Going beyond consensus genome sequences: An innovative SNP-based methodology reconstructs different Ugandan cassava brown streak virus haplotypes at a nationwide scale in Rwanda.

Cassava Brown Streak Disease (CBSD), which is caused by cassava brown streak virus (CBSV) and Ugandan cassava brown streak virus (UCBSV), represents one of the most devastating threats to cassava production in Africa, including in Rwanda where a dramatic epidemic in 2014 dropped cassava yield from 3.3 million to 900,000 tonnes (1). Studying viral genetic diversity at the genome level is essential in disease management, as it can provide valuable information on the origin and dynamics of epidemic events. To fill the current lack of genome-based diversity studies of UCBSV, we performed a nationwide survey of cassava ipomovirus genomic sequences in Rwanda by high-throughput sequencing (HTS) of pools of plants sampled from 130 cassava fields in thirteen cassava-producing districts, spanning seven agro-ecological zones with contrasting climatic conditions and different cassava cultivars. HTS allowed the assembly of a nearly complete consensus genome of UCBSV in twelve districts. The phylogenetic analysis revealed high homology between UCBSV genome sequences, with a maximum of 0.8 per cent divergence between genomes at the nucleotide level. An in-depth investigation based on Single Nucleotide Polymorphisms (SNPs) was conducted to explore the genome diversity beyond the consensus sequences. First, to ensure the validity of the result, a panel of SNPs was confirmed by independent reverse transcription polymerase chain reaction (RT-PCR) and Sanger sequencing. Furthermore, the combination of fixation index (FST) calculation and Principal Component Analysis (PCA) based on SNP patterns identified three different UCBSV haplotypes geographically clustered. The haplotype 2 (H2) was restricted to the central regions, where the NAROCAS 1 cultivar is predominantly farmed. RT-PCR and Sanger sequencing of individual NAROCAS1 plants confirmed their association with H2. Haplotype 1 was widely spread, with a 100 per cent occurrence in the Eastern region, while Haplotype 3 was only found in the Western region. These haplotypes' associations with specific cultivars or regions would need further confirmation. Our results prove that a much more complex picture of genetic diversity can be deciphered beyond the consensus sequences, with practical implications on virus epidemiology, evolution, and disease management. Our methodology proposes a high-resolution analysis of genome diversity beyond the consensus between and within samples. It can be used at various scales, from individual plants to pooled samples of virus-infected plants. Our findings also showed how subtle genetic differences could be informative on the potential impact of agricultural practices, as the presence and frequency of a virus haplotype could be correlated with the dissemination and adoption of improved cultivars.

Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest: A case study in Rwanda.

As found in the health studies literature, the levels of climate association between epidemiological diseases have been found to vary across regions. Therefore, it seems reasonable to allow for the possibility that relationships might vary spatially within regions. We implemented the geographically weighted random forest (GWRF) machine learning method to analyze ecological disease patterns caused by spatially non-stationary processes using a malaria incidence dataset for Rwanda. We first compared the geographically weighted regression (WGR), the global random forest (GRF), and the geographically weighted random forest (GWRF) to examine the spatial non-stationarity in the non-linear relationships between malaria incidence and their risk factors. We used the Gaussian areal kriging model to disaggregate the malaria incidence at the local administrative cell level to understand the relationships at a fine scale since the model goodness of fit was not satisfactory to explain malaria incidence due to the limited number of sample values. Our results show that in terms of the coefficients of determination and prediction accuracy, the geographical random forest model performs better than the GWR and the global random forest model. The coefficients of determination of the geographically weighted regression (R2), the global RF (R2), and the GWRF (R2) were 4.74, 0.76, and 0.79, respectively. The GWRF algorithm achieves the best result and reveals that risk factors (rainfall, land surface temperature, elevation, and air temperature) have a strong non-linear relationship with the spatial distribution of malaria incidence rates, which could have implications for supporting local initiatives for malaria elimination in Rwanda.

Risk factors associated with cassava brown streak disease dissemination through seed pathways in Eastern D.R. Congo.

Vegetatively propagated crops are particularly prone to disease dissemination through their seed systems. Strict phytosanitary measures are important to limit the impact of diseases as illustrated by the potato seed system in Europe. Cassava brown streak disease (CBSD) is a devastating disease caused by two viral species collectively named cassava brown streak viruses (CBSVs). CBSD can cause substantial root yield losses of up to 100% in the worst affected areas and is easily transmitted through stem cuttings. In Eastern and Central Africa, the epidemiology of CBSVs in the local socio-economical context of production remains poorly known while a better understanding would be an asset to properly manage the disease. This lack of information explains partially the limited efficiency of current regulatory schemes in increasing the availability of quality seed to smallholders and mitigating the spread of pests and diseases. This study surveyed the epidemiology of CBSVs in Uvira territory, Eastern D.R. Congo, and its drivers using a multivariate approach combining farmer's interview, field observation, sampling and molecular detection of CBSVs. Investigation on the epidemiology of CBSD revealed that three clusters in the study area could be identified using five most significant factors: (i) symptoms incidence, (ii) number of whiteflies, (iii) types of foliar symptoms, (iv) cutting's pathways and (v) plant age. Among the three clusters identified, one proved to be potentially interesting for seed multiplication activities since the disease pressure was the lowest. Through risk assessment, we also identified several key socio-economic determinants on disease epidemy: (i) factors related to farmer's knowledge and awareness (knowledge of cassava pests and diseases, knowledge of management practices, support from extension services and management strategies applied), (ii) factors related to the geographical location of farmer's fields (proximity to borders, proximity to town, distance to acquire cuttings), as well as (iii) the pathways used to acquire cuttings.

Assessing the effects of air temperature and rainfall on malaria incidence: an epidemiological study across Rwanda and Uganda.

We investigate the short-term effects of air temperature, rainfall, and socioeconomic indicators on malaria incidence across Rwanda and Uganda from 2002 to 2011. Delayed and nonlinear effects of temperature and rainfall data are estimated using generalised additive mixed models with a distributed lag nonlinear specification. A time series cross-validation algorithm is implemented to select the best subset of socioeconomic predictors and to define the degree of smoothing of the weather variables. Our findings show that trends in malaria incidence agree well with variations in both temperature and rainfall in both countries, although factors other than climate seem to play an important role too. The estimated short-term effects of air temperature and precipitation are nonlinear, in agreement with previous research and the ecology of the disease. These effects are robust to the effects of temporal correlation. The effects of socioeconomic data are difficult to ascertain and require further evaluation with longer time series. Climate-informed models had lower error estimates compared to models with no climatic information in 77 and 60% of the districts in Rwanda and Uganda, respectively. Our results highlight the importance of using climatic information in the analysis of malaria surveillance data, and show potential for the development of climate informed malaria early warning systems.

Modelling homogeneous regions of social vulnerability to malaria in Rwanda.

Despite the decline in malaria incidence due to intense interventions, potentials for malaria transmission persist in Rwanda. To eradicate malaria in Rwanda, strategies need to expand beyond approaches that focus solely on malaria epidemiology and also consider the socioeconomic, demographic and biological/disease-related factors that determine the vulnerability of potentially exposed populations. This paper analyses current levels of social vulnerability to malaria in Rwanda by integrating a set of weighted vulnerability indicators. The paper uses regionalisation techniques as a spatially explicit approach for delineating homogeneous regions of social vulnerability to malaria. This overcomes the limitations of administrative boundaries for modelling the trans-boundary social vulnerability to malaria. The utilised approach revealed high levels of social vulnerability to malaria in the highland areas of Rwanda, as well as in remote areas where populations are more susceptible. Susceptibility may be due to the populations' lacking the capacity to anticipate mosquito bites, or lacking resilience to cope with or recover from malaria infection. By highlighting the most influential indicators of social vulnerability to malaria, the applied approach indicates which vulnerability domains need to be addressed, and where appropriate interventions are most required. Interventions to improve the socioeconomic development in highly vulnerable areas could prove highly effective, and provide sustainable outcomes against malaria in Rwanda. This would ultimately increase the resilience of the population and their capacity to better anticipate, cope with, and recover from possible infection.

Assessing the social vulnerability to malaria in Rwanda.

BACKGROUND: Since 2004, malaria interventions in Rwanda have resulted in substantial decline of malaria incidence. However, this achievement is fragile as potentials for local malaria transmissions remain. The risk of getting malaria infection is partially explained by social conditions of vulnerable populations. Since vulnerability to malaria is both influenced by social and environmental factors, its complexity cannot be measured by a single value. The aim of this paper is, therefore, to apply a composite indicator approach for assessing social vulnerability to malaria in Rwanda. This assessment informs the decision-makers in targeting malaria interventions and allocating limited resources to reduce malaria burden in Rwanda. METHODS: A literature review was used to conceptualize the social vulnerability to malaria and to select the appropriate vulnerability indicators. Indicators used in the index creation were classified into susceptibility and lack of resilience vulnerability domains. The main steps followed include selection of indicators and datasets, imputation of missing values, descriptive statistics, normalization and weighting of indicators, local sensitivity analysis and indicators aggregation. Correlation analysis helped to empirically evidence the association between the indicators and malaria incidence. RESULTS: The high values of social vulnerability to malaria are found in Gicumbi, Rusizi, Nyaruguru and Gisagara, and low values in Muhanga, Nyarugenge, Kicukiro and Nyanza. The most influential susceptibility indicators to increase malaria are population change (r = 0.729), average number of persons per bedroom (r = 0.531), number of households affected by droughts and famines (r = 0.591), and area used for irrigation (r = 0.611). The bed net ownership (r = -0.398) and poor housing wall materials (0.378) are the lack of resilience indicators that significantly correlate with malaria incidence. CONCLUSIONS: The developed composite index social vulnerability to malaria indicates which indicators need to be addressed and in which districts. The results from this study are salient for public health policy- and decision makers in malaria control in Rwanda and timely support the national integrated malaria initiative. Future research development should focus on spatial explicit vulnerability assessment by combining environmental and social drivers to achieve an integrated and complete assessment of vulnerability to malaria.

Malaria prevalence, spatial clustering and risk factors in a low endemic area of Eastern Rwanda: a cross sectional study.

BACKGROUND: Rwanda reported significant reductions in malaria burden following scale up of control intervention from 2005 to 2010. This study sought to; measure malaria prevalence, describe spatial malaria clustering and investigate for malaria risk factors among health-centre-presumed malaria cases and their household members in Eastern Rwanda. METHODS: A two-stage health centre and household-based survey was conducted in Ruhuha sector, Eastern Rwanda from April to October 2011. At the health centre, data, including malaria diagnosis and individual level malaria risk factors, was collected. At households of these Index cases, a follow-up survey, including malaria screening for all household members and collecting household level malaria risk factor data, was conducted. RESULTS: Malaria prevalence among health centre attendees was 22.8%. At the household level, 90 households (out of 520) had at least one malaria-infected member and the overall malaria prevalence for the 2634 household members screened was 5.1%. Among health centre attendees, the age group 5-15 years was significantly associated with an increased malaria risk and a reported ownership of ≥4 bednets was significantly associated with a reduced malaria risk. At the household level, age groups 5-15 and >15 years and being associated with a malaria positive index case were associated with an increased malaria risk, while an observed ownership of ≥4 bednets was associated with a malaria risk-protective effect. Significant spatial malaria clustering among household cases with clusters located close to water- based agro-ecosystems was observed. CONCLUSIONS: Malaria prevalence was significantly higher among health centre attendees and their household members in an area with significant household spatial malaria clustering. Circle surveillance involving passive case finding at health centres and proactive case detection in households can be a powerful tool for identifying household level malaria burden, risk factors and clustering.