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Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

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Related Experiment Video

Updated: May 17, 2026

A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease
04:23

A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease

Published on: April 28, 2019

Optimal lead time for dengue forecast.

Yien Ling Hii1, Joacim Rocklöv, Stig Wall

  • 1Umeå Centre for Global Health Research, Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden. yienling.hii@epiph.umu.se

Plos Neglected Tropical Diseases
|October 31, 2012
PubMed
Summary
This summary is machine-generated.

A dengue early warning system can be optimized by issuing forecasts three months in advance. This lead time allows authorities sufficient time for vector control and mitigation strategies to prevent dengue outbreaks.

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Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1
06:18

Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1

Published on: March 13, 2018

Area of Science:

  • Epidemiology
  • Public Health
  • Environmental Science

Background:

  • Dengue outbreaks pose a significant public health risk, necessitating effective early warning systems.
  • Accurate dengue case prediction allows for timely interventions and preventive measures by local authorities.

Purpose of the Study:

  • To determine the optimal lead time for dengue case warnings in Singapore.
  • To align forecast timing with the duration required for effective outbreak mitigation.

Main Methods:

  • A Poisson regression model analyzed dengue case risks based on temperature and rainfall, incorporating lag times of 1-5 months.
  • Vector control durations from 2000-2010 informed the time needed to mitigate outbreaks.
  • The gap between forecast and control was assessed to identify optimal warning timing.

Main Results:

  • Increased weekly mean temperature and cumulative rainfall preceded dengue case increases by 4-20 weeks and 8-20 weeks, respectively.
  • Forecast windows ranged from 1-5 months based on weather data.
  • Mitigating dengue outbreaks required 1-3 months, with a median of 2 months.

Conclusions:

  • Issuing dengue forecasts three months ahead provides sufficient time for authorities to mitigate outbreaks.
  • Optimizing forecast timing enhances the functional value and cost-effectiveness of early warning systems.
  • Considering forecast-mitigation gaps is crucial for developing effective dengue forecasting models.