Impact of network centrality and income on slowing infection spread after outbreaks.

The COVID-19 pandemic has shed light on how the spread of infectious diseases worldwide are importantly shaped by both human mobility networks and socio-economic factors. However, few studies look at how both socio-economic conditions and the complex network properties of human mobility patterns interact, and how they influence outbreaks together. We introduce a novel methodology, called the Infection Delay Model, to calculate how the arrival time of an infection varies geographically, considering both effective distance-based metrics and differences in regions' capacity to isolate-a feature associated with socio-economic inequalities. To illustrate an application of the Infection Delay Model, this paper integrates household travel survey data with cell phone mobility data from the São Paulo metropolitan region to assess the effectiveness of lockdowns to slow the spread of COVID-19. Rather than operating under the assumption that the next pandemic will begin in the same region as the last, the model estimates infection delays under every possible outbreak scenario, allowing for generalizable insights into the effectiveness of interventions to delay a region's first case. The model sheds light on how the effectiveness of lockdowns to slow the spread of disease is influenced by the interaction of mobility networks and socio-economic levels. We find that a negative relationship emerges between network centrality and the infection delay after a lockdown, irrespective of income. Furthermore, for regions across all income and centrality levels, outbreaks starting in less central locations were more effectively slowed by a lockdown. Using the Infection Delay Model, this paper identifies and quantifies a new dimension of disease risk faced by those most central in a mobility network.

Impacts of detection and contact tracing on the epidemic spread in time-varying networks

• Quantify the effectiveness of detection and contact tracing in time-varying networks. • Propose the SEIRDH model with detection and contact tracing. • Derive the epidemic thresholds under different conditions of detection and contact tracing. • Analyze the effect of detection and contact tracing in suppressing epidemic spread. In the situation of insufficient vaccines and rapid mutation of the virus, detection and contact tracing have been argued to be effective interventions in the containment of emergent epidemics. However, most of previous studies are devoted to data-driven, leading to insufficient understanding of quantifying their effectiveness, especially when individuals' interactions evolve with time. Here, we aim at quantifying the effectiveness of detection and contact tracing interventions in suppressing the epidemic in time-varying networks. We propose the Susceptible-Exposed-Infected-Removed-Dead-Hospitalized (SEIRDH) model with detection and contact tracing. Under the framework of time-varying networks and with a mean-field approach, we analyze the epidemic thresholds under different situations. Experimental results show that detection can effectively suppress the epidemic spread with an increased epidemic threshold, while the role of tracing depends on the characteristics of the epidemic. When an epidemic is infectious in the incubation period, contact tracing has an obvious effect in suppressing the epidemic spread, but not when the epidemic is not infectious in the incubation. Thus, we apply this framework in real networks to explore possible contact tracing measures by taking use of individuals' properties. We find that contact tracing based on activity and historical information is more efficient than random contact tracing. Moreover, individuals' attractiveness and aging effects also affect the efficiency of detection and contact tracing. In conclusion, making full use of individuals' properties can remarkably improve the effectiveness of detection and contact tracing. The proposed method is expected to provide theoretical guidance for coping with the COVID-19 or other emergent epidemics. [ FROM AUTHOR]

Impacts of detection and contact tracing on the epidemic spread in time-varying networks

In the situation of insufficient vaccines and rapid mutation of the virus, detection and contact tracing have been argued to be effective interventions in the containment of emergent epidemics. However, most of previous studies are devoted to data-driven, leading to insufficient understanding of quantifying their effectiveness, especially when individuals’ interactions evolve with time. Here, we aim at quantifying the effectiveness of detection and contact tracing interventions in suppressing the epidemic in time-varying networks. We propose the Susceptible-Exposed-Infected-Removed-Dead-Hospitalized (SEIRDH) model with detection and contact tracing. Under the framework of time-varying networks and with a mean-field approach, we analyze the epidemic thresholds under different situations. Experimental results show that detection can effectively suppress the epidemic spread with an increased epidemic threshold, while the role of tracing depends on the characteristics of the epidemic. When an epidemic is infectious in the incubation period, contact tracing has an obvious effect in suppressing the epidemic spread, but not when the epidemic is not infectious in the incubation. Thus, we apply this framework in real networks to explore possible contact tracing measures by taking use of individuals’ properties. We find that contact tracing based on activity and historical information is more efficient than random contact tracing. Moreover, individuals’ attractiveness and aging effects also affect the efficiency of detection and contact tracing. In conclusion, making full use of individuals’ properties can remarkably improve the effectiveness of detection and contact tracing. The proposed method is expected to provide theoretical guidance for coping with the COVID-19 or other emergent epidemics.

Optimal Epidemic Control as a Contextual Combinatorial Bandit with Budget

In light of the COVID-19 pandemic, it is an open challenge and critical practical problem to find a optimal way to dynamically prescribe the best policies that balance both the governmental resources and epidemic control in different countries and regions. To solve this multi-dimensional tradeoff of exploitation and exploration, we formulate this technical challenge as a contextual combinatorial bandit problem that jointly optimizes a multi-criteria reward function. Given the historical daily cases in a region and the past intervention plans in place, the agent should generate useful intervention plans that policy makers can implement in real time to minimizing both the number of daily COVID-19 cases and the stringency of the recommended interventions. We prove this concept with simulations of multiple realistic policy making scenarios and demonstrate a clear advantage in providing a pareto optimal solution in the epidemic intervention problem. (1)