Advancing operational flood forecasting, early warning and risk management with new emerging science: Gaps, opportunities and barriers in Kenya

Kenya and the wider East African region suffer from significant flood risk, as illustrated by major losses of lives, livelihoods and assets in the most recent years. This is likely to increase in future as exposure rises and rainfall intensifies under climate change. Accordingly, flood risk management is a priority action area in Kenya's national climate change adaptation planning. Here, we outline the opportunities and challenges to improve end-to-end flood early warning systems, considering the scientific, technical and institutional/governance dimensions. We demonstrate improvements in rainfall forecasts, river flow, inundation and baseline flood risk information. Notably, East Africa is a ‘sweetspot’ for rainfall predictability at sub-seasonal to seasonal timescales for extending forecast lead times beyond a few days and for ensemble flood forecasting. Further, we demonstrate coupled ensemble flow forecasting, new flood inundation simulation, vulnerability and exposure data to support Impact based Forecasting (IbF). We illustrate these advances in the case of fluvial and urban flooding and reflect on the potential for improved flood preparedness action. However, we note that, unlike for drought, there remains no national flood risk management framework in Kenya and there is need to enhance institutional capacities and arrangements to take full advantage of these scientific advances

Determinants of Adoption and Dis-Adoption of Integrated Pest Management Practices in the Suppression of Mango Fruit Fly Infestation: Evidence from Embu County, Kenya

This study evaluates the drivers of the adoption and dis-adoption of Integrated Pest Management (IPM) practices in the suppression of mango fruit-fly infestation in Embu County, Kenya. It employs a Correlated Random Effects Probit Model and a Discrete-time Proportional Hazard Model on two-wave panel data of 149 mango farmers selected using a cluster sampling technique. The descriptive results show that 59% and 17% of the respondents were adopters and dis-adopters of mango fruit fly IPM practices, respectively. Empirical findings reveal that the cost of IPM and training on IPM positively and significantly influenced adoption, while the unavailability of the technology had a negative and significant effect on adoption. For dis-adoption, the results indicate that farm size and the quality of IPM positively influenced the hazard of exit from IPM use, and hence, enhanced the sustained adoption of IPM. The study recommends capacity building for mango farmers through training and increased access to extension services to enhance the adoption of this technology and prevent dis-adoption

Determinants of Adoption and Dis-Adoption of Integrated Pest Management Practices in the Suppression of Mango Fruit Fly Infestation: Evidence from Embu County, Kenya

This study evaluates the drivers of the adoption and dis-adoption of Integrated Pest Management (IPM) practices in the suppression of mango fruit-fly infestation in Embu County, Kenya. It employs a Correlated Random Effects Probit Model and a Discrete-time Proportional Hazard Model on two-wave panel data of 149 mango farmers selected using a cluster sampling technique. The descriptive results show that 59% and 17% of the respondents were adopters and dis-adopters of mango fruit fly IPM practices, respectively. Empirical findings reveal that the cost of IPM and training on IPM positively and significantly influenced adoption, while the unavailability of the technology had a negative and significant effect on adoption. For dis-adoption, the results indicate that farm size and the quality of IPM positively influenced the hazard of exit from IPM use, and hence, enhanced the sustained adoption of IPM. The study recommends capacity building for mango farmers through training and increased access to extension services to enhance the adoption of this technology and prevent dis-adoption

Advancing operational flood forecasting, early warning and risk management with new emerging science: Gaps, opportunities and barriers in Kenya

Kenya and the wider East African region suffer from significant flood risk, as illustrated by major losses of lives, livelihoods and assets in the most recent years. This is likely to increase in future as exposure rises and rainfall intensifies under climate change. Accordingly, flood risk management is a priority action area in Kenya's national climate change adaptation planning. Here, we outline the opportunities and challenges to improve end-to-end flood early warning systems, considering the scientific, technical and institutional/governance dimensions. We demonstrate improvements in rainfall forecasts, river flow, inundation and baseline flood risk information. Notably, East Africa is a ‘sweetspot’ for rainfall predictability at sub-seasonal to seasonal timescales for extending forecast lead times beyond a few days and for ensemble flood forecasting. Further, we demonstrate coupled ensemble flow forecasting, new flood inundation simulation, vulnerability and exposure data to support Impact based Forecasting (IbF). We illustrate these advances in the case of fluvial and urban flooding and reflect on the potential for improved flood preparedness action. However, we note that, unlike for drought, there remains no national flood risk management framework in Kenya and there is need to enhance institutional capacities and arrangements to take full advantage of these scientific advances

Using research to prepare for outbreaks of severe acute respiratory infection

Severe acute respiratory infections (SARI) remain one of the leading causes of mortality around the world in all age groups. There is large global variation in epidemiology, clinical management and outcomes, including mortality. We performed a short period observational data collection in critical care units distributed globally during regional peak SARI seasons from 1 January 2016 until 31 August 2017, using standardised data collection tools. Data were collected for 1 week on all admitted patients who met the inclusion criteria for SARI, with follow-up to hospital discharge. Proportions of patients across regions were compared for microbiology, management strategies and outcomes. Regions were divided geographically and economically according to World Bank definitions. Data were collected for 682 patients from 95 hospitals and 23 countries. The overall mortality was 9.5%. Of the patients, 21.7% were children, with case fatality proportions of 1% for those less than 5 years. The highest mortality was in those above 60 years, at 18.6%. Case fatality varied by region: East Asia and Pacific 10.2% (21 of 206), Sub-Saharan Africa 4.3% (8 of 188), South Asia 0% (0 of 35), North America 13.6% (25 of 184), and Europe and Central Asia 14.3% (9 of 63). Mortality in low-income and low-middle-income countries combined was 4% as compared with 14% in high-income countries. Organ dysfunction scores calculated on presentation in 560 patients where full data were available revealed Sequential Organ Failure Assessment (SOFA) scores on presentation were significantly associated with mortality and hospital length of stay. Patients in East Asia and Pacific (48%) and North America (24%) had the highest SOFA scores of >12. Multivariable analysis demonstrated that initial SOFA score and age were independent predictors of hospital survival. There was variability across regions and income groupings for the critical care management and outcomes of SARI. Intensive care unit-specific factors, geography and management features were less reliable than baseline severity for predicting ultimate outcome. These findings may help in planning future outbreak severity assessments, but more globally representative data are required

Using research to prepare for outbreaks of severe acute respiratory infection

Abstract Severe acute respiratory infections (SARI) remain one of the leading causes of mortality around the world in all age groups. There is large global variation in epidemiology, clinical management and outcomes, including mortality. We performed a short period observational data collection in critical care units distributed globally during regional peak SARI seasons from 1 January 2016 until 31 August 2017, using standardised data collection tools. Data were collected for 1 week on all admitted patients who met the inclusion criteria for SARI, with follow-up to hospital discharge. Proportions of patients across regions were compared for microbiology, management strategies and outcomes. Regions were divided geographically and economically according to World Bank definitions. Data were collected for 682 patients from 95 hospitals and 23 countries. The overall mortality was 9.5%. Of the patients, 21.7% were children, with case fatality proportions of 1% for those less than 5 years. The highest mortality was in those above 60 years, at 18.6%. Case fatality varied by region: East Asia and Pacific 10.2% (21 of 206), Sub-Saharan Africa 4.3% (8 of 188), South Asia 0% (0 of 35), North America 13.6% (25 of 184), and Europe and Central Asia 14.3% (9 of 63). Mortality in low-income and low-middle-income countries combined was 4% as compared with 14% in high-income countries. Organ dysfunction scores calculated on presentation in 560 patients where full data were available revealed Sequential Organ Failure Assessment (SOFA) scores on presentation were significantly associated with mortality and hospital length of stay. Patients in East Asia and Pacific (48%) and North America (24%) had the highest SOFA scores of >12. Multivariable analysis demonstrated that initial SOFA score and age were independent predictors of hospital survival. There was variability across regions and income groupings for the critical care management and outcomes of SARI. Intensive care unit-specific factors, geography and management features were less reliable than baseline severity for predicting ultimate outcome. These findings may help in planning future outbreak severity assessments, but more globally representative data are required