Predicting the Spread and Management of the Cassava Brown Streak Disease Epidemic

Cassava Brown Streak disease (CBSD) is a viral disease of cassava that causes necrosis of the edible root tissue, which reduces both consumable and marketable yield. In 2004, CBSD emerged in Uganda and has since been spreading rapidly through previously unaffected regions of East Africa and into Central Africa. Preventing spread to West Africa is a major food security and development priority, along with mitigating the impact of CBSD in endemic regions. This thesis focuses on the development of a landscape-scale spatial model of the CBSD epidemic to inform management. Currently, there is disparate information on the epidemiology of CBSD and significant associated uncertainty. We begin with a review of CBSD from an epidemiological perspective. The review focuses on: mechanisms and rates of pathogen dispersal, surveillance, disease impact and management efficacy to inform the structure of the CBSD model. Prior to model development, it was necessary to aggregate all available data on the historic spread of the epidemic. Minimal surveillance data were available in the literature. Therefore, it was necessary to work extensively with East African collaborators to acquire and digitise over 10 years of previously unavailable surveillance records from Uganda and surrounding countries. Extensive post-processing was performed to minimise errors in the data. In parallel with digitisation of the surveillance data, we describe work to enable digital data collection via the creation of a cassava disease surveillance app, along with extensive training. The goal was to minimise errors in data collection and reduce the time lag between disease surveillance and reporting in surveillance programmes. The second section of the thesis describes the development, parameterisation, and validation of a stochastic, spatio-temporal epidemic model for CBSD. Using digitised Ugandan surveillance data from 2005-2010, and estimates of cassava density throughout Uganda and immediately surrounding regions, we apply Approximate Bayesian Computation (ABC) to estimate dispersal parameters, providing methodological details on the development and validation of summary statistics. The model fitting also takes account of empirical data for vector density across Uganda and surrounding regions. The model fits the data well for the training set for 2005-2010. Survey data from Uganda and the surrounding region from 2011-2017 are then used as a rigorous independent test to validate model predictions. The third section of the thesis describes the application of the model to address questions concerning historic, current and future epidemic spread. We use the model to identify reasons why, although there were historically high levels of CBSD infection in Malawi, negligible epidemic spread occurred into Zambia from Malawi showing that low density of cassava cultivation in south east Zambia could account for the inhibition of spread. The model does successfully predict the incursion of the epidemic into north east Zambia from the Democratic Republic of Congo (DRC). We run cross-continent simulations to predict the spatiotemporal spread of the epidemic through central Africa, including DRC and the Central African Republic, where there is very little disease surveillance and reporting for CBSD. The simulations allow us to compute the likely distributions of arrival times of the epidemic in West African countries. We also simulate rates of spread of the disease in West African countries following direct introduction for example by importation and by natural spread from adjoint countries. Finally, we simulate management interventions in Nigeria, to identify the scale and speed at which management programmes would need to be deployed to contain the epidemic. The thesis concludes with a review of the principal results and critical assumptions underlying the results. Some proposals are presented for future work in epidemiological modelling to address practical problems of the management of CBSD.BBSR

In vivo co-localization of enzymes on RNA scaffolds increases metabolic production in a geometrically dependent manner

Co-localization of biochemical processes plays a key role in the directional control of metabolic fluxes toward specific products in cells. Here, we employ in vivo scaffolds made of RNA that can bind engineered proteins fused to specific RNA binding domains. This allows proteins to be co-localized on RNA scaffolds inside living Escherichia coli. We assembled a library of eight aptamers and corresponding RNA binding domains fused to partial fragments of fluorescent proteins. New scaffold designs could co-localize split green fluorescent protein fragments to produce activity as measured by cell-based fluorescence. The scaffolds consisted of either single bivalent RNAs or RNAs designed to polymerize in one or two dimensions. The new scaffolds were used to increase metabolic output from a two-enzyme pentadecane production pathway that contains a fatty aldehyde intermediate, as well as three and four enzymes in the succinate production pathway. Pentadecane synthesis depended on the geometry of enzymes on the scaffold, as determined through systematic reorientation of the acyl-ACP reductase fusion by rotation via addition of base pairs to its cognate RNA aptamer. Together, these data suggest that intra-cellular scaffolding of enzymatic reactions may enhance the direct channeling of a variety of substrates

Computational models to improve surveillance for cassava brown streak disease and minimize yield loss.

Cassava brown streak disease (CBSD) is a rapidly spreading viral disease that affects a major food security crop in sub-Saharan Africa. Currently, there are several proposed management interventions to minimize loss in infected fields. Field-scale data comparing the effectiveness of these interventions individually and in combination are limited and expensive to collect. Using a stochastic epidemiological model for the spread and management of CBSD in individual fields, we simulate the effectiveness of a range of management interventions. Specifically we compare the removal of diseased plants by roguing, preferential selection of planting material, deployment of virus-free 'clean seed' and pesticide on crop yield and disease status of individual fields with varying levels of whitefly density crops under low and high disease pressure. We examine management interventions for sustainable production of planting material in clean seed systems and how to improve survey protocols to identify the presence of CBSD in a field or quantify the within-field prevalence of CBSD. We also propose guidelines for practical, actionable recommendations for the deployment of management strategies in regions of sub-Saharan Africa under different disease and whitefly pressure

Expansion of the cassava brown streak pandemic in Uganda revealed by annual field survey data for 2004 to 2017

Funder: Uganda Government Association for Strengthening Agricultural Research in Eastern and Central AfricaFunder: Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation)Abstract: Cassava brown streak disease (CBSD) is currently the most devastating cassava disease in eastern, central and southern Africa affecting a staple crop for over 700 million people on the continent. A major outbreak of CBSD in 2004 near Kampala rapidly spread across Uganda. In the following years, similar CBSD outbreaks were noted in countries across eastern and central Africa, and now the disease poses a threat to West Africa including Nigeria - the biggest cassava producer in the world. A comprehensive dataset with 7,627 locations, annually and consistently sampled between 2004 and 2017 was collated from historic paper and electronic records stored in Uganda. The survey comprises multiple variables including data for incidence and symptom severity of CBSD and abundance of the whitefly vector (Bemisia tabaci). This dataset provides a unique basis to characterize the epidemiology and dynamics of CBSD spread in order to inform disease surveillance and management. We also describe methods used to integrate and verify extensive field records for surveys typical of emerging epidemics in subsistence crops

Qualité de la prise en charge de l'enfant admis en salle d'urgence polyvalente.

AIM: By a prospective study, authors tried to analyse the quality of management of the pediatric patient admitted in the emergency department. POPULATION AND METHODS: One hundred admission files were prospectively analysed for characteristics of age (mean age: 70 months), effectiveness of measurement of physiological parameters, evaluation of appropriateness of nursing management according to recorded parameters, length of stay in the emergency department according to the need for hospitalization, blood tests, X-rays and the seniority of the attending medical staff. RESULTS: Parameters were not, or only incompletely, recorded in 65 files. . Although all were recorded in the remaining 35 files, subsequent management was inadequate in seven cases. Mean length of stay in the emergency department was 116 minutes, influenced by the need for hospitalization (145 minutes compared to 102 minutes for the non-hospitalized children), timing of admission (mean: 125 minutes from 8 am to 6 pm, compared to 94 minutes from 6 pm to 8 am), need for blood tests, X-rays or both (mean: 122, 107 and 170 minutes respectively, compared to 55 minutes when no complementary exam was asked) and seniority of attending medical staff (mean: 65 minutes for permanent staff compared to 116 minutes for fellows). CONCLUSIONS: Measurement of physiological parameters must be standard practise in the management of pediatric patients admitted to the emergency department and must lead to appropriate management without undue delay. In order to reach this goal, emergency departments should be more adequately staffed with nurses and senior doctors specifically trained in the care of the pediatric patient. Blood tests and X-rays should be more readily available

Sweet's syndrome with arthritis in an 8-month-old boy

Sweet's syndrome was diagnosed in a 4-month-old boy. He was successfully treated with systemic corticosteroids. At the age of 8 months, he developed acute arthritis in his right knee. The synovial fluid was analyzed and revealed a very high neutrophil count and neutrophil activation with a detectable level of intraarticular granulocyte-monocyte colony stimulating factor (GM-CSF). Prednisone injection into the knee led to dramatic improvement. No recurrence occurred. Although arthritis and/or arthralgia are common features in adult patients with Sweet's syndrome, this is the first reported case of Sweet's arthritis in a child

Computational models to improve surveillance for cassava brown streak disease and minimize yield loss.

Cassava brown streak disease (CBSD) is a rapidly spreading viral disease that affects a major food security crop in sub-Saharan Africa. Currently, there are several proposed management interventions to minimize loss in infected fields. Field-scale data comparing the effectiveness of these interventions individually and in combination are limited and expensive to collect. Using a stochastic epidemiological model for the spread and management of CBSD in individual fields, we simulate the effectiveness of a range of management interventions. Specifically we compare the removal of diseased plants by roguing, preferential selection of planting material, deployment of virus-free 'clean seed' and pesticide on crop yield and disease status of individual fields with varying levels of whitefly density crops under low and high disease pressure. We examine management interventions for sustainable production of planting material in clean seed systems and how to improve survey protocols to identify the presence of CBSD in a field or quantify the within-field prevalence of CBSD. We also propose guidelines for practical, actionable recommendations for the deployment of management strategies in regions of sub-Saharan Africa under different disease and whitefly pressure

Developing a predictive model for an emerging epidemic on cassava in sub-Saharan Africa.

Funder: Bill and Melinda Gates Foundation; doi: http://dx.doi.org/10.13039/100000865Funder: Foreign, Commonwealth and Development Office; doi: http://dx.doi.org/10.13039/501100020171Funder: Biotechnology and Biological Sciences Research Council; doi: http://dx.doi.org/10.13039/501100000268The agricultural productivity of smallholder farmers in sub-Saharan Africa (SSA) is severely constrained by pests and pathogens, impacting economic stability and food security. An epidemic of cassava brown streak disease, causing significant yield loss, is spreading rapidly from Uganda into surrounding countries. Based on sparse surveillance data, the epidemic front is reported to be as far west as central DRC, the world's highest per capita consumer, and as far south as Zambia. Future spread threatens production in West Africa including Nigeria, the world's largest producer of cassava. Using innovative methods we develop, parameterise and validate a landscape-scale, stochastic epidemic model capturing the spread of the disease throughout Uganda. The model incorporates real-world management interventions and can be readily extended to make predictions for all 32 major cassava producing countries of SSA, with relevant data, and lays the foundations for a tool capable of informing policy decisions at a national and regional scale

Developing a predictive model for an emerging epidemic on cassava in sub-Saharan Africa

Funder: Bill and Melinda Gates Foundation; doi: http://dx.doi.org/10.13039/100000865Funder: Foreign, Commonwealth and Development Office; doi: http://dx.doi.org/10.13039/501100020171Funder: Biotechnology and Biological Sciences Research Council; doi: http://dx.doi.org/10.13039/501100000268The agricultural productivity of smallholder farmers in sub-Saharan Africa (SSA) is severely constrained by pests and pathogens, impacting economic stability and food security. An epidemic of cassava brown streak disease, causing significant yield loss, is spreading rapidly from Uganda into surrounding countries. Based on sparse surveillance data, the epidemic front is reported to be as far west as central DRC, the world’s highest per capita consumer, and as far south as Zambia. Future spread threatens production in West Africa including Nigeria, the world’s largest producer of cassava. Using innovative methods we develop, parameterise and validate a landscape-scale, stochastic epidemic model capturing the spread of the disease throughout Uganda. The model incorporates real-world management interventions and can be readily extended to make predictions for all 32 major cassava producing countries of SSA, with relevant data, and lays the foundations for a tool capable of informing policy decisions at a national and regional scale

Developing a predictive model for an emerging epidemic on cassava in sub-Saharan Africa

Abstract The agricultural productivity of smallholder farmers in sub-Saharan Africa (SSA) is severely constrained by pests and pathogens, impacting economic stability and food security. An epidemic of cassava brown streak disease, causing significant yield loss, is spreading rapidly from Uganda into surrounding countries. Based on sparse surveillance data, the epidemic front is reported to be as far west as central DRC, the world’s highest per capita consumer, and as far south as Zambia. Future spread threatens production in West Africa including Nigeria, the world’s largest producer of cassava. Using innovative methods we develop, parameterise and validate a landscape-scale, stochastic epidemic model capturing the spread of the disease throughout Uganda. The model incorporates real-world management interventions and can be readily extended to make predictions for all 32 major cassava producing countries of SSA, with relevant data, and lays the foundations for a tool capable of informing policy decisions at a national and regional scale