A multidisciplinary team from the National Institute of Public Health (INSP) of the Ministry of Health and the Mathematics Research Center (CIMAT) developed the EggCountATT algorithm, a tool designed to automate the counting of Aedes aegypti mosquito eggs, the main vector of dengue in the country. This technological innovation was conceived to reduce analysis times in the field, minimize human errors, and improve the accuracy of reports, key elements for strengthening epidemiological risk models. Its implementation allows for a clearer identification of priority areas for vector control interventions, optimizing the response of public health programs. Dr. Kenia Mayela Valdez Delgado, a researcher at INSP's Regional Center for Public Health Research, explained that the tool uses advanced image processing techniques to analyze the cards collected in ovitraps, devices used to monitor mosquito populations. This approach allows for more precise information about the presence of the vector, contributing to a more informed and timely decision-making process in preventing dengue outbreaks. The specialist emphasized that the development of the algorithm has a direct impact on epidemiological surveillance by strengthening the monitoring of Aedes aegypti and facilitating the more accurate identification of risk areas. She also highlighted that this technology helps evaluate the effectiveness of control strategies, such as the use of mosquitoes infected with Wolbachia, promoted by the National Center for Prevention and Disease Control (CENAPRECE). 'Technology does not replace human work, but rather expands knowledge and allows for real-time decision-making,' she explained, emphasizing that tools like this favor the construction of healthier and safer communities. The EggCountATT algorithm demonstrated a mean accuracy of 92% in egg counting, surpassing both manual methods and other available automated solutions. These results were documented in a study published on February 17, 2026, in the journal Signal, Image and Video Processing, which supports its scientific validity and potential application in public health programs. Since 2008, mosquito monitoring in Mexico has been based on the placement of ovitraps in homes, whose analysis required manual counting using microscopes or magnifying glasses, a laborious, demanding, and human-error-prone process. The incorporation of this algorithm represents a significant advance by transforming a traditional process into an automated, consistent, and scalable system. Towards a more efficient epidemiological surveillance The development of EggCountATT evidenced the potential of integrating data science and public health to strengthen epidemiological surveillance systems in Mexico. This type of innovation positions the country in the adoption of technological tools aimed at improving prevention, optimizing resources, and strengthening the response to vector-borne diseases.
Mexico Develops Algorithm to Automatically Count Dengue Mosquito Eggs
Mexican scientists have created the EggCountATT tool, which automatically and accurately counts Aedes aegypti mosquito eggs, significantly improving dengue fever monitoring and prevention.
