App/Software

Development of a mobile phone application that allows for the detection of mosquito species using acoustic surveillance through global crowdsourcing. Mosquito-generated species-specific sounds can be recorded on a cellphone, and the distinct frequencies are processed to build biomarkers for a given species, and together with phone-based metadata enable the species-specific identification of mosquitoes in near real-time.

Develop an interactive user-friendly tool that allows for nearly real-time monitoring of population movement and related Zika risk flows, thus enabling identification of new areas susceptible to Zika introduction, and prioritizing small-scale areas where Zika interventions would have the highest impact.

Develop and deploy a user-friendly tool to estimate and visualize risks posed by outbreak events reported on ProMED to the rest of the world by combining multiple data streams into a single probabilistic framework.

This project will leverage one of the most widely used Frontline Worker systems, CommCare, and cutting edge geospatial and predictive algorithms from ATLAS to identify cold spots and compute their risks for different diseases across Latin America. This information can be acted upon to gather more information to detect and manage disease outbreaks.

This program will enhance existing surveillance and vector control efforts by deploying a mobile-based data and analytics platform, which measures hyperlocal ground truth in real time. A network of local data contributors in the selected municipalities will collect geotagged data on environmental risk factors, which will be transformed into a daily heat map to enable real-time reporting for vector control workers and community leaders.

Chinedu Chugbo of Avigo Health L.L.C. in the U.S. will develop an approach to crowdsource reports of infant births and deaths from community members by health workers to better monitor vaccine coverage in low- to middle-income countries. In Nigeria, only 30% of births are registered, making it difficult to estimate numbers of vaccine-eligible children and ensure every child is properly vaccinated. Current methods for estimating population sizes include household surveys, which are costly, or records from health clinics, which suffer from limited coverage.

Aaditeshwar Seth of OnionDev Technologies Pvt. Ltd. in India in collaboration with the University of Montreal Hospital Research Centre (CRCHUM) via the Tika Vaani project, will develop a smartphone application and digital processing techniques to digitize childhood immunization data from photographs of vaccination cards taken by health workers during clinic visits and store the data in a cloud to monitor adherence and send reminders to families.

Monica Nolan of MU-JHU Care Limited in Uganda will adapt the existing open source Smart Register Platform, which digitally stores health records, for the real-time collection and transfer of immunization data, to improve vaccine coverage and other healthcare services for women and children in Uganda. In many low- to middle-income countries, records of childhood vaccinations are usually written by hand and can be poor quality.

Menale Kassie of the International Centre of Insect Physiology and Ecology in Kenya along with Ram Fishman and Opher Mendelsohn from Tel Aviv University in Israel will take a community-based crowdsourcing approach to crop protection of smallholder farms in low-resource settings by developing a simple software platform for basic feature phones to monitor pest incidence. Human-based monitoring of crops is the most accurate way to identify pests, but there are too few public monitoring agents in low-resource settings, leaving the majority of farms unprotected.

Christopher Gilligan of the University of Cambridge in the United Kingdom will develop a data collection and analysis platform for crop diseases that uses Bayesian modelling frameworks to better integrate data from diverse sources and identifies cost-effective pest and disease control solutions for small-holder farmers. Current crop disease surveillance programs generally collect data from limited sources and lack the capacity to use the data to advise farmers how to manage any disease outbreaks.