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      Optimizing the deployment of ultra-low volume and targeted indoor residual spraying for dengue outbreak response

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          Abstract

          Recent years have seen rising incidence of dengue and large outbreaks of Zika and chikungunya, which are all caused by viruses transmitted by Aedes aegypti mosquitoes. In most settings, the primary intervention against Aedes-transmitted viruses is vector control, such as indoor, ultra-low volume (ULV) spraying. Targeted indoor residual spraying (TIRS) has the potential to more effectively impact Aedes-borne diseases, but its implementation requires careful planning and evaluation. The optimal time to deploy these interventions and their relative epidemiological effects are, however, not well understood. We used an agent-based model of dengue virus transmission calibrated to data from Iquitos, Peru to assess the epidemiological effects of these interventions under differing strategies for deploying them. Specifically, we compared strategies where spray application was initiated when incidence rose above a threshold based on incidence in recent years to strategies where spraying occurred at the same time(s) each year. In the absence of spraying, the model predicted 361,000 infections [inter-quartile range (IQR): 347,000–383,000] in the period 2000–2010. The ULV strategy with the fewest median infections was spraying twice yearly, in March and October, which led to a median of 172,000 infections [IQR: 158,000–183,000], a 52% reduction from baseline. Compared to spraying once yearly in September, the best threshold-based strategy utilizing ULV had fewer median infections (254,000 vs. 261,000), but required more spraying (351 vs. 274 days). For TIRS, the best strategy was threshold-based, which led to the fewest infections of all strategies tested (9,900; [IQR: 8,720–11,400], a 94% reduction), and required fewer days spraying than the equivalent ULV strategy (280). Although spraying twice each year is likely to avert the most infections, our results indicate that a threshold-based strategy can become an alternative to better balance the translation of spraying effort into impact, particularly if used with a residual insecticide.

          Author summary

          Over half of the world’s population is at risk of infection by dengue virus (DENV) from Aedes a egypti mosquitoes. While most infected people experience mild or asymptomatic infections, dengue can cause severe symptoms, such as hemorrhage, shock, and death. A vaccine against dengue exists, but it can increase the risk of severe disease in people who have not been previously infected by one of the four DENV serotypes. In many places, therefore, the best currently available way to prevent outbreaks is by controlling the mosquito population. Our study used a simulation model to explore alternative strategies for deploying insecticide in the city of Iquitos in the Peruvian Amazon. Our simulations closely matched empirical patterns from studies of dengue's ecology and epidemiology in Iquitos, such as mosquito population dynamics, human household structure, demography, human and mosquito movement, and virus transmission. Our results indicate that an insecticide that has a long-lasting, residual effect will have the biggest impact on reducing DENV transmission. For non-residual insecticides, we find that it is best to begin spraying close to the start of the dengue transmission season, as mosquito populations can rebound quickly and resume previous levels of transmission.

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          A methodology for performing global uncertainty and sensitivity analysis in systems biology.

          Accuracy of results from mathematical and computer models of biological systems is often complicated by the presence of uncertainties in experimental data that are used to estimate parameter values. Current mathematical modeling approaches typically use either single-parameter or local sensitivity analyses. However, these methods do not accurately assess uncertainty and sensitivity in the system as, by default, they hold all other parameters fixed at baseline values. Using techniques described within we demonstrate how a multi-dimensional parameter space can be studied globally so all uncertainties can be identified. Further, uncertainty and sensitivity analysis techniques can help to identify and ultimately control uncertainties. In this work we develop methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models. We compare two specific types of global sensitivity analysis indexes that have proven to be among the most robust and efficient. Through familiar and new examples of mathematical and computer models, we provide a complete methodology for performing these analyses, in both deterministic and stochastic settings, and propose novel techniques to handle problems encountered during these types of analyses.
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            House-to-house human movement drives dengue virus transmission.

            Dengue is a mosquito-borne disease of growing global health importance. Prevention efforts focus on mosquito control, with limited success. New insights into the spatiotemporal drivers of dengue dynamics are needed to design improved disease-prevention strategies. Given the restricted range of movement of the primary mosquito vector, Aedes aegypti, local human movements may be an important driver of dengue virus (DENV) amplification and spread. Using contact-site cluster investigations in a case-control design, we demonstrate that, at an individual level, risk for human infection is defined by visits to places where contact with infected mosquitoes is likely, independent of distance from the home. Our data indicate that house-to-house human movements underlie spatial patterns of DENV incidence, causing marked heterogeneity in transmission rates. At a collective level, transmission appears to be shaped by social connections because routine movements among the same places, such as the homes of family and friends, are often similar for the infected individual and their contacts. Thus, routine, house-to-house human movements do play a key role in spread of this vector-borne pathogen at fine spatial scales. This finding has important implications for dengue prevention, challenging the appropriateness of current approaches to vector control. We argue that reexamination of existing paradigms regarding the spatiotemporal dynamics of DENV and other vector-borne pathogens, especially the importance of human movement, will lead to improvements in disease prevention.
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              Is Dengue Vector Control Deficient in Effectiveness or Evidence?: Systematic Review and Meta-analysis

              Background Although a vaccine could be available as early as 2016, vector control remains the primary approach used to prevent dengue, the most common and widespread arbovirus of humans worldwide. We reviewed the evidence for effectiveness of vector control methods in reducing its transmission. Methodology/Principal Findings Studies of any design published since 1980 were included if they evaluated method(s) targeting Aedes aegypti or Ae. albopictus for at least 3 months. Primary outcome was dengue incidence. Following Cochrane and PRISMA Group guidelines, database searches yielded 960 reports, and 41 were eligible for inclusion, with 19 providing data for meta-analysis. Study duration ranged from 5 months to 10 years. Studies evaluating multiple tools/approaches (23 records) were more common than single methods, while environmental management was the most common method (19 studies). Only 9/41 reports were randomized controlled trials (RCTs). Two out of 19 studies evaluating dengue incidence were RCTs, and neither reported any statistically significant impact. No RCTs evaluated effectiveness of insecticide space-spraying (fogging) against dengue. Based on meta-analyses, house screening significantly reduced dengue risk, OR 0.22 (95% CI 0.05–0.93, p = 0.04), as did combining community-based environmental management and water container covers, OR 0.22 (95% CI 0.15–0.32, p 0.5), but insecticide aerosols (OR 2.03; 95% CI 1.44–2.86) and mosquito coils (OR 1.44; 95% CI 1.09–1.91) were associated with higher dengue risk (p = 0.01). Although 23/41 studies examined the impact of insecticide-based tools, only 9 evaluated the insecticide susceptibility status of the target vector population during the study. Conclusions/Significance This review and meta-analysis demonstrate the remarkable paucity of reliable evidence for the effectiveness of any dengue vector control method. Standardised studies of higher quality to evaluate and compare methods must be prioritised to optimise cost-effective dengue prevention.
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                Author and article information

                Contributors
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Writing – review & editing
                Role: ConceptualizationRole: Project administrationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: SoftwareRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: VisualizationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                20 April 2020
                April 2020
                : 16
                : 4
                : e1007743
                Affiliations
                [1 ] Department of Biological Sciences & Eck Institute of Global Health, University of Notre Dame, Notre Dame, Indiana, United States of America
                [2 ] Department of Mathematics & Biomathematics Graduate Program, North Carolina State University, Raleigh, North Carolina, United States of America
                [3 ] Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
                [4 ] Department of Environmental Sciences, Emory University, Atlanta, Georgia, United States of America
                [5 ] U.S. Naval Medical Research Unit Six, Lima, Peru
                [6 ] Department of Entomology and Nematology, University of California, Davis, Davis, California, United States of America
                [7 ] Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, Davis, California, United States of America
                [8 ] Institute of Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
                University of Chicago, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-2559-797X
                http://orcid.org/0000-0002-9915-8056
                http://orcid.org/0000-0002-6389-6321
                http://orcid.org/0000-0001-5002-8886
                http://orcid.org/0000-0002-8630-1378
                http://orcid.org/0000-0002-0710-270X
                http://orcid.org/0000-0003-2947-8123
                http://orcid.org/0000-0001-6224-5984
                http://orcid.org/0000-0003-1056-7919
                http://orcid.org/0000-0002-7518-4014
                Article
                PCOMPBIOL-D-19-01721
                10.1371/journal.pcbi.1007743
                7200023
                32310958
                4fac4427-c3ff-4ecf-b6d8-ef8ece74c5e5

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 3 October 2019
                : 24 February 2020
                Page count
                Figures: 10, Tables: 2, Pages: 28
                Funding
                Funded by: National Institute of Allergy and Infectious Diseases
                Award ID: P01AI098670
                Award Recipient :
                SMC, GE, GMV-P, ACM, TWS, RCR, and TAP were supported by grant P01AI098670 (TWS, PI) from the National Institutes of Health, National Institute for Allergy and Infectious Disease ( https://www.niaid.nih.gov). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and life sciences
                Organisms
                Viruses
                RNA viruses
                Flaviviruses
                Dengue Virus
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbial Pathogens
                Viral Pathogens
                Flaviviruses
                Dengue Virus
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Pathogens
                Microbial Pathogens
                Viral Pathogens
                Flaviviruses
                Dengue Virus
                Biology and Life Sciences
                Organisms
                Viruses
                Viral Pathogens
                Flaviviruses
                Dengue Virus
                Medicine and Health Sciences
                Infectious Diseases
                Disease Vectors
                Insect Vectors
                Mosquitoes
                Biology and Life Sciences
                Species Interactions
                Disease Vectors
                Insect Vectors
                Mosquitoes
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Insects
                Mosquitoes
                Medicine and Health Sciences
                Infectious Diseases
                Infectious Disease Control
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Death Rates
                Biology and Life Sciences
                Agriculture
                Agrochemicals
                Insecticides
                Medicine and Health Sciences
                Infectious Diseases
                Disease Vectors
                Insect Vectors
                Mosquitoes
                Aedes Aegypti
                Biology and Life Sciences
                Species Interactions
                Disease Vectors
                Insect Vectors
                Mosquitoes
                Aedes Aegypti
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Insects
                Mosquitoes
                Aedes Aegypti
                Biology and Life Sciences
                Population Biology
                Population Dynamics
                Research and Analysis Methods
                Simulation and Modeling
                Custom metadata
                vor-update-to-uncorrected-proof
                2020-05-05
                Data and code are available at https://github.com/scavany/dengue_outbreak_response.

                Quantitative & Systems biology
                Quantitative & Systems biology

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