Evaluation of measles surveillance systems in Afghanistan-2010

Jawad, Mofleh; Jamil, Ansari

doi:10.5897/jphe2013.0643

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…In this unstable setting, not much is known about the intensity, magnitude, and temporal dependence of epidemic patterns over time. Only recently has a systemic surveillance system been put in place [13], but the scope is limited and mostly confined to accessible regions. Reporting is based on passive case finding from facilities by health professionals.…”

Section: Introductionmentioning

confidence: 99%

Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence

Anwar

Lewnard

Parikh

et al. 2016

Malar J

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BackgroundMalaria remains endemic in Afghanistan. National control and prevention strategies would be greatly enhanced through a better ability to forecast future trends in disease incidence. It is, therefore, of interest to develop a predictive tool for malaria patterns based on the current passive and affordable surveillance system in this resource-limited region.MethodsThis study employs data from Ministry of Public Health monthly reports from January 2005 to September 2015. Malaria incidence in Afghanistan was forecasted using autoregressive integrated moving average (ARIMA) models in order to build a predictive tool for malaria surveillance. Environmental and climate data were incorporated to assess whether they improve predictive power of models.ResultsTwo models were identified, each appropriate for different time horizons. For near-term forecasts, malaria incidence can be predicted based on the number of cases in the four previous months and 12 months prior (Model 1); for longer-term prediction, malaria incidence can be predicted using the rates 1 and 12 months prior (Model 2). Next, climate and environmental variables were incorporated to assess whether the predictive power of proposed models could be improved. Enhanced vegetation index was found to have increased the predictive accuracy of longer-term forecasts.ConclusionResults indicate ARIMA models can be applied to forecast malaria patterns in Afghanistan, complementing current surveillance systems. The models provide a means to better understand malaria dynamics in a resource-limited context with minimal data input, yielding forecasts that can be used for public health planning at the national level.Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-016-1602-1) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence

Anwar

Lewnard

Parikh

et al. 2016

Malar J

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Measles outbreak amplified in a pediatric ward: Lyantonde District, Uganda, August 2017

Biribawa¹,

Atuhairwe²,

Bulage³

et al. 2020

BMC Infect Dis

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Background: Measles is a highly infectious viral disease. In August 2017, Lyantonde District, Uganda reported a measles outbreak to Uganda Ministry of Health. We investigated the outbreak to assess the scope, factors facilitating transmission, and recommend control measures. Methods: We defined a probable case as sudden onset of fever and generalized rash in a resident of Lyantonde, Lwengo, or Rakai Districts from 1 June-30 September 2017, plus ≥1 of the following: coryza, conjunctivitis, or cough. A confirmed case was a probable case with serum positivity of measles-specific IgM. We conducted a neighborhood-and age-matched case-control study to identified exposure factors, and used conditional logistic regression to analyze the data. We estimated vaccine effectiveness and vaccination coverage. Results: We identified 81 cases (75 probable, 6 confirmed); 4 patients (4.9%) died. In the case-control study, 47% of case-patients and 2.3% of controls were hospitalized at Lyantonde Hospital pediatric department for non-measles conditions 7-21 days before case-patient's onset (OR adj = 34, 95%CI: 5.1-225). Estimated vaccine effectiveness was 95% (95%CI: 75-99%) and vaccination coverage was 76% (95%CI: 68-82%). During the outbreak, an "isolation" ward was established inside the general pediatric ward where there was mixing of both measles and non-measles patients. Conclusions: This outbreak was amplified by nosocomial transmission and facilitated by low vaccination coverage. We recommended moving the isolation ward outside of the building, supplemental vaccination, and vaccinating pediatric patients during measles outbreaks.

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