Publications by authors named "Ahmad Mohiddin Mohd-Ngesom"

Background: Despite significant reductions in recent malaria cases and deaths globally, the persistence of this health concern necessitates a shift from traditional top-down approaches. Consequently, malaria control initiatives increasingly focus on empowering local communities through community-centred strategies. Therefore, this scoping review protocol systematically explores diverse community knowledge approaches adopted in malaria programmes worldwide and their associated outcomes.

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Background: Leptospirosis is a water-related zoonotic disease. The disease is primarily transmitted from animals to humans through pathogenic bacteria in contaminated water and soil. Rivers have a critical role in transmissions, while co-infection potentials with other waterborne bacteria might increase the severity and death risk of the disease.

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Machine learning algorithms (ML) are receiving a lot of attention in the development of predictive models for monitoring dengue transmission rates. Previous work has focused only on specific weather variables and algorithms, and there is still a need for a model that uses more variables and algorithms that have higher performance. In this study, we use vector indices and meteorological data as predictors to develop the ML models.

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Pathogenic is the causative agent of leptospirosis, an emerging zoonotic disease affecting animals and humans worldwide. The risk of host infection following interaction with environmental sources depends on the ability of to persist, survive, and infect the new host to continue the transmission chain. may coexist with other pathogens, thus providing a suitable condition for the development of other pathogens, resulting in multi-pathogen infection in humans.

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Background: Dengue is a significant public health issue that is caused by Aedes spp. mosquitoes. The current vector control methods are unable to effectively reduce Aedes populations and thus fail to decrease dengue transmission.

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We aim to investigate the effect of large-scale human movement restrictions during the COVID-19 lockdown on both the dengue transmission and vector occurrences. This study compared the weekly dengue incidences during the period of lockdown to the previous years (2015 to 2019) and a Seasonal Autoregressive Integrated Moving Average (SARIMA) model that expected no movement restrictions. We found that the trend of dengue incidence during the first two weeks (stage 1) of lockdown decreased significantly with the incidences lower than the lower confidence level (LCL) of SARIMA.

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