tetano
Editor, Senior Moderator
Sci Rep
. 2021 Nov 18;11(1):22512.
doi: 10.1038/s41598-021-01857-4.
Socioeconomic and environmental patterns behind H1N1 spreading in Sweden
András Bota[SUP] 1 2 [/SUP], Martin Holmberg[SUP] 3 [/SUP], Lauren Gardner[SUP] 4 [/SUP], Martin Rosvall[SUP] 3 [/SUP]
Affiliations
Abstract
Identifying the critical factors related to influenza spreading is crucial in predicting and mitigating epidemics. Specifically, uncovering the relationship between epidemic onset and various risk indicators such as socioeconomic, mobility and climate factors can reveal locations and travel patterns that play critical roles in furthering an outbreak. We study the 2009 A(H1N1) influenza outbreaks in Sweden's municipalities between 2009 and 2015 and use the Generalized Inverse Infection Method (GIIM) to assess the most significant contributing risk factors. GIIM represents an epidemic spreading process on a network: nodes correspond to geographical objects, links indicate travel routes, and transmission probabilities assigned to the links guide the infection process. Our results reinforce existing observations that the influenza outbreaks considered in this study were driven by the country's largest population centers, while meteorological factors also contributed significantly. Travel and other socioeconomic indicators have a negligible effect. We also demonstrate that by training our model on the 2009 outbreak, we can predict the epidemic onsets in the following five seasons with high accuracy.
. 2021 Nov 18;11(1):22512.
doi: 10.1038/s41598-021-01857-4.
Socioeconomic and environmental patterns behind H1N1 spreading in Sweden
András Bota[SUP] 1 2 [/SUP], Martin Holmberg[SUP] 3 [/SUP], Lauren Gardner[SUP] 4 [/SUP], Martin Rosvall[SUP] 3 [/SUP]
Affiliations
- PMID: 34795338
- DOI: 10.1038/s41598-021-01857-4
Abstract
Identifying the critical factors related to influenza spreading is crucial in predicting and mitigating epidemics. Specifically, uncovering the relationship between epidemic onset and various risk indicators such as socioeconomic, mobility and climate factors can reveal locations and travel patterns that play critical roles in furthering an outbreak. We study the 2009 A(H1N1) influenza outbreaks in Sweden's municipalities between 2009 and 2015 and use the Generalized Inverse Infection Method (GIIM) to assess the most significant contributing risk factors. GIIM represents an epidemic spreading process on a network: nodes correspond to geographical objects, links indicate travel routes, and transmission probabilities assigned to the links guide the infection process. Our results reinforce existing observations that the influenza outbreaks considered in this study were driven by the country's largest population centers, while meteorological factors also contributed significantly. Travel and other socioeconomic indicators have a negligible effect. We also demonstrate that by training our model on the 2009 outbreak, we can predict the epidemic onsets in the following five seasons with high accuracy.