Modelling of malaria risk,rates, and trends: A spatiotemporal approach for identifying and targeting sub-national areas of high and low burden

While mortality from malaria continues to decline globally, incidence rates in many countries are rising. Within countries, spatial and temporal patterns of malaria vary across communities due to many different physical and social environmental factors. To identify those areas most suitable for malaria elimination or targeted control interventions, we used Bayesian models to estimate the spatiotemporal variation of malaria risk, rates, and trends to determine areas of high or low malaria burden compared to their geographical neighbours. We present a methodology using Bayesian hierarchical models with a Markov Chain Monte Carlo (MCMC) based inference to fit a generalised linear mixed model with a conditional autoregressive structure. We modelled clusters of similar spatiotemporal trends in malaria risk, using trend functions with constrained shapes and visualised high and low burden districts using a multi-criterion index derived by combining spatiotemporal risk, rates and trends of districts in Zambia. Our results indicate that over 3 million people in Zambia live in high-burden districts with either high mortality burden or high incidence burden coupled with an increasing trend over 16 years (2000 to 2015) for all age, under-five and over-five cohorts. Approximately 1.6 million people live in high-incidence burden areas alone. Using our method, we have developed a platform that can enable malaria programs in countries like Zambia to target those high-burden areas with intensive control measures while at the same time pursue malaria elimination efforts in all other areas. Our method enhances conventional approaches and measures to identify those districts which had higher rates and increasing trends and risk. This study provides a method and a means that can help policy makers evaluate intervention impact over time and adopt appropriate geographically targeted strategies that address the issues of both high-burden areas, through intensive control approaches, and low-burden areas, via specific elimination programs.     

Sap flow variation in selected riparian woodland species in the Okavango Delta,Botswana

In the tropical Okavango Delta, transpiration by trees is an important process partly responsible for maintaining the basin as a freshwater environment. Quantification of evapotranspiration from terrestrial landforms of the delta, fringed by riparian woodlands, is one of the main contributors to uncertainty in current hydrological modelling. We investigated sap flow of common trees in the distal, mid‐ and upper delta in July–August 2012, November–December 2012 and February–April 2013 using the compensation heat pulse velocity method. In the distal delta, four Diospyros mespiliformis individuals of different sizes were studied. Four trees of different species were studied in the mid‐ and upper delta. Sap flow density (SFD; flow per unit cross‐sectional area) was used as a common unit to facilitate comparison. Sap flow varied with tree size, species, season and location. It was positively correlated with tree size (r² = 0.67). Sap flow variation between seasons and across locations in all the species studied indicated two distinct groups. Group 1 transpired the least during the hottest season, November–December, and Group 2 the most. In Group 1, the highest average SFD was 1.17 l cm^?2 day^?1 during July–August; in Group 2, it was 1.07 l cm^?2 day^?1 during November–December. Changes in the hydrology of the delta would negatively affect the riparian woodland.