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A Multi-detection Assay for Malaria Transmitting Mosquitoes
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mHAT app for automated malaria rapid test result analysis and aggregation: a pilot study.

Carson Moore1, Thomas Scherr2, Japhet Matoba3

  • 1Department of Chemistry, Vanderbilt University, 1234 Stevenson Center Lane, Nashville, TN, 37212, USA.

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|May 27, 2021
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Summary

The mHAT mobile app accurately detects malaria using rapid diagnostic tests (RDTs), improving data collection for surveillance in low-resource settings. This technology enhances accuracy and speed in reporting malaria cases.

Keywords:
Application developmentMalariaMobile healthRapid diagnostic test

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Area of Science:

  • Malariology
  • Public Health
  • Mobile Health Technology

Background:

  • Current malaria surveillance methods like active and reactive case detection face scalability and sustainability challenges.
  • Mobile health (mHealth) interventions offer potential solutions by automating and standardizing surveillance processes.
  • Existing data aggregation methods present challenges in terms of accuracy and timeliness.

Purpose of the Study:

  • To quantify challenges in current malaria surveillance data aggregation.
  • To present a web-based mobile phone application (mHAT) for reporting rapid diagnostic test (RDT) results.
  • To reduce the burden of RDT data reporting in low-resource settings.

Main Methods:

  • Collected de-identified RDTs from 14 rural clinics in Zambia.
  • Utilized the mHAT web application to image and analyze RDT signal intensity for binary results.
  • Validated app performance through comparative limits of detection and receiver operating characteristic analysis against expert visual inspection and laboratory readers.

Main Results:

  • The mHAT app demonstrated 91.9% sensitivity and 91.4% specificity compared to visual analysis.
  • Analysis of surveillance data (Jan 2017-Feb 2019) revealed 36% of data points required correction.
  • Data submission delays were observed, with 44.8% of reports received after the expected date, though 65.1% arrived within 2 days.

Conclusions:

  • The mHAT mobile app is a sensitive and specific tool for malaria RDT analysis.
  • Automating and standardizing lateral flow assay (LFA) data collection improves accuracy and speed.
  • This mHealth solution offers a vital improvement for low-resource health facilities in malaria surveillance campaigns.