• Energy Research

Abstract Background Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in malaria incidence. The study investigated the effect of environmental covariates on malaria incidence in high transmission settings of Uganda. Methods This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period. Estimates of monthly malaria incidence (MI) were derived from MRCs’ catchment areas. Environmental data including monthly temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed lag nonlinear model was used to investigate the effect of environmental covariates on malaria incidence. Results Overall, the median (range) monthly temperature was 30 °C (26–47), rainfall 133.0 mm (3.0–247), NDVI 0.66 (0.24–0.80) and MI was 790 per 1000 person-years (73–3973). Temperature of 35 °C was significantly associated with malaria incidence compared to the median observed temperature (30 °C) at month lag 2 (IRR: 2.00, 95% CI: 1.42–2.83) and the increased cumulative IRR of malaria at month lags 1–4, with the highest cumulative IRR of 8.16 (95% CI: 3.41–20.26) at lag-month 4. Rainfall of 200 mm significantly increased IRR of malaria compared to the median observed rainfall (133 mm) at lag-month 0 (IRR: 1.24, 95% CI: 1.01–1.52) and the increased cumulative IRR of malaria at month lags 1–4, with the highest cumulative IRR of 1.99(95% CI: 1.22–2.27) at lag-month 4. Average NVDI of 0.72 significantly increased the cumulative IRR of malaria compared to the median observed NDVI (0.66) at month lags 2–4, with the highest cumulative IRR of 1.57(95% CI: 1.09–2.25) at lag-month 4. Conclusions In high-malaria transmission settings, high values of environmental covariates were associated with increased cumulative IRR of malaria, with IRR peaks at variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.

Bayesian model averaging was employed to study the dynamics of aircraft departure delay based on airport operational data of aviation and meteorological parameters collected on daily basis for the period 2004 through 2008 in matrix X. Models were evaluated using the R programming language mainly to establish the combinations of variables that could formulate the best model through assessing their importance. Findings showed that out of the sixteen covariates, 62.5% were suitable for model inclusion to determine aircraft departure delay of which 40% exhibited negative coefficients. The following parameters were found to negatively affect departure delay; number of aircrafts that departed on time (-0.562), number of persons on board of the arriving aircrafts (-0.002), daily average visibility (-0.001) and year (-1.605). Comparison between Posterior Model Probabilities (PMP Exact) and that based on Markov Chain Monte Carlo (PMP MCMC) revealed a high correlation (0.998; p<0.01).The study recommended the MCMC as providing a more efficient approach to modelling the determinants of aircraft departure delay at an airport.

Abstract Background Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in the incidence of symptomatic malaria. The study aim was to investigate the associations between environmental covariates and malaria incidence in high transmission settings of Uganda.Methods This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period (January 2019 - December 2020). Estimates of monthly malaria incidence (MI) were derived from MRCs’ catchment areas. Environmental data including monthy average measures of temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed non-linear lagged model was used to investigate the quantitative relationship between environmental covariates and malaria incidence. Results Overall, the median (range) monthly temperature was 30oC (26-47), rainfall 133.0 mm (3.0-247), NDVI 0.66 (0.24-0.80) and MI was 790 per 1000 person-years (73-3973). A non-linear relationship between environmental covariates and malaria incidence was observed. An average monthly temperature of 35oC was associated with significant increases in malaria incidence compared to the median observed temperature (30oC) at month lag 2 (IRR: 2.00, 95% CI: 1.42-2.83) and the cumulative increases in MI significantly at month lags 1-4, with the highest cumulative IRR of 8.16 (95% CI: 3.41-20.26) at lag month 4. An average monthly rainfall of 200mm was associated with significant increases in malaria incidence compared to the median observed rainfall (133mm) at lag month 0 (IRR: 1.24, 95% CI: 1.01-1.52) and the cumulative IRR increases of malaria at month lags 1-4, with the highest cumulative IRR of 1.99(95% CI: 1.22-2.27) at lag month 4. An average NVDI of 0.72 was associated with significant cumulative increases in IRR of malaria as compared to the median observed NDVI (0.66) at month lag 2-4, with the highest cumulative IRR of 1.57(95% CI: 1.09-2.25) at lag month 4. The rate of increase in cumulative IRR of malaria was highest within lag months 1-2 as compared to lag months 3-4 for all the environmental covariates.Conclusions In high-malaria transmission settings, high values of environmental covariates were associated with cumulative increases in the incidence of malaria, with peak associations occurring after variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.