Figure 3 code

This file includes the code used to create Figure 3. It includes data (in form of matrices) that was generated using separate files. One such data generating file is provided separately here. See "Note" at the top of the code in this file

Figure 6 data generation file Sp=0.05

This file includes the code used to create one of the panels in Figure 6. It includes the life cycle loop iterated over multiple generations across a large number of combinations of parameter values

Unsustainable transgenic interventions. The epidemiological effects of integrating a vaccine-based intervention strategy with transgenic vector manipulation over a 10-year time horizon when the effects of the transgenic strategy are unsustainable.

<p>Here, and in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004695#pcbi.1004695.g006" target="_blank">Fig 6</a>, resistance arises (or, in the case of population reduction, transgenic releases end) two thirds of the way into a transgenic manipulation regime unless noted otherwise. For the case of population replacement, we model initial invasion of the resistant pathogen by having a single vector carrying the mutant resistant pathogen appear at <i>t</i> = <i>τ</i>_<i>R</i>; in this, and in subsequent figures, the parameter values other than <i>G</i>(<i>t</i>) are modeled to be the same for vectors and hosts infected with the resistant pathogen. (A) The total number of cases (cumulative incidence) relative to the total number of cases without any public health management program when transgenic vector manipulation aims at population replacement. (B) The total number of cases relative to the total number of cases without any public health management program when transgenic vector manipulation aims at reducing vector recruitment. (C-D) The maximum total number of cases, relative to the same quantity without intervention, when the vaccination fraction is low (i.e., evaluated at the time point <i>T</i>_<i>H</i> where is maximized). In contrast to other figures, panels (C) and (D) illustrate the total number of cases at a given point in time rather than the total number of cases over the 10-year time horizon, with the white region of the plots corresponding to regions where the total number of cases with the intervention is always below the incidence in the absence of an intervention. We note that when a relatively small fraction of new hosts are vaccinated, abruptly ending a population reduction program can cause transient oscillations, leading to the nonlinearities apparent in panels (B,D). Panels (E-F) illustrate how the total number of cases can temporarily increase relative to no intervention if transgenic population replacement is unsustainable, although over much longer time horizons eventually falls below one. Panel (E) shows how vaccination can maintain a lower total number of cases relative to the situation in the absence of interventions, but still results in an increase in the total number of cases as vector competence recovers. In panel (F), 5% of new hosts are effectively vaccinated, but the decline <i>G</i>(<i>t</i>) in vector competence is not sustainable.</p

The long-term epidemiological effects of integrating clinical interventions with transgenic population reduction.

(A) The prevalence (fraction of hosts infectious) at equilibrium after combined vaccination and transgenic population reduction relative to prevalence before the interventions begin, and (B) the corresponding effective type reproductive number at the equilibrium in (A) in the absence of an antimicrobial medication. (C-D) describe analogous results assuming vaccination and transgenic population reduction are combined with an antimicrobial medication strategy that removes infectious hosts at the same rate as the background recovery rate, while (E-F) describe the results when an antimicrobial medication strategy that removes infectious hosts at twice the background recovery rate is used. Panels (A-B) illustrate how absent transgenic manipulation long-term pathogen elimination is only possible in our model if the fraction of hosts vaccinated exceeds approximately 0.78, which corresponds to roughly 1 − 1/TR (e.g., [85]) for the parameter values in Table 1.</p

Integrating Transgenic Vector Manipulation with Clinical Interventions to Manage Vector-Borne Diseases

<div><p>Many vector-borne diseases lack effective vaccines and medications, and the limitations of traditional vector control have inspired novel approaches based on using genetic engineering to manipulate vector populations and thereby reduce transmission. Yet both the short- and long-term epidemiological effects of these transgenic strategies are highly uncertain. If neither vaccines, medications, nor transgenic strategies can by themselves suffice for managing vector-borne diseases, integrating these approaches becomes key. Here we develop a framework to evaluate how clinical interventions (i.e., vaccination and medication) can be integrated with transgenic vector manipulation strategies to prevent disease invasion and reduce disease incidence. We show that the ability of clinical interventions to accelerate disease suppression can depend on the nature of the transgenic manipulation deployed (e.g., whether vector population reduction or replacement is attempted). We find that making a specific, individual strategy highly effective may not be necessary for attaining public-health objectives, provided suitable combinations can be adopted. However, we show how combining only partially effective antimicrobial drugs or vaccination with transgenic vector manipulations that merely temporarily lower vector competence can amplify disease resurgence following transient suppression. Thus, transgenic vector manipulation that cannot be sustained can have adverse consequences—consequences which ineffective clinical interventions can at best only mitigate, and at worst temporarily exacerbate. This result, which arises from differences between the time scale on which the interventions affect disease dynamics and the time scale of host population dynamics, highlights the importance of accounting for the potential delay in the effects of deploying public health strategies on long-term disease incidence. We find that for systems at the disease-endemic equilibrium, even modest perturbations induced by weak interventions can exhibit strong, albeit transient, epidemiological effects. This, together with our finding that under some conditions combining strategies could have transient adverse epidemiological effects suggests that a relatively long time horizon may be necessary to discern the efficacy of alternative intervention strategies.</p></div

Oppenheim phenotypes and genotypes

File includes phenotypes and marker genotypes of all assayed insects

Model parameters and their numerical values.

Model parameters and their numerical values.</p

Time <i>t</i>_<i>s</i> to suppression.

<p>Time <i>t</i>_<i>s</i> to suppression.</p

Transient effects on incidence of combining transgenic vector manipulation with antimicrobial medications of varying efficacies.

<p>(A-B) results for transgenic manipulation aimed at population replacement. (C-D) The same analysis for the case when transgenic manipulation aims at population reduction. Panel (A) assumes <i>α</i> = 10⁻²; panel (B) assumes <i>ρ</i> = (<i>U</i>^⋆ + <i>V</i>^⋆). Panels (B, D) assume an additional medication-induced recovery rate that is 10% of the natural recovery rate for all simulations, but vary <i>α</i> (B) or the time <i>τ</i>_<i>m</i> until releases stop (D). The vertical dotted-dashed line represents the point in time where resistance arises or releases end.</p

Illustrative time series for the effects of applying a single clinical intervention strategy.

<p>Here, and in subsequent figures, in the absence of any intervention the type reproductive number <i>T</i>_<i>R</i> at the disease-free equilibrium is ≈ 4.56; note, however, that at the disease-endemic equilibrium, the effective type reproductive number is one. Here, and in subsequent figures, the solid grey line represents the total number of cases or the effective type reproductive number in the absence of any management strategy. (A) The effects of an antimicrobial medication that renders infectious hosts recovered at different rates on the total number of cases. The natural recovery rate is 1/6 ≈ 0.17 per day. We highlight that even a very weak effect from antimicrobial medications (0.01; approximately 5% of the background natural recovery rate) can cause large transient fluctuations at the disease-endemic equilibrium. The type reproductive number at the disease-free equilibrium is below one when the medication-induced recovery rate is above 0.63 per day. (B) The effects of an antimicrobial medication with different recovery rates on the effective type reproductive number at a given point in time. (C) The effects of vaccinating a fraction <i>ϵ</i> of newborns on the total number of cases and (D) the effective type reproductive number at a given point in time. In this, and in subsequent figures, the cumulative incidence relative to no intervention over time is defined as the running aggregate (see the main text for details).</p