The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication e...The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.展开更多
A study was conducted in Northern Province of Rwanda, from the College of Agriculture and Veterinary Medicine, Busogo Campus located in Musanze district to evaluate the effect of feed type on rabbit growth in rabbit i...A study was conducted in Northern Province of Rwanda, from the College of Agriculture and Veterinary Medicine, Busogo Campus located in Musanze district to evaluate the effect of feed type on rabbit growth in rabbit intensification systems in Rwanda. The Complete Randomized Design (CRD) was used and data were collected on rabbit growth weekly for a period of 12 weeks. The experiment was composed of three treatments replicated ten times. The treatments included three types of feeds namely;cabbage combined with Mucuna pruriens added to local forage (I), cabbages combined with Leucaena leucocephala added to local forage (II) and a control composed of other varieties of locally available forage, such as Bidens pilosa, Crassocephalum vitellium and Galinsoga parviflora (III) which was considered as the control (Farmers practice). The feeds were given to ten rabbits separated in individual cages, and each rabbit was considered a replicate. Water was given ad libitum. One month old rabbits (weaners) were used and data were collected after one week of adaptation for 12 weeks. The results showed that the mean of weight gain after 12 weeks was 783.3 g, 760.7 g and 705.7 g for feed type I, II and III respectively. The difference between means of feed types after 12 weeks was not significant (p > 0.5), which implied that rabbit growth did not depend on the feed type. The mean weight gain after 8 weeks was 707.5 g, 661.4 g and 577.1 g for feed type I, II and III respectively. At 8 weeks, the difference between means of feed types was significant (p Mucuna pruriens combined with cabbage and local forage were growing faster than rabbits from other treatments at 8 weeks. The researchers recommended that farmers should be facilitated with feeding materials by the concerned institutions. Training of rabbit farmers and further researches on locally available feeding materials were also given as recommendations at the end of this study.展开更多
文摘The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.
文摘A study was conducted in Northern Province of Rwanda, from the College of Agriculture and Veterinary Medicine, Busogo Campus located in Musanze district to evaluate the effect of feed type on rabbit growth in rabbit intensification systems in Rwanda. The Complete Randomized Design (CRD) was used and data were collected on rabbit growth weekly for a period of 12 weeks. The experiment was composed of three treatments replicated ten times. The treatments included three types of feeds namely;cabbage combined with Mucuna pruriens added to local forage (I), cabbages combined with Leucaena leucocephala added to local forage (II) and a control composed of other varieties of locally available forage, such as Bidens pilosa, Crassocephalum vitellium and Galinsoga parviflora (III) which was considered as the control (Farmers practice). The feeds were given to ten rabbits separated in individual cages, and each rabbit was considered a replicate. Water was given ad libitum. One month old rabbits (weaners) were used and data were collected after one week of adaptation for 12 weeks. The results showed that the mean of weight gain after 12 weeks was 783.3 g, 760.7 g and 705.7 g for feed type I, II and III respectively. The difference between means of feed types after 12 weeks was not significant (p > 0.5), which implied that rabbit growth did not depend on the feed type. The mean weight gain after 8 weeks was 707.5 g, 661.4 g and 577.1 g for feed type I, II and III respectively. At 8 weeks, the difference between means of feed types was significant (p Mucuna pruriens combined with cabbage and local forage were growing faster than rabbits from other treatments at 8 weeks. The researchers recommended that farmers should be facilitated with feeding materials by the concerned institutions. Training of rabbit farmers and further researches on locally available feeding materials were also given as recommendations at the end of this study.
