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Schematic of metapopulation system. (a) A metapopulation system with several relatively isolated populations. These populations are synchronised by individual flows to some degree but not fully. At the beginning of the simulation, the epidemic outbreaks in population P 1 , while other populations stay susceptible. (b) A long-distance traveller i travels from P 1 to P 2 , and at the same time, another long-distance traveller j travels from P 2 to P 1 . Thus individuals in P 2 are exposed to the risk of infection. With no MEs, every infective in P 1 leads to equivalent risk to j . (c) The population structure in P 1 . Individuals are organised into groups and these groups may be overlapped. The long-distance traveller j stays in group C 1 when visiting P 1 . In this case, j is more likely to be infected by 2 than by other infectives. And owing to the higher transmission rate within the groups, j will suffer from a higher risk. (d) The population structure in P 2 . i stays in group C 1 when visiting P 2 and susceptibles in C 1 are more likely to be infected compared to other susceptibles in P 2 . The higher risk could then be transferred to group C 2 through k . This process is repeated and thus accelerates the spreading of infectious diseases.
Source publication
The study analyses the role of long-distance travel behaviours on the large-scale spatial spreading of directly transmitted infectious diseases, focusing on two different travel types in terms of the travellers travelling to a specific group or not. For this purpose, we have formulated and analysed a metapopulation model in which the individuals in...
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Citations
... We previously established two individual-based computer models to analyse the impact of Wuhan's lockdown and that of asymptomatic-infected individuals on the pandemic's spread [16,17]. Additionally, many studies have shown the value of complex network-based tools to simulate and analyse the spread of disease [18][19][20][21]. These pioneering interdisciplinary studies have contributed to the development of theoretical epidemiology. ...
Family feasting during the Spring Festival is a Chinese tradition. However, close contact during this period is likely to promote the spread of coronavirus disease 2019 (COVID-19). This study developed a dynamic infectious disease model in which the feast gatherings of families were considered the sole mode of transmission. The model simulates COVID-19 transmission via family feast gatherings through a social contact network. First, a kinship-based, virtual social contact network was constructed, with nodes representing families and connections representing kinships. Families in kinship with each other comprised of the largest globally coupled network, also known as a clique, in which a feast gathering was generated by randomly selecting two or more families willing to gather. The social contact network in the model comprised of 215 cliques formed among 608 families with 1517 family members. The modelling results indicated that when there is only one patient on day 0, the number of new infections will reach a peak on day 29, and almost all families and their members in the social contact network will be infected by day 60. This study demonstrated that COVID-19 can spread rapidly through continuous feast gatherings through social contact networks and that the disease will run rampant throughout the network.
... An explanation for the influence of assortative mixing and multiple peaks is provided in [40] by considering that the pathogen tends to move gradually from one sexual activity group to the next, where the multiple peaks then correspond to emerging infection within-group epidemics. A similar finding is reported in [41]. On the other hand, in [42] the authors are able to generate multiple peaks of the infection by tuning the parameters of a multi-compartment model. ...
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The COVID-19 crisis has upset the way of life of our society. The objective of this study was to apprehend the consequences of public health policies on mobility through the lens of gender. The analyses are based on a representative sample of 3000 people living in France. Travel behaviour was quantified using three mobility indicators (number of daily trips, daily distance travelled and daily travel time) that we regressed on individual and contextual explanatory variables. Two periods were studied: lockdown (March 17, 2020 until May 11, 2020), and post-lockdown (a curfew period: January-February 2021). For the lockdown period, our results show: (i) a statistically significant gender difference for the three mobility indicators. On average, women made 1.19 daily trips versus 1.46 for men, travelled 12 km whereas versus 17 km for men and spent less time on travel (23 min) than men (30 min); (ii) the degree of mobility was particularly sensitive to access to a car, according to a gender difference. For the post-lockdown period, our results reveal that: (i) women were more likely than men to make a higher number of daily trips (OR = 1.10, 95% CI = [1.04-1.17]); (ii) having only one or no car in the household impacted the mobility of women during the post-lockdown period; (iii) women regained some mobility but without reaching the pre-lockdown level. A better understanding of the factors influencing mobility behaviour, in lockdown and curfew periods, can provide some pathways to improve transport planning and help public authorities while tackling gender inequalites.
Objectives:
To examine recent literature in order to present a comprehensive overview of the current trends as regards the computational models used to represent the propagation of an infectious outbreak in a population, paying particular attention to those that represent network-based transmission.
Methods:
a systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Papers published in English between 2010 and September 2021 were sought in the ACM Digital Library, IEEE Xplore, PubMed and Scopus databases.
Results:
Upon considering their titles and abstracts, 832 papers were obtained, of which 192 were selected for a full content-body check. Of these, 112 studies were eventually deemed suitable for quantitative and qualitative analysis. Emphasis was placed on the spatial and temporal scales studied, the use of networks or graphs, and the granularity of the data used to evaluate the models. The models principally used to represent the spreading of outbreaks have been stochastic (55.36%), while the type of networks most frequently used are relationship networks (32.14%). The most common spatial dimension used is a region (19.64%) and the most used unit of time is a day (28.57%). Synthetic data as opposed to an external source were used in 51.79% of the papers. With regard to the granularity of the data sources, aggregated data such as censuses or transportation surveys are the most common.
Conclusion:
We identified a growing interest in the use of networks to represent disease transmission. We detected that research is focused on only certain combinations of the computational model, type of network (in both the expressive and the structural sense) and spatial scale, while the search for other interesting combinations has been left for the future.
The Gompertz function is one of the most widely used models in the description of growth processes in many different fields. We obtain a networked version of the Gompertz function as a worst-case scenario for the exact solution to the SIS model on networks. This function is shown to be asymptotically equivalent to the classical scalar Gompertz function for sufficiently large times. It proves to be very effective both as an approximate solution of the networked SIS equation within a wide range of the parameters involved and as a fitting curve for the most diverse empirical data. As an instance, we perform some computational experiments, applying this function to the analysis of two real networks of sexual contacts. The numerical results highlight the analogies and the differences between the exact description provided by the SIS model and the upper bound solution proposed here, observing how the latter amplifies some empirically observed behaviors such as the presence of multiple and successive peaks in the contagion curve.
In a recent study published in this journal, Jones et al [1] reported that a 6-week program of enhanced physiotherapy and structured exercise (PEPSE) and an essential amino acid supplement drink (GEAA) may enhance physical recovery and reduce anxiety and depression. This is a particularly important finding because intensive care unit–acquired weakness can be prolonged after hospital discharge in survivors of critical illness [2], whereas a combination of both rehabilitation and nutritional care management may improve outcomes in disabled, elderly individuals with malnutrition and sarcopenia [3].
