A spatially structured mixed vector control strategy.

<p>The proposed mixed strategy involves vertical control targeted at areas of predicted high risk of domestic infestation clustering (circles and solid lines) and horizontal control based on community participation in the communities predicted to be at medium to low risk (squares and dashed lines).</p

Risk maps of <i>T. infestans</i> domestic infestation.

<p>(A) Map showing the predicted prevalence of domestic infestation. (B) Map showing the probability of membership in a cluster of high domestic infestation. Both maps were estimated from the coefficients of the best fitting models. The spatial resolution of the map is 1×1 km.</p

Factors associated with the high prevalence of domestic infestation by <i>T. infestans</i> in the Moreno Department, Santiago del Estero, Argentina.

1<p>Variables: Den, density of rural houses (# per sq. km); Dist, distance from a community to the nearest T. infestans infested community (meters); LST, mean maximum land surface temperature (°C); NDVI, Normalized Difference Vegetation Index (no units); Elev, mean elevation of each community (meters above sea level); Deg, percentage of landscape within 2 km of a village that was degraded (see text for details); Def, percentage of landscape within 2 km of a community that was deforested (see text for details); Crops, percentage of landscape within 2 km of a village that was modified for soy production.</p><p>Symbols: X (variable tested in model); — (variable not tested in model); − (negative association) + (positive association);</p>**<p>(<i>P</i>≤0,01);</p>*<p>(0,01<<i>P</i>≤0,05); NS (not significant).</p><p>Δ_i = AIC_i−AIC_min.</p><p>ω_i = exp (−1/2 Δ_i)/Σ exp (−1/2 Δ_i).</p><p>Σ ω_i(<i>j</i>): sum of ω_i values from every model in which variable <i>i</i> was present. Indicates the relative importance of each independent variable in predicting the data.</p>2<p>Lowest AIC = 701.8.</p

Spatially explicit insecticide spraying schemes in the Moreno Department.

<p>(A) Implementation of a spatially contiguous strategy (i.e., visiting the nearest neighbor of each community). (B) Strategy targeting interventions according to risk (i.e., only high-risk communities are treated). Color squares indicate the location of Moreno's main cities (Quimili in pink and Tintina in light blue) where spraying teams initiate their journeys. Spraying was performed by two trucks (one stationed on each city) with two technicians each (represented by lines of the same color as the square indicating the city where they are based at). Black circles indicate the communities first visited by each spraying team in each control scenario.</p

Impacts of hypothetical scenarios of delayed response of vector control to Dengue virus outbreaks.

<p>The basic reproduction number, <i>R₀</i> (the average number of secondary cases after the introduction of an infection) for the 2003 and 2009 dengue fever outbreaks that affected the city of Cairns, Australia, was estimated by fitting an exponential function to the observed weekly epidemic curves before vector control interventions began (6 weeks in 2003 and 4 weeks in 2009). The effective reproduction number, <i>R_t</i> (the average number of secondary cases per primary case at time <i>t</i>) of each outbreak was estimated by accumulating the number of cases in biweekly periods (the average generation time of dengue is ∼14 days) and computing the ratio between consecutive two-week periods. The hypothetical epidemic curves for the 2003 (<b>A</b>) and 2009 (<b>B</b>) outbreaks under different scenarios for response times (<i>res</i>) of vector control activities to a dengue introduction (<i>res</i> = 2, 4, 6 and 8 weeks) were computed by estimating the number of cases in the absence of control (between <i>t₀</i> and <i>res</i>) using <i>R₀</i>, and then generating the rest of each epidemic time series by multiplying the number of cases by the estimated post intervention <i>R_t</i> in the original series. Blue lines indicate a faster response time than in the actual outbreak, red lines indicate scenarios where the response is delayed in comparison to the actual outbreak, and green lines indicate the actual outbreak. Values on top of the green lines are estimates for <i>R_t</i>. Cumulative cost (in 2009 US$) of each <i>res</i> scenario were estimated for the 2003 (<b>C</b>) and 2009 (<b>D</b>) outbreaks. Figure legends refer to each <i>res</i> scenario (A,B) and to the final epidemic size of each scenario (C,D).</p

Assessing the costs of spraying communities predicted to be at high-risk of domestic infestation clustering.

1<p>Assumes all communities are visited. Blanket control is performed based on the rule of contiguity (i.e. the nearest neighbor first). Targeted control assumes only communities predicted as high-risk (from the risk map) are visited.</p>2<p>Refers to the city where spraying brigades are based.</p>3<p>Communities with prevalence of domestic infestation by <i>T. infestans</i> higher than 10% are slated for blanket spraying (Tintina = 66 communities and 880 houses; Quimili = 76 communities and 822 houses).</p>4<p>Selected from communities estimated in 3.</p>5<p>The total cost for a Blanket contiguous strategy was estimated to be US$69,779 and for a Targeted strategy US$35,552. Costs were based on Vazquez-Prokopec et al. 2009 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001788#pntd.0001788-VazquezProkopec1" target="_blank">[5]</a> estimates and include cost of insecticides (US$6.9 per sprayed house), salaries (US$22 per-diem and US$11.2 wages per technician per day) and mobility (US$1 per km).</p

Factors associated with membership of a community in a cluster of high <i>T. infestans</i> infestation in the Moreno Department, Santiago del Estero, Argentina.

1<p>Variables: Den, density of rural houses (# per sq. km); Dist, distance from a community to the nearest T. infestans infested community (meters); LST, mean maximum land surface temperature (°C); NDVI, Normalized Difference Vegetation Index (no units); Elev, mean elevation of each community (meters above sea level); Deg, percentage of landscape within 2 km of a village that was degraded (see text for details); Def, percentage of landscape within 2 km of a community that was deforested (see text for details); Crops, percentage of landscape within 2 km of a village that was modified for soy production.</p><p>Symbols: X (variable tested in model); — (variable not tested in model); − (negative association) + (positive association);</p>**<p>(<i>P</i>≤0,01);</p>*<p>(0,01<<i>P</i>≤0,05); NS (not significant).</p><p>Δ_i = AIC_i−AIC_min.</p><p>ω_i = exp (−1/2 Δ_i)/Σ exp (−1/2 Δ_i).</p><p>Σ ω_i(<i>j</i>): sum of ω_i values from every model in which variable <i>i</i> was present. Indicates the relative importance of each independent variable in predicting the data.</p>2<p>Lowest AIC = 60.8.</p

Spatial distribution of <i>T. infestans</i> domestic infestation.

<p>Prevalence of domestic infestation by <i>T. infestans</i> (assessed by householders' collections) during 1999–2002 in the Moreno Department, Santiago del Estero, Argentina. ND refers to communities for which infestation data were not available.</p

Cost estimates per month (during surveillance) and per case (during an outbreak) to prevent and control dengue fever introductions in Cairns, Australia.

1<p>DART's responsibility is to implement mosquito prevention and control. In large outbreaks DART is supplemented by environmental health and municipal agents.</p>2<p>Vector control encompasses selective indoor insecticide residual spraying (SC 2.5% lambda-cyhalothrin, Demand) and larval control/source reduction activities (removal of small containers and treatment of large containers with S-methophene pellets or residual surface sprays) in premises within 100 meters of a case.</p>3<p>Serum samples are forwarded to the reference laboratory where they are screened for the presence of anti-dengue IgM and IgG using a combined pool of flavivirus antigens in capture ELISA assays. Positive IgM samples are further analyzed using flavivirus-specific IgM ELISA capture assays in order to identify the serotype of the infecting dengue virus. Additionally, real-time TaqMan reverse transcriptase-polymerase chain reaction is performed to detect dengue virus RNA.</p>4<p>Information provided by the Australian Red Cross Blood Service.</p>5<p>Each cased was assumed to loose, on average, 5 work days. Daily costs were estimated by dividing the median monthly income in Cairns (US$ 25,419; source: Australian Bureau of Statistics) by the number of working days (250).</p

Projecting the Long-Term Impact of School- or Community-Based Mass-Treatment Interventions for Control of <em>Schistosoma</em> Infection

<div><h3>Background</h3><p>Schistosomiasis remains a significant health burden in many areas of the world. <em>Morbidity control</em>, focused on limiting infection intensity through periodic delivery of anti-schistosomal medicines, is the thrust of current World Health Organization guidelines (2006) for reduction of <em>Schistosoma</em>-related disease. A new appreciation of the lifetime impact of repeated <em>Schistosoma</em> infection has directed attention toward strategies for greater suppression of parasite infection <em>per se</em>, with the goal of <em>transmission interruption</em>. Variations in drug schedules involving increased population coverage and/or treatment frequency are now undergoing field trials. However, their relative effectiveness in long-term infection suppression is presently unknown.</p> <h3>Methodology/Principal Findings</h3><p>Our study used available field data to calibrate advanced network models of village-level <em>Schistosoma</em> transmission to project outcomes of six different community- or school age-based programs, as compared to the impact of current 2006 W.H.O. recommended control strategies. We then scored the number of years each of 10 typical villages would remain below 10% infection prevalence (a practicable level associated with minimal prevalence of disease). All strategies that included four annual treatments effectively reduced community prevalence to less than 10%, while programs having yearly gaps (‘holidays’) failed to reach this objective in half of the communities. Effective post-program suppression of infection prevalence persisted in half of the 10 villages for 7–10 years, whereas in five high-risk villages, program effects on prevalence lasted zero to four years only.</p> <h3>Conclusions/Significance</h3><p>At typical levels of treatment adherence (60 to 70%), current WHO recommendations will likely not achieve effective suppression of <em>Schistosoma</em> prevalence unless implemented for ≥6 years. Following more aggressive 4 year annual intervention, some communities may be able to continue without further intervention for 8–10 years, while in higher-risk communities, annual treatment may prove necessary until eco-social factors fostering transmission are removed. Effective ongoing surveillance and locally targeted annual intervention must then become their mainstays of control.</p> </div