Malaria Risk Stratification and Modeling the Effect of Rainfall on Malaria Incidence in Eritrea

Screening for antifolate and artemisinin resistance in Plasmodium falciparum dried-blood spots from three hospitals of Eritrea

Background

Antimalarial drug resistance is a major challenge hampering malaria control and elimination. About three-quarters of Eritrea's population resides in the malaria-endemic western lowlands of the country. Plasmodium falciparum, the leading causative parasite species, has developed resistance to basically all antimalarials. Continued surveillance of drug resistance using genetic markers provides important molecular data for treatment policies which complements clinical studies, and strengthens control efforts. This study sought to genotype point mutations associated with P. falciparum resistance to sulfadoxine-pyrimethamine and artemisinin, in dried-blood spots from three hospitals in the western lowlands of Eritrea.

Methods

Dried-blood spot samples were collected from patients visiting Adi Quala, Keren and Gash Barka Hospitals, between July and October, 2014. The patients were followed up after treatment with first line artesunate-amodiaquine, and dried-blood spots were collected on day three after treatment. Nested polymerase chain reaction and Sanger sequencing techniques were employed to genotype point mutations in the Pfdhfr (PF3D7_0417200), Pfdhps (PF3D7_0810800) and PfK13 (PF3D7_1343700) partial gene regions.

Results

Sequence data analyses of PCR-positive isolates found wild-type artemisinin haplotypes associated with resistance (Y493Y, R539R, I543I) in three isolates, whereas four mutant antifolate haplotypes associated with resistance were observed in six isolates. These included the triple-mutant Pfdhfr (S108N, C59R, N51I) haplotype, the double-mutant Pfdhfr (N51I, S108N) haplotype, the single-mutant Pfdhfr (K540E) haplotype, and the mixed-mutant Pfdhfr-Pfdhps (S108N, N51I + K540E) haplotype. Other findings observed were, a rare non-synonymous Pfdhfr V45A mutation in four isolates, and a synonymous Pfdhps R449R in one isolate.

Conclusions

The mutant antifolate haplotypes observed indicate a likely existence of full SP resistance. Further studies can be carried out to estimate the prevalence of SP resistance. The wild-type artemisinin haplotypes observed suggest artemisinin is still an effective treatment. Continuous monitoring of point mutations associated with delayed parasite clearance in ART clinical studies is recommended.

Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model

Climatic factors influence malaria transmission via the effect on the Anopheles vector and Plasmodium parasite. Modelling and understanding the complex effects that climate has on malaria incidence can enable important early warning capabilities. Deep learning applications across fields are proving valuable, however the field of epidemiological forecasting is still in its infancy with a lack of applied deep learning studies for malaria in southern Africa which leverage quality datasets. Using a novel high resolution malaria incidence dataset containing 23 years of daily data from 1998 to 2021, a statistical model and XGBOOST machine learning model were compared to a deep learning Transformer model by assessing the accuracy of their numerical predictions. A novel loss function, used to account for the variable nature of the data yielded performance around + 20% compared to the standard MSE loss. When numerical predictions were converted to alert thresholds to mimic use in a real-world setting, the Transformer's performance of 80% according to AUROC was 20-40% higher than the statistical and XGBOOST models and it had the highest overall accuracy of 98%. The Transformer performed consistently with increased accuracy as more climate variables were used, indicating further potential for this prediction framework to predict malaria incidence at a daily level using climate data for southern Africa.

Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania

As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017-2019 to predict risks at the ward level, the lowest decision-making unit in mainland Tanzania. To quantify the associated uncertainty, the probability of malaria TPR exceeding programmatic threshold was estimated. Results showed a marked spatial heterogeneity in malaria TPR across wards. 17.7 million people resided in areas where malaria TPR was high (≥ 30; 90% certainty) in the North-West and South-East parts of Tanzania. Approximately 11.7 million people lived in areas where malaria TPR was very low (< 5%; 90% certainty). HF data can be used to identify different epidemiological strata and guide malaria interventions at micro-planning units in Tanzania. These data, however, are imperfect in many settings in Africa and often require application of geo-spatial modelling techniques for estimation.

El Niño and other climatic drivers of epidemic malaria in Ethiopia: new tools for national health adaptation plans

BACKGROUND

Ethiopia has a history of climate related malaria epidemics. An improved understanding of malaria-climate interactions is needed to inform malaria control and national adaptation plans.

METHODS

Malaria-climate associations in Ethiopia were assessed using (a) monthly climate data (1981-2016) from the Ethiopian National Meteorological Agency (NMA), (b) sea surface temperatures (SSTs) from the eastern Pacific, Indian Ocean and Tropical Atlantic and (c) historical malaria epidemic information obtained from the literature. Data analysed spanned 1950-2016. Individual analyses were undertaken over relevant time periods. The impact of the El Niño Southern Oscillation (ENSO) on seasonal and spatial patterns of rainfall and minimum temperature (Tmin) and maximum temperature (Tmax) was explored using NMA online Maprooms. The relationship of historic malaria epidemics (local or widespread) and concurrent ENSO phases (El Niño, Neutral, La Niña) and climate conditions (including drought) was explored in various ways. The relationships between SSTs (ENSO, Indian Ocean Dipole and Tropical Atlantic), rainfall, Tmin, Tmax and malaria epidemics in Amhara region were also explored.

RESULTS

El Niño events are strongly related to higher Tmax across the country, drought in north-west Ethiopia during the July-August-September (JAS) rainy season and unusually heavy rain in the semi-arid south-east during the October-November-December (OND) season. La Niña conditions approximate the reverse. At the national level malaria epidemics mostly occur following the JAS rainy season and widespread epidemics are commonly associated with El Niño events when Tmax is high, and drought is common. In the Amhara region, malaria epidemics were not associated with ENSO, but with warm Tropical Atlantic SSTs and higher rainfall.

CONCLUSION

Malaria-climate relationships in Ethiopia are complex, unravelling them requires good climate and malaria data (as well as data on potential confounders) and an understanding of the regional and local climate system. The development of climate informed early warning systems must, therefore, target a specific region and season when predictability is high and where the climate drivers of malaria are sufficiently well understood. An El Niño event is likely in the coming years. Warming temperatures, political instability in some regions, and declining investments from international donors, implies an increasing risk of climate-related malaria epidemics.

Community knowledge and practice of malaria prevention in Ghindae, Eritrea, a Cross-sectional study

BACKGROUND

Little is known about community knowledge and practice towards malaria prevention in Ghindae, Eritrea.

METHODOLOGY

A community based cross-sectional study design was employed among 380 households. Participants were selected systematically.

RESULT

More than eight-tenth (86.5%) of the respondents had heard information about malaria preceding the survey; health facilities (54.1%), television (23.7%). Majority (94.2%) mentioned mosquito bite as the main mode of malaria transmission. Fever was the predominantly (89.2%) identified sign/symptoms of malaria. ITN (84.4%) and environmental sanitation (67.3%) were well recognized preventive measures for malaria. Though most households (91%) possess bed nets, but only 37% were ragged on observation. Overall, 64% of the respondents have satisfactory knowledge and 57.3% had adequate practice towards malaria prevention. Malaria knowledge was significantly associated with increased age (p=0.001) and district areas (p=0.022). Malaria prevention practice was significantly associated with Tigrigna and Saho ethnic group (p=0.013), and districts (p=0.02). Districts showed significant difference with an OR=4.56 (95%CI, 1.29-16.09) on knowledge for district 04 and OR=1.98(95%-CI, 1.21-3.26) on practice for district 03 compared to district 01. Knowledge was associated with prevention (OR=1.99, 95%CI, 1.28-3.09).

CONCLUSION

Overall community knowledge and practice towards malaria prevention were satisfactory. Furthermore, comprehensive community interventions are paramount for effective sustainable control.

Regional heterogeneity of malaria prevalence and associated risk factors among children under five in Togo: evidence from a national malaria indicators survey

BACKGROUND

Malaria remains a major cause of morbidity and death among children less than 5 years of age. In Togo, despite intensification of malaria control interventions, malaria remained highly prevalent, with significant heterogeneity from one region to another. The aim of this study is to explore further such regional differences in malaria prevalence and to determine associated risk factors.

METHODS

Data from a 2017 cross-sectional nationally representative malaria indicator survey was used. Children aged 6-59 months in selected households were tested for malaria using a rapid diagnostic test (RDT), confirmed by microscopy. Univariate and multivariate logistic regression analysis were performed using Generalized Linear Models.

RESULTS

A total of 2131 children aged 6-59 months (1983 in rural areas, 989 in urban areas) were enrolled. Overall 28% of children tested positive for malaria, ranging from 7.0% in the Lomé Commune region to 4% 7.1 in the Plateaux region. In multivariate analysis, statistically significant differences between regions persisted. Independent risk factors identified were higher children aged (aOR = 1.46, 95% CI [1.13-1.88]) for those above 24 months compared to those below; households wealth quintile (aOR = 0.22, 95% CI [0.11-0.41]) for those richest compared to those poorest quintiles; residence in rural areas (aOR = 2.02, 95% CI [1.32-3.13]).

CONCLUSION

Interventions that target use of combined prevention measures should prioritise on older children living in poorest households in rural areas, particularly in the regions of high malaria prevalence.

Epidemiological Trends of Malaria in Five Years and under Children of Nsanje District in Malawi, 2015-2019

Background: Malaria continues to be a major public health problem in Malawi and the greatest load of mortality and morbidity occurs in children five years and under. However, there is no information yet regarding trends and predictions of malaria incidence in children five years and under at district hospital level, particularly at Nsanje district hospital. Aim: Therefore, this study aimed at investigating the trends of malaria morbidity and mortality in order to design appropriate interventions on the best approach to contain the disease in the near future. Methodology: Trend analysis of malaria morbidity and mortality together with time series analysis using the SARIMA (Seasonal Autoregressive Integrated Moving Average) model was used to predict malaria incidence in Nsanje district. Results: The SARIMA model used malaria cases from 2015 to 2019 and created the best model to forecast the malaria cases in Nsanje from 2020 to 2022. An SARIMA (0, 1, 2) (0,1,1)₁₂ was suitable for forecasting the incidence of malaria for Nsanje. Conclusion: The mortality and morbidity trend showed that malaria cases were growing at a fluctuating rate at Nsanje district hospital. The relative errors between the actual values and predicted values indicated that the predicted values matched the actual values well. Therefore, the model proved that it was adequate to forecast monthly malaria cases and it had a good fit, hence, was appropriate for this study

Spatial and spatio-temporal methods for mapping malaria risk: a systematic review

Background

Approaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this review aimed to systematically retrieve, summarise methods and examine covariates that have been used for mapping malaria risk in sub-Saharan Africa (SSA).

Methods

A systematic search of malaria risk mapping studies was conducted using PubMed, EBSCOhost, Web of Science and Scopus databases. The search was restricted to refereed studies published in English from January 1968 to April 2020. To ensure completeness, a manual search through the reference lists of selected studies was also undertaken. Two independent reviewers completed each of the review phases namely: identification of relevant studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, data extraction and methodological quality assessment using a validated scoring criterion.

Results

One hundred and seven studies met the inclusion criteria. The median quality score across studies was 12/16 (range: 7–16). Approximately half (44%) of the studies employed variable selection techniques prior to mapping with rainfall and temperature selected in over 50% of the studies. Malaria incidence (47%) and prevalence (35%) were the most commonly mapped outcomes, with Bayesian geostatistical models often (31%) the preferred approach to risk mapping. Additionally, 29% of the studies employed various spatial clustering methods to explore the geographical variation of malaria patterns, with Kulldorf scan statistic being the most common. Model validation was specified in 53 (50%) studies, with partitioning data into training and validation sets being the common approach.

Conclusions

Our review highlights the methodological diversity prominent in malaria risk mapping across SSA. To ensure reproducibility and quality science, best practices and transparent approaches should be adopted when selecting the statistical framework and covariates for malaria risk mapping. Findings underscore the need to periodically assess methods and covariates used in malaria risk mapping; to accommodate changes in data availability, data quality and innovation in statistical methodology.

Effects of rainfall, temperature and topography on malaria incidence in elimination targeted district of Ethiopia

BACKGROUND

Climate and environmental factors could be one of the primary factors that drive malaria transmission and it remains to challenge the malaria elimination efforts. Hence, this study was aimed to evaluate the effects of meteorological factors and topography on the incidence of malaria in the Boricha district in Sidama regional state of Ethiopia.

METHODS

Malaria morbidity data recorded from 2010 to 2017 were obtained from all public health facilities of Boricha District in the Sidama regional state of Ethiopia. The monthly malaria cases, rainfall, and temperature (minimum, maximum, and average) were used to fit the ARIMA model to compute the malaria transmission dynamics and also to forecast future incidence. The effects of the meteorological variables and altitude were assessed with a negative binomial regression model using R version 4.0.0. Cross-correlation analysis was employed to compute the delayed effects of meteorological variables on malaria incidence.

RESULTS

Temperature, rainfall, and elevation were the major determinants of malaria incidence in the study area. A regression model of previous monthly rainfall at lag 0 and Lag 2, monthly mean maximum temperature at lag 2 and Lag 3, and monthly mean minimum temperature at lag 3 were found as the best prediction model for monthly malaria incidence. Malaria cases at 1801-1900 m above sea level were 1.48 times more likely to occur than elevation ≥ 2000 m.

CONCLUSIONS

Meteorological factors and altitude were the major drivers of malaria incidence in the study area. Thus, evidence-based interventions tailored to each determinant are required to achieve the malaria elimination target of the country.

Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period

In moving toward malaria elimination, finer scale malaria risk maps are required to identify hotspots for implementing surveillance–response activities, allocating resources, and preparing health facilities based on the needs and necessities at each specific area. This study aimed to demonstrate the use of multi-criteria decision analysis (MCDA) in conjunction with geographic information systems (GISs) to create a spatial model and risk maps by integrating satellite remote-sensing and malaria surveillance data from 18 counties of Yunnan Province along the China–Myanmar border. The MCDA composite and annual models and risk maps were created from the consensus among the experts who have been working and know situations in the study areas. The experts identified and provided relative factor weights for nine socioeconomic and disease ecology factors as a weighted linear combination model of the following: ([Forest coverage × 0.041] + [Cropland × 0.086] + [Water body × 0.175] + [Elevation × 0.297] + [Human population density × 0.043] + [Imported case × 0.258] + [Distance to road × 0.030] + [Distance to health facility × 0.033] + [Urbanization × 0.036]). The expert-based model had a good prediction capacity with a high area under curve. The study has demonstrated the novel integrated use of spatial MCDA which combines multiple environmental factors in estimating disease risk by using decision rules derived from existing knowledge or hypothesized understanding of the risk factors via diverse quantitative and qualitative criteria using both data-driven and qualitative indicators from the experts. The model and fine MCDA risk map developed in this study could assist in focusing the elimination efforts in the specifically identified locations with high risks.