Interplay between mobility, multi-seeding and lockdowns shapes COVID-19 local impact

Abstract

Assessing the impact of mobility on epidemic spreading is of crucial importance for understanding the effect of policies like mass quarantines and selective re-openings. While many factors affect disease incidence at a local level, making it more or less homogeneous with respect to other areas, the importance of multi-seeding has often been overlooked. Multi-seeding occurs when several independent (non-clustered) infected individuals arrive at a susceptible population. This can lead to independent outbreaks that spark from distinct areas of the local contact (social) network. Such mechanism has the potential to boost incidence, making control efforts and contact tracing less effective. Here, through a modeling approach we show that the effect produced by the number of initial infections is non-linear on the incidence peak and peak time. When case importations are carried by mobility from an already infected area, this effect is further enhanced by the local demography and underlying mixing patterns: the impact of every seed is larger in smaller populations. Finally, both in the model simulations and the analysis, we show that a multi-seeding effect combined with mobility restrictions can explain the observed spatial heterogeneities in the first wave of COVID-19 incidence and mortality in five European countries. Our results allow us for identifying what we have called epidemic epicenter: an area that shapes incidence and mortality peaks in the entire country. The present work further clarifies the nonlinear effects that mobility can have on the evolution of an epidemic and highlight their relevance for epidemic control.

Impact of human mobility and networking on spread of COVID-19 at the time of the 1st and 2nd epidemic waves in Japan: An effective distance approach

“…Studies that have estimated the effective distance indicate that travel restrictions and international airline suspensions have contributed to the spread of COVID-19 [ 13 , 14 ]. In addition, currently, there are some reports that confronted the effect of different mobility data, including people flow statistics, on the spatiotemporal distributions of SARS-CoV2 at sub-national level [ 15 – 17 ]. Thus, we hypothesized that the number of people traveling between Japanese cities may also affect the domestic spread of COVID-19.…”

Multiplex mobility network and metapopulation epidemic simulations of Italy based on Open Data

“…Making informed decisions, both at the top-down and bottom-up levels, requires timely and quality data about the current stage of the outbreak and contact patterns of individuals [19,20]. In particular, these data serve to inform computational epidemic models [21], which produce detailed scenario analyses that can be fundamental to inform strategies of response and mitigation of a disease [3,[22][23][24][25][26][27] and have been widely exploited to face Covid-19 [28][29][30][31][32][33][34][35][36][37][38][39][40][41].…”

Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra

“…Researchers have also combined mobile phone data from multiple sources to better understand the spatiotemporal dynamics of how the virus can spread. This includes work that simulated relationships between the number of virus cases imported to an area, subsequent population mobility, and virus spread in multiple European countries [22]. Whereas another study tracked a specific fast-spreading lineage of COVID-19 in the United Kingdom by combining aggregated mobility metrics from both Google and the O2 telecommunications service provider with genomic data [23].…”

“…European countries [22]. Whereas another study tracked a specific fast-spreading lineage of COVID-19 in the United Kingdom by combining aggregated mobility metrics from both Google and the O2 telecommunications service provider with genomic data [23].…”