Objective: To assess the effectiveness of seasonal malaria chemoprevention (SMC) in reducing under-five malaria morbidity and mortalityDesign: Under-five malaria data for confirmed episodes, deaths, and number of children dosed per cycle of SMC campaign were extracted from the District Health Information Management System (DHIMS-2) for 2018-2019. Data verification was done to compare extracted data with the source for completeness and consistency. Association be-tween SMC and the main outcome variables (malaria cases and mortality) was computed from 2X2 tables and reported as rate ratios at a 95% confidence level.Setting: All seven (7) districts in Savannah Region, GhanaParticipants: Children under five yearsIntervention: Sulphadoxine-Pyrimethamine and Amodiaquine (SPAQ) prophylaxis given monthly, four times, during the rainy season (July to October)Main outcome measures: SMC coverage per cycle and under-five malaria morbidity and mortality ratiosResults: Over 370,000 dose packs of SPAQ were administered with an average cycle coverage of 93%. There was approximately 17% (p<0.01) and 67% (p=0.047) reduction in malaria-related morbidity and mortality, respectively, in the implementation year compared with the baseline. This translated into nearly 9,300 episodes of all forms of malaria and nine malaria-attributable deaths averted by the intervention.Conclusion: SMC (combined with existing control measures) wields prospects of accelerating the regional/national malaria elimination efforts if the implementation is optimised. Expansion of the intervention to other high-prevalence regions with seasonal variation in disease burden may be worthwhile.
A drive-by download is a download that occurs without users action or knowledge. It usually triggers an exploit of vulnerability in a browser to downloads an unknown file. The malicious program in the downloaded file installs itself on the victims machine. Moreover, the downloaded file can be camouflaged as an installer that would further install malicious software. Drive-by downloads is a very good example of the exponential increase in malicious activity over the Internet and how it affects the daily use of the web. In this paper, we try to address the problem caused by drive-by downloads from different standpoints. We provide in-depth understanding of the difficulties in dealing with drive-by downloads and suggest appropriate solutions. We propose machine learning and feature selection solutions to remedy the drive-by download problem. Experimental results reported 98.2% precision, 98.2% F-Measure and 97.2% ROC area.
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