The perfect mix: combining participatory influenza surveillance with modeling and forecasting

Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. New participatory surveillance approaches provide an alternative way of monitoring and forecasting the evolution of these outbreaks, strengthening real-time epidemic science and allowing a more rigorous understanding of epidemic conditions.

A new paper published in JMIR Public Health and Surveillance explores three of these alternative approaches. An international scientific team, including ISI Foundation Research Leader Daniela Paolotti, ISI Principal Researcher Michele Tizzoni, ISI PhD Student, Daniela Perrotta, ISI Scientific Advisory Board vice-chair Alessandro Vespignani and 2016 Lagrange-CRT Foundation Prize awardee John Brownstein analyze WISDM (Widely Internet Sourced Distributed Monitoring), Influenzanet and Flu Near You (FNY) systems and how modeling and simulation can be or has been combined with participatory disease surveillance to measure the non-response bias in a participatory surveillance sample (WISDM) and to nowcast and forecast influenza activity in different parts of the world (Influenzanet and Flu Near You).

Researchers show how the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, while the major limits are in the lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long-term forecasting of influenza activity in data-poor parts of the world, thus supporting decision makers in designing effective interventions and allocating resources to mitigate their impact.

“Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches”, John S. Brownstein, Shuyu Chu, Achla Marathe, Madhav V. Marathe, Andre T. Nguyen, Daniela Paolotti, Nicola Perra, Daniela Perrotta, Mauricio Santillana, Samarth Swarup, Michele Tizzoni, Alessandro Vespignani, Anil Kumar S. Vullikanti, Mandy L. Wilson, Qian Zhang, JMIR Public Health and Surveillance, November 2017. Link: