An innovative forecasting and dashboard system for Malaysian dengue trends

Jamil, Jastini Mohd; Shaharanee, Izwan Nizal Mohd

doi:10.1063/1.4960910

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…Four main public health objectives could be identified to tackle these challenges. (A) Threatening situations shall be controlled, for example, through surveillance or risk prevention ( 77 ) ( n = 40). (B) Information management has to be improved ( n = 26) by for instance enhancing knowledge ( 75 ) or addressing target groups ( 78 ).…”

Section: Results: Answering the Research Questionsmentioning

confidence: 99%

“…In this context, the cross-verification ( 68 ) and early warning ( 34 ) of outbreak or other adverse events as well as the possibility to trace back and rapidly detect respective crises situations ( 74 ), were repeatedly underlined as objectives of data visualization as well as aggregation via dashboards. However, the discussed dashboards are not just limited to the immediate surveillance of crises events, but also aim at the prediction of outbreaks and other adverse events, as was noted for the dashboard, focused on in Jamil et al ( 77 ). More so, dashboards aim to present relevant information and thus reduce time spent searching for information ( 44 ).…”

Section: Results: Answering the Research Questionsmentioning

confidence: 99%

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Digital dashboards visualizing public health data: a systematic review

Schulze

Brand

Geppert

et al. 2023

Front. Public Health

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IntroductionPublic health is not only threatened by diseases, pandemics, or epidemics. It is also challenged by deficits in the communication of health information. The current COVID-19 pandemic demonstrates that impressively. One way to deliver scientific data such as epidemiological findings and forecasts on disease spread are dashboards. Considering the current relevance of dashboards for public risk and crisis communication, this systematic review examines the state of research on dashboards in the context of public health risks and diseases.MethodNine electronic databases where searched for peer-reviewed journal articles and conference proceedings. Included articles (n = 65) were screened and assessed by three independent reviewers. Through a methodological informed differentiation between descriptive studies and user studies, the review also assessed the quality of included user studies (n = 18) by use of the Mixed Methods Appraisal Tool (MMAT).Results65 articles were assessed in regards to the public health issues addressed by the respective dashboards, as well as the data sources, functions and information visualizations employed by the different dashboards. Furthermore, the literature review sheds light on public health challenges and objectives and analyzes the extent to which user needs play a role in the development and evaluation of a dashboard. Overall, the literature review shows that studies that do not only describe the construction of a specific dashboard, but also evaluate its content in terms of different risk communication models or constructs (e.g., risk perception or health literacy) are comparatively rare. Furthermore, while some of the studies evaluate usability and corresponding metrics from the perspective of potential users, many of the studies are limited to a purely functionalistic evaluation of the dashboard by the respective development teams.ConclusionThe results suggest that applied research on public health intervention tools like dashboards would gain in complexity through a theory-based integration of user-specific risk information needs.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=200178, identifier: CRD42020200178.

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Section: Results: Answering the Research Questionsmentioning

confidence: 99%

Section: Results: Answering the Research Questionsmentioning

confidence: 99%

Digital dashboards visualizing public health data: a systematic review

Schulze

Brand

Geppert

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Knowing the dangerous effect of dengue fever, thus one of the solutions is to implement an innovative forecasting and dashboard system of dengue spread in Malaysia, with emphasize on an early prediction of dengue outbreak. The dashboard will deliver the message to health policy makers such as The Ministry of Health Malaysia (MOH), practitioners, and researchers of the importance to integrate their collaboration in exploring the potential strategies in order to reduce the future burden of the increase in dengue transmission cases in Malaysia [8]. Another study from D. Mould (2015) about Dashboard systems: Pharmacokinetic/pharmacodynamic mediated dose optimization for monoclonal antibodies.…”

Section: Related Workmentioning

confidence: 99%

Dashboard Model Design for Tuna Fish Processing (PT. XYZ)

Putra¹,

Labasariyani²

2023

Global J. Eng. Technol. Adv.

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The dashboard systems provide visualization model help the management to make decision which shows the data summary in each processing stage and comparison of total result of main product and side product in specific periods of time at fish processors. This research proposes dashboard model to designed in fish processors which huge of complex processing data available and have difficulties to analyze and require some times to process data analytical to display the production result in each processing stage. We design analytical dashboard to show the quantity fish processed and seafood product summary analysis in each processing stage i.e. total weight of receiving, cutting, retouching, packing and shipment. The dashboard system helps the management to make quick decision based on analytical information related to how many fish processed, product result per each processing stage, and also help to compare yield in cutting and retouching process based on type of raw materials (dirty loin and clean loin). This dashboard also displayed details total production and total side product (by-product) i.e. black meat, belly, red meat, and residue. The scope of this research is to display summary analytical data from the periods September until November of 2022. Based on the calculation, the yield of cutting process during the period September to November was 63.7%, while the yield for retouching process was 57.65%. In this research it explained that by-product percentage for dirty loin cutting shows a higher percentage compared to clean loin because the dirty loin has more parts to remove.

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An innovative forecasting and dashboard system for Malaysian dengue trends

Cited by 2 publications

References 4 publications

Digital dashboards visualizing public health data: a systematic review

Digital dashboards visualizing public health data: a systematic review

Dashboard Model Design for Tuna Fish Processing (PT. XYZ)

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