Evaluating seasonal variations in human contact patterns and their impact on the transmission of respiratory infectious diseases

Abstract

Considerable uncertainties surround the seasonality of respiratory infectious diseases. To which extent the observed seasonality is associated with biological reasons (e.g., virus survival rates, host immune dynamics) or human behavior remains unclear. Here, we investigate the association between temperature and human contact patterns using data collected through a contact diary-based survey between December 24, 2017 and May 30, 2018 in Shanghai, China. We found a significant inverse relationship between number of contacts and temperature seasonal trend (p=0.003) and temperature daily variation (p=0.009), with contacts increasing from 19.6 (95%CI: 14.9-22.2) in December to 24.4 (95%CI: 19.0-28.0) in January and declining to 10.9 (95%CI: 10.1-11.9) in May. This seasonal trend in number of contacts translates into a seasonal trend in the basic reproduction number - mean number of secondary cases generated by a typical infector in a fully susceptible population. By setting the basic reproduction number at 1.4 on December 24, weekly mean estimates showed a clear increasing trend during the fall, beginning at 1.14 (95%CI: 0.78-1.39) in October and reaching 2.02 (95%CI: 1.60-1.35) in February and then remaining below 1 in the summer. Epidemic dynamics comparable with those of seasonal influenza are obtained through model simulations when the infection is seeded during the fall; however, their dynamics become more complex when seeded after February (e.g., double peaks or no epidemic until after the summer). Our findings indicate a distinct seasonal trend among human contact patterns and highlight a behavioral mechanism contributing to the seasonality of respiratory infectious diseases.

Mathematical assessment of the role of human behavior changes on SARS-CoV-2 transmission dynamics

“…Specifically, some of these models have accounted for the behavior change due to the transmission of information through contacts between humans [14][15][16][17][18] or due to the prevalence of the pandemic [14,15,19]. For instance, using data collected through a contact diary-based survey, Kummer et al [20] showed the impact of the seasonal change in the number of contacts made between individuals on the spread of the disease. Furthermore, d'Onofrio et al used a deterministic compartmental model to study vaccination uptake behavior as a function of prevalence [21,22].…”

Disentangling the rhythms of human activity in the built environment for airborne transmission risk: An analysis of large-scale mobility data

“…The relative importance of these disparate mechanisms varies across directly-transmitted pathogens and is still largely unexplained ( Martinez, 2018 ; Grassly and Fraser, 2006 ). The influence of seasonal host behavior on respiratory disease seasonality remains particularly understudied ( Fisman, 2012 ; Kronfeld-Schor et al, 2021 ) except for a few notable examples ( Bharti et al, 2011 ; Few et al, 2013 ; Kummer et al, 2022 ).…”

The effects of weather and mobility on respiratory viruses dynamics before and after the COVID-19 pandemic

“…In particular, there is a strong link between weather and human behaviour, with the first strongly influencing the second. For example, outdoor weather conditions influence the amount of time spent indoors [47] and therefore can potentially have an effect on both viral viability, and social contact rates or length [43, 48].…”