Alemayehu Midekisa

Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia

BackgroundMalaria is one of the leading public health problems in most of sub-Saharan Africa, particularly in Ethiopia. Almost all demographic groups are at risk of malaria because of seasonal and unstable transmission of the disease. Therefore, there is a need to develop malaria early-warning systems to enhance public health decision making for control and prevention of malaria epidemics. Data from orbiting earth-observing sensors can monitor environmental risk factors that trigger malaria epidemics. Remotely sensed environmental indicators were used to examine the influences of climatic and environmental variability on temporal patterns of malaria cases in the Amhara region of Ethiopia.MethodsIn this study seasonal autoregressive integrated moving average (SARIMA) models were used to quantify the relationship between malaria cases and remotely sensed environmental variables, including rainfall, land-surface temperature (LST), vegetation indices (NDVI and EVI), and actual evapotranspiration (ETa) with lags ranging from one to three months. Predictions from the best model with environmental variables were compared to the actual observations from the last 12 months of the time series.ResultsMalaria cases exhibited positive associations with LST at a lag of one month and positive associations with indicators of moisture (rainfall, EVI and ETa) at lags from one to three months. SARIMA models that included these environmental covariates had better fits and more accurate predictions, as evidenced by lower AIC and RMSE values, than models without environmental covariates.ConclusionsMalaria risk indicators such as satellite-based rainfall estimates, LST, EVI, and ETa exhibited significant lagged associations with malaria cases in the Amhara region and improved model fit and prediction accuracy. These variables can be monitored frequently and extensively across large geographic areas using data from earth-observing sensors to support public health decisions.

Seasonal associations of climatic drivers and malaria in the highlands of Ethiopia

BackgroundThe impacts of interannual climate fluctuations on vector-borne diseases, especially malaria, have received considerable attention in the scientific literature. These effects can be significant in semi-arid and high-elevation areas such as the highlands of East Africa because cooler temperature and seasonally dry conditions limit malaria transmission. Many previous studies have examined short-term lagged effects of climate on malaria (weeks to months), but fewer have explored the possibility of longer-term seasonal effects.MethodsThis study assessed the interannual variability of malaria occurrence from 2001 to 2009 in the Amhara region of Ethiopia. We tested for associations of climate variables summarized during the dry (January–April), early transition (May–June), and wet (July–September) seasons with malaria incidence in the early peak (May–July) and late peak (September–December) epidemic seasons using generalized linear models. Climate variables included land surface temperature (LST), rainfall, actual evapotranspiration (ET), and the enhanced vegetation index (EVI).ResultsWe found that both early and late peak malaria incidence had the strongest associations with meteorological conditions in the preceding dry and early transition seasons. Temperature had the strongest influence in the wetter western districts, whereas moisture variables had the strongest influence in the drier eastern districts. We also found a significant correlation between malaria incidence in the early and the subsquent late peak malaria seasons, and the addition of early peak malaria incidence as a predictor substantially improved models of late peak season malaria in both of the study sub-regions.ConclusionsThese findings suggest that climatic effects on malaria prior to the main rainy season can carry over through the rainy season and affect the probability of malaria epidemics during the late malaria peak. The results also emphasize the value of combining environmental monitoring with epidemiological surveillance to develop forecasts of malaria outbreaks, as well as the need for spatially stratified approaches that reflect the differential effects of climatic variations in the different sub-regions.

Spatial synchrony of malaria outbreaks in a highland region of Ethiopia

To understand the drivers and consequences of malaria in epidemic‐prone regions, it is important to know whether epidemics emerge independently in different areas as a consequence of local contingencies, or whether they are synchronised across larger regions as a result of climatic fluctuations and other broad‐scale drivers. To address this question, we collected historical malaria surveillance data for the Amhara region of Ethiopia and analysed them to assess the consistency of various indicators of malaria risk and determine the dominant spatial and temporal patterns of malaria within the region. We collected data from a total of 49 districts from 1999–2010. Data availability was better for more recent years and more data were available for clinically diagnosed outpatient malaria cases than confirmed malaria cases. Temporal patterns of outpatient malaria case counts were correlated with the proportion of outpatients diagnosed with malaria and confirmed malaria case counts. The proportion of outpatients diagnosed with malaria was spatially clustered, and these cluster locations were generally consistent from year to year. Outpatient malaria cases exhibited spatial synchrony at distances up to 300 km, supporting the hypothesis that regional climatic variability is an important driver of epidemics. Our results suggest that decomposing malaria risk into separate spatial and temporal components may be an effective strategy for modelling and forecasting malaria risk across large areas. They also emphasise both the value and limitations of working with historical surveillance datasets and highlight the importance of enhancing existing surveillance efforts.

Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia.

Malaria is a major global public health problem, particularly in Sub-Saharan Africa. The spatial heterogeneity of malaria can be affected by factors such as hydrological processes, physiography, and land cover patterns. Tropical wetlands, for example, are important hydrological features that can serve as mosquito breeding habitats. Mapping and monitoring of wetlands using satellite remote sensing can thus help to target interventions aimed at reducing malaria transmission. The objective of this study was to map wetlands and other major land cover types in the Amhara region of Ethiopia and to analyze district-level associations of malaria and wetlands across the region. We evaluated three random forests classification models using remotely sensed topographic and spectral data based on Shuttle Radar Topographic Mission (SRTM) and Landsat TM/ETM+ imagery, respectively. The model that integrated data from both sensors yielded more accurate land cover classification than single-sensor models. The resulting map of wetlands and other major land cover classes had an overall accuracy of 93.5%. Topographic indices and subpixel level fractional cover indices contributed most strongly to the land cover classification. Further, we found strong spatial associations of percent area of wetlands with malaria cases at the district level across the dry, wet, and fall seasons. Overall, our study provided the most extensive map of wetlands for the Amhara region and documented spatiotemporal associations of wetlands and malaria risk at a broad regional level. These findings can assist public health personnel in developing strategies to effectively control and eliminate malaria in the region. Key Points Remote sensing produced an accurate wetland map for the Ethiopian highlands Wetlands were associated with spatial variability in malaria risk Mapping and monitoring wetlands can improve malaria spatial decision support

Mapping Malaria Risk in Low Transmission Settings: Challenges and Opportunities

As malaria transmission declines, it becomes increasingly focal and prone to outbreaks. Understanding and predicting patterns of transmission risk becomes an important component of an effective elimination campaign, allowing limited resources for control and elimination to be targeted cost-effectively. Malaria risk mapping in low transmission settings is associated with some unique challenges. This article reviews the main challenges and opportunities related to risk mapping in low transmission areas including recent advancements in risk mapping low transmission malaria, relevant metrics, and statistical approaches and risk mapping in post-elimination settings.

Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing.

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth’s land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources.

Community-wide Prevalence of Malaria Parasitemia in HIV-Infected and Uninfected Populations in a High-Transmission Setting in Uganda

BACKGROUND Malaria control strategies depend on identifying individuals with parasitemia, who may be asymptomatic but retain the ability to transmit disease. Population-level survey data on parasitemia are limited and traditionally exclude adults and human immunodeficiency virus (HIV)-infected individuals. METHODS We performed a cross-sectional survey of residents aged 18 months to 94 years in Nankoma, Uganda. Blood specimens were collected using the dried blood spot technique from 9629 residents (87.6%), and samples from a subset of 4131 were tested for malaria parasites, using loop-mediated isothermal amplification. Population-level prevalence was estimated using a weighted proportion, and predictors of parasitemia were identified using a multivariate Poisson regression model. RESULTS The community prevalence of parasitemia was 83.8% (95% confidence interval [CI], 82.9%-84.6%). Parasite prevalence was highest among children aged 5-14 years (94.7%) and lowest among adults (61.9%). In analysis that controlled for age, HIV-infected individuals with an undetectable viral load had a lower risk of parasitemia, compared with HIV-uninfected individuals (adjusted relative risk, 0.16; 95% CI, .10-.27; P < .001). CONCLUSIONS In a rural Ugandan community, 2 years after distribution of long-lasting insecticide-treated bed nets, the prevalence of malaria parasitemia was high across all ages, peaking in school-aged children. Persons with well-controlled HIV infection had a lower risk of parasitemia, presumably reflecting access to HIV care.

Intercalibration and Gaussian Process Modeling of Nighttime Lights Imagery for Measuring Urbanization Trends in Africa 2000–2013

Sub-Saharan Africa currently has the world’s highest urban population growth rate of any continent at roughly 4.2% annually. A better understanding of the spatiotemporal dynamics of urbanization across the continent is important to a range of fields including public health, economics, and environmental sciences. Nighttime lights imagery (NTL), maintained by the National Oceanic and Atmospheric Administration, offers a unique vantage point for studying trends in urbanization. A well-documented deficiency of this dataset is the lack of intra- and inter-annual calibration between satellites, which makes the imagery unsuitable for temporal analysis in their raw format. Here we have generated an ‘intercalibrated’ time series of annual NTL images for Africa (2000–2013) by building on the widely used invariant region and quadratic regression method (IRQR). Gaussian process methods (GP) were used to identify NTL latent functions independent from the temporal noise signals in the annual datasets. The corrected time series was used to explore the positive association of NTL with Gross Domestic Product (GDP) and urban population (UP). Additionally, the proportion of change in ‘lit area’ occurring in urban areas was measured by defining urban agglomerations as contiguously lit pixels of >250 km2, with all other pixels being rural. For validation, the IRQR and GP time series were compared as predictors of the invariant region dataset. Root mean square error values for the GP smoothed dataset were substantially lower. Correlation of NTL with GDP and UP using GP smoothing showed significant increases in R2 over the IRQR method on both continental and national scales. Urban growth results suggested that the majority of growth in lit pixels between 2000 and 2013 occurred in rural areas. With this study, we demonstrated the effectiveness of GP to improve conventional intercalibration, used NTL to describe temporal patterns of urbanization in Africa, and detected NTL responses to environmental and humanitarian crises. The smoothed datasets are freely available for further use.

Predicting residential structures from open source remotely enumerated data using machine learning

Having accurate maps depicting the locations of residential buildings across a region benefits a range of sectors. This is particularly true for public health programs focused on delivering services at the household level, such as indoor residual spraying with insecticide to help prevent malaria. While open source data from OpenStreetMap (OSM) depicting the locations and shapes of buildings is rapidly improving in terms of quality and completeness globally, even in settings where all buildings have been mapped, information on whether these buildings are residential, commercial or another type is often only available for a small subset. Using OSM building data from Botswana and Swaziland, we identified buildings for which ‘type’ was indicated, generated via on the ground observations, and classified these into two classes, “sprayable” and “not-sprayable”. Ensemble machine learning, using building characteristics such as size, shape and proximity to neighbouring features, was then used to form a model to predict which of these 2 classes every building in these two countries fell into. Results show that an ensemble machine learning approach performed marginally, but statistically, better than the best individual model and that using this ensemble model we were able to correctly classify >86% (using independent test data) of structures correctly as sprayable and not-sprayable across both countries.

Low-quality housing is associated with increased risk of malaria infection: A national population-based study from the low transmission setting of Swaziland

Abstract Background Low-quality housing may confer risk of malaria infection, but evidence in low transmission settings is limited. Methods To examine the relationship between individual level housing quality and locally acquired infection in children and adults, a population-based cross-sectional analysis was performed using existing surveillance data from the low transmission setting of Swaziland. From 2012 to 2015, cases were identified through standard diagnostics in health facilities and by loop-mediated isothermal amplification in active surveillance, with uninfected subjects being household members and neighbors. Housing was visually assessed in a home visit and then classified as low, high, or medium quality, based on housing components being traditional, modern, or both, respectively. Results Overall, 11426 individuals were included in the study: 10960 uninfected and 466 infected (301 symptomatic and 165 asymptomatic). Six percent resided in low-quality houses, 26% in medium-quality houses, and 68% in high-quality houses. In adjusted models, low- and medium-quality construction was associated with increased risk of malaria compared with high-quality construction (adjusted odds ratio [AOR], 2.11 and 95% confidence interval [CI], 1.26–3.53 for low vs high; AOR, 1.56 and 95% CI, 1.15–2.11 for medium vs high). The relationship was independent of vector control, which also conferred a protective effect (AOR, 0.67; 95% CI, .50–.90) for sleeping under an insecticide-treated bed net or a sprayed structure compared with neither. Conclusions Our study adds to the limited literature on housing quality and malaria risk from low transmission settings. Housing improvements may offer an attractive and sustainable additional strategy to support countries in malaria elimination.