A computational approach to investigate patterns of acute respiratory illness dynamics in the regions with distinct seasonal climate transitions

Abstract

In the current work we present a set of computational algorithms aimed to analyze the acute respiratory infection (ARI) incidence data in the regions with distinct seasonal climate transitions. Their capabilities include: (a) collecting incidence data, fixing the under-reporting; (b) distinguishing phases of seasonal ARI dynamics (lower ARI level, higher ARI level, level transitions, epidemic outbreak); (c) finding the connections between the ARI dynamics (epidemic and interepidemic) and the weather factors. The algorithms are tested on the data for Saint Petersburg, Moscow and Novosibirsk and compared with the results for Ile-de-France region (Paris and its suburbs). The results are used to clarify the underlying mechanisms of ARI dynamics in temperate regions.