Modelling the influence of temperature and rainfall on the population dynamics of Anopheles arabiensis

Abiodun, Gbenga J.; Maharaj, Rajendra; Witbooi, Peter J.; Okosun, Kazeem Oare

doi:10.1186/s12936-016-1411-6

Cited by 108 publications

(107 citation statements)

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“…In Burkina Faso, the epidemiological features of malaria show increasing prevalence according to the variation in rainfall, from low to high, respectively in the Sahelian, Sudano‐Sahelian, and Sudanese climate regions. [ 3,4 ] Temperature, dry‐season, and rainfall impact the vector density and metabolism of An. gambiae (s.l.).…”

Section: Introductionmentioning

confidence: 99%

Proteomic Investigation on Anopheles gambiae in Burkina Faso Related to Insecticide Pressures from Different Climatic Regions

et al. 2020

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In Sub-Saharan Africa, An. gambiae sensu lato (s.l.) Giles 190, largely contributes to malaria transmission. Therefore, the authors carry out a proteomic analysis to compare its metabolic state, depending on different pesticide pressures by selecting areas with/without cotton crops. The proteomes data are available via ProteomeXchange with identifier PXD016300. From a total of 1.182 identified proteins, 648 are retained for further statistical analysis and are attributed to biological functions, the most important of which being energy metabolism (120 proteins) followed by translation-biogenesis (74), cytoskeleton (71), stress response (62), biosynthetic process (60), signalling (44), cellular respiration (38), cell redox homeostasis (25), DNA processing (17), pheromone binding (10), protein folding (9), RNA processing (9), other proteins (26) and unknown functions (83). In the Sudano-Sahelian region, 421 (91.3%) proteins are found in samples from areas both with and without cotton crops. By contrast, in the Sahelian region, only 271 (55.0%) are common to both crop areas, and 233 proteins are up-regulated from the cotton area. The focus is placed on proteins with putative roles in insecticide resistance, according to literature. This study provides the first whole-body proteomic characterisation of An. gambiae s.l. in Burkina Faso, as a framework to strengthen vector control strategies.

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Section: Introductionmentioning

confidence: 99%

Proteomic Investigation on Anopheles gambiae in Burkina Faso Related to Insecticide Pressures from Different Climatic Regions

et al. 2020

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“… over KwaZulu‐Natal province—one of the epidemic provinces in South Africa. Several other studies have explored the impacts of environmental variables on malaria transmission and mosquito abundance. We reference the work of Ref.…”

Section: Introductionmentioning

confidence: 99%

Development and analysis of a malaria transmission mathematical model with seasonal mosquito life‐history traits

et al. 2019

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In this paper, we develop and analyze a malaria model with seasonality of mosquito life-history traits: periodicmosquitoes per capita birth rate, -mosquitoes death rate, -probability of mosquito to human disease transmission, -probability of human to mosquito disease transmission, and -mosquitoes biting rate. All these parameters are assumed to be time dependent leading to a nonautonomous differential equation system. We provide a global analysis of the model depending on two threshold parameters  0 and  0 < 1 (with  0 ≤  0 ). When  0 < 1, then the disease-free stationary state is locally asymptotically stable. In the presence of the human disease-induced mortality, the global stability of the disease-free stationary state is guarantied when  0 < 1. On the contrary, if  0 > 1, the disease persists in the host population in the long term and the model admits at least one positive periodic solution.Moreover, by a numerical simulation, we show that a sub-critical (backward) bifurcation is possible at  0 = 1. Finally, the simulation results are in accordance with the seasonal variation of the reported cases of a malariaepidemic region in Mpumalanga province in South Africa. K E Y W O R D S basic reproduction number, global stability, periodic solution, seasonal pattern, uniform persistence Stud Appl Math. 2020;144:389-411.wileyonlinelibrary.com/journal/sapm

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“…Okuneye and Gumel [12] designed a new non-autonomous model to assess the impact of variability in temperature and rainfall on the transmission dynamics of malaria in KwaZulu-Natal province of South Africa. Similarly, in the recent study of Abiodun et al [13], a climate-based mosquito model was presented to explore the impact of temperature and rainfall on mosquito population dynamics over Dondotha village in KwaZulu-Natal province, South Africa. The model demonstrates and quantifies the influence of temperature and rainfall on the abundance of Anopheles arabiensis over time and presents the strong seasonal variability over the region.…”

Section: Introductionmentioning

confidence: 99%

“…The current study aims to further develop the mosquito model presented in [13] to investigate the impact of climate variability on malaria transmission over KwaZulu-Natal province during the period 1970-2005. The newly developed mosquito-human malaria model will be used to analyse the temporal dynamics of the diseases over the province.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Impact of Climate Variability on Malaria Transmission Using a Dynamic Mosquito-Human Malaria Model

Abiodun¹,

Witbooi²,

Okosun³

et al. 2018

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Introduction: The reasons for malaria resurgence mostly in Africa are yet to be well understood. Although the causes are often linked to regional climate change, it is important to understand the impact of climate variability on the dynamics of the disease. However, this is almost impossible without adequate long-term malaria data over the study areas. Methods: In this study, we develop a climate-based mosquito-human malaria model to study malaria dynamics in the human population over KwaZulu-Natal, one of the epidemic provinces in South Africa, from 1970-2005. We compare the model output with available observed monthly malaria cases over the province from September 1999 to December 2003. We further use the model outputs to explore the relationship between the climate variables (rainfall and temperature) and malaria incidence over the province using principal component analysis, wavelet power spectrum and wavelet coherence analysis. The model produces a reasonable fit with the observed data and in particular, it captures all the spikes in malaria prevalence. Results: Our results highlight the importance of climate factors on malaria transmission and show the seasonality of malaria epidemics over the province. Results from the principal component analyses further suggest that, there are two principal factors associated with climates variables and the model outputs. One of the factors indicate high loadings on Susceptible, Exposed and Infected human, while the other is more correlated with Susceptible and Recovered humans. However, both factors reveal the inverse correlation between Susceptible-Infected and Susceptible-Recovered humans respectively. Through the spectrum analysis, we notice a strong annual cycle of malaria incidence over the province and ascertain a dominant of one year periodicity. Consequently, our findings indicate that an average of 0 to 120-day lag is generally noted over the study period, but the 120-day lag is more associated with temperature than rainfall. This is consistence with other results obtained from our analyses that malaria transmission is more tightly coupled with temperature than with rainfall in KwaZulu-Natal province.

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Modelling the influence of temperature and rainfall on the population dynamics of Anopheles arabiensis

Cited by 108 publications

References 36 publications

Proteomic Investigation on Anopheles gambiae in Burkina Faso Related to Insecticide Pressures from Different Climatic Regions

Proteomic Investigation on Anopheles gambiae in Burkina Faso Related to Insecticide Pressures from Different Climatic Regions

Development and analysis of a malaria transmission mathematical model with seasonal mosquito life‐history traits

Exploring the Impact of Climate Variability on Malaria Transmission Using a Dynamic Mosquito-Human Malaria Model

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