Background A surveillance system is the foundation for disease prevention and control. Malaria surveillance is crucial for tracking regional and temporal patterns in disease incidence, assisting in recorded details, timely reporting, and frequency of analysis. Objective In this study, we aim to develop an integrated surveillance graphical app called FeverTracker, which has been designed to assist the community and health care workers in digital surveillance and thereby contribute toward malaria control and elimination. Methods FeverTracker uses a geographic information system and is linked to a web app with automated data digitization, SMS text messaging, and advisory instructions, thereby allowing immediate notification of individual cases to district and state health authorities in real time. Results The use of FeverTracker for malaria surveillance is evident, given the archaic paper-based surveillance tools used currently. The use of the app in 19 tribal villages of the Dhalai district in Tripura, India, assisted in the surveillance of 1880 suspected malaria patients and confirmed malaria infection in 93.4% (114/122; Plasmodium falciparum), 4.9% (6/122; P vivax), and 1.6% (2/122; P falciparum/P vivax mixed infection) of cases. Digital tools such as FeverTracker will be critical in integrating disease surveillance, and they offer instant data digitization for downstream processing. Conclusions The use of this technology in health care and research will strengthen the ongoing efforts to eliminate malaria. Moreover, FeverTracker provides a modifiable template for deployment in other disease systems.

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Coronavirus (COVID-19) disease is a major pandemic which has taken the world by storm. More than 524,000 citizens of the globe have succumbed to the disease as on 3 rd July, 2020. Accurate modeling of the dynamics of the disease spread is required to curb the virus. With the availability of large amount of data made available publicly by the Government agency as well as the other live crowdsourcing media, it is possible to develop an accurate local prediction tool. In this study, we analyze the dynamics of local outbreaks of COVID-19 for few states of India with major outbreaks and for India as a whole. The large amount of data available from the COVID-19 Tracker India platform was utilized to estimate the impact of lockdown in the country and the states with major outbreaks. The effectiveness of the lockdown implementation by the respective states is studied for the analysis. The lockdown was categorized into strict, moderate, and lenient. The model is deployed on a web based platform to disseminate the alert to the public. We further extend the ordinary differential Equation (ODE) based model to generate district level vulnerability index for the whole country based on the rate of change of infected people, breach in social distancing, and population. Around 47% of districts of the country were not found vulnerable; however, 13% of the districts were identified as high risk for the disease outbreak.

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