Spatiotemporal Trends and Distributions of Malaria Incidence in the Northwest Ethiopia

Understanding and extracting noticeable patterns of malaria surveillance data at the district level are crucial for malaria prevention, control, and elimination progress. This study aimed to analyze spatiotemporal trends and nonparametric dynamics of malaria incidences in northwest Ethiopia, considering spatial and temporal correlations. The data were analyzed using count regression spatiotemporal models under the Bayesian setups, and parameters were estimated using integrated nested Laplace approximations (INLA). The region had a declining linear trend, and the average annual malaria incidence rate was 24.8 per 1,000 persons between 2012 and 2020. The malaria incidence rate was decreased by 0.984 (95% CI: 0.983, 0.986) per unit increase in months between July 2012 and June 2020. Districts found in the western and northwestern parts of the region had a steeper trend, while districts in the eastern and southern parts had a less steep trend than the average trend of the region. Compared to the regional level trend, the decreasing rate of malaria incidence trends was lower in most town administrations. The nonparametric dynamics showed that the monthly malaria incidence had a sinusoidal wave shape that varied throughout study periods. Malaria incidence had a decreasing linear trend changed across districts of the study region, and the steepness of trends of districts might not depend on incidences. Thus, an intervention and controlling mechanism that considers malaria incidences and district-specific differential trends would be indispensable to mitigate malaria transmission in the region.

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

Background

Malaria 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.

Methods

This 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.

Results

Two 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.

Conclusion

Results 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 material

The online version of this article (doi:10.1186/s12936-016-1602-1) contains supplementary material, which is available to authorized users.

Status of insecticide resistance in high-risk malaria provinces in Afghanistan

BACKGROUND

Insecticide resistance seriously threatens the efficacy of vector control interventions in malaria endemic countries. In Afghanistan, the status of insecticide resistance is largely unknown while distribution of long-lasting insecticidal nets has intensified in recent years. The main objective of this study was thus to measure the level of resistance to four classes of insecticides in provinces with medium to high risk of malaria transmission.

METHODS

Adult female mosquitoes were reared from larvae successively collected in the provinces of Nangarhar, Kunar, Badakhshan, Ghazni and Laghman from August to October 2014. WHO insecticide susceptibility tests were performed with DDT (4 %), malathion (5 %), bendiocarb (0.1 %), permethrin (0.75 %) and deltamethrin (0.05 %). In addition, the presence of kdr mutations was investigated in deltamethrin resistant and susceptible Anopheles stephensi mosquitoes collected in the eastern provinces of Nangarhar and Kunar.

RESULTS

Analyses of mortality rates revealed emerging resistance against all four classes of insecticides in the provinces located east and south of the Hindu Kush mountain range. Resistance is observed in both An. stephensi and Anopheles culicifacies, the two dominant malaria vectors in these provinces. Anopheles superpictus in the northern province of Badakhshan shows a different pattern of susceptibility with suspected resistance observed only for deltamethrin and bendiocarb. Genotype analysis of knock down resistance (kdr) mutations at the voltage-gated channel gene from An. stephensi mosquitoes shows the presence of the known resistant alleles L1014S and L1014F. However, a significant fraction of deltamethrin-resistant mosquitoes were homozygous for the 1014L wild type allele indicating that other mechanisms must be considered to account for the observed pyrethroid resistance.

CONCLUSIONS

This study confirms the importance of monitoring insecticide resistance for the development of an integrated vector management in Afghanistan. The validation of the kdr genotyping PCR assay applied to An. stephensi collected in Afghanistan paves the way for further studies into the mechanisms of insecticide resistance of malaria vectors in this region.

Detection of infectious disease outbreaks in twenty-two fragile states, 2000-2010: a systematic review

Fragile states are home to a sixth of the world's population, and their populations are particularly vulnerable to infectious disease outbreaks. Timely surveillance and control are essential to minimise the impact of these outbreaks, but little evidence is published about the effectiveness of existing surveillance systems. We did a systematic review of the circumstances (mode) of detection of outbreaks occurring in 22 fragile states in the decade 2000-2010 (i.e. all states consistently meeting fragility criteria during the timeframe of the review), as well as time lags from onset to detection of these outbreaks, and from detection to further events in their timeline. The aim of this review was to enhance the evidence base for implementing infectious disease surveillance in these complex, resource-constrained settings, and to assess the relative importance of different routes whereby outbreak detection occurs.We identified 61 reports concerning 38 outbreaks. Twenty of these were detected by existing surveillance systems, but 10 detections occurred following formal notifications by participating health facilities rather than data analysis. A further 15 outbreaks were detected by informal notifications, including rumours.There were long delays from onset to detection (median 29 days) and from detection to further events (investigation, confirmation, declaration, control). Existing surveillance systems yielded the shortest detection delays when linked to reduced barriers to health care and frequent analysis and reporting of incidence data.Epidemic surveillance and control appear to be insufficiently timely in fragile states, and need to be strengthened. Greater reliance on formal and informal notifications is warranted. Outbreak reports should be more standardised and enable monitoring of surveillance systems' effectiveness.

Human malaria in the highlands of Yemen