Scholarlink

<p>Dengue is an arboviral disease that clinically ranges from mild febrile illness to life-threatening hemorrhagic fever and shock syndrome. In Bangladesh, dengue incidence rose sharply over the past decade, culminating in the country’s largest outbreak in 2023. There are 321,179 confirmed cases and 1,705 deaths (case fatality rate: 0.53%) and the outbreak has not ended. Effective disease surveillance is essential for early outbreak detection, monitoring, and response. As the dengue burden continues to grow, the capacity of Bangladesh’s surveillance systems is increasingly constrained by challenges related to coordination, coverage, data integration, and insufficient funding. This narrative review synthesizes evidence from 20 peer-reviewed studies published between 2018 and 2024, using thematic analysis and an adapted PRISMA approach to examine existing dengue surveillance systems and identify opportunities to strengthen the capacity in Bangladesh. Two core surveillance modalities were identified, passive surveillance and active surveillance, supplemented by two predictive analytics approaches: forecasting and predictive modeling. Across these modalities, two primary surveillance approaches emerged: hospitalization and patient-record reporting, and community-level monitoring. Early warning systems were also identified within the forecasting and predictive modeling literature. Findings reveal substantial variability in data quality, timeliness, and interoperability across surveillance modalities and analytics tools. Passive and hospital-based reporting offer nationwide coverage but are limited by underreporting and delays, while active surveillance provides earlier signals yet lacks systematic integration with national platforms. Forecasting and predictive models show promise for anticipatory outbreak detection but require more robust validation and standardized data inputs. Overall, this review underscores the need for a more cohesive and integrated dengue surveillance architecture in Bangladesh. By strengthening data sharing, standardizing reporting practices, and adopting digital surveillance tools, public health systems could significantly enhance outbreak detection, preparedness, and response. Such an integrated system may also serve as a model for other dengue-endemic countries.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Evaluating surveillance systems to understand dengue burden in Bangladesh: a narrative review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月20日

查看全文
  • Tasnim Salam,
  • Mst. Noorjahan Begum,
  • Mustafizur Rahman

摘要

Dengue is an arboviral disease that clinically ranges from mild febrile illness to life-threatening hemorrhagic fever and shock syndrome. In Bangladesh, dengue incidence rose sharply over the past decade, culminating in the country’s largest outbreak in 2023. There are 321,179 confirmed cases and 1,705 deaths (case fatality rate: 0.53%) and the outbreak has not ended. Effective disease surveillance is essential for early outbreak detection, monitoring, and response. As the dengue burden continues to grow, the capacity of Bangladesh’s surveillance systems is increasingly constrained by challenges related to coordination, coverage, data integration, and insufficient funding. This narrative review synthesizes evidence from 20 peer-reviewed studies published between 2018 and 2024, using thematic analysis and an adapted PRISMA approach to examine existing dengue surveillance systems and identify opportunities to strengthen the capacity in Bangladesh. Two core surveillance modalities were identified, passive surveillance and active surveillance, supplemented by two predictive analytics approaches: forecasting and predictive modeling. Across these modalities, two primary surveillance approaches emerged: hospitalization and patient-record reporting, and community-level monitoring. Early warning systems were also identified within the forecasting and predictive modeling literature. Findings reveal substantial variability in data quality, timeliness, and interoperability across surveillance modalities and analytics tools. Passive and hospital-based reporting offer nationwide coverage but are limited by underreporting and delays, while active surveillance provides earlier signals yet lacks systematic integration with national platforms. Forecasting and predictive models show promise for anticipatory outbreak detection but require more robust validation and standardized data inputs. Overall, this review underscores the need for a more cohesive and integrated dengue surveillance architecture in Bangladesh. By strengthening data sharing, standardizing reporting practices, and adopting digital surveillance tools, public health systems could significantly enhance outbreak detection, preparedness, and response. Such an integrated system may also serve as a model for other dengue-endemic countries.