| 41. |
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| 42. |
- Quam, Mikkel B., et al.
(författare)
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Dissecting Japan's Dengue Outbreak in 2014
- 2016
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Ingår i: American Journal of Tropical Medicine and Hygiene. - : American Society of Tropical Medicine and Hygiene. - 0002-9637 .- 1476-1645. ; 94:2, s. 409-412
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Tidskriftsartikel (refereegranskat)abstract
- Despite Japan's temperate climate, a dengue outbreak occurred in Tokyo for the first time in over 70 years in 2014. We dissected this dengue outbreak based on phylogenetic analysis, travel interconnectivity, and environmental drivers for dengue epidemics. Comparing the available dengue virus 1 (DENV1) E gene sequence from this outbreak with 3,282 unique DENV1 sequences in National Center for Biotechnology Information suggested that the DENV might have been imported from China, Indonesia, Singapore, or Vietnam. With travelers arriving into Japan, Guangzhou (China) may have been the source of DENV introduction, given that Guangzhou also reported a large-scale dengue outbreak in 2014. Coinciding with the 2014 outbreak, Tokyo's climate conditions permitted the amplification of Aedes vectors and the annual peak of vectorial capacity. Given suitable vectors and climate conditions in addition to increasing interconnectivity with endemic areas of Asia, Tokyo's 2014 outbreak did not come as a surprise and may foretell more to come.
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| 43. |
- Rahman, Md. Siddikur, et al.
(författare)
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Ecological, social and other environmental determinants of dengue vector abundance in urban and rural areas of Northeastern Thailand
- 2021
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Ingår i: International Journal of Environmental Research and Public Health. - : MDPI. - 1661-7827 .- 1660-4601. ; 18:11
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Tidskriftsartikel (refereegranskat)abstract
- Aedes aegypti is the main vector of dengue globally. The variables that influence the abundance of dengue vectors are numerous and complex. This has generated a need to focus on areas at risk of disease transmission, the spatial-temporal distribution of vectors, and the factors that modulate vector abundance. To help guide and improve vector-control efforts, this study identified the ecological, social, and other environmental risk factors that affect the abundance of adult female and immature Ae. aegypti in households in urban and rural areas of northeastern Thailand. A one-year entomological study was conducted in four villages of northeastern Thailand between January and December, 2019. Socio-demographic; self-reported prior dengue infections; housing conditions; durable asset ownership; water management; characteristics of water containers; knowledge, atti-tudes, and practices (KAP) regarding climate change and dengue; and climate data were collected. Household crowding index (HCI), premise condition index (PCI), socio-economic status (SES), and entomological indices (HI, CI, BI, and PI) were calculated. Negative binomial generalized linear models (GLMs) were fitted to identify the risk factors associated with the abundance of adult females and immature Ae. aegypti. Urban sites had higher entomological indices and numbers of adult Ae. aegypti mosquitoes than rural sites. Overall, participants’ KAP about climate change and dengue were low in both settings. The fitted GLM showed that a higher abundance of adult female Ae. aegypti was significantly (p < 0.05) associated with many factors, such as a low education level of household respondents, crowded households, poor premise conditions, surrounding house den-sity, bathrooms located indoors, unscreened windows, high numbers of wet containers, a lack of adult control, prior dengue infections, poor climate change adaptation, dengue, and vector-related practices. Many of the above were also significantly associated with a high abundance of immature mosquito stages. The GLM model also showed that maximum and mean temperature with four-and one-to-two weeks of lag were significant predictors (p < 0.05) of the abundance of adult and immature mosquitoes, respectively, in northeastern Thailand. The low KAP regarding climate change and dengue highlights the engagement needs for vector-borne disease prevention in this region. The identified risk factors are important for the critical first step toward developing routine Aedes surveillance and reliable early warning systems for effective dengue and other mosquito-borne disease prevention and control strategies at the household and community levels in this region and similar settings elsewhere.
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| 44. |
- Rahman, M.S., et al.
(författare)
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Mapping the spatial distribution of the dengue vector Aedes aegypti and predicting its abundance in northeastern Thailand using machine-learning approach
- 2021
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Ingår i: One Health. - : Elsevier. - 2352-7714. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Background: Mapping the spatial distribution of the dengue vector Aedes (Ae.) aegypti and accurately predicting its abundance are crucial for designing effective vector control strategies and early warning tools for dengue epidemic prevention. Socio-ecological and landscape factors influence Ae. aegypti abundance. Therefore, we aimed to map the spatial distribution of female adult Ae. aegypti and predict its abundance in northeastern Thailand based on socioeconomic, climate change, and dengue knowledge, attitude and practices (KAP) and/or landscape factors using machine learning (ML)-based system.Method: A total of 1066 females adult Ae. aegypti were collected from four villages in northeastern Thailand during January–December 2019. Information on household socioeconomics, KAP regarding climate change and dengue, and satellite-based landscape data were also acquired. Geographic information systems (GIS) were used to map the household-based spatial distribution of female adult Ae. aegypti abundance (high/low). Five popular supervised learning models, logistic regression (LR), support vector machine (SVM), k-nearest neighbor (kNN), artificial neural network (ANN), and random forest (RF), were used to predict females adult Ae. aegypti abundance (high/low). The predictive accuracy of each modeling technique was calculated and evaluated. Important variables for predicting female adult Ae. aegypti abundance were also identified using the best-fitted model.Results: Urban areas had higher abundance of female adult Ae. aegypti compared to rural areas. Overall, study respondents in both urban and rural areas had inadequate KAP regarding climate change and dengue. The average landscape factors per household in urban areas were rice crop (47.4%), natural tree cover (17.8%), built-up area (13.2%), permanent wetlands (21.2%), and rubber plantation (0%), and the corresponding figures for rural areas were 12.1, 2.0, 38.7, 40.1 and 0.1% respectively. Among all assessed models, RF showed the best prediction performance (socioeconomics: area under curve, AUC = 0.93, classification accuracy, CA = 0.86, F1 score = 0.85; KAP: AUC = 0.95, CA = 0.92, F1 = 0.90; landscape: AUC = 0.96, CA = 0.89, F1 = 0.87) for female adult Ae. aegypti abundance. The combined influences of all factors further improved the predictive accuracy in RF model (socioeconomics + KAP + landscape: AUC = 0.99, CA = 0.96 and F1 = 0.95). Dengue prevention practices were shown to be the most important predictor in the RF model for female adult Ae. aegypti abundance in northeastern Thailand.Conclusion: The RF model is more suitable for the prediction of Ae. aegypti abundance in northeastern Thailand. Our study exemplifies that the application of GIS and machine learning systems has significant potential for understanding the spatial distribution of dengue vectors and predicting its abundance. The study findings might help optimize vector control strategies, future mosquito suppression, prediction and control strategies of epidemic arboviral diseases (dengue, chikungunya, and Zika). Such strategies can be incorporated into One Health approaches applying transdisciplinary approaches considering human-vector and agro-environmental interrelationships.
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| 45. |
- Ramadona, Aditya Lia, et al.
(författare)
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A combination of incidence data and mobility proxies from social media predicts the intra-urban spread of dengue in Yogyakarta, Indonesia
- 2019
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Ingår i: PLoS Neglected Tropical Diseases. - : Public Library of Science (PLoS). - 1935-2727 .- 1935-2735. ; 13:4
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Tidskriftsartikel (refereegranskat)abstract
- Only a few studies have investigated the potential of using geotagged social media data for predicting the patterns of spatio-temporal spread of vector-borne diseases. We herein demonstrated the role of human mobility in the intra-urban spread of dengue by weighting local incidence data with geo-tagged Twitter data as a proxy for human mobility across 45 neighborhoods in Yogyakarta city, Indonesia. To estimate the dengue virus importation pressure in each study neighborhood monthly, we developed an algorithm to estimate a dynamic mobility-weighted incidence index (MI), which quantifies the level of exposure to virus importation in any given neighborhood. Using a Bayesian spatio-temporal regression model, we estimated the coefficients and predictiveness of the MI index for lags up to 6 months. Specifically, we used a Poisson regression model with an unstructured spatial covariance matrix. We compared the predictability of the MI index to that of the dengue incidence rate over the preceding months in the same neighborhood (autocorrelation) and that of the mobility information alone. We based our estimates on a volume of 1·302·405 geotagged tweets (from 118·114 unique users) and monthly dengue incidence data for the 45 study neighborhoods in Yogyakarta city over the period from August 2016 to June 2018. The MI index, as a standalone variable, had the highest explanatory power for predicting dengue transmission risk in the study neighborhoods, with the greatest predictive ability at a 3-months lead time. The MI index was a better predictor of the dengue risk in a neighborhood than the recent transmission patterns in the same neighborhood, or just the mobility patterns between neighborhoods. Our results suggest that human mobility is an important driver of the spread of dengue within cities when combined with information on local circulation of the dengue virus. The geotagged Twitter data can provide important information on human mobility patterns to improve our understanding of the direction and the risk of spread of diseases, such as dengue. The proposed MI index together with traditional data sources can provide useful information for the development of more accurate and efficient early warning and response systems.
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| 46. |
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| 47. |
- Ramadona, Aditya Lia, et al.
(författare)
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Predicting the dengue cluster outbreak dynamics in Yogyakarta, Indonesia : a modelling study
- 2023
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Ingår i: The Lancet Regional Health - Southeast Asia. - : Elsevier. - 2772-3682. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Background: Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia.Methods: We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model.Findings: When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale.Interpretation: The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue.
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| 48. |
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| 49. |
- Ramadona, Aditya Lia, et al.
(författare)
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Validating search protocols for mining of health and disease events on Twitter
- 2016
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Ingår i: International Conference on Public Health: Accelerating the achievment of sustainable development goals for the improvement and equitable distribution of population health. - 9786027148413 ; , s. 142-143
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In the year of 2016, there were more than 24 million Indonesian twitter users sharing news, events, as well as personal feelings and experiences on Twitter. This study seeks to validate a search protocol of health-related terms using real-time Twitter data which can later be used to understand if, and how, twitter can reveal information on the current health situation in Indonesia. In this validation study of mining protocols, we extracted geo-located conversations related to health and disease postings on Twitter using a set of pre-defined keywords, assessed the prevalence, frequency and timing of such content in these conversations, and validated how this search protocol was able to detect relevant disease tweets.Groups of words and phrases relevant to disease symptoms and health outcomes were used in a protocol developed in the Indonesian language in order to extract relevant content from geo-tagged Twitter feeds. A supervised learning algorithm using Classification and Regression Trees was used to validate search protocols of disease and health hits comparing to those identified by a team of human experts. The experts categorized tweets as positive or negative in respect to health events. The model fit was evaluated based on prediction performance.We observed 390 tweets from historical Twitter feeds and 1,145,649 tweets from Twitter stream feeds during the period July 26th to August 1st, 2016. Only twitter hits with health related keywords in the Indonesian language were obtained. The accuracy of predictions of mined hits versus expert validated hits using the CART algorithm showed good validity with AUC beyond 0.8.Our study shows that monitoring of public sentiment on Twitter, combined with contextual knowledge about the disease, can detect health and disease tweets and potentially be used as a valuable real-time proxy for health events over space and time.
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| 50. |
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