Multihost vector-borne infectious diseases form a significant fraction of the global infectious disease burden. In this study we explore the relationship between host diversity, vector behavior, and disease risk. To this end, we have developed a new dynamic model which includes two distinct host species and one vector species with variable preferences. With the aid of the model we were able to compute the basic reproductive rate, R 0, a well-established measure of disease risk that serves as a threshold parameter for disease outbreak. The model analysis reveals that the system has two different qualitative behaviors: (i) the well-known dilution effect, where the maximal R0 is obtained in a community which consists a single host (ii) a new amplification effect, denoted by us as diversity amplification, where the maximal R0 is attained in a community which consists both hosts. The model analysis extends on previous results by underlining the mechanism of both, diversity amplification and the dilution, and specifies the exact conditions for their occurrence. We have found that diversity amplification occurs where the vector prefers the host with the highest transmission ability, and dilution is obtained when the vector does not show any preference, or it prefers to bite the host with the lower transmission ability. The mechanisms of dilution and diversity amplification are able to account for the different and contradictory patterns often observed in nature (i.e., in some cases disease risk is increased while in other is decreased when the diversity is increased). Implication of the diversity amplification mechanism also challenges current premises about the interaction between biodiversity, climate change, and disease risk and calls for retrospective thinking in planning intervention policies aimed at protecting the preferred host species.

BackgroundAn important factor influencing the transmission dynamics of vector-borne diseases is the contribution of hosts with different parasitemia (no. of parasites per ml of blood) to the infected vector population. Today, estimation of this contribution is often impractical since it relies exclusively on limited-scale xenodiagnostic or artificial feeding experiments (i.e., measuring the proportion of vectors that become infected after feeding on infected blood/host).MethodologyWe developed a novel mechanistic model that facilitates the quantification of the contribution of hosts with different parasitemias to the infection of the vectors from data on the distribution of these parasitemias within the host population. We applied the model to an ample data set of Leishmania donovani carriers, the causative agent of visceral leishmaniasis in Ethiopia.ResultsCalculations facilitated by the model quantified the host parasitemias that are mostly responsible for the infection of vector, the sand fly Phlebotomus orientalis. Our findings indicate that a 3.2% of the most infected people were responsible for the infection of between 53% and 79% (mean – 62%) of the infected sand fly vector population.SignificanceOur modeling framework can easily be extended to facilitate the calculation of the contribution of other host groups (such as different host species, hosts with different ages) to the infected vector population. Identifying the hosts that contribute most towards infection of the vectors is crucial for understanding the transmission dynamics, and planning targeted intervention policy of visceral leishmaniasis as well as other vector borne infectious diseases (e.g., West Nile Fever).

Most bacterial habitats are topographically complex in the micro scale. Important examples include the gastrointestinal and tracheal tracts, and the soil. Although there are myriad theoretical studies that explore the role of spatial structures on antagonistic interactions (predation, competition) among animals, there are many fewer experimental studies that have explored, validated and quantified their predictions. In this study, we experimentally monitored the temporal dynamic of the predatory bacterium Bdellovibrio bacteriovorus, and its prey, the bacterium Burkholderia stabilis in a structured habitat consisting of sand under various regimes of wetness. We constructed a dynamic model, and estimated its parameters by further developing the direct integral method, a novel estimation procedure that exploits the separability of the states and parameters in the model. We also verified that one of our parameter estimates was consistent with its known, directly measured value from the literature. The ability of the model to fit the data combined with realistic parameter estimates indicate that bacterial predation in the sand can be described by a relatively simple model, and stress the importance of prey refuge on predation dynamics in heterogeneous environments.

Personal protection (PP) techniques, such as insecticide-treated nets, repellents and medications, include some of the most important and commonest ways used today to protect individuals from vector-borne infectious diseases. In this study, we explore the possibility that a PP intervention with partial coverage may have the counterintuitive effect of increasing disease burden at the population level, by increasing the biting intensity on the unprotected portion of the population. To this end, we have developed a dynamic model which incorporates parameters that describe the potential effects of PP on vector searching and biting behaviour and calculated its basic reproductive rate, R 0 . R 0 is a well-established threshold of disease risk; the higher R 0 is above unity, the stronger the disease onset intensity. When R 0 is below unity, the disease is typically unable to persist. The model analysis revealed that partial coverage with popular PP techniques can realistically lead to a substantial increase in the reproductive number. An increase in R 0 implies an increase in disease burden and difficulties in eradication efforts within certain parameter regimes. Our findings therefore stress the importance of studying vector behavioural patterns in response to PP interventions for future mitigation of vector-borne diseases.