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Current methods for the detection of contagious outbreaks give contemporaneous information about the course of an epidemic at best. Individuals at the center of a social network are likely to be infected sooner, on average, than those at the periphery. However, mapping a whole network to identify central individuals whom to monitor is typically very difficult. We propose an alternative strategy that does not require ascertainment of global network structure, namely, monitoring the friends of randomly selected individuals. Such individuals are known to be more central. To evaluate whether such a friend group could indeed provide early detection, we studied a flu outbreak at Harvard College in late 2009. We followed 744 students divided between a random group and a friend group. Based on clinical diagnoses, the progression of the epidemic in the friend group occurred 14.7 days (95% C.I. 11.7-17.6) in advance of the randomly chosen group (i.e., the population as a whole). The friend group also showed a significant lead time (p<0.05) on day 16 of the epidemic, a full 46 days before the peak in daily incidence in the population as a whole. This sensor method could provide significant additional time to react to epidemics in small or large populations under surveillance. Moreover, the method could in principle be generalized to other biological, psychological, informational, or behavioral contagions that spread in networks.
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Recent research has focused on the monitoring of global-scale online data for improved detection of epidemics, mood patterns, movements in the stock market, political revolutions, box-office revenues, consumer behaviour and many other important phenomena. However, privacy considerations and the sheer scale of data available online are quickly making global monitoring infeasible, and existing methods do not take full advantage of local network structure to identify key nodes for monitoring. Here, we develop a model of the contagious spread of information in a global-scale, publicly-articulated social network and show that a simple method can yield not just early detection, but advance warning of contagious outbreaks. In this method, we randomly choose a small fraction of nodes in the network and then we randomly choose a friend of each node to include in a group for local monitoring. Using six months of data from most of the full Twittersphere, we show that this friend group is more central in the network and it helps us to detect viral outbreaks of the use of novel hashtags about 7 days earlier than we could with an equal-sized randomly chosen group. Moreover, the method actually works better than expected due to network structure alone because highly central actors are both more active and exhibit increased diversity in the information they transmit to others. These results suggest that local monitoring is not just more efficient, it is more effective, and it is possible that other contagious processes in global-scale networks may be similarly monitored.
The dynamics of epidemics depend on how peoples behavior changes during an outbreak. The impact of this effect due to control interventions on the morbidity rate is obvious and supported by numerous studies based on SIR-type models. However, the existing models do not explain the difference in outbreak profiles in countries with different intrinsic socio-cultural features and are rather specific for describing the complex dynamics of an outbreak. A system of models of the COVID-19 pandemic is proposed, combining the dynamics of social stress described by the tools of sociophysics8 with classical epidemic models. Even the combination of a dynamic SIR model with the classic triad of stages of general adaptation syndrome, Alarm-Resistance-Exhaustion, makes it possible to describe the available statistics for various countries of the world with a high degree of accuracy. The conceptualization of social stress leads to the division of the vulnerable population into different groups according to behavior mode, which can be tracked in detail. The sets of kinetic constants corresponding to optimal fit of model to data clearly characterize the society ability to focus efforts on protection against pandemic and keep this concentration for a considerable time. Such characterization can further help in the development of management strategies specific to a particular society: country, region, or social group.
The COVID-19 pandemic poses challenges for continuing economic activity while reducing health risks. While these challenges can be mitigated through testing, testing budget is often limited. Here we study how institutions, such as nursing homes, should utilize a fixed test budget for early detection of an outbreak. Using an extended network-SEIR model, we show that given a certain budget of tests, it is generally better to test smaller subgroups of the population frequently than to test larger groups but less frequently. The numerical results are consistent with an analytical expression we derive for the size of the outbreak at detection in an exponential spread model. Our work provides a simple guideline for institutions: distribute your total tests over several batches instead of using them all at once. We expect that in the appropriate scenarios, this easy-to-implement policy recommendation will lead to earlier detection and better mitigation of local COVID-19 outbreaks.
Epidemic spreading has been studied for a long time and most of them are focused on the growing aspect of a single epidemic outbreak. Recently, we extended the study to the case of recurrent epidemics (Sci. Rep. {bf 5}, 16010 (2015)) but limited only to a single network. We here report from the real data of coupled regions or cities that the recurrent epidemics in two coupled networks are closely related to each other and can show either synchronized outbreak phase where outbreaks occur simultaneously in both networks or mixed outbreak phase where outbreaks occur in one network but do not in another one. To reveal the underlying mechanism, we present a two-layered network model of coupled recurrent epidemics to reproduce the synchronized and mixed outbreak phases. We show that the synchronized outbreak phase is preferred to be triggered in two coupled networks with the same average degree while the mixed outbreak phase is preferred for the case with different average degrees. Further, we show that the coupling between the two layers is preferred to suppress the mixed outbreak phase but enhance the synchronized outbreak phase. A theoretical analysis based on microscopic Markov-chain approach is presented to explain the numerical results. This finding opens a new window for studying the recurrent epidemics in multi-layered networks.
We analyze the paper of Nathan D. Grubaugh et al. (Nature 546, 401-405, 2017) and find that it does not offer a convincing quantitative explanation for what generated the temporal distribution of human Zika virus (ZIKV) cases shown in their paper (Fig. 1d). We criticize this aspect because it is this understanding of how human cases develop from day-today and week-to-week within an area such as these Ground Zeros, that policymakers need in order to mitigate future outbreaks. We present results that strongly suggest that the missing piece is everyday human visit-revisit behavior. These results reproduce the human outbreak data in the key areas of Miami in 2016 very well, and give policymakers specific predictions for how changes in human flow through these areas will affect, and hence can be used to mitigate, future ZIka outbreaks in Miami and beyond.
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