Citizen Science as an Approach for Responding to the Threat of Anopheles stephensi in Africa

Prevalence of Anopheles stephensi in Horn of Africa: a systematic review and meta-analysis

BACKGROUND

Anopheles stephensi has been a primary cause of malaria spread in urban areas. The World Health Organization announced a threat alert in 2019 that highlighted the An. stephensi outbreak in the Horn of Africa. Currently, there is insufficient information on the prevalence of An. stephensi. Therefore, this systematic review and meta-analysis aimed to determine the pooled prevalence of An. stephensi in Horn of Africa.

METHODS

Systematic search was performed to retrieve articles from PubMed, Scopus, Science Direct databases, and Google Scholar search engine. Fourteen potential studies that provided important data on An. stephensi were systematically reviewed and analyzed. The prevalence of An. stephensi was extracted separately into Microsoft Excel and analyzed using STATA 17.0. The Inverse of variance was done to evaluate heterogeneity across studies. A funnel plot and an Egger's test were used to evaluate the potential publication bias. A trim-and-fill-meta-analysis was carried out to generate a bias-adjusted effect estimate. A random effect model was used to determine the pooled prevalence of An. stephensi. Subgroup analysis was performed based on year of publication and country.

RESULTS

A total of 14 studies were included for this systematic review and meta-analysis. From this meta-analysis, the pooled prevalence of An. stephensi was 41.26%. The subgroup analysis based on year of publication showed that the pooled prevalence of An. stephensi in studies conducted 2014-2018 was 99%, while it was 36.82% in studies conducted 2019-2024. On the other hand, country-based analysis showed that the pooled prevalence of An. stephensi in Djibouti, Eritrea, Ethiopia, Kenya, Somaliland, and Sudan was 10.5%, 17.3%, 55.30%, 9.90%, 48.70%, and 24.63%, respectively.

CONCLUSIONS

This systematic review and meta-analysis reveal a high prevalence of An. stephensi across the Horn of Africa region. This underscores the significant challenges in managing malaria infections transmitted by An. stephensi in the region. Health officials must adjust current vector control measures to incorporate An. stephensi with the local malaria vector species. Also, regular identification and limiting of An. stephensi must be sustained to ensure the effectiveness of malaria prevention.

Community-based intervention in mosquito control strategy: A systematic review

As part of the World Health Organization's One Health Initiative, vector-borne disease control requires multidisciplinary and community involvement. This review examined community-based mosquito control intervention methods, their efficacy, and limitations. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, data were extracted from four medical databases: PubMed, Clinical Key, ProQuest, and ScienceDirect, covering the period from 2014 to 2023. The search used the keywords "community intervention," "vector control," and "mosquito." Filters were applied for full text, primary sources, scholarly journals, and publications within the last ten years (2014-2023). Studies without community intervention components were excluded. The initial search retrieved 1,035 articles, and 32 full-text articles were selected and assessed for eligibility, with 15 papers included in the final analysis. The included studies focused on arbovirus or malaria vectors and used randomized controlled trials (RCTs), pre- and post-intervention surveys, community-based implementation surveys, or qualitative research designs. Commonly applied interventions included community-driven vector population control and community education. Overall, the studies reported improvements in outcome measures such as entomological indices, community knowledge and practices, costs, and disease incidence or prevalence. However, some studies reported challenges with community perception and acceptance. In conclusion, this review consistently demonstrated a positive impact of community interventions on managing mosquito control.

Global mosquito observations dashboard (GMOD): creating a user-friendly web interface fueled by citizen science to monitor invasive and vector mosquitoes

BACKGROUND

Mosquitoes and the diseases they transmit pose a significant public health threat worldwide, causing more fatalities than any other animal. To effectively combat this issue, there is a need for increased public awareness and mosquito control. However, traditional surveillance programs are time-consuming, expensive, and lack scalability. Fortunately, the widespread availability of mobile devices with high-resolution cameras presents a unique opportunity for mosquito surveillance. In response to this, the Global Mosquito Observations Dashboard (GMOD) was developed as a free, public platform to improve the detection and monitoring of invasive and vector mosquitoes through citizen science participation worldwide.

METHODS

GMOD is an interactive web interface that collects and displays mosquito observation and habitat data supplied by four datastreams with data generated by citizen scientists worldwide. By providing information on the locations and times of observations, the platform enables the visualization of mosquito population trends and ranges. It also serves as an educational resource, encouraging collaboration and data sharing. The data acquired and displayed on GMOD is freely available in multiple formats and can be accessed from any device with an internet connection.

RESULTS

Since its launch less than a year ago, GMOD has already proven its value. It has successfully integrated and processed large volumes of real-time data (~ 300,000 observations), offering valuable and actionable insights into mosquito species prevalence, abundance, and potential distributions, as well as engaging citizens in community-based surveillance programs.

CONCLUSIONS

GMOD is a cloud-based platform that provides open access to mosquito vector data obtained from citizen science programs. Its user-friendly interface and data filters make it valuable for researchers, mosquito control personnel, and other stakeholders. With its expanding data resources and the potential for machine learning integration, GMOD is poised to support public health initiatives aimed at reducing the spread of mosquito-borne diseases in a cost-effective manner, particularly in regions where traditional surveillance methods are limited. GMOD is continually evolving, with ongoing development of powerful artificial intelligence algorithms to identify mosquito species and other features from submitted data. The future of citizen science holds great promise, and GMOD stands as an exciting initiative in this field.