Rift Valley fever vector diversity and impact of meteorological and environmental factors on Culex pipiens dynamics in the Okavango Delta, Botswana

Entomologic investigations were conducted in the Al-Darb, Al-Reath, Al-Aridah, Abuareesh, Al-Ahad, Samttah, Sabyah, Damad and Beash areas by CO2-baited CDC miniature light traps in the Jazan region. Vectors were identified morphologically, as well as COI gene segment amplification and sequencing. The relative abundance (RA%) and pattern of occurrence (C%) were recorded. The presence of the Rift Valley fever virus (RVFV) in pooled mosquito samples was investigated by reverse transcriptase-polymerase chain reaction (RT-PCR). Culex pipiens (C. pipiens) and Culex tritaeniorhynchus (C. tritaeniorhynchus) were found with RA% values of 96% and 4%, respectively, in the region. Significant variations in vector population densities were observed in different districts. The C. pipiens was found highly abundant in all districts and RA% value (100%) was recorded in the Al-Darb, Al-Reath, Al-Aridah, Samttah and Damad areas, whereas RA% values (93.75%, 93.33%, 92.30% and 91.66%) were noted in Al-Ahad, Sabyah, Abuareesh and Beash districts, respectively. RA% values for C. tritaeniorhynchus were recorded as 8.33%, 7.70%, 6.66% and 6.25% in Beash, Abuareesh, Sabyah and Al-Ahad areas, respectively. The pattern of occurrence for C. pipiens and C. tritaeniorhynchus was recorded as 100% and 44.4% in the region. Phylogenetic analysis of C. pipiens and C. tritaeniorhynchus exhibited a close relationship with mosquitoes from Kenya and Turkey, respectively. All mosquito samples tested by RT-PCR were found negative for RVFV. In summary, the current study assessed the composition, abundance, distribution of different mosquito vectors and presence of RVFV in different areas of the Jazan region. Our data will help risk assessments of RVFV future re-emergence in the region.

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A One Health perspective to identify environmental factors that affect Rift Valley fever transmission in Gezira state, Central Sudan

Tropical Medicine and Health ◽

10.1186/s41182-019-0178-1 ◽

2019 ◽

Vol 47 (1) ◽

Author(s):

Rania Salah Eldien Bashir ◽

Osama Ahmed Hassan

Keyword(s):

Remote Sensing ◽

Logistic Regression ◽

Environmental Factors ◽

Soil Type ◽

Rift Valley ◽

Rift Valley Fever ◽

Logistic Regression Model ◽

Multilevel Logistic Regression ◽

Valley Fever ◽

Multilevel Logistic Regression Model

Abstract Background Rift Valley fever (RVF) is a zoonotic viral vector-borne disease that affects both animals and humans and leads to severe economic consequences. RVF outbreaks are triggered by a favorable environment and flooding, which enable mosquitoes to proliferate and spread the virus further. RVF is endemic to Africa and has spread to Saudi Arabia and Yemen. There is great concern that RVF may spread to previously unaffected geographic regions due to climate change. We aimed to better understand the spatiotemporal pattern of the 2007 RVF outbreak at the human–animal–environment interface and to determine environmental factors that may have effects on RVF occurrence in Gezira state, Sudan. Materials and methods We compiled epidemiological, environmental, and spatiotemporal data across time and space using remote sensing and a geographical information system (GIS). The epidemiological data included 430 RVF human cases as well as human and animal population demographic data for each locality. The cases were collected from 41 locations in Gezira state. The environmental data represent classified land cover during 2007, the year of the RVF outbreak, and the average of the Normalized Difference Vegetation Index (NDVI) for 6 months of 2007 is compared with those of 2010 and 2014, when there was no RVF outbreak. To determine the effect of the environmental factors such as NDVI, soil type, and RVF case’s location on the Blue Nile riverbank on RVF incidence in Gezira state, a multilevel logistic regression model was carried out. Results We found that the outbreak in Gezira state occurred as a result of interaction among animals, humans, and the environment. The multilevel logistic regression model (F = 43,858, df = 3, p = 0.000) explained 23% of the variance in RVF incidence due to the explanatory variables. Notably, soil type (β = 0.613, t = 11.284, p = 0.000) and NDVI (β = − 0.165, t = − 3.254, p = 0.001) were the explanatory environmental factors that had significant effects on RVF incidence in 2007 in Gezira state, Sudan. Conclusions Precise remote sensing and the GIS technique, which rely on environmental indices such as NDVI and soil type that are satellite-derived, can contribute to establishing an early warning system for RVF in Sudan. Future preparedness and strengthening the capacity of regional laboratories are necessary for early notification of outbreaks in animals and humans.

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