Preliminary study on temporal variations in biting activity of Simulium damnosum s.l. in Abeokuta North LGA, Ogun State Nigeria

<p>Abstract</p> <p>Background</p> <p><it>Simulum damnosum </it>Theobald <it>sensu lato </it>(<it>s.l</it>.) is the vector of the parasitic filarial worm <it>Onchocerca volvulus </it>Leuckart which causes onchocerciasis. In order to understand the vector population dynamics, a preliminary 12 months entomological evaluation was carried out at Abeokuta, the Southwest Zone of Nigeria, an onchocerciasis endemic area, where vector control has not been previously initiated. <it>S.damnosum s.l</it>. flies were caught on human attractants between 700 to1800 hours each day, for 4 days each month, from August 2007 to July 2008. The flies caught were classified as either forest-dwelling or savanna-dwelling groups based on the colour of certain morphological characters. Climatic data such as rainfall, humidity and temperature were also collected monthly during the period of survey.</p> <p>Results</p> <p>A total of 1,139 flies were caught, 596 (52.33%) were forest-dwelling group while 543 (47.67%) were savanna-dwelling group of <it>S. damnosum s.l</it>. The highest percentage of forest-dwelling group was caught in the month of August 2007 (78.06%) and the least percentage of forest-dwelling groups was caught in November 2007 (8.14%). The highest percentage of savannah-dwelling group was caught in the month of November 2007 (91.86%) and the least percentage of savannah-dwelling group was caught in August 2007 (21.94%). There was no significant difference between the population of forest and savannah-dwelling groups of the fly when the means of the fly population were compared (<it>P </it>= 0.830). Spearman correlation analysis showed a significant relationship between monthly fly population with monthly average rainfall (<it>r </it>= 0.550, n = 12, <it>P </it>= 0.033), but no significant relationship with monthly average temperature (<it>r </it>= 0.291, <it>n </it>= 12, <it>P </it>= 0.179). There was also a significant relationship between monthly fly population and monthly average relative humidity (<it>r </it>= 0.783, <it>n </it>= 12 <it>P </it>= 0.001). There was no significant correlation between the population of forest-dwelling group of <it>S. damnosum s.l</it>. and monthly average rainfall (<it>r </it>= 0.466, <it>n </it>= 12, <it>P </it>= 0.064) and monthly average temperature (<it>r </it>= 0.375, n = 12, <it>P </it>= 0.115) but there was significant correlation with monthly average relative humidity (<it>r </it>= 0.69, <it>n </it>= 12, <it>P </it>= 0.006). There was significant correlation between savannah-dwelling group and monthly average rainfall (<it>r </it>= 0.547, <it>n </it>= 12, <it>P </it>= 0.033), and monthly average relative humidity (<it>r </it>= 0.504, <it>n </it>= 12, <it>P </it>= 0.047) but there was no significant correlation with monthly average temperature (<it>r </it>= 0.142, <it>n </it>= 12, <it>P </it>= 0.329)</p> <p>Conclusion</p> <p>The results from this study showed that both the forest and the savannah dwelling groups of <it>S. damnosum s.l</it>. were caught biting in the study area. This could have implications on the transmission and epidemiology of human onchocerciasis if not monitored.</p

Geographical information system and predictive risk maps of urinary schistosomiasis in Ogun State, Nigeria

<p>Abstract</p> <p>Background</p> <p>The control of urinary schistosomiasis in Ogun State, Nigeria remains inert due to lack of reliable data on the geographical distribution of the disease and the population at risk. To help in developing a control programme, delineating areas of risk, geographical information system and remotely sensed environmental images were used to developed predictive risk maps of the probability of occurrence of the disease and quantify the risk for infection in Ogun State, Nigeria.</p> <p>Methods</p> <p>Infection data used were derived from carefully validated morbidity questionnaires among primary school children in 2001–2002, in which school children were asked among other questions if they have experienced "blood in urine" or urinary schistosomiasis. The infection data from 1,092 schools together with remotely sensed environmental data such as rainfall, vegetation, temperature, soil-types, altitude and land cover were analysis using binary logistic regression models to identify environmental features that influence the spatial distribution of the disease. The final regression equations were then used in Arc View 3.2a GIS software to generate predictive risk maps of the distribution of the disease and population at risk in the state.</p> <p>Results</p> <p>Logistic regression analysis shows that the only significant environmental variable in predicting the presence and absence of urinary schistosomiasis in any area of the State was Land Surface Temperature (LST) (B = 0.308, p = 0.013). While LST (B = -0.478, p = 0.035), rainfall (B = -0.006, p = 0.0005), ferric luvisols (B = 0.539, p = 0.274), dystric nitosols (B = 0.133, p = 0.769) and pellic vertisols (B = 1.386, p = 0.008) soils types were the final variables in the model for predicting the probability of an area having an infection prevalence equivalent to or more than 50%. The two predictive risk maps suggest that urinary schistosomiasis is widely distributed and occurring in all the Local Government Areas (LGAs) in State. The high-risk areas (≥ 50% prevalence) however, are confined to scatter foci in the north western part of the State. The model also estimated that 98.99% of schools aged children (5–14 years) are living in areas suitable for urinary schistosomiasis transmission and are at risk of infection.</p> <p>Conclusion</p> <p>The risk maps developed will hopefully be useful to the state health officials, by providing them with detailed distribution of urinary schistosomiasis, help to delineate areas for intervention, assesses population at risk thereby helping in optimizing scarce resources.</p

Risk model map of presence of urinary schistosomiasis in Ogun State as observed and predicted through logistic regression

<p><b>Copyright information:</b></p><p>Taken from "Geographical information system and predictive risk maps of urinary schistosomiasis in Ogun State, Nigeria"</p><p>http://www.biomedcentral.com/1471-2334/8/74</p><p>BMC Infectious Diseases 2008;8():74-74.</p><p>Published online 31 May 2008</p><p>PMCID:PMC2438363.</p><p></p

Risk model map of presence of high-risk schools for urinary schistosomiasis in Ogun State as observed and predicted through logistic regression

<p><b>Copyright information:</b></p><p>Taken from "Geographical information system and predictive risk maps of urinary schistosomiasis in Ogun State, Nigeria"</p><p>http://www.biomedcentral.com/1471-2334/8/74</p><p>BMC Infectious Diseases 2008;8():74-74.</p><p>Published online 31 May 2008</p><p>PMCID:PMC2438363.</p><p></p

Risk map of suitable areas for urinary schistosomiasis transmission in Ogun State based on predicted probability of 0

75.<p><b>Copyright information:</b></p><p>Taken from "Geographical information system and predictive risk maps of urinary schistosomiasis in Ogun State, Nigeria"</p><p>http://www.biomedcentral.com/1471-2334/8/74</p><p>BMC Infectious Diseases 2008;8():74-74.</p><p>Published online 31 May 2008</p><p>PMCID:PMC2438363.</p><p></p