Simulation Modelling of Population Dynamics of Mosquito Vectors for Rift Valley Fever Virus in a Disease Epidemic Setting

Abstract

BackgroundRift Valley Fever (RVF) is weather dependent arboviral infection of livestock and humans. Population dynamics of mosquito vectors is associated with disease epidemics. In our study, we use daily temperature and rainfall as model inputs to simulate dynamics of mosquito vectors population in relation to disease epidemics.Methods/FindingsTime-varying distributed delays (TVDD) and multi-way functional response equations were implemented to simulate mosquito vectors and hosts developmental stages and to establish interactions between stages and phases of mosquito vectors in relation to vertebrate hosts for infection introduction in compartmental phases. An open-source modelling platforms, Universal Simulator and Qt integrated development environment were used to develop models in C++ programming language. Developed models include source codes for mosquito fecundity, host fecundity, water level, mosquito infection, host infection, interactions, and egg time. Extensible Markup Language (XML) files were used as recipes to integrate source codes in Qt creator with Universal Simulator plug-in. We observed that Floodwater Aedines and Culicine population continued to fluctuate with temperature and water level over simulation period while controlled by availability of host for blood feeding. Infection in the system was introduced by floodwater Aedines. Culicines pick infection from infected host once to amplify disease epidemic. Simulated mosquito population show sudden unusual increase between December 1997 and January 1998 a similar period when RVF outbreak occurred in Ngorongoro district.Conclusion/SignificanceFindings presented here provide new opportunities for weather-driven RVF epidemic simulation modelling. This is an ideal approach for understanding disease transmission dynamics towards epidemics prediction, prevention and control. This approach can be used as an alternative source for generation of calibrated RVF epidemics data in different settings.

The Role of Temperature in Transmission of Zoonotic Arboviruses

“…However, a mechanistic model based on water temperature and surface area was used to examine a range of conditions theoretically expected to be favorable to RVFV persistence and outbreaks [164] and in Kenya, minimum temperature was among the significant variables included in a RVFV early-warning system [266]. Temperature factored into a mechanistic model for RVFV in East Africa [267]. Temperature and precipitation effects were also included in a SEIR model of RVFV in Tanzania, where an increased risk at low temperatures was found, despite an increase in overall risk under climate change ([165], but see [268] where no effect of temperature was observed).…”

A Stochastic Model to Study Rift Valley Fever Persistence with Different Seasonal Patterns of Vector Abundance: New Insights on the Endemicity in the Tropical Island of Mayotte

“…Concerning RVF, the first models were based on vector populations of constant sizes [ 20 , 21 ]. More realistic seasonal vector population dynamics were later introduced to study RVF behaviour in specific areas like California [ 23 ], Texas [ 24 ], South Africa [ 25 ], the Netherlands [ 26 ] or Tanzania [ 27 ]. Vector population dynamics of Aedes and Culex were obtained from local vector trapping [ 23 , 26 ] or from more general equations for birth and development rates using local precipitation and temperature data [ 28 , 29 ].…”

Inter-epidemic abundance and distribution of potential mosquito vectors for Rift Valley fever virus in Ngorongoro district, Tanzania

“…Malambo and Pinyinyi villages were the most hit by the 2006–2007 RVF epidemics followed by Digodigo ( 5 , 42 ). Based on our field-based observations, these villages have shown characteristic vegetation types that could predispose environmental conditions favouring major RVF epidemics and as a source of spread of infection to different areas ( 48 ). This abundance–distribution phenomenon could also explain that the disease does not spread like other classical contagious diseases but with some spreading mechanism involving animal movements locally or from neighbouring endemic countries.…”