To what extent does climate explain variations in reported malaria cases in early 20th century Uganda?

Abstract

Malaria case statistics were analysed for the period 1926 to 1960 to identify inter-annual variations in malaria cases for the Uganda Protectorate. The analysis shows the mid-to-late 1930s to be a period of increased reported cases. After World War II, malaria cases trend down to a relative minimum in the early 1950s, before increasing rapidly after 1953 to the end of the decade. Data for the Western Province confirm these national trends, which at the time were attributed to a wide range of causes, including land development and management schemes, population mobility, interventions and misdiagnosis. Climate was occasionally proposed as a contributor to enhanced case numbers, and unusual precipitation patterns were held responsible; temperature was rarely, if ever, considered. In this study, a dynamical malaria model was driven with available precipitation and temperature data from the period for five stations located across a range of environments in Uganda. In line with the historical data, the simulations produced relatively enhanced transmission in the 1930s, although there is considerable variability between locations. In all locations, malaria transmission was low in the late 1940s and early 1950s, steeply increasing after 1954. Results indicate that past climate variability explains some of the variations in numbers of reported malaria cases. The impact of multiannual variability in temperature, while only on the order of 0.5°C, was sufficient to drive some of the trends observed in the statistics and thus the role of climate was likely underestimated in the contemporary reports. As the elimination campaigns of the 1960s followed this partly climate-driven increase in malaria, this emphasises the need to account for climate when planning and evaluating intervention strategies.

Assessment of malaria transmission changes in Africa, due to the climate impact of land use change using Coupled Model Intercomparison Project Phase 5 earth system models

“…Similarly, in our analysis only a single malaria model was used, and recent work by Piontek et al (2014);Caminade et al (2014) and Wallace et al (2014) has highlighted the considerable uncertainty in individual impacts models. Tompkins et al (2016b) parameter ensembles for single location runs. Thus the limited ensemble size of initial conditions and modelling systems implies that the results of this study should be taken as merely indicative of potential impacts.…”

Impact of an accelerated melting of Greenland on malaria distribution over Africa

“…These data provide 1900s malaria endemicity estimates for all Plasmodium parasites before human intervention occurred at a global scale. This dataset is a better estimate of the impact of environmental conditions on malaria burden, as limited malaria control measures were undertaken prior to the 1920s 55 . These data are binned into five categories: free, PR < 1%; hypoendemic, PR > 1% and PR < 10% ; mesoendemic, PR > 10% and < 50%; hyperendemic, PR > 50% and < 75%; and holoendemic transmission, PR > 75% 56 .…”

Uncertainty in malaria simulations in the highlands of Kenya: Relative contributions of model parameter setting, driving climate and initial condition errors

“…The methodology examined the output Jacobians (the matrix of all first order function derivatives), a method that has also been employed for atmospheric models [ 42 – 44 ], but has the drawback of being dependent on the basic state employed. Parallel to the climate modeling approach, some studies employ ensembles of health models integrations with stochastic parameter perturbations [ 45 – 47 ]. These studies showed limited perturbations of a single model parameter could lead to a very large spread in the simulated transmission intensity in highland setting where temperature was close to the 18°C threshold.…”