MalariaNCT07909746
VectorCAM: A Digital Innovation to Advance Entomological Surveillance
A study testing VectorCam and Routine Entomological Surveillance in people with Malaria and Surveillance, run by Johns Hopkins University at 1 site in Uganda.
Trial summary
Mosquitoes spread diseases such as malaria, but tracking mosquito populations is often slow and depends on a small number of highly trained specialists. VectorCam is a smartphone-based tool that uses artificial intelligence (AI) to help identify mosquitoes from photographs and automatically report surveillance… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Malaria, Surveillance, Entomology
- What is being tested?
- VectorCam and Routine Entomological Surveillance
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 48 participants
- Where
- 1 siteUganda
- Study dates
- 1 Sep 2023to 30 Sep 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Required to be at least 18 years of age
- Provide informed consent
- Be able to communicate in English
- No plans to relocate during the study period
- Households' owners additionally:
- Be able to participate in routine mosquito collection activities within their homes throughout the study period
Who cannot join
- Not actively working within the selected surveillance area
- Unable to complete the required study training
Trial locations and contacts
1 site in 1 country.
About this study
Mosquitoes spread diseases such as malaria, but tracking mosquito populations is often slow and depends on a small number of highly trained specialists. VectorCam is a smartphone-based tool that uses artificial intelligence (AI) to help identify mosquitoes from photographs and automatically report surveillance information. In this study, community health workers in Uganda used VectorCam during routine mosquito collection activities, while a comparison group followed standard methods that required mosquito identification by trained specialists using microscopes. The study evaluated how accurately mosquitoes could be identified, how quickly information could be reported, how complete the collected data were, and how easy the system was to use. The goal was to determine whether AI-enabled technology could help communities collect and share mosquito surveillance information more quickly and efficiently, while maintaining data quality under real-world conditions.
Read the detailed description
This randomized controlled trial evaluated whether VectorCam, a smartphone-based artificial intelligence (AI) tool for mosquito surveillance, can support a task-shifting model in Uganda. In this model, routine entomological surveillance moves from trained Vector Control Officers (VCOs) to community-based Village Health Teams (VHTs) who have no formal entomology training. VectorCam lets users photograph mosquito specimens, classify them automatically with AI, and report surveillance data electronically.
The study compared surveillance by VHTs using VectorCam (the task-shifting model) with standard surveillance by VCOs. The primary objective was to find out whether VHTs, supported by VectorCam, could perform routine mosquito surveillance with accuracy, completeness, timeliness, cost-effectiveness, and feasibility comparable to standard VCO-led surveillance under real-world field conditions.
VectorCam is a smartphone-based mosquito surveillance platform that combines specimen imaging, artificial intelligence (AI)-based classification, and digital reporting tools. The system was developed to support routine entomological surveillance by enabling community-based personnel to identify and report mosquito specimens using a smartphone.
This randomized controlled trial was conducted in Adjumani and Mayuge districts in Uganda. Village Health Teams (VHTs) in the intervention arm used VectorCam during routine mosquito surveillance activities, while the control arm followed standard surveillance procedures conducted by trained Vector Control Officers (VCOs).
Mosquito specimens collected through routine surveillance methods were classified using VectorCam and compared with conventional morphological identification using a microscope and molecular verification methods. The study evaluated the accuracy of mosquito identification, completeness of surveillance data, timeliness of reporting, and the cost associated with the tool.
The findings will help determine whether AI-enabled digital tools can support task-shifting of mosquito surveillance to community health workers and strengthen entomological surveillance systems in malaria-endemic settings.
- Conditions
- Malaria, Surveillance, Entomology, Vector-Borne Diseases
- Being tested
- VectorCam, Routine Entomological Surveillance
- Keywords
- Artificial Intelligence, Vector Surveillance, Task-Shifting, Malaria Control, Subnational Tailoring, Community Health Workers, Health Systems Strengthening, Digital Health
- Medical subject headings
- Malaria, Vector Borne Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
VectorCamDevice
VectorCam is an artificial intelligence (AI)-enabled mosquito surveillance platform designed to support community-based entomological surveillance through task shifting. The system enables trained Village Health Teams (VHTs) to image mosquito specimens using a smartphone-based device and receive automated classifications without internet connectivity. During this study, the AI model classified specimens into seven categories: Anopheles gambiae s.l., Anopheles funestus s.l., Anopheles Other, Culex spp., Mansonia spp., Aedes spp., and Non-Mosquito specimens. The platform incorporates digital reporting workflows aligned with Uganda Ministry of Health surveillance requirements, allowing mosquito surveillance data to be collected and electronically reported. The intended use of VectorCam is to expand access to routine mosquito surveillance by enabling community health workers to perform species identification and reporting activities traditionally conducted by trained entomologists.
Routine Entomological SurveillanceOther
The control arm followed routine Ministry of Health mosquito surveillance procedures. Village Health Teams (VHTs) collected mosquito specimens using standard collection methods, including pyrethrum spray catches and Centers for Disease Control (CDC) light traps, and recorded household-level surveillance data on paper forms. Specimens were packaged, labeled, and transported to district laboratories, where trained Vector Control Officers (VCOs) conducted morphological identification under light microscopy according to national standard operating procedures. Mosquitoes were classified by species, sex, and abdominal status, and results were manually recorded on surveillance forms. Surveillance data were subsequently entered into DHIS2 platform by district biostatisticians, following existing reporting workflows used within Uganda's national malaria surveillance program.
| Study group | Role | What they receive |
|---|---|---|
| VectorCam arm, study arm | Experimental | Participants assigned to the intervention arm conducted routine mosquito surveillance using VectorCam, an artificial intelligence (AI)-enabled smartphone platform. Village Health Teams (VHTs) collected mosquito specimens, captured specimen images using the VectorCam device, received automated mosquito classifications, and electronically submitted surveillance data through the mobile application. Mosquito specimens were retained for subsequent morphological and molecular validation by the district entomologist for validation purposes. Receives: Device: VectorCam |
| Routine Arm, control arm | Active comparator | Participants assigned to the control arm conducted routine mosquito surveillance according to standard Ministry of Health procedures. Village Health Teams (VHTs) collected mosquito specimens and submitted them to district Vector Control Officers (VCOs) for species identification using paper-based and microscopy methods. Surveillance data were recorded in paper and later on submitted through the existing Ministry of Health reporting systems (DHIS2) Receives: Other: Routine Entomological Surveillance |
What is being measured
The primary outcome is the main question the study is designed to answer.
Mosquito Species Identification Accuracy
Accuracy of mosquito species classification by VectorCam compared to molecular or sequencing results. For each taxonomic class, sensitivity, specificity, positive predictive value (PPV), and overall accuracy were calculated. A weighted F1 score accounting for class imbalance across taxa was additionally reported to provide an aggregate performance measure across species groups.
Measured: From the beginning of the steady stage (6 months) up to 12 months
Throughput Efficiency (Per-specimen identification time )
Operational throughput was defined as the time required to progress from identification of one mosquito specimen to identification of the subsequent specimen under routine operating conditions.
Measured: From the beginning of the steady stage (6 months) up to 12 months
Completeness (proportion of required surveillance reporting fields successfully captured and available)
Data completeness was defined as the proportion of required surveillance reporting fields that were successfully captured and available for review within each arm's reporting system. For each routine surveillance data element captured on the paper-based VHT forms and subsequently entered into DHIS2, the corresponding field, if any, collected by the VectorCam application was identified, and vice versa. Data elements that existed in only one system, or that could not be mapped to an equivalent field in the other, were excluded from the completeness comparison; only fields common to both arms were retained.
Measured: From the beginning of the steady stage (6 months) up to 12 months
Timeliness (delay between field specimen collection and availability of surveillance data)
Timeliness was defined as the elapsed time between surveillance activity and availability of the resulting record in the arm-specific digital reporting system. In the control arm, reporting delay was calculated as the number of days between the field collection date recorded by the VHT and the date on which the corresponding surveillance record became available in Uganda's DHIS2 system. In the study arm, reporting delay was calculated from initiation of the VectorCam imaging session to successful upload of the corresponding record to the secure VectorCam cloud server.
Measured: From the beginning of the steady stage (6 months) up to 12 months
Secondary outcomes (3)
Classification performance accuracy for mosquito sex
In the VectorCam (study) arm, classification of sex will be compared against morphological identification by trained VCOs under light microscopy for a subset of specimens. Performance will be measured as accuracy: the proportion of specimens correctly classified. For sex, accuracy will be reported overall and separately for females and males.
Measured: From the beginning of the steady stage (6 months) up to 12 months
Classification performance accuracy for mosquito abdominal status
In the VectorCam (study) arm, classification of abdominal status will be compared against morphological identification by trained VCOs under light microscopy for a subset of specimens. Performance will be measured as accuracy: the proportion of specimens correctly classified.. Abdominal status, will be reported overall and for each category (unfed, fed, and gravid).
Measured: From the beginning of the steady stage (6 months) up to 12 months
Summative Usability Assessment (SUS Scale)
The SUS questionnaire was administered to all participating VHTs assigned to the intervention arm. Responses were scored according to standard SUS methodology, generating a total usability score ranging from 0 to 100. Results were analyzed descriptively to characterize perceived usability and acceptability. A System Usability Scale (SUS) score is considered good when it is above 68, with scores above 80.3 considered excellent or top-tier.
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Nov 2026
- Study start1 Sep 2023
- Primary completion11 Aug 2025
- Study completion30 Sep 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for MalariaStudies of the same condition, open and under way.
- More trials run by Johns Hopkins UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 48 participants (actual)
Sponsor and investigators
- Lead sponsor
- Johns Hopkins University
- Sponsor type
- University, hospital or other
- Collaborators
- Makerere University, University of Notre Dame, Ministry of Health, Uganda, Bill and Melinda Gates Foundation
- Responsible party
- Sponsor
- Investigators
- Soumyadipta Acharya, MBBS, MS, PhD, Principal investigator, Johns Hopkins University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07909746
- Sponsor's protocol ID
- IRB00376201
- Other IDs
- INV-043944
- Status last verified
- Oct 2026
- First submitted
- 15 Oct 2026
- Last updated
- 2 Nov 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data (IPD) will not be shared to protect participant confidentiality and in accordance with the data governance agreements established with the Uganda Ministry of Health and the study's ethical approvals. De-identified aggregate entomological surveillance data, study protocols, and AI model architectures may be shared upon reasonable request to enable external validation and reproduction of the study findings using independent datasets. Data sharing will be subject to applicable institutional policies, ethical approvals, and data use agreements.
Publications
Burkot TR, Farlow R, Min M, Espino E, Mnzava A, Russell TL. A global analysis of National Malaria Control Programme vector surveillance by elimination and control status in 2018. Malar J. 2019 Dec 4;18(1):399. doi: 10.1186/s12936-019-3041-2.
PubMed 31801543Lowe R, Stewart-Ibarra AM, Petrova D, Garcia-Diez M, Borbor-Cordova MJ, Mejia R, Regato M, Rodo X. Climate services for health: predicting the evolution of the 2016 dengue season in Machala, Ecuador. Lancet Planet Health. 2017 Jul;1(4):e142-e151. doi: 10.1016/S2542-5196(17)30064-5. Epub 2017 Jul 7.
PubMed 29851600Nsubuga P, White ME, Thacker SB, Anderson MA, Blount SB, Broome CV, Chiller TM, Espitia V, Imtiaz R, Sosin D, Stroup DF, Tauxe RV, Vijayaraghavan M, Trostle M. Public Health Surveillance: A Tool for Targeting and Monitoring Interventions. In: Jamison DT, Breman JG, Measham AR, Alleyne G, Claeson M, Evans DB, Jha P, Mills A, Musgrove P, editors. Disease Control Priorities in Developing Countries. 2nd edition. Washington (DC): The International Bank for Reconstruction and Development / The World Bank; 2006. Chapter 53. Available from http://www.ncbi.nlm.nih.gov/books/NBK11770/
PubMed 21250345Semwal A, Melvin LMJ, Mohan RE, Ramalingam B, Pathmakumar T. AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot. Sensors (Basel). 2022 Jun 29;22(13):4921. doi: 10.3390/s22134921.
PubMed 35808427Lee C, Vythilingam I, Chong CS, Abdul Razak MA, Tan CH, Liew C, Pok KY, Ng LC. Gravitraps for management of dengue clusters in Singapore. Am J Trop Med Hyg. 2013 May;88(5):888-892. doi: 10.4269/ajtmh.12-0329. Epub 2013 Mar 11.
PubMed 23478581Gama RA, Silva EM, Silva IM, Resende MC, Eiras AE. Evaluation of the sticky MosquiTRAP for detecting Aedes (Stegomyia) aegypti (L.) (Diptera: Culicidae) during the dry season in Belo Horizonte, Minas Gerais, Brazil. Neotrop Entomol. 2007 Mar-Apr;36(2):294-302. doi: 10.1590/s1519-566x2007000200018.
PubMed 17607465Crepeau TN, Healy SP, Bartlett-Healy K, Unlu I, Farajollahi A, Fonseca DM. Effects of Biogents Sentinel Trap field placement on capture rates of adult Asian tiger mosquitoes, Aedes albopictus. PLoS One. 2013;8(3):e60524. doi: 10.1371/journal.pone.0060524. Epub 2013 Mar 29.
PubMed 23555987Minakshi M, Bharti P, Bhuiyan T, Kariev S, Chellappan S. A framework based on deep neural networks to extract anatomy of mosquitoes from images. Sci Rep. 2020 Aug 3;10(1):13059. doi: 10.1038/s41598-020-69964-2.
PubMed 32747744World malaria report 2025: Addressing the threat of antimalarial drug resistance.
Study at a glance
From the registry record, last updated by the sponsor on 2 Nov 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- VectorCAM: A Digital Innovation to Advance Entomological Surveillance
- ClinicalTrials.gov ID
- NCT07909746
- Acronym / sponsor ID
- IRB00376201
- Conditions
- Malaria, Surveillance, Entomology, Vector-Borne Diseases
- Keywords
- Artificial Intelligence, Vector Surveillance, Task-Shifting, Malaria Control, Subnational Tailoring, Community Health Workers, Health Systems Strengthening, Digital Health
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 48 participants (actual)
- Lead sponsor
- Johns Hopkins UniversityUniversity, hospital or other
- Collaborators
- Makerere University, University of Notre Dame, Ministry of Health, Uganda, Bill and Melinda Gates Foundation
- Study start
- 1 Sep 2023 (actual)
- Primary completion
- 11 Aug 2025 (actual)
- Study completion
- 30 Sep 2025 (actual)
- First posted
- 2 Nov 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Mosquitoes spread diseases such as malaria, but tracking mosquito populations is often slow and depends on a small number of highly trained specialists. VectorCam is a smartphone-based tool that uses artificial intelligence (AI) to help identify mosquitoes from photographs and automatically report surveillance information. In this study, community health workers in Uganda used VectorCam during routine mosquito collection activities, while a comparison group followed standard methods that required mosquito identification by trained specialists using microscopes. The study evaluated how accurately mosquitoes could be identified, how quickly information could be reported, how complete the collected data were, and how easy the system was to use. The goal was to determine whether AI-enabled technology could help communities collect and share mosquito surveillance information more quickly and efficiently, while maintaining data quality under real-world conditions.
Detailed description
This randomized controlled trial evaluated whether VectorCam, a smartphone-based artificial intelligence (AI) tool for mosquito surveillance, can support a task-shifting model in Uganda. In this model, routine entomological surveillance moves from trained Vector Control Officers (VCOs) to community-based Village Health Teams (VHTs) who have no formal entomology training. VectorCam lets users photograph mosquito specimens, classify them automatically with AI, and report surveillance data electronically.
The study compared surveillance by VHTs using VectorCam (the task-shifting model) with standard surveillance by VCOs. The primary objective was to find out whether VHTs, supported by VectorCam, could perform routine mosquito surveillance with accuracy, completeness, timeliness, cost-effectiveness, and feasibility comparable to standard VCO-led surveillance under real-world field conditions.
VectorCam is a smartphone-based mosquito surveillance platform that combines specimen imaging, artificial intelligence (AI)-based classification, and digital reporting tools. The system was developed to support routine entomological surveillance by enabling community-based personnel to identify and report mosquito specimens using a smartphone.
This randomized controlled trial was conducted in Adjumani and Mayuge districts in Uganda. Village Health Teams (VHTs) in the intervention arm used VectorCam during routine mosquito surveillance activities, while the control arm followed standard surveillance procedures conducted by trained Vector Control Officers (VCOs).
Mosquito specimens collected through routine surveillance methods were classified using VectorCam and compared with conventional morphological identification using a microscope and molecular verification methods. The study evaluated the accuracy of mosquito identification, completeness of surveillance data, timeliness of reporting, and the cost associated with the tool.
The findings will help determine whether AI-enabled digital tools can support task-shifting of mosquito surveillance to community health workers and strengthen entomological surveillance systems in malaria-endemic settings.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| VectorCam arm, study arm | Experimental | Device: VectorCamParticipants assigned to the intervention arm conducted routine mosquito surveillance using VectorCam, an artificial intelligence (AI)-enabled smartphone platform. Village Health Teams (VHTs) collected mosquito specimens, captured specimen images using the VectorCam device, received automated mosquito classifications, and electronically submitted surveillance data through the mobile application. Mosquito specimens were retained for subsequent morphological and molecular validation by the district entomologist for validation purposes. |
| Routine Arm, control arm | Active comparator | Other: Routine Entomological SurveillanceParticipants assigned to the control arm conducted routine mosquito surveillance according to standard Ministry of Health procedures. Village Health Teams (VHTs) collected mosquito specimens and submitted them to district Vector Control Officers (VCOs) for species identification using paper-based and microscopy methods. Surveillance data were recorded in paper and later on submitted through the existing Ministry of Health reporting systems (DHIS2) |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- Required to be at least 18 years of age
- Provide informed consent
- Be able to communicate in English
- No plans to relocate during the study period
- Be able to participate in routine mosquito collection activities within their homes throughout the study period
Key exclusion criteria (2 listed)
- Not actively working within the selected surveillance area
- Unable to complete the required study training
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| VectorCamVectorCam is an artificial intelligence (AI)-enabled mosquito surveillance platform designed to support community-based entomological surveillance through task shifting. The system enables trained Village Health Teams (VHTs) to image mosquito specimens using a smartphone-based device and receive automated classifications without internet connectivity. During this study, the AI model classified specimens into seven categories: Anopheles gambiae s.l., Anopheles funestus s.l., Anopheles Other, Culex spp., Mansonia spp., Aedes spp., and Non-Mosquito specimens. The platform incorporates digital reporting workflows aligned with Uganda Ministry of Health surveillance requirements, allowing mosquito surveillance data to be collected and electronically reported. The intended use of VectorCam is to expand access to routine mosquito surveillance by enabling community health workers to perform species identification and reporting activities traditionally conducted by trained entomologists. | Device | VectorCam arm, study arm |
| Routine Entomological SurveillanceThe control arm followed routine Ministry of Health mosquito surveillance procedures. Village Health Teams (VHTs) collected mosquito specimens using standard collection methods, including pyrethrum spray catches and Centers for Disease Control (CDC) light traps, and recorded household-level surveillance data on paper forms. Specimens were packaged, labeled, and transported to district laboratories, where trained Vector Control Officers (VCOs) conducted morphological identification under light microscopy according to national standard operating procedures. Mosquitoes were classified by species, sex, and abdominal status, and results were manually recorded on surveillance forms. Surveillance data were subsequently entered into DHIS2 platform by district biostatisticians, following existing reporting workflows used within Uganda's national malaria surveillance program. | Other | Routine Arm, control arm |
Primary outcome
Mosquito Species Identification Accuracy
Time frame: From the beginning of the steady stage (6 months) up to 12 months
Accuracy of mosquito species classification by VectorCam compared to molecular or sequencing results. For each taxonomic class, sensitivity, specificity, positive predictive value (PPV), and overall accuracy were calculated. A weighted F1 score accounting for class imbalance across taxa was additionally reported to provide an aggregate performance measure across species groups.
Throughput Efficiency (Per-specimen identification time )
Time frame: From the beginning of the steady stage (6 months) up to 12 months
Operational throughput was defined as the time required to progress from identification of one mosquito specimen to identification of the subsequent specimen under routine operating conditions.
Completeness (proportion of required surveillance reporting fields successfully captured and available)
Time frame: From the beginning of the steady stage (6 months) up to 12 months
Data completeness was defined as the proportion of required surveillance reporting fields that were successfully captured and available for review within each arm's reporting system. For each routine surveillance data element captured on the paper-based VHT forms and subsequently entered into DHIS2, the corresponding field, if any, collected by the VectorCam application was identified, and vice versa. Data elements that existed in only one system, or that could not be mapped to an equivalent field in the other, were excluded from the completeness comparison; only fields common to both arms were retained.
Timeliness (delay between field specimen collection and availability of surveillance data)
Time frame: From the beginning of the steady stage (6 months) up to 12 months
Timeliness was defined as the elapsed time between surveillance activity and availability of the resulting record in the arm-specific digital reporting system. In the control arm, reporting delay was calculated as the number of days between the field collection date recorded by the VHT and the date on which the corresponding surveillance record became available in Uganda's DHIS2 system. In the study arm, reporting delay was calculated from initiation of the VectorCam imaging session to successful upload of the corresponding record to the secure VectorCam cloud server.
3 secondary outcomes
Classification performance accuracy for mosquito sex
Time frame: From the beginning of the steady stage (6 months) up to 12 months
Classification performance accuracy for mosquito abdominal status
Time frame: From the beginning of the steady stage (6 months) up to 12 months
Summative Usability Assessment (SUS Scale)
Time frame: 12 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Nov 2026.
- Background: PubMed 31801543Burkot TR, Farlow R, Min M, Espino E, Mnzava A, Russell TL. A global analysis of National Malaria Control Programme vector surveillance by elimination and control status in 2018. Malar J. 2019 Dec 4;18(1):399. doi: 10.1186/s12936-019-3041-2.
- Background: PubMed 29851600Lowe R, Stewart-Ibarra AM, Petrova D, Garcia-Diez M, Borbor-Cordova MJ, Mejia R, Regato M, Rodo X. Climate services for health: predicting the evolution of the 2016 dengue season in Machala, Ecuador. Lancet Planet Health. 2017 Jul;1(4):e142-e151. doi: 10.1016/S2542-5196(17)30064-5. Epub 2017 Jul 7.
- Background: PubMed 21250345Nsubuga P, White ME, Thacker SB, Anderson MA, Blount SB, Broome CV, Chiller TM, Espitia V, Imtiaz R, Sosin D, Stroup DF, Tauxe RV, Vijayaraghavan M, Trostle M. Public Health Surveillance: A Tool for Targeting and Monitoring Interventions. In: Jamison DT, Breman JG, Measham AR, Alleyne G, Claeson M, Evans DB, Jha P, Mills A, Musgrove P, editors. Disease Control Priorities in Developing Countries. 2nd edition. Washington (DC): The International Bank for Reconstruction and Development / The World Bank; 2006. Chapter 53. Available from http://www.ncbi.nlm.nih.gov/books/NBK11770/
- Background: PubMed 35808427Semwal A, Melvin LMJ, Mohan RE, Ramalingam B, Pathmakumar T. AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot. Sensors (Basel). 2022 Jun 29;22(13):4921. doi: 10.3390/s22134921.
- Background: PubMed 23478581Lee C, Vythilingam I, Chong CS, Abdul Razak MA, Tan CH, Liew C, Pok KY, Ng LC. Gravitraps for management of dengue clusters in Singapore. Am J Trop Med Hyg. 2013 May;88(5):888-892. doi: 10.4269/ajtmh.12-0329. Epub 2013 Mar 11.
- Background: PubMed 17607465Gama RA, Silva EM, Silva IM, Resende MC, Eiras AE. Evaluation of the sticky MosquiTRAP for detecting Aedes (Stegomyia) aegypti (L.) (Diptera: Culicidae) during the dry season in Belo Horizonte, Minas Gerais, Brazil. Neotrop Entomol. 2007 Mar-Apr;36(2):294-302. doi: 10.1590/s1519-566x2007000200018.
- Background: PubMed 23555987Crepeau TN, Healy SP, Bartlett-Healy K, Unlu I, Farajollahi A, Fonseca DM. Effects of Biogents Sentinel Trap field placement on capture rates of adult Asian tiger mosquitoes, Aedes albopictus. PLoS One. 2013;8(3):e60524. doi: 10.1371/journal.pone.0060524. Epub 2013 Mar 29.
- Background: PubMed 32747744Minakshi M, Bharti P, Bhuiyan T, Kariev S, Chellappan S. A framework based on deep neural networks to extract anatomy of mosquitoes from images. Sci Rep. 2020 Aug 3;10(1):13059. doi: 10.1038/s41598-020-69964-2.
- BackgroundWorld malaria report 2025: Addressing the threat of antimalarial drug resistance.
- Individual participant dataSharing planned: No. Individual participant data (IPD) will not be shared to protect participant confidentiality and in accordance with the data governance agreements established with the Uganda Ministry of Health and the study's ethical approvals. De-identified aggregate entomological surveillance data, study protocols, and AI model architectures may be shared upon reasonable request to enable external validation and reproduction of the study findings using independent datasets. Data sharing will be subject to applicable institutional policies, ethical approvals, and data use agreements.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Soumyadipta Acharya, MBBS, MS, PhD principal investigator
Johns Hopkins University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing VectorCam and Routine Entomological Surveillance in people with Malaria and Surveillance, run by Johns Hopkins University at 1 site in Uganda.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 2 Nov 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Uganda. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Mosquito Species Identification Accuracy (time frame: From the beginning of the steady stage (6 months) up to 12 months). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07909746, which also lists 9 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Nov 2026.