Ali N. Hassan

Modeling the distribution of the West Nile and Rift Valley Fever vector Culex pipiens in arid and semi-arid regions of the Middle East and North Africa

BackgroundThe Middle East North Africa (MENA) region is under continuous threat of the re-emergence of West Nile virus (WNV) and Rift Valley Fever virus (RVF), two pathogens transmitted by the vector species Culex pipiens. Predicting areas at high risk for disease transmission requires an accurate model of vector distribution, however, most Cx. pipiens distribution modeling has been confined to temperate, forested habitats. Modeling species distributions across a heterogeneous landscape structure requires a flexible modeling method to capture variation in mosquito response to predictors as well as occurrence data points taken from a sufficient range of habitat types.MethodsWe used presence-only data from Egypt and Lebanon to model the population distribution of Cx. pipiens across a portion of the MENA that also encompasses Jordan, Syria, and Israel. Models were created with a set of environmental predictors including bioclimatic data, human population density, hydrological data, and vegetation indices, and built using maximum entropy (Maxent) and boosted regression tree (BRT) methods. Models were created with and without the inclusion of human population density.ResultsPredictions of Maxent and BRT models were strongly correlated in habitats with high probability of occurrence (Pearson’s r = 0.774, r = 0.734), and more moderately correlated when predicting into regions that exceeded the range of the training data (r = 0.666,r = 0.558). All models agreed in predicting high probability of occupancy around major urban areas, along the banks of the Nile, the valleys of Israel, Lebanon, and Jordan, and southwestern Saudi Arabia. The most powerful predictors of Cx. pipiens habitat were human population density (60.6% Maxent models, 34.9% BRT models) and the seasonality of the enhanced vegetation index (EVI) (44.7% Maxent, 16.3% BRT). Maxent models tended to be dominated by a single predictor. Areas of high probability corresponded with sites of independent surveys or previous disease outbreaks.ConclusionsCx. pipiens occurrence was positively associated with areas of high human population density and consistent vegetation cover, but was not significantly driven by temperature and rainfall, suggesting human-induced habitat change such as irrigation and urban infrastructure has a greater influence on vector distribution in this region than in temperate zones.

Linking land cover and species distribution models to project potential ranges of malaria vectors: an example using Anopheles arabiensis in Sudan and Upper Egypt

BackgroundAnopheles arabiensis is a particularly opportunistic feeder and efficient vector of Plasmodium falciparum in Africa and may invade areas outside its normal range, including areas separated by expanses of barren desert. The purpose of this paper is to demonstrate how spatial models can project future irrigated cropland and potential, new suitable habitat for vectors such as An. arabiensis.MethodsTwo different but complementary spatial models were linked to demonstrate their synergy for assessing re-invasion potential of An. arabiensis into Upper Egypt as a function of irrigated cropland expansion by 2050. The first model (The Land Change Modeler) was used to simulate changes in irrigated cropland using a Markov Chain approach, while the second model (MaxEnt) uses species occurrence points, land cover and other environmental layers to project probability of species presence. Two basic change scenarios were analysed, one involving a more conservative business-as-usual (BAU) assumption and second with a high probability of desert-to-cropland transition (Green Nile) to assess a broad range of potential outcomes by 2050.ResultsThe results reveal a difference of 82,000 sq km in potential An. arabiensis range between the BAU and Green Nile scenarios. The BAU scenario revealed a highly fragmented set of small, potential habitat patches separated by relatively large distances (maximum distance = 64.02 km, mean = 12.72 km, SD = 9.92), while the Green Nile scenario produced a landscape characterized by large patches separated by relatively shorter gaps (maximum distance = 49.38, km, mean = 4.51 km, SD = 7.89) that may be bridged by the vector.ConclusionsThis study provides a first demonstration of how land change and species distribution models may be linked to project potential changes in vector habitat distribution and invasion potential. While gaps between potential habitat patches remained large in the Green Nile scenario, the models reveal large areas of future habitat connectivity that may facilitate the re-invasion of An. arabiensis from Sudan into Upper Egypt. The methods used are broadly applicable to other land cover changes as they influence vector distribution, particularly those related to tropical deforestation and urbanization processes.

A Modeling Approach to Investigate Epizootic Outbreaks and Enzootic Maintenance of Rift Valley Fever Virus

We propose a mathematical model to investigate the transmission dynamics of Rift Valley fever (RVF) virus among ruminants. Our findings indicate that in endemic areas RVF virus maintains at a very low level among ruminants after outbreaks and subsequent outbreaks may occur when new susceptible ruminants are recruited into endemic areas or abundant numbers of mosquitoes emerge when herd immunity decreases. Many factors have been shown to have impacts on the severity of RVF outbreaks; a higher probability of death due to RVF among ruminants, a higher mosquito:ruminant ratio, or a shorter lifespan of animals can amplify the magnitude of the outbreaks; vaccination helps to reduce the magnitude of RVF outbreaks and the loss of animals efficiently, and the maximum vaccination effort (a high vaccination rate and a larger number of vaccinated animals) is recommended before the commencement of an outbreak but can be reduced later during the enzootic.

Isolation of West Nile and Sindbis Viruses from Mosquitoes Collected in the Nile Valley of Egypt During an Outbreak of Rift Valley Fever

Abstract As part of an evaluation of potential vectors of arboviruses during a Rift Valley fever (RVF) outbreak in the Nile Valley of Egypt in August 1993, we collected mosquitoes in villages with known RVF viral activity. Mosquitoes were sorted to species, pooled, and processed for virus isolation both by intracerebral inoculation into suckling mice and by inoculation into cell culture. A total of 33 virus isolates was made from 36,024 mosquitoes. Viruses were initially identified by indirect fluorescent antibody testing and consisted of 30 flaviviruses (all members of the Japanese encephalitis complex, most probably West Nile [WN] virus) and three alphaviruses (all members of western equine encephalitis complex, most probably Sindbis). The identity of selected viruses was confirmed by reverse transcriptase-polymerase chain reaction and sequencing. Culex antennatus (Becker) and Culex perexiguus Theobald accounted for five (17%) and 23 (77%) of the WN virus isolations, respectively. Despite isolation of viruses from 32 pools of mosquitoes (both WN and Sindbis viruses were isolated from a single pool), RVF virus was not isolated from these mosquitoes, even though most of them are known competent vectors collected during an ongoing RVF outbreak. Thus, it should be remembered, that even during a known arbovirus outbreak, other arboviruses may still be circulating and causing disease.

A Statistical Model of Rift Valley Fever Activity in Egypt

ABSTRACT: Rift Valley fever (RVF) is a viral disease of animals and humans and a global public health concern due to its ecological plasticity, adaptivity, and potential for spread to countries with a temperate climate. In many places, outbreaks are episodic and linked to climatic, hydrologic, and socioeconomic factors. Although outbreaks of RVF have occurred in Egypt since 1977, attempts to identify risk factors have been limited. Using a statistical learning approach (lasso-regularized generalized linear model), we tested the hypotheses that outbreaks in Egypt are linked to (1) River Nile conditions that create a mosquito vector habitat, (2) entomologic conditions favorable to transmission, (3) socio-economic factors (Islamic festival of Greater Bairam), and (4) recent history of transmission activity. Evidence was found for effects of rainfall and river discharge and recent history of transmission activity. There was no evidence for an effect of Greater Bairam. The model predicted RVF activity correctly in 351 of 358 months (98.0%). This is the first study to statistically identify risk factors for RVF outbreaks in a region of unstable transmission.

A model for the coupling of the Greater Bairam and local environmental factors in promoting Rift-Valley Fever epizootics in Egypt

OBJECTIVES Rift-Valley Fever (RVF) is a zoonotic mosquito-borne disease in Africa and the Arabian Peninsula. Drivers for this disease vary by region and are not well understood for North African countries such as Egypt. A deeper understanding of RVF risk factors would inform disease management policies. STUDY DESIGN The present study employs mathematical and computational modeling techniques to ascertain the extent to which the severity of RVF epizootics in Egypt differs depending on the interaction between imported ruminant and environmentally-constrained mosquito populations. METHODS An ordinary differential system of equations, a numerical model, and an individual-based model (IBM) were constructed to represent RVF disease dynamics between localized mosquitoes and ruminants being imported into Egypt for the Greater Bairam. Four cases, corresponding to the Greater Bairams occurrence during distinct quarters of the solar year, were set up in both models to assess whether the different season-associated mosquito populations present during the Greater Bairam resulted in RVF epizootics of variable magnitudes. RESULTS The numerical model and the IBM produced nearly identical results: ruminant and mosquito population plots for both models were similar in shape and magnitude for all four cases. In both models, all four cases differed in the severity of their corresponding simulated RVF epizootics. The four cases, ranked by the severity of the simulated RVF epizootics in descending order, correspond with the occurrence of the Greater Bairam on the following months: July, October, April, and January. The numerical model was assessed for sensitivity with respect to parameter values and exhibited a high degree of robustness. CONCLUSIONS Limiting the importation of infected ruminants beginning one month prior to the Greater Bairam festival (on years in which the festival falls between the months of July and October: 2014-2022) might be a feasible way of mitigating future RVF epizootics in Egypt.