing expansion of dengue burden in Malaysia, setting up proactive control strategies is critical. Despite some limitations of the trial such as the use of passive surveillance to identify cases the results will be informative for a better understanding of effectiveness of proactive IVM approach in the control of dengue. Evidence from this trial may help justify investment in preventive IVM approaches as preferred to reactive case management strategies. Trial registration: ISRCTN, ISRCTN81915073, retrospectively registered on 17 April 2020 (https://doi.org/10.1186/ISRCTN81915073)
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Background and aims: Dengue is a vector-borne viral disease which is one of the major causes of public health problem in India, and its control is often the major challenges of municipal bodies in the country, especially in West Bengal. The previous outbreaks of the disease can be used to forecast the future occurrence and burden, so that authorities may optimize the available resources in order to contain and minimize the impact. Materials and methods: Weekly disease outbreak data were extracted from Integrated Disease Surveillance Programme website and arranged as monthly data. Mann-Kendall test was used to determine the significance of the disease trends in various districts of Gangetic West Bengal. Time series analysis was done by using Seasonal ARIMA method to predict the number of Dengue outbreak cases for the year 2020. Results: Murshidabad was the only district of Gangetic West Bengal that had a significant upward Dengue cases outbreak trend. Nadia had a downward trend but it was not statistically significant. Model SARIMA (1,0,0) (1,0,0) 12 was chosen to forecast the Dengue outbreak cases which showed that the cases might start from the month of June, peak in August and wane off in October 2020. However, this prediction was not significant. Conclusion: Gangetic West Bengal might experience similar dengue cases as the previous year, but their numbers would be low. Only the district of Murshidabad would have upward trend. Knowledge in advance about periods of disease occurrence may enable health authorities to initiate control measures during the start of the outbreak season.
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Brucellosis is one of the major public health problems in China, and human brucellosis represents a serious public health concern in Xinjiang and requires a prediction analysis to help making early planning and putting forward science preventive and control countermeasures. According to the characteristics of the time series of monthly reported cases of human brucellosis in Xinjiang from January 2008 to June 2020, we used seasonal autoregressive integrated moving average (SARIMA) method and nonlinear autoregressive regression neural network (NARNN) method, which are widely prevalent and have high prediction accuracy, to construct prediction models and make prediction analysis. Finally, we established the SARIMA((1,4,5,7),0,0)(0,1,2) ¹² model and the NARNN model with a time lag of 5 and a hidden layer neuron of 10. Both models have high fitting performance. After comparing the accuracies of two established models, we found that the SARIMA((1,4,5,7),0,0)(0,1,2) ¹² model was better than the NARNN model. We used the SARIMA((1,4,5,7),0,0)(0,1,2) ¹² model to predict the number of monthly reported cases of human brucellosis in Xinjiang from July 2020 to December 2021, and the results showed that the fluctuation of the time series from July 2020 to December 2021 was similar to that of the last year and a half while maintaining the current prevention and control ability. The methodology applied here and its prediction values of this study could be useful to give a scientific reference for prevention and control human brucellosis.
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Since 1968, Dengue Harmonic Fever’s incidence in Indonesia has continued to rise and has become a public health issue. Indonesia has the largest number of Dengue Harmonic Fever cases than 30 other epidemic countries worldwide. It is very important to carry out research related to dengue cases’ prediction to prevent the spread of Dengue. This literature review is intended to determine the extent of the dengue prediction approach carried out by previous researchers, and a research gap will be obtained. The algorithm used to cluster articles is a modularity algorithm, using several open-source tools to process data. The online databases used are Google Scholar and Crossref by using keywords: journal, algorithm, prediction, and Dengue. The data are taken from the expansion of 1928-2020. This study’s results are 200 articles that are suitable and divided into four clusters of important articles. Also, several important parameters were obtained in the prediction study of dengue fever, namely humidity, temperature, rainfall, and population density.
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Machine learning (ML) methods can be leveraged to prevent the spread of deadly infectious disease outbreak (e.g., COVID-19). This can be done by applying machine learning methods in predicting and detecting the deadly infectious disease. Most reviews did not discuss about the machine learning algorithms, datasets and performance measurements used for various applications in predicting and detecting the deadly infectious disease. In contrast, this paper outlines the literature review based on two major ways (e.g., prediction, detection) to limit the spread of deadly disease outbreaks. Hence, this study aims to investigate the state of the art, challenges and future works of leveraging ML methods to detect and predict deadly disease outbreaks according to two categories mentioned earlier. Specifically, this study provides a review on various approaches (e.g., individual and ensemble models), types of datasets, parameters or variables and performance measures used in the previous works. The literature review included all articles from journals and conference proceedings published from 2010 through 2020 in Scopus indexed databases using the search terms Predicting Disease Outbreaks and/or Detecting Disease using Machine Learning. The findings from this review focus on commonly used machine learning approaches, challenges and future works to limit the spread of deadly disease outbreaks through preventions and detections.
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Changes in climate factors such as temperature, rainfall, humidity, and wind speed are natural processes that could significantly impact the incidence of infectious diseases. Dengue is a widespread disease that has often been documented when it comes to the impact of climate change. It has become a significant concern, especially for the Malaysian health authorities, due to its rapid spread and serious effects, leading to loss of life. Several statistical models were performed to identify climatic factors associated with infectious diseases. However, because of the complex and nonlinear interactions between climate variables and disease components, modelling their relationships have become the main challenge in climate-health studies. Hence, this study proposed a Generalized Linear Model (GLM) via Poisson and Negative Binomial to examine the effects of the climate factors on dengue incidence by considering the collinearity between variables. This study focuses on the dengue hot spots in Malaysia for the year 2014. Since there exists collinearity between climate factors, the analysis was done separately using three different models. The study revealed that rainfall, temperature, humidity, and wind speed were statistically significant with dengue incidence, and most of them shown a negative effect. Of all variables, wind speed has the most significant impact on den-gue incidence. Having this kind of relationships, policymakers should formulate better plans such that precautionary steps can be taken to reduce the spread of dengue diseases.
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Background A proactive approach to preventing and responding to emerging infectious diseases is critical to global health security. We present a three-stage approach to modeling the spatial distribution of outbreak vulnerability to Aedes aegypti -vectored diseases in Perú. Methods Extending a framework developed for modeling hemorrhagic fever vulnerability in Africa, we modeled outbreak vulnerability in three stages: index case potential (stage 1), outbreak receptivity (stage 2), and epidemic potential (stage 3), stratifying scores on season and El Niño events. Subsequently, we evaluated the validity of these scores using dengue surveillance data and spatial models. Results We found high validity for stage 1 and 2 scores, but not stage 3 scores. Vulnerability was highest in Selva Baja and Costa, and in summer and during El Niño events, with index case potential (stage 1) being high in both regions but outbreak receptivity (stage 2) being generally high in Selva Baja only. Conclusions Stage 1 and 2 scores are well-suited to predicting outbreaks of Ae. aegypti -vectored diseases in this setting, however stage 3 scores appear better suited to diseases with direct human-to-human transmission. To prevent outbreaks, measures to detect index cases should be targeted to both Selva Baja and Costa, while Selva Baja should be prioritized for healthcare system strengthening. Successful extension of this framework from hemorrhagic fevers in Africa to an arbovirus in Latin America indicates its broad utility for outbreak and pandemic preparedness and response activities.
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Background: This research addresses two questions: (1) how El Niño Southern Oscillation (ENSO) affects climate variability and how it influences dengue transmission in the Metropolitan Region of Recife (MRR), and (2) whether the epidemic in MRR municipalities has any connection and synchronicity. Methods: Wavelet analysis and cross-correlation were applied to characterize seasonality, multiyear cycles, and relative delays between the series. This study was developed into two distinct periods. Initially, we performed periodic dengue incidence and intercity epidemic synchronism analyses from 2001 to 2017. We then defined the period from 2001 to 2016 to analyze the periodicity of climatic variables and their coherence with dengue incidence. Results: Our results showed systematic cycles of 3-4 years with a recent shortening trend of 2-3 years. Climatic variability, such as positive anomalous temperatures and reduced rainfall due to changes in sea surface temperature (SST), is partially linked to the changing epidemiology of the disease, as this condition provides suitable environments for the Aedes aegypti lifecycle. Conclusion: ENSO may have influenced the dengue temporal patterns in the MRR, transiently reducing its main way of multiyear variability (3-4 years) to 2-3 years. Furthermore, when the epidemic coincided with El Niño years, it spread regionally and was highly synchronized.
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Dengue has long been a public health problem in tropical and subtropical countries. In 2015, a dengue outbreak occurred in Taiwan, where 43,784 cases were reported. This study aims to assess the impact of dengue on Southern Taiwan’s economic growth according to the economic growth model-based regression approach recommended by the World Health Organization (WHO). Herein, annual data from Southern Taiwan on the number of dengue cases, income growth, and demographics from 2010–2015 were analyzed. The percentage of reduction of the average income per capita in 2015 due to the dengue outbreak was estimated. Dengue was determined to have a negative linear economic impact on Southern Taiwan’s economic growth. In particular, a reduction of 0.26% in the average income per capita was estimated in Southern Taiwan due to the 2015 outbreak. If the model is applied alongside other dengue outbreak forecast models, then the forecast for economic reduction due to a future dengue outbreak may also be estimated. Prevention and recovery policies may subsequently be decided upon based on not only the number of dengue cases but also the degree of economic burden resulting from an outbreak.
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Background & Objectives Dengue is a climate-sensitive infectious disease. Climate-based dengue early warning may be a simple, low-cost, and effective tool for enhancing surveillance and control. Scientific studies on climate and dengue in local context form the basis for advancing the development of a climate-based early warning system. This study aims to review the current status of scientific studies in climate and dengue and the prospect or challenges of such research on a climate-based dengue early warning system in a dengue-endemic country, taking Malaysia as a case study. Method We reviewed the relationship between climate and dengue derived from statistical modeling, laboratory tests, and field studies. We searched electronic databases including PubMed, Scopus, EBSCO (MEDLINE), Web of Science, and the World Health Organization publications, and assessed climate factors and their influence on dengue cases, mosquitoes, and virus and recent development in the field of climate and dengue. Results & Discussion Few studies in Malaysia have emphasized the relationship between climate and dengue. Climatic factors such as temperature, rainfall, and humidity are associated with dengue; however, these relationships were not consistent. Climate change projections for Malaysia show a mounting risk for dengue in the future. Scientific studies on climate and dengue enhance dengue surveillance in the long run. Conclusion It is essential for institutions in Malaysia to promote research on climate and vector-borne diseases to advance the development of climate-based early warning systems. Together, effective strategies that improve existing research capacity, maximize the use of limited resources, and promote local-international partnership are crucial for sustaining research on climate and health.
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Dengue fever, an arbovirus disease transmitted by Aedes mosquitoes, has recently spread rapidly, especially in the tropical countries of the Americas and Asia-Pacific regions. It is endemic in Malaysia, with an annual average of 37,937 reported dengue cases from 2007 to 2012. This study measured the overall economic impact of dengue in Malaysia, and estimated the costs of dengue prevention. In 2010, Malaysia spent US$73.5 million or 0.03% of the country's GDP on its National Dengue Vector Control Program. This spending represented US$1,591 per reported dengue case and US$2.68 per capita population. Most (92.2%) of this spending occurred in districts, primarily for fogging. The inclusion of preventive activities increases the substantial estimated cost of dengue to US$176 million, or 72% above illness costs alone. If innovative technologies for dengue vector control prove efficacious, and a dengue vaccine was introduced, substantial existing spending could be rechanneled to fund them.
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Dengue is a mosquito-transmitted virus infection that causes epidemics of febrile illness and hemorrhagic fever across the tropics and subtropics worldwide. Annual epidemics are commonly observed, but there is substantial spatiotemporal heterogeneity in intensity. A better understanding of this heterogeneity in dengue transmission could lead to improved epidemic prediction and disease control. Time series decomposition methods enable the isolation and study of temporal epidemic dynamics with a specific periodicity (e.g., annual cycles related to climatic drivers and multiannual cycles caused by dynamics in population immunity). We collected and analyzed up to 18 y of monthly dengue surveillance reports on a total of 3.5 million reported dengue cases from 273 provinces in eight countries in Southeast Asia, covering ∼10(7) km(2). We detected strong patterns of synchronous dengue transmission across the entire region, most markedly during a period of high incidence in 1997-1998, which was followed by a period of extremely low incidence in 2001-2002. This synchrony in dengue incidence coincided with elevated temperatures throughout the region in 1997-1998 and the strongest El Niño episode of the century. Multiannual dengue cycles (2-5 y) were highly coherent with the Oceanic Niño Index, and synchrony of these cycles increased with temperature. We also detected localized traveling waves of multiannual dengue epidemic cycles in Thailand, Laos, and the Philippines that were dependent on temperature. This study reveals forcing mechanisms that drive synchronization of dengue epidemics on a continental scale across Southeast Asia.
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Background: Weather variables affect dengue transmission. This study aimed to identify a dengue weather correlation pattern in Kandy, Sri Lanka, compare the results with results of similar studies, and establish ways for better control and prevention of dengue. Method: We collected data on reported dengue cases in Kandy and mid-year population data from 2003 to 2012, and calculated weekly incidences. We obtained daily weather data from two weather stations and converted it into weekly data. We studied correlation patterns between dengue incidence and weather variables using the wavelet time series analysis, and then calculated cross-correlation coefficients to find magnitudes of correlations. Results: We found a positive correlation between dengue incidence and rainfall in millimeters, the number of rainy and wet days, the minimum temperature, and the night and daytime, as well as average, humidity, mostly with a five- to seven-week lag. Additionally, we found correlations between dengue incidence and maximum and average temperatures, hours of sunshine, and wind, with longer lag periods. Dengue incidences showed a negative correlation with wind run. Conclusion: Our results showed that rainfall, temperature, humidity, hours of sunshine, and wind are correlated with local dengue incidence. We have suggested ways to improve dengue management routines and to control it in these times of global warming. We also noticed that the results of dengue weather correlation studies can vary depending on the data analysis.
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Em curto espaço de tempo, um aumento na temperatura e precipitação pode afetar a população de vetores e conseqüentemente, as doenças por eles transmitidas. Nesse estudo, analisou-se o efeito de pequenas variações na temperatura e umidade, sobre fecundidade, fertilidade e sobrevivência de Aedes aegypti. Esses parâmetros foram investigados usando-se fêmeas individuais nas temperaturas: 23-27 ° C (média 25 ° C), 28-32 ° C (média 30 ° C) e 33-37 ° C (média 35 º C) associada à umidade relativa: 60 ± 8% e 80 ± 6%. As fêmeas responderam ao aumento da temperatura com redução na produção de ovos, tempo de oviposição e mudança nos padrões de postura. A 25 º C e 80%, fêmeas sobreviveram duas vezes mais e produziram 40% mais ovos, que aquelas mantidas a 35 º C e 80%. No entanto, nos grupos a 35 º C e 60% a postura foi inibida em 45% das fêmeas e apenas 15% puseram mais de 100 ovos, sugerindo que a intensidade do efeito da temperatura seja influenciado pela umidade. Reduções graduais na fertilidade a 60% de umidade relativa foram observadas com o aumento da temperatura, embora esse efeito não tenha sido registrado na umidade de 80%, nas temperaturas de 25 º C e 30 º C. Esses resultados sugerem que a redução na densidade populacional nas zonas tropicais durante estações, em que a temperatura se eleva acima de 35 º C pode ser fortemente influenciada pela interação temperatura e umidade, afetando negativamente diversos aspectos da biologia do mosquito.
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Dengue and malaria are vector-borne diseases and major public health problems worldwide. Changes in climatic factors influence incidences of these diseases. The objective of this study was to investigate the relationship between vector-borne disease incidences and meteorological data, and hence to predict disease risk in a global outreach tourist setting. The retrospective data of dengue and malaria incidences together with local meteorological factors (temperature, rainfall, humidity) registered from 2001 to 2011 on Koh Chang, Thailand were used in this study. Seasonal distribution of disease incidences and its correlation with local climatic factors were analyzed. Seasonal patterns in disease transmission differed between dengue and malaria. Monthly meteorological data and reported disease incidences showed good predictive ability of disease transmission patterns. These findings provide a rational basis for identifying the predictive ability of local meteorological factors on disease incidence that may be useful for the implementation of disease prevention and vector control programs on the tourism island, where climatic factors fluctuate.
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Background Dengue is a disease that has undergone significant expansion over the past hundred years. Understanding what factors limit the distribution of transmission can be used to predict current and future limits to further dengue expansion. While not the only factor, temperature plays an important role in defining these limits. Previous attempts to analyse the effect of temperature on the geographic distribution of dengue have not considered its dynamic intra-annual and diurnal change and its cumulative effects on mosquito and virus populations. Methods Here we expand an existing modelling framework with new temperature-based relationships to model an index proportional to the basic reproductive number of the dengue virus. This model framework is combined with high spatial and temporal resolution global temperature data to model the effects of temperature on Aedes aegypti and Ae. albopictus persistence and competence for dengue virus transmission. Results Our model predicted areas where temperature is not expected to permit transmission and/or Aedes persistence throughout the year. By reanalysing existing experimental data our analysis indicates that Ae. albopictus, often considered a minor vector of dengue, has comparable rates of virus dissemination to its primary vector, Ae. aegypti, and when the longer lifespan of Ae. albopictus is considered its competence for dengue virus transmission far exceeds that of Ae. aegypti. Conclusions These results can be used to analyse the effects of temperature and other contributing factors on the expansion of dengue or its Aedes vectors. Our finding that Ae. albopictus has a greater capacity for dengue transmission than Ae. aegypti is contrary to current explanations for the comparative rarity of dengue transmission in established Ae. albopictus populations. This suggests that the limited capacity of Ae. albopictus to transmit DENV is more dependent on its ecology than vector competence. The recommendations, which we explicitly outlined here, point to clear targets for entomological investigation.
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Dengue is a mosquito-borne viral disease that occurs mainly in the tropics and subtropics but has a high potential to spread to new areas. Dengue infections are climate sensitive, so it is important to better understand how changing climate factors affect the potential for geographic spread and future dengue epidemics. Vectorial capacity (VC) describes a vector's propensity to transmit dengue taking into account human, virus, and vector interactions. VC is highly temperature dependent, but most dengue models only take mean temperature values into account. Recent evidence shows that diurnal temperature range (DTR) plays an important role in influencing the behavior of the primary dengue vector Aedes aegypti. In this study, we used relative VC to estimate dengue epidemic potential (DEP) based on the temperature and DTR dependence of the parameters of A. aegypti. We found a strong temperature dependence of DEP; it peaked at a mean temperature of 29.3°C when DTR was 0°C and at 20°C when DTR was 20°C. Increasing average temperatures up to 29°C led to an increased DEP, but temperatures above 29°C reduced DEP. In tropical areas where the mean temperatures are close to 29°C, a small DTR increased DEP while a large DTR reduced it. In cold to temperate or extremely hot climates where the mean temperatures are far from 29°C, increasing DTR was associated with increasing DEP. Incorporating these findings using historical and predicted temperature and DTR over a two hundred year period (1901-2099), we found an increasing trend of global DEP in temperate regions. Small increases in DEP were observed over the last 100 years and large increases are expected by the end of this century in temperate Northern Hemisphere regions using climate change projections. These findings illustrate the importance of including DTR when mapping DEP based on VC.
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Many environmental factors, biotic and abiotic interact to influence organismal development. Given the importance of Aedes aegypti as a vector of human pathogens including dengue and yellow fever, understanding the impact of environmental factors such as temperature, resource availability, and intraspecific competition during development is critical for population control purposes. Despite known associations between developmental traits and factors of diet and density, temperature has been considered the primary driver of development rate and survival. To determine the relative importance of these critical factors, wide gradients of conditions must be considered. We hypothesize that 1) diet and density, as well as temperature influence the variation in development rate and survival, 2) that these factors interact, and this interaction is also necessary to understand variation in developmental traits. Temperature, diet, density, and their two-way interactions are significant factors in explaining development rate variation of the larval stages of Ae. aegypti mosquitoes. These factors as well as two and three-way interactions are significantly associated with the development rate from hatch to emergence. Temperature, but not diet or density, significantly impacted juvenile mortality. Development time was heteroskedastic with the highest variation occurring at the extremes of diet and density conditions. All three factors significantly impacted survival curves of experimental larvae that died during development. Complex interactions may contribute to variation in development rate. To better predict variation in development rate and survival in Ae. aegypti, factors of resource availability and intraspecific density must be considered in addition, but never to the exclusion of temperature.
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The survival of adult female Aedes mosquitoes is a critical component of their ability to transmit pathogens such as dengue viruses. One of the principal determinants of Aedes survival is temperature, which has been associated with seasonal changes in Aedes populations and limits their geographical distribution. The effects of temperature and other sources of mortality have been studied in the field, often via mark-release-recapture experiments, and under controlled conditions in the laboratory. Survival results differ and reconciling predictions between the two settings has been hindered by variable measurements from different experimental protocols, lack of precision in measuring survival of free-ranging mosquitoes, and uncertainty about the role of age-dependent mortality in the field. Here we apply generalised additive models to data from 351 published adult Ae. aegypti and Ae. albopictus survival experiments in the laboratory to create survival models for each species across their range of viable temperatures. These models are then adjusted to estimate survival at different temperatures in the field using data from 59 Ae. aegypti and Ae. albopictus field survivorship experiments. The uncertainty at each stage of the modelling process is propagated through to provide confidence intervals around our predictions. Our results indicate that adult Ae. albopictus has higher survival than Ae. aegypti in the laboratory and field, however, Ae. aegypti can tolerate a wider range of temperatures. A full breakdown of survival by age and temperature is given for both species. The differences between laboratory and field models also give insight into the relative contributions to mortality from temperature, other environmental factors, and senescence and over what ranges these factors can be important. Our results support the importance of producing site-specific mosquito survival estimates. By including fluctuating temperature regimes, our models provide insight into seasonal patterns of Ae. aegypti and Ae. albopictus population dynamics that may be relevant to seasonal changes in dengue virus transmission. Our models can be integrated with Aedes and dengue modelling efforts to guide and evaluate vector control, better map the distribution of disease and produce early warning systems for dengue epidemics.
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Objective To develop a forecasting model for the incidence of dengue cases in Subang Jaya using time series analysis. Methods The model was performed using the Autoregressive Integrated Moving Average (ARIMA) based on data collected from 2005 to 2010. The fitted model was then used to predict dengue incidence for the year 2010 by extrapolating dengue patterns using three different approaches (i.e. 52, 13 and 4 weeks ahead). Finally cross correlation between dengue incidence and climate variable was computed over a range of lags in order to identify significant variables to be included as external regressor. Results The result of this study revealed that the ARIMA (2,0,0) (0,0,1)52 model developed, closely described the trends of dengue incidence and confirmed the existence of dengue fever cases in Subang Jaya for the year 2005 to 2010. The prediction per period of 4 weeks ahead for ARIMA (2,0,0)(0,0,1)52 was found to be best fit and consistent with the observed dengue incidence based on the training data from 2005 to 2010 (Root Mean Square Error=0.61). The predictive power of ARIMA (2,0,0) (0,0,1)52 is enhanced by the inclusion of climate variables as external regressor to forecast the dengue cases for the year 2010. Conclusions The ARIMA model with weekly variation is a useful tool for disease control and prevention program as it is able to effectively predict the number of dengue cases in Malaysia.
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The number of dengue cases has been increasing on a global level in recent years, and particularly so in Malaysia, yet little is known about the effects of weather for identifying the short-term risk of dengue for the population. The aim of this paper is to estimate the weather effects on dengue disease accounting for non-linear temporal effects in Selangor, Kuala Lumpur and Putrajaya, Malaysia, from 2008 to 2010. We selected the weather parameters with a Poisson generalized additive model, and then assessed the effects of minimum temperature, bi-weekly accumulated rainfall and wind speed on dengue cases using a distributed non-linear lag model while adjusting for trend, day-of-week and week of the year. We found that the relative risk of dengue cases is positively associated with increased minimum temperature at a cumulative percentage change of 11.92% (95% CI: 4.41-32.19), from 25.4 °C to 26.5 °C, with the highest effect delayed by 51 days. Increasing bi-weekly accumulated rainfall had a positively strong effect on dengue cases at a cumulative percentage change of 21.45% (95% CI: 8.96, 51.37), from 215 mm to 302 mm, with the highest effect delayed by 26-28 days. The wind speed is negatively associated with dengue cases. The estimated lagged effects can be adapted in the dengue early warning system to assist in vector control and prevention plan.
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There is much uncertainty about the future impact of climate change on vector-borne diseases. Such uncertainty reflects the difficulties in modelling the complex interactions between disease, climatic and socioeconomic determinants. We used a comprehensive panel dataset from Mexico covering 23 years of province-specific dengue reports across nine climatic regions to estimate the impact of weather on dengue, accounting for the effects of non-climatic factors. Using a Generalized Additive Model, we estimated statistically significant effects of weather and access to piped water on dengue. The effects of weather were highly nonlinear. Minimum temperature (Tmin) had almost no effect on dengue incidence below 5°C, but Tmin values above 18°C showed a rapidly increasing effect. Maximum temperature above 20°C also showed an increasing effect on dengue incidence with a peak around 32°C, after which the effect declined. There is also an increasing effect of precipitation as it rose to about 550 mm, beyond which such effect declines. Rising access to piped water was related to increasing dengue incidence. We used our model estimations to project the potential impact of climate change on dengue incidence under three emission scenarios by 2030, 2050, and 2080. An increase of up to 40% in dengue incidence by 2080 was estimated under climate change while holding the other driving factors constant. Our results indicate that weather significantly influences dengue incidence in Mexico and that such relationships are highly nonlinear. These findings highlight the importance of using flexible model specifications when analysing weather-health interactions. Climate change may contribute to an increase in dengue incidence. Rising access to piped water may aggravate dengue incidence if it leads to increased domestic water storage. Climate change may therefore influence the success or failure of future efforts against dengue.
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Dengue is currently regarded globally as the most important mosquito-borne viral disease. A history of symptoms compatible with dengue can be traced back to the Chin Dynasty of 265-420 AD. The virus and its vectors have now become widely distributed throughout tropical and subtropical regions of the world, particularly over the last half-century. Significant geographic expansion has been coupled with rapid increases in incident cases, epidemics, and hyperendemicity, leading to the more severe forms of dengue. Transmission of dengue is now present in every World Health Organization (WHO) region of the world and more than 125 countries are known to be dengue endemic. The true impact of dengue globally is difficult to ascertain due to factors such as inadequate disease surveillance, misdiagnosis, and low levels of reporting. Currently available data likely grossly underestimates the social, economic, and disease burden. Estimates of the global incidence of dengue infections per year have ranged between 50 million and 200 million; however, recent estimates using cartographic approaches suggest this number is closer to almost 400 million. The expansion of dengue is expected to increase due to factors such as the modern dynamics of climate change, globalization, travel, trade, socioeconomics, settlement and also viral evolution. No vaccine or specific antiviral therapy currently exists to address the growing threat of dengue. Prompt case detection and appropriate clinical management can reduce the mortality from severe dengue. Effective vector control is the mainstay of dengue prevention and control. Surveillance and improved reporting of dengue cases is also essential to gauge the true global situation as indicated in the objectives of the WHO Global Strategy for Dengue Prevention and Control, 2012-2020. More accurate data will inform the prioritization of research, health policy, and financial resources toward reducing this poorly controlled disease. The objective of this paper is to review historical and current epidemiology of dengue worldwide and, additionally, reflect on some potential reasons for expansion of dengue into the future.
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The World Health Organization (WHO) reported that the 1997/98 El Nino might have been the cause of the dengue fever epidemics in many tropical countries. Because of the interaction between the atmosphere and the ocean, the warm El Nino and the cold La Nina phases of the El Nino Southern Oscillation (ENSO) engender significant temperature and precipitation anomalies around the world. This paper presents the results of a correlation analysis of past ENSO events with dengue epidemics across the Indonesian archipelago and northern South America. Our analysis shows that there is a statistically significant correlation at the 95% confidence level between El Nino and dengue epidemics in French Guiana and Indonesia and at the 90% confidence level in Colombia and Surinam. These regions experience statistically significant warmer temperatures and less rainfall during El Nino years. Public health officials could therefore strongly benefit from El Nino forecasts, and they should emphasise control activities such as insecticide sprayings and media campaigns concerning the potential breeding sites of dengue mosquitoes during these years.
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Background Environmental factors such as temperature can alter mosquito vector competence for arboviruses. Results from recent studies indicate that daily fluctuations around an intermediate mean temperature (26°C) reduce vector competence of Aedes aeygpti for dengue viruses (DENV). Theoretical predictions suggest that the mean temperature in combination with the magnitude of the diurnal temperature range (DTR) mediate the direction of these effects. Methodology/Principal Findings We tested the effect of temperature fluctuations on Ae. aegypti vector competence for DENV serotype-1 at high and low mean temperatures, and confirmed this theoretical prediction. A small DTR had no effect on vector competence around a high (30°C) mean, but a large DTR at low temperature (20°C) increased the proportion of infected mosquitoes with a disseminated infection by 60% at 21 and 28 days post-exposure compared to a constant 20°C. This effect resulted from a marked shortening of DENV extrinsic incubation period (EIP) in its mosquito vector; i.e., a decrease from 29.6 to 18.9 days under the fluctuating vs. constant temperature treatment. Conclusions Our results indicate that Ae. aegypti exposed to large fluctuations at low temperatures have a significantly shorter virus EIP than under constant temperature conditions at the same mean, leading to a considerably greater potential for DENV transmission. These results emphasize the value of accounting for daily temperature variation in an effort to more accurately understand and predict the risk of mosquito-borne pathogen transmission, provide a mechanism for sustained DENV transmission in endemic areas during cooler times of the year, and indicate that DENV transmission could be more efficient in temperate regions than previously anticipated.
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