دورية أكاديمية
Real-time, spatial decision support to optimize malaria vector control: The case of indoor residual spraying on Bioko Island, Equatorial Guinea.
| العنوان: | Real-time, spatial decision support to optimize malaria vector control: The case of indoor residual spraying on Bioko Island, Equatorial Guinea. |
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| المؤلفون: | García GA; Medical Care Development International, Silver Spring, MD, United States of America., Atkinson B; Medical Care Development International, Silver Spring, MD, United States of America., Donfack OT; Medical Care Development International, Malabo, Equatorial Guinea., Hilton ER; Institute for Health Metrics and Evaluation, Univeristy of Washington, Seattle, WA, United States of America., Smith JM; Medical Care Development International, Malabo, Equatorial Guinea., Eyono JNM; Medical Care Development International, Malabo, Equatorial Guinea., Iyanga MM; Medical Care Development International, Malabo, Equatorial Guinea., Vaz LM; Medical Care Development International, Malabo, Equatorial Guinea., Mba Nguema Avue R; Medical Care Development International, Malabo, Equatorial Guinea., Pollock J; Medical Care Development, Augusta, ME, United States of America., Ratsirarson J; Medical Care Development International, Silver Spring, MD, United States of America., Aldrich EM; Medical Care Development, Augusta, ME, United States of America., Phiri WP; Medical Care Development International, Malabo, Equatorial Guinea., Smith DL; Institute for Health Metrics and Evaluation, Univeristy of Washington, Seattle, WA, United States of America., Schwabe C; Medical Care Development, Augusta, ME, United States of America., Guerra CA; Medical Care Development International, Silver Spring, MD, United States of America. |
| المصدر: | PLOS digital health [PLOS Digit Health] 2022 May 12; Vol. 1 (5), pp. e0000025. Date of Electronic Publication: 2022 May 12 (Print Publication: 2022). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: PLOS Country of Publication: United States NLM ID: 9918335064206676 Publication Model: eCollection Cited Medium: Internet ISSN: 2767-3170 (Electronic) Linking ISSN: 27673170 NLM ISO Abbreviation: PLOS Digit Health Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: San Francisco, California : PLOS, [2022]- |
| مستخلص: | Public health interventions require evidence-based decision-making to maximize impact. Spatial decision support systems (SDSS) are designed to collect, store, process and analyze data to generate knowledge and inform decisions. This paper discusses how the use of a SDSS, the Campaign Information Management System (CIMS), to support malaria control operations on Bioko Island has impacted key process indicators of indoor residual spraying (IRS): coverage, operational efficiency and productivity. We used data from the last five annual IRS rounds (2017 to 2021) to estimate these indicators. IRS coverage was calculated as the percentage of houses sprayed per unit area, represented by 100x100 m map-sectors. Optimal coverage was defined as between 80% and 85%, and under and overspraying as coverage below 80% and above 85%, respectively. Operational efficiency was defined as the fraction of map-sectors that achieved optimal coverage. Daily productivity was expressed as the number of houses sprayed per sprayer per day (h/s/d). These indicators were compared across the five rounds. Overall IRS coverage (i.e. percent of total houses sprayed against the overall denominator by round) was highest in 2017 (80.2%), yet this round showed the largest proportion of oversprayed map-sectors (36.0%). Conversely, despite producing a lower overall coverage (77.5%), the 2021 round showed the highest operational efficiency (37.7%) and the lowest proportion of oversprayed map-sectors (18.7%). In 2021, higher operational efficiency was also accompanied by marginally higher productivity. Productivity ranged from 3.3 h/s/d in 2020 to 3.9 h/s/d in 2021 (median 3.6 h/s/d). Our findings showed that the novel approach to data collection and processing proposed by the CIMS has significantly improved the operational efficiency of IRS on Bioko. High spatial granularity during planning and deployment together with closer follow-up of field teams using real-time data supported more homogeneous delivery of optimal coverage while sustaining high productivity. Competing Interests: The authors have declared that no competing interests exist. (Copyright: © 2022 García et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
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| معلومات مُعتمدة: | R01 AI163398 United States AI NIAID NIH HHS |
| تواريخ الأحداث: | Date Created: 20230222 Latest Revision: 20240413 |
| رمز التحديث: | 20240413 |
| مُعرف محوري في PubMed: | PMC9931250 |
| DOI: | 10.1371/journal.pdig.0000025 |
| PMID: | 36812503 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 2767-3170 |
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| DOI: | 10.1371/journal.pdig.0000025 |