Predicting the Timing and Magnitude of Tropical Mosquito Population Peaks for Maximizing Control Efficiency

Abstract

The transmission of mosquito-borne diseases is strongly linked to the abundance of the host vector. Identifying the environmental and biological precursors which herald the onset of peaks in mosquito abundance would give health and land-use managers the capacity to predict the timing and distribution of the most efficient and cost-effective mosquito control. We analysed a 15-year time series of monthly abundance of Aedes vigilax, a tropical mosquito species from northern Australia, to determine periodicity and drivers of population peaks (high-density outbreaks). Two sets of density-dependent models were used to examine the correlation between mosquito abundance peaks and the environmental drivers of peaks or troughs (low-density periods). The seasonal peaks of reproduction (r) and abundance () occur at the beginning of September and early November, respectively. The combination of low mosquito abundance and a low frequency of a high tide exceeding 7 m in the previous low-abundance (trough) period were the most parsimonious predictors of a peak's magnitude, with this model explaining over 50% of the deviance in . Model weights, estimated using AICc, were also relatively high for those including monthly maximum tide height, monthly accumulated tide height or total rainfall per month in the trough, with high values in the trough correlating negatively with the onset of a high-abundance peak. These findings illustrate that basic environmental monitoring data can be coupled with relatively simple density feedback models to predict the timing and magnitude of mosquito abundance peaks. Decision-makers can use these methods to determine optimal levels of control (i.e., least-cost measures yielding the largest decline in mosquito abundance) and so reduce the risk of disease outbreaks in human populations.

Early warning of West Nile virus mosquito vector: climate and land use models successfully explain phenology and abundance of Culex pipiens mosquitoes in north-western Italy

“…In particular, early predictions of both the timing and intensity of future mosquito abundance will help to enable decision makers to apply effective prevention and control plans [10]. …”

A Model of Malaria Epidemiology Involving Weather, Exposure and Transmission Applied to North East India

“…On the other hand transmission can not occur if the development time of the pathogen exceeds the life span of the insect [31]. If water temperature rises, the larvae take a shorter time to mature and consequently there is a greater capacity to produce more offspring during the transmission period [4].The threshold for vector genesis and survival were adopted (Table 2) from the values available in the literature.…”

“…However, pro-active vector sanitation can significantly reduce the risk of infection. In particular, identification of the peaks in vector population which would precede the disease outbreak by a typical incubation time of the parasite in the human host can enable pro-active control of malaria [4]. Vector controls are effective for mitigation only if they are carried out at the time of maximum exposure rather than at the time of detection of infection.…”

Quantifying the Drivers of Larval Density Patterns in Two Tropical Mosquito Species to Maximize Control Efficiency

“…We logtransformed lagged adult abundance to linearize the response. Other variables included the following: Spray, to examine the potential effectiveness of past insecticide management interventions at decreasing larval abundance, we included the total number of spraying hours from the previous month; Monthly rainfall, total monthly rainfall (mm); Tide height, maximum monthly tide height (m); Lagged tide frequency, frequency of high tides above 7.4 m from the previous month as a proxy for accumulated tidal water in the swamp (Yang et al 2009); Lagged monthly rainfall, rainfall (mm) from the previous month to account for accumulated rain water in the swamp.…”

“…Unfortunately, such models are often based on either broad temporal averages or snapshots of mosquito occurrence and environmental conditions at particular times (Russell 1986, Rejmankova et al 1991, Chase and Knight 2003, Leisnham et al 2005, Pope et al 2005, Sattler et al 2005, Vezzani et al 2005, Barrera et al 2006, Grieco et al 2006, Lindsay et al 2007, Zeilhofer et al 2007) and therefore cannot accurately represent the long-term availability and quality of ephemeral larval habitats. Studies with longer time series of data permit examination of the longterm spatio-temporal trends in mosquito dynamics and habitats, yet these studies often rely on making inferences between adult occurrence and the suspected drivers of larval abundance (Dhileepan 1996;Shaman et al 2002;Hachiya et al 2007;Whelan 2007a;Yang et al 2008bYang et al , 2009. Inferences based on adult dynamics and habitat relationships are largely decoupled from processes driving larval abundance patterns and so alone are generally unreliable tools for maximizing the efÞciency of larval control.…”