Evaluating the impact of test-trace-isolate for COVID-19 management and alternative strategies.

There are many contrasting results concerning the effectiveness of Test-Trace-Isolate (TTI) strategies in mitigating SARS-CoV-2 spread. To shed light on this debate, we developed a novel static-temporal multiplex network characterizing both the regular (static) and random (temporal) contact patterns of individuals and a SARS-CoV-2 transmission model calibrated with historical COVID-19 epidemiological data. We estimated that the TTI strategy alone could not control the disease spread: assuming R0 = 2.5, the infection attack rate would be reduced by 24.5%. Increased test capacity and improved contact trace efficiency only slightly improved the effectiveness of the TTI. We thus investigated the effectiveness of the TTI strategy when coupled with reactive social distancing policies. Limiting contacts on the temporal contact layer would be insufficient to control an epidemic and contacts on both layers would need to be limited simultaneously. For example, the infection attack rate would be reduced by 68.1% when the reactive distancing policy disconnects 30% and 50% of contacts on static and temporal layers, respectively. Our findings highlight that, to reduce the overall transmission, it is important to limit contacts regardless of their types in addition to identifying infected individuals through contact tracing, given the substantial proportion of asymptomatic and pre-symptomatic SARS-CoV-2 transmission.

What is the role of aerosol transmission in SARS-Cov-2 Omicron spread in Shanghai?

The Omicron transmission has infected nearly 600,000 people in Shanghai from March 26 to May 31, 2022. Combined with different control measures taken by the government in different periods, a dynamic model was constructed to investigate the impact of medical resources, shelter hospitals and aerosol transmission generated by clustered nucleic acid testing on the spread of Omicron. The parameters of the model were estimated by least square method and MCMC method, and the accuracy of the model was verified by the cumulative number of asymptomatic infected persons and confirmed cases in Shanghai from March 26 to May 31, 2022. The result of numerical simulation demonstrated that the aerosol transmission figured prominently in the transmission of Omicron in Shanghai from March 28 to April 30. Without aerosol transmission, the number of asymptomatic subjects and symptomatic cases would be reduced to 130,000 and 11,730 by May 31, respectively. Without the expansion of shelter hospitals in the second phase, the final size of asymptomatic subjects and symptomatic cases might reach 23.2 million and 4.88 million by May 31, respectively. Our results also revealed that expanded vaccination played a vital role in controlling the spread of Omicron. However, even if the vaccination rate were 100%, the transmission of Omicron should not be completely blocked. Therefore, other control measures should be taken to curb the spread of Omicron, such as widespread antiviral therapies, enhanced testing and strict tracking quarantine measures. This perspective could be utilized as a reference for the transmission and prevention of Omicron in other large cities with a population of 10 million like Shanghai.

Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted.

BACKGROUND: Contact patterns play a key role in the spread of respiratory infectious diseases in human populations. During the COVID-19 pandemic, the regular contact patterns of the population have been disrupted due to social distancing both imposed by the authorities and individual choices. Many studies have focused on age-mixing patterns before the COVID-19 pandemic, but they provide very little information about the mixing patterns in the COVID-19 era. In this study, we aim at quantifying human heterogeneous mixing patterns immediately after lockdowns implemented to contain COVID-19 spread in China were lifted. We also provide an illustrative example of how the collected mixing patterns can be used in a simulation study of SARS-CoV-2 transmission. METHODS AND RESULTS: In this work, a contact survey was conducted in Chinese provinces outside Hubei in March 2020, right after lockdowns were lifted. We then leveraged the estimated mixing patterns to calibrate a mathematical model of SARS-CoV-2 transmission. Study participants reported 2.3 contacts per day (IQR: 1.0-3.0) and the mean per-contact duration was 7.0 h (IQR: 1.0-10.0). No significant differences in average contact number and contact duration were observed between provinces, the number of recorded contacts did not show a clear trend by age, and most of the recorded contacts occurred with family members (about 78%). The simulation study highlights the importance of considering age-specific contact patterns to estimate the COVID-19 burden. CONCLUSIONS: Our findings suggest that, despite lockdowns were no longer in place at the time of the survey, people were still heavily limiting their contacts as compared to the pre-pandemic situation.

Reactive school closure weakens the network of social interactions and reduces the spread of influenza.

School-closure policies are considered one of the most promising nonpharmaceutical interventions for mitigating seasonal and pandemic influenza. However, their effectiveness is still debated, primarily due to the lack of empirical evidence about the behavior of the population during the implementation of the policy. Over the course of the 2015 to 2016 influenza season in Russia, we performed a diary-based contact survey to estimate the patterns of social interactions before and during the implementation of reactive school-closure strategies. We develop an innovative hybrid survey-modeling framework to estimate the time-varying network of human social interactions. By integrating this network with an infection transmission model, we reduce the uncertainty surrounding the impact of school-closure policies in mitigating the spread of influenza. When the school-closure policy is in place, we measure a significant reduction in the number of contacts made by students (14.2 vs. 6.5 contacts per day) and workers (11.2 vs. 8.7 contacts per day). This reduction is not offset by the measured increase in the number of contacts between students and nonhousehold relatives. Model simulations suggest that gradual reactive school-closure policies based on monitoring student absenteeism rates are capable of mitigating influenza spread. We estimate that without the implemented reactive strategies the attack rate of the 2015 to 2016 influenza season would have been 33% larger. Our study sheds light on the social mixing patterns of the population during the implementation of reactive school closures and provides key instruments for future cost-effectiveness analyses of school-closure policies.

The COVID-19 outbreak in Sichuan, China: Epidemiology and impact of interventions.

In January 2020, a COVID-19 outbreak was detected in Sichuan Province of China. Six weeks later, the outbreak was successfully contained. The aim of this work is to characterize the epidemiology of the Sichuan outbreak and estimate the impact of interventions in limiting SARS-CoV-2 transmission. We analyzed patient records for all laboratory-confirmed cases reported in the province for the period of January 21 to March 16, 2020. To estimate the basic and daily reproduction numbers, we used a Bayesian framework. In addition, we estimated the number of cases averted by the implemented control strategies. The outbreak resulted in 539 confirmed cases, lasted less than two months, and no further local transmission was detected after February 27. The median age of local cases was 8 years older than that of imported cases. We estimated R0 at 2.4 (95% CI: 1.6-3.7). The epidemic was self-sustained for about 3 weeks before going below the epidemic threshold 3 days after the declaration of a public health emergency by Sichuan authorities. Our findings indicate that, were the control measures be adopted four weeks later, the epidemic could have lasted 49 days longer (95% CI: 31-68 days), causing 9,216 more cases (95% CI: 1,317-25,545).

Detecting critical slowing down in high-dimensional epidemiological systems.

Despite medical advances, the emergence and re-emergence of infectious diseases continue to pose a public health threat. Low-dimensional epidemiological models predict that epidemic transitions are preceded by the phenomenon of critical slowing down (CSD). This has raised the possibility of anticipating disease (re-)emergence using CSD-based early-warning signals (EWS), which are statistical moments estimated from time series data. For EWS to be useful at detecting future (re-)emergence, CSD needs to be a generic (model-independent) feature of epidemiological dynamics irrespective of system complexity. Currently, it is unclear whether the predictions of CSD-derived from simple, low-dimensional systems-pertain to real systems, which are high-dimensional. To assess the generality of CSD, we carried out a simulation study of a hierarchy of models, with increasing structural complexity and dimensionality, for a measles-like infectious disease. Our five models included: i) a nonseasonal homogeneous Susceptible-Exposed-Infectious-Recovered (SEIR) model, ii) a homogeneous SEIR model with seasonality in transmission, iii) an age-structured SEIR model, iv) a multiplex network-based model (Mplex) and v) an agent-based simulator (FRED). All models were parameterised to have a herd-immunity immunization threshold of around 90% coverage, and underwent a linear decrease in vaccine uptake, from 92% to 70% over 15 years. We found evidence of CSD prior to disease re-emergence in all models. We also evaluated the performance of seven EWS: the autocorrelation, coefficient of variation, index of dispersion, kurtosis, mean, skewness, variance. Performance was scored using the Area Under the ROC Curve (AUC) statistic. The best performing EWS were the mean and variance, with AUC > 0.75 one year before the estimated transition time. These two, along with the autocorrelation and index of dispersion, are promising candidate EWS for detecting disease emergence.

Measurability of the epidemic reproduction number in data-driven contact networks.

The basic reproduction number is one of the conceptual cornerstones of mathematical epidemiology. Its classical definition as the number of secondary cases generated by a typical infected individual in a fully susceptible population finds a clear analytical expression in homogeneous and stratified mixing models. Along with the generation time (the interval between primary and secondary cases), the reproduction number allows for the characterization of the dynamics of an epidemic. A clear-cut theoretical picture, however, is hardly found in real data. Here, we infer from highly detailed sociodemographic data two multiplex contact networks representative of a subset of the Italian and Dutch populations. We then simulate an infection transmission process on these networks accounting for the natural history of influenza and calibrated on empirical epidemiological data. We explicitly measure the reproduction number and generation time, recording all individual-level transmission events. We find that the classical concept of the basic reproduction number is untenable in realistic populations, and it does not provide any conceptual understanding of the epidemic evolution. This departure from the classical theoretical picture is not due to behavioral changes and other exogenous epidemiological determinants. Rather, it can be simply explained by the (clustered) contact structure of the population. Finally, we provide evidence that methodologies aimed at estimating the instantaneous reproduction number can operationally be used to characterize the correct epidemic dynamics from incidence data.

[Epidemiology, Treatment, and Epidemic Prevention and Control of the Coronavirus Disease 2019: a Review].

This review summarizes the ongoing researches regarding etiology, epidemiology, transmission dynamics, treatment, and prevention and control strategies of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with comparison to severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV) and pandemic H1N1 virus. SARS-CoV-2 may be originated from bats, and the patients and asymptomatic carriers are the source of epidemic infection. The virus can be transmitted human-to-human through droplets and close contact, and people at all ages are susceptible to this virus. The main clinical symptoms of the patients are fever and cough, accompanied with leukocytopenia and lymphocytopenia. Effective drugs have been not yet available thus far. In terms of the prevention and control strategies, vaccine development as the primary prevention should be accelerated. Regarding the secondary prevention, ongoing efforts of the infected patients and close contacts quarantine, mask wearing promotion, regular disinfection in public places should be continued. Meanwhile, rapid detection kit for serological monitoring of the virus in general population is expected so as to achieve early detection, early diagnosis, early isolation and early treatment. In addition, public health education on this disease and prevention should be enhanced so as to mitigate panic and mobilize the public to jointly combat the epidemic.

Coevolution spreading in complex networks.

The propagations of diseases, behaviors and information in real systems are rarely independent of each other, but they are coevolving with strong interactions. To uncover the dynamical mechanisms, the evolving spatiotemporal patterns and critical phenomena of networked coevolution spreading are extremely important, which provide theoretical foundations for us to control epidemic spreading, predict collective behaviors in social systems, and so on. The coevolution spreading dynamics in complex networks has thus attracted much attention in many disciplines. In this review, we introduce recent progress in the study of coevolution spreading dynamics, emphasizing the contributions from the perspectives of statistical mechanics and network science. The theoretical methods, critical phenomena, phase transitions, interacting mechanisms, and effects of network topology for four representative types of coevolution spreading mechanisms, including the coevolution of biological contagions, social contagions, epidemic-awareness, and epidemic-resources, are presented in detail, and the challenges in this field as well as open issues for future studies are also discussed.

Social contagions on interconnected networks of heterogeneous populations.

Recently, the dynamics of social contagions ranging from the adoption of a new product to the diffusion of a rumor have attracted more and more attention from researchers. However, the combined effects of individual's heterogenous adoption behavior and the interconnected structure on the social contagions processes have yet to be understood deeply. In this paper, we study theoretically and numerically the social contagions with heterogeneous adoption threshold in interconnected networks. We first develop a generalized edge-based compartmental approach to predict the evolution of social contagion dynamics on interconnected networks. Both the theoretical predictions and numerical results show that the growth of the final recovered fraction with the intralayer propagation rate displays double transitions. When increasing the initial adopted proportion or the adopted threshold, the first transition remains continuous within different dynamic parameters, but the second transition gradually vanishes. When decreasing the interlayer propagation rate, the change in the double transitions mentioned above is also observed. The heterogeneity of degree distribution does not affect the type of first transition, but increasing the heterogeneity of degree distribution results in the type change of the second transition from discontinuous to continuous. The consistency between the theoretical predictions and numerical results confirms the validity of our proposed analytical approach.

Interactive social contagions and co-infections on complex networks.

What we are learning about the ubiquitous interactions among multiple social contagion processes on complex networks challenges existing theoretical methods. We propose an interactive social behavior spreading model, in which two behaviors sequentially spread on a complex network, one following the other. Adopting the first behavior has either a synergistic or an inhibiting effect on the spread of the second behavior. We find that the inhibiting effect of the first behavior can cause the continuous phase transition of the second behavior spreading to become discontinuous. This discontinuous phase transition of the second behavior can also become a continuous one when the effect of adopting the first behavior becomes synergistic. This synergy allows the second behavior to be more easily adopted and enlarges the co-existence region of both behaviors. We establish an edge-based compartmental method, and our theoretical predictions match well with the simulation results. Our findings provide helpful insights into better understanding the spread of interactive social behavior in human society.

Impacts of opinion leaders on social contagions.

Opinion leaders are ubiquitous in both online and offline social networks, but the impacts of opinion leaders on social behavior contagions are still not fully understood, especially by using a mathematical model. Here, we generalize the classical Watts threshold model and address the influences of the opinion leaders, where an individual adopts a new behavior if one of his/her opinion leaders adopts the behavior. First, we choose the opinion leaders randomly from all individuals in the network and find that the impacts of opinion leaders make other individuals adopt the behavior more easily. Specifically, the existence of opinion leaders reduces the lowest mean degree of the network required for the global behavior adoption and increases the highest mean degree of the network that the global behavior adoption can occur. Besides, the introduction of opinion leaders accelerates the behavior adoption but does not change the adoption order of individuals. The developed theoretical predictions agree with the simulation results. Second, we randomly choose the opinion leaders from the top h% of the highest degree individuals and find an optimal h% for the network with the lowest mean degree that the global behavior adoption can occur. Meanwhile, the influences of opinion leaders on accelerating the adoption of behaviors become less significant and can even be ignored when reducing the value of h%.

[Clinical application evaluation and revision suggestions of clinical practice guideline on traditional Chinese medicine therapy alone or combined with antibiotics for upper respiratory tract infection in children].

Questionnaires and case investigation methods were taken in this paper, taking the clinical practice guideline on traditional Chinese medicine therapy alone or combined with antibiotics for upper respiratory tract infection in children published by the Chinese Medicine Association as the research object. Doctors from 187 hospitals in 29 regions across the country were invited to evaluate the applicability of the Guideline and clinical application effects, so as to collect the opinions on revising the Guideline. Clinicians about 508 accepted the applicability survey of the Guideline, and considered that the structure and content of the Guideline were reasonable, with the proportions being as high as 98.23% and 98.03%, respectively. In the content of syndrome differentiation-based treatment, the factors with higher rationality included therapeutic principle and method (99.41%), diagnosis elements (98.82%), and syndrome differentiation classification (98.03%); while the factors with lower rationality included the rehabilitation and health preserving (97.05%) and complication prevention (97.24%). 98.03% of the clinicians considered theat the Guideline was to be fully applicable and basically applicable, and 1.97% of the clinicians considered it to be applicable after revision. By observing 491 cases, the Guide was applied for evaluation and analysis. The factors with higher compliance included diagnosis of Western medicine disease (100%) and the diagnosis of TCM disease (99.18%); while the factors with lower compliance included the treatment measures, with a compliance rate of 77.18% and 83.05% respectively for simple preparations and other treatment method. The safety and economy of the Guideline were good, 97.35%, 93.89%, respectively. The comprehensive evaluation was good, and 99.41% of the respondents were willing to follow the treatment schemes recommended in the Guideline, suitable for clinical application. The opinions on revision were mainly focused on dialectical treatment, complication prevention and rehabilitation. It indicates that only by actively and extensively soliciting opinions to revise the Guideline, can we improve the quality of the Guideline for clinical practice, so as to raise the level of clinical efficacy.

[A commentary of literature research of traditional Chinese medicine for acute upper respiratory tract infection in children].

Acute upper respiratory tract infection is the most common infectious disease in children's respiratory system. The pathogen to the main virus, can account for more than 90% of the primary upper respiratory tract infectio. However, there is no specific anti-viral drugs specifically for the disease, in addition to the existence of excessive, widespread use or even abuse of antibiotics.Long-term clinical practice has confirmed that Chinese medicine is safe and effective in treating acute upper respiratory tract infection in children. The author reviews the literatures of multiple databases, and analyzes the advantages of Chinese patent medicine in the treatment of acute upper respiratory tract infection in children from the perspective of clinical research and experimental basic research. It also puts forward the existing problems and possible research directions of Chinese patent medicine in the treatment of acute upper respiratory tract infection in children.

[Measurement of external deviation angle of patellar tendon and its application on diagnosis of recurrent patellar dislocation].

OBJECTIVE: To compare computed tomography (CT) measurement results of external deviation angle of patellar tendon and tibia tubercle-trochlea groove (TT-TG), as well as the diagnostic ability and pathology in recurrent patellar dislocation threshold. METHODS: From January 2015 to March 2020, 46 patients with recurrent patella dislocation and 112 patients with non-patella dislocation were retrospectively analyzed. Forty-six patients with recurrent patella dislocation were divived into 2 groups according to TT-TG value, 11 patients with patellar dislocation with TT-TG≥20 mm(group A), including 7 males and 4 females, aged from 16 to 27 years old with an average of(21.00±3.98) years old; 35 patients with patellar dislocation with TT-TG<20 mm(group B), including 14 males and 21 females, aged from 16 to 37 years old with an average of(22.83±6.09) years old. While 112 patients with non-patella dislocation(group C) included 63 males and 49 females, aged 16 to 36 years old with an average of(22.87±5.69) years old. The measurement data of external deviation angle of patellar tendon and TT-TG value among three groups were compared. Spearman analysis was used to analyze correlation among them. Intraclass correlation coefficient (ICC) was used to determine repeatability within group. Receiver operating characteristic (ROC) area under the curve was used to evaluate diagnostic ability of parameters, and calculate osteotomy parameters of external deviation angle of patellar tendon, as well as external deviation angle of patellar tendon and TT-TG value in the diagnosis of recurrent patella diagnostic parameters of dislocation. RESULTS: External deviation angle of patellar tendon in group A, B and C was (22.04±3.09)°, (17.20±4.43)°and (10.22±3.45)° respectively;while TT-TG value was(21.15±0.71) mm, (15.97±2.69) mm and (11.12±3.77) mm, there were significance among three groups (P<0.01), and had difference between group A and B(P<0.01). There was strong positive correlation between external deviation angle of patellar tendon and TT-TG value (r=0.735, P<0.000 1). The intra-group ICC value(0.980, 0.982) of external deviation angle of patellar tendon in group A and B have better repeatability than TT-TG value (0.594, 0.775). The external deviation angle of patellar tendon in group C(0.956) and repeatability of TT-TG value(0.906) was very good. In the diagnosis of recurrent patellar dislocation, the area under ROC curve of external deviation angle of patellar tendon (0.916) was greater than TT-TG value(0.886), and diagnostic parameters were 13.84°and 14.69 mm, respectively;in tibial osteotomy, the area under ROC curve of external deviation angle of patellar tendon was 0.821, and osteotomy parameter was 20.15°. CONCLUSION: CT imaging could reliably measure external deviation angle of patellar tendon.There is a strong positive correlation between external deviation angle of patellar tendon and value of TT-TG, which could be used to determine pathological state of recurrent patellar dislocation, and external deviation angle of patellar tendon is superior to the TT-TG value in the diagnosis of recurrent patellar dislocation. The external deviation angle of patellar tendon could also be used to guide the formulation of the tibial osteotomy plan for recurrent patellar dislocation.

Model-based evaluation of alternative reactive class closure strategies against COVID-19.

There are contrasting results concerning the effect of reactive school closure on SARS-CoV-2 transmission. To shed light on this controversy, we developed a data-driven computational model of SARS-CoV-2 transmission. We found that by reactively closing classes based on syndromic surveillance, SARS-CoV-2 infections are reduced by no more than 17.3% (95%CI: 8.0-26.8%), due to the low probability of timely identification of infections in the young population. We thus investigated an alternative triggering mechanism based on repeated screening of students using antigen tests. Depending on the contribution of schools to transmission, this strategy can greatly reduce COVID-19 burden even when school contribution to transmission and immunity in the population is low. Moving forward, the adoption of antigen-based screenings in schools could be instrumental to limit COVID-19 burden while vaccines continue to be rolled out.

Model-based evaluation of alternative reactive class closure strategies against COVID-19.

There are contrasting results concerning the effect of reactive school closure on SARS-CoV-2 transmission. To shed light on this controversy, here we develop a data-driven computational model of SARS-CoV-2 transmission to investigate mechanistically the effect on COVID-19 outbreaks of school closure strategies based on syndromic surveillance and antigen screening of students. We found that by reactively closing classes based on syndromic surveillance, SARS-CoV-2 infections are reduced by no more than 13.1% (95%CI: 8.6%-20.2 %), due to the low probability of timely symptomatic case identification among the young population. We thus investigated an alternative triggering mechanism based on repeated screening of students using antigen tests. Should population-level social distancing measures unrelated to schools enable maintaining the reproduction number ( R ) at 1.3 or lower, an antigen-based screening strategy is estimated to fully prevent COVID-19 outbreaks in the general population. Depending on the contribution of schools to transmission, this strategy can either prevent COVID-19 outbreaks for R up to 1.9 or to at least greatly reduce outbreak size in very conservative scenarios about school contribution to transmission. Moving forward, the adoption of antigen-based screenings in schools could be instrumental to limit COVID-19 burden while vaccines continue to roll out through 2021, especially in light of possible continued emergence of SARS-CoV-2 variants.

Inferring high-resolution human mixing patterns for disease modeling.

Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for realism in addressing complex public health questions is, however, calling for accurate models of the human contact patterns that govern the disease transmission processes. Here we present a data-driven approach to generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features. We produce age-stratified contact matrices for 35 countries, including 277 sub-national administratvie regions of 8 of those countries, covering approximately 3.5 billion people and reflecting the high degree of cultural and societal diversity of the focus countries. We use the derived contact matrices to model the spread of airborne infectious diseases and show that sub-national heterogeneities in human mixing patterns have a marked impact on epidemic indicators such as the reproduction number and overall attack rate of epidemics of the same etiology. The contact patterns derived here are made publicly available as a modeling tool to study the impact of socio-economic differences and demographic heterogeneities across populations on the epidemiology of infectious diseases.

Synergistic interactions promote behavior spreading and alter phase transitions on multiplex networks.

Synergistic interactions are ubiquitous in the real world. Recent studies have revealed that, for a single-layer network, synergy can enhance spreading and even induce an explosive contagion. There is at the present a growing interest in behavior spreading dynamics on multiplex networks. What is the role of synergistic interactions in behavior spreading in such networked systems? To address this question, we articulate a synergistic behavior spreading model on a double layer network, where the key manifestation of the synergistic interactions is that the adoption of one behavior by a node in one layer enhances its probability of adopting the behavior in the other layer. A general result is that synergistic interactions can greatly enhance the spreading of the behaviors in both layers. A remarkable phenomenon is that the interactions can alter the nature of the phase transition associated with behavior adoption or spreading dynamics. In particular, depending on the transmission rate of one behavior in a network layer, synergistic interactions can lead to a discontinuous (first-order) or a continuous (second-order) transition in the adoption scope of the other behavior with respect to its transmission rate. A surprising two-stage spreading process can arise: due to synergy, nodes having adopted one behavior in one layer adopt the other behavior in the other layer and then prompt the remaining nodes in this layer to quickly adopt the behavior. Analytically, we develop an edge-based compartmental theory and perform a bifurcation analysis to fully understand, in the weak synergistic interaction regime where the dynamical correlation between the network layers is negligible, the role of the interactions in promoting the social behavioral spreading dynamics in the whole system.

Explosive spreading on complex networks: The role of synergy.

In spite of the vast literature on spreading dynamics on complex networks, the role of local synergy, i.e., the interaction of elements that when combined produce a total effect greater than the sum of the individual elements, has been studied but only for irreversible spreading dynamics. Reversible spreading dynamics are ubiquitous but their interplay with synergy has remained unknown. To fill this knowledge gap, we articulate a model to incorporate local synergistic effect into the classical susceptible-infected-susceptible process, in which the probability for a susceptible node to become infected through an infected neighbor is enhanced when the neighborhood of the latter contains a number of infected nodes. We derive master equations incorporating the synergistic effect, with predictions that agree well with the numerical results. A striking finding is that when a parameter characterizing the strength of the synergy reinforcement effect is above a critical value, the steady-state density of the infected nodes versus the basic transmission rate exhibits an explosively increasing behavior and a hysteresis loop emerges. In fact, increasing the synergy strength can promote the spreading and reduce the invasion and persistence thresholds of the hysteresis loop. A physical understanding of the synergy promoting explosive spreading and the associated hysteresis behavior can be obtained through a mean-field analysis.