Human mobility networks and persistence of rapidly mutating pathogens

Abstract

Rapidly mutating pathogens may be able to persist in the population and reach an endemic equilibrium by escaping hosts’ acquired immunity. For such diseases, multiple biological, environmental and population-level mechanisms determine the dynamics of the outbreak, including pathogen's epidemiological traits (e.g. transmissibility, infectious period and duration of immunity), seasonality, interaction with other circulating strains and hosts’ mixing and spatial fragmentation. Here, we study a susceptible-infected-recovered-susceptible model on a metapopulation where individuals are distributed in sub-populations connected via a network of mobility flows. Through extensive numerical simulations, we explore the phase space of pathogen's persistence and map the dynamical regimes of the pathogen following emergence. Our results show that spatial fragmentation and mobility play a key role in the persistence of the disease whose maximum is reached at intermediate mobility values. We describe the occurrence of different phenomena including local extinction and emergence of epidemic waves, and assess the conditions for large-scale spreading. Findings are highlighted in reference to previous studies and to real scenarios. Our work uncovers the crucial role of hosts’ mobility on the ecological dynamics of rapidly mutating pathogens, opening the path for further studies on disease ecology in the presence of a complex and heterogeneous environment.

Mechanisms for lyssavirus persistence in non-synanthropic bats in Europe: insights from a modeling study

“…In the ecological context under study, the northern portion of the migratory path is entirely responsible for viral persistence at the estimated immunity waning, likely because it is composed by a more complex spatial structure including a larger number of patches, thus creating more opportunities for seeding events sustaining coupled but non-synchronous patch epidemics 62 that cannot be otherwise obtained with the southern path only. Increasing spatial resolution and resolving shelters sharing similar ecological and environmental conditions (such as the ones collectively grouped in Summer refuges) would not substantially alter our predictions.…”

A data-driven assessment of early travel restrictions related to the spreading of the novel COVID-19 within mainland China

“…In 2020, the Spring Festival was celebrated on Jan. 25th, while in 2019 it took place on Feb. 5th. The existence of recurrent local outbreaks is a feature that some models have anticipated [20]. As a consequence, we start the simulations on Jan. 12th, 2019 and Jan. 1st, 2020, so that in both years the period between the first reported cases and the celebration of the Spring Festival is the same.…”

“…Specifically, we built a basic metapopulation model [19,20] of 31 regions in China (except HongKong, Macau and Taiwan). Specifically, we built a basic metapopulation model [19,20] of 31 regions in China (except HongKong, Macau and Taiwan).…”

Multinational patterns of seasonal asymmetry in human movement influence infectious disease dynamics

“…The connectivity of human populations defines the spread of infectious diseases: cities that are visited frequently experience more introductions of vector borne 1 , 2 and directly transmitted 3 , 4 infections. Estimates of mobility have been used to inform targeting of control efforts 5 and to characterize the spatial spread of emergent pathogens 2 , 6 – 8 , demonstrating the public health implications of human connectivity. Despite the connection between travel and infectious disease spread, one major feature of human mobility has been neglected in most applications to date: human mobility is generally seasonal 9 – 12 .…”

“…Seasonal travel has wide-ranging health implications. Beyond re-introducing pathogens into locations where they have otherwise been controlled, seasonal mobility and resulting human aggregation may also increase transmission of directly transmitted pathogens 8 . Term-times, when children are in schools, are thought to be associated with higher measles 23 , rubella 24 , and influenza transmission 25 , 26 ; the dry seasons when agricultural workers return to urban settings are also associated with higher measles transmission in Niger 27 .…”