A Process-based Model with Temperature, Water, and Lab-derived Data Improves Predictions of Daily Culex pipiens/restuans Mosquito Density.

While the number of human cases of mosquito-borne diseases has increased in North America in the last decade, accurate modeling of mosquito population density has remained a challenge. Longitudinal mosquito trap data over the many years needed for model calibration, and validation is relatively rare. In particular, capturing the relative changes in mosquito abundance across seasons is necessary for predicting the risk of disease spread as it varies from year to year. We developed a discrete, semi-stochastic, mechanistic process-based mosquito population model that captures life-cycle egg, larva, pupa, adult stages, and diapause for Culex pipiens (Diptera, Culicidae) and Culex restuans (Diptera, Culicidae) mosquito populations. This model combines known models for development and survival into a fully connected age-structured model that can reproduce mosquito population dynamics. Mosquito development through these stages is a function of time, temperature, daylight hours, and aquatic habitat availability. The time-dependent parameters are informed by both laboratory studies and mosquito trap data from the Greater Toronto Area. The model incorporates city-wide water-body gauge and precipitation data as a proxy for aquatic habitat. This approach accounts for the nonlinear interaction of temperature and aquatic habitat variability on the mosquito life stages. We demonstrate that the full model predicts the yearly variations in mosquito populations better than a statistical model using the same data sources. This improvement in modeling mosquito abundance can help guide interventions for reducing mosquito abundance in mitigating mosquito-borne diseases like West Nile virus.

Enemies and turncoats: bovine tuberculosis exposes pathogenic potential of Rift Valley fever virus in a common host, African buffalo (Syncerus caffer).

The ubiquity and importance of parasite co-infections in populations of free-living animals is beginning to be recognized, but few studies have demonstrated differential fitness effects of single infection versus co-infection in free-living populations. We investigated interactions between the emerging bacterial disease bovine tuberculosis (BTB) and the previously existing viral disease Rift Valley fever (RVF) in a competent reservoir host, African buffalo, combining data from a natural outbreak of RVF in captive buffalo at a buffalo breeding facility in 2008 with data collected from a neighbouring free-living herd of African buffalo in Kruger National Park. RVF infection was twice as likely in individual BTB+ buffalo as in BTB- buffalo, which, according to a mathematical model, may increase RVF outbreak size at the population level. In addition, co-infection was associated with a far higher rate of fetal abortion than other infection states. Immune interactions between BTB and RVF may underlie both of these interactions, since animals with BTB had decreased innate immunity and increased pro-inflammatory immune responses. This study is one of the first to demonstrate how the consequences of emerging infections extend beyond direct effects on host health, potentially altering the dynamics and fitness effects of infectious diseases that had previously existed in the ecosystem on free-ranging wildlife populations.

Inter-epidemic and between-season persistence of rift valley fever: vertical transmission or cryptic cycling?

Rift Valley fever (RVF) is an emerging zoonotic mosquito-borne infectious disease that has been identified as a risk for spread to other continents and can cause mass livestock mortality. In equatorial Africa, outbreaks of RVF are associated with high rainfall, when vector populations are at their highest. It is, however, unclear how RVF virus persists during the inter-epidemic periods and between seasons. Understanding inter-epidemic persistence as well as the role of vectors and hosts is paramount to creating effective management programmes for RVF control. We created a mathematical model for the spread of RVF and used the model to explore different scenarios of persistence including vertical transmission and alternate wildlife hosts, with a case study on buffalo in Kruger National Park, South Africa. Our results suggest that RVF persistence is a delicate balance between numerous species of susceptible hosts, mosquito species, vertical transmission and environmental stochasticity. Further investigations should not focus on a single species, but should instead consider a myriad of susceptible host species when seeking to understand disease dynamics.

Towards a Hybrid Agent-based Model for Mosquito Borne Disease.

Agent-based models (ABM) are used to simulate the spread of infectious disease through a population. Detailed human movement, demography, realistic business location networks, and in-host disease progression are available in existing ABMs, such as the Epidemic Simulation System (EpiSimS). These capabilities make possible the exploration of pharmaceutical and non-pharmaceutical mitigation strategies used to inform the public health community. There is a similar need for the spread of mosquito borne pathogens due to the re-emergence of diseases such as chikungunya and dengue fever. A network-patch model for mosquito dynamics has been coupled with EpiSimS. Mosquitoes are represented as a "patch" or "cloud" associated with a location. Each patch has an ordinary differential equation (ODE) mosquito dynamics model and mosquito related parameters relevant to the location characteristics. Activities at each location can have different levels of potential exposure to mosquitoes based on whether they are inside, outside, or somewhere in-between. As a proof of concept, the hybrid network-patch model is used to simulate the spread of chikungunya through Washington, DC. Results are shown for a base case, followed by varying the probability of transmission, mosquito count, and activity exposure. We use visualization to understand the pattern of disease spread.

Coupling Vector-host Dynamics with Weather Geography and Mitigation Measures to Model Rift Valley Fever in Africa.

We present and characterize a multi-host epidemic model of Rift Valley fever (RVF) virus in East Africa with geographic spread on a network, rule-based mitigation measures, and mosquito infection and population dynamics. Susceptible populations are depleted by disease and vaccination and are replenished with the birth of new animals. We observe that the severity of the epidemics is strongly correlated with the duration of the rainy season and that even severe epidemics are abruptly terminated when the rain stops. Because naturally acquired herd immunity is established, total mortality across 25 years is relatively insensitive to many mitigation approaches. Strong reductions in cattle mortality are expected, however, with sufficient reduction in population densities of either vectors or susceptible (ie. unvaccinated) hosts. A better understanding of RVF epidemiology would result from serology surveys to quantify the importance of herd immunity in epidemic control, and sequencing of virus from representative animals to quantify the realative importance of transportation and local reservoirs in nucleating yearly epidemics. Our results suggest that an effective multi-layered mitigation strategy would include vector control, movement control, and vaccination of young animals yearly, even in the absence of expected rainfall.

Spatiotemporal model of barley and cereal yellow dwarf virus transmission dynamics with seasonality and plant competition.

Many generalist pathogens are influenced by the spatial distributions and relative abundances of susceptible host species. The spatial structure of host populations can influence patterns of infection incidence (or disease outbreaks), and the effects of a generalist pathogen on host community dynamics in a spatially heterogeneous community may differ from predictions derived via simple models. In this paper, we model the transmission of a generalist pathogen within a patch framework that incorporates the movement of vectors between discrete host patches to investigate the effects of local host community composition and vector movement rates on disease dynamics.We use barley and cereal yellow dwarf viruses (B/CYDV), a suite of generalist, aphid-vectored pathogens of grasses, and their interactions with a range of host species as our case study. We examine whether B/CYDV can persist locally or in a patch framework across a range of host community configurations. We then determine how pathogen-mediated interactions between perennial and annual competitors are altered at the local and regional scale when the host populations are spatially structured. We find that the spatial configuration of the patch system, host composition within patches, and patch connectivity affect not only the ability of the pathogen to invade a fragmented system, but also determine whether the pathogen facilitates the invasion of a non-native host species. Further, our results suggest that connectivity can interact with arrival time and host infection tolerance to determine the success or failure of establishment for newly arriving species.