Malaria Vector Surveillance, Prevalence, Diversity, Behavioural Patterns and Their Implications to Tourist Activities in Akwa Ibom State, Nigeria | Chapter 13 | Current Trends in Medicine and Medical Research Vol. 1
Aims: Malaria poses a medical and public health challenge in Nigeria. The burden of the disease has been a major source of concern to tourists in Uyo. Knowledge on the biting behaviour and the Human Biting Rates (HBR) are needed to assess the epidemiology of the disease and in estimating the vector - human contact.
Study Design: A six months study was carried out in Uyo, Nigeria where no information exists on the major malaria vectors associated with human malaria. Sample collection was carried out between May and October 2013 using Knockdown and Human Landing Catches (HLC) techniques.
Methodology: Adults mosquitoes were collected in two areas in Ewet Offot Community. Nine households were randomly selected in each location using Simple Random Sampling Method. Mosquitoes belonging to the Anopheles gambiae complex were further characterized and identified by a molecular method using Polymerase Chain Reaction (PCR).
Results: Two anophelines species were collected by the sampling methods consisting of 21(23.3%) Anopheles nili and 69(76.7%) Anophele gambiae complex. A Polymerase Chain Reaction (PCR) based test on the An. gambiae complex identified 66(96.0%) as An. gambiae sensu stricto. The study also revealed that the resting behaviour of An. gambiae complex species in this area is endophilic whereas the resting behaviour of An. nili is exophagic/exophilic. The peak biting activity of An. gambiae complex species occurred at 11 pm (indoor) and 7 pm (outdoor) in July whereas that of An. nili occurred at 10 pm (indoor) and 6 pm (outdoor) in June. The Human Biting Rates (HBR) recorded for An. gambiae was higher than An. nili and the total number An. gambiae collected was more than An. nili.
Conclusion: Better understanding of the behaviour of the sibling species within the complex is important to help identify their roles in disease transmission and to facilitate vector control.
Biography of author(s)
Inyang A. Atting
Department of Medical Microbiology and Parasitology, Faculty of Clinical Sciences, College of Health Sciences, University of Uyo, Uyo, Nigeria.
Mfonobong E. Akpan
Department of Medical Microbiology and Parasitology, Faculty of Clinical Sciences, College of Health Sciences, University of Uyo, Uyo, Nigeria
Nsima Ibanga G. Udoidung
Department of Animal and Environmental Biology, Faculty of Science, University of Uyo, Uyo, Nigeria.
Read full article: http://bp.bookpi.org/index.php/bpi/catalog/view/22/43/127-1
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The original idea for this site actually stemmed from another idea to provide a way of benchmarking the popularity of a video against the general population of YouTube videos. There are probably sites that do this by now, but there wasn’t when we started out. Anyway, in order to figure out how popular any one video is, you need a pretty large sample of videos to rank it against. The challenge is that the sample needs to be very random in order to properly rank a video and YouTube doesn’t appear to provide a way to obtain large numbers of random video IDs.
Alternative random YouTube videos generator: YouTuBeRandom
Even if you search on YouTube for a random string, the set of results that will be returned will still be based on popularity, so if you’re using this approach to build up your sample, you’re already in trouble. It turns out there is a multitude of ways in which the YouTube search function makes it very difficult to retrieve truly random results.
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