Timely surveillance and temporal calibration of disease response against human infectious diseases

Background Disease surveillance and response are critical components of epidemic preparedness. The disease response, in most cases, is a set of reactive measures that follow the outcomes of the disease surveillance. Hence, timely surveillance is a prerequisite for an effective response. Methodology/principal findings We apply epidemiological soundness criteria in combination with the Latent Influence Point Process and time-to-event models to construct a disease spread network. The network implicitly quantifies the fertility (whether a case leads to secondary cases) and reproduction (number of secondary cases per infectious case) of the cases as well as the size and generations (of the infection chain) of the outbreaks. We test our approach by applying it to historic dengue case data from Australia. Using the data, we empirically confirm that high morbidity relates positively with delay in disease response. Moreover, we identify what constitutes timely surveillance by applying various thresholds of disease response delay to the network and report their impact on case fertility, reproduction, number of generations and ultimately, outbreak size. We observe that enforcing a response delay threshold of 5 days leads to a large average reduction across all parameters (occurrence 87%, reproduction 83%, outbreak size 80% and outbreak generations 47%), whereas extending the threshold to 10 days, in comparison, significantly limits the effectiveness of the response actions. Lastly, we identify the components of the disease surveillance system that can be calibrated to achieve the identified thresholds. Conclusion We identify practically achievable, timely surveillance thresholds (on temporal scale) that lead to an effective response and identify how they can be satisfied. Our approach can be utilized to provide guidelines on spatially and demographically targeted resource allocation for public awareness campaigns as well as to improve diagnostic abilities and turn-around times for the doctors and laboratories involved.

The impact of negative edges on spreading in signed networks

In signed networks, negative edges represent negative relationships; the increase or gathering of negative interpersonal relationships can lead to social turmoil, which will affect the spreading of diseases or information. Therefore, it is significant to study the impact of the proportion and configuration of negative edges on spreading. In this paper, to study the impact of negative relationships on spreading, we propose a heterogeneous spreading model using signed networks. In this model, we use the balance of the local structure to quantify the probability of contact between individuals, making the contacts heterogeneous. We then examine the impact of negative edges on spreading using numerical simulations. We find that the balance of the network and the spreading coverage (i.e. outbreak size) gradually decrease with the proportion of negative edges. Compared with preference configurations (in which negative signs are placed on edges that have an important impact on spreading), a random configuration (in which negative signs are placed on random edges) has a suppressive effect on spreading. This provides information for epidemic prevention. Finally, we find that there are two important factors — contact probability and spreading paths — that could explain the observed spreading phenomena.

Impact of temporal correlations on high risk outbreaks of independent and cooperative SIR dynamics

We first propose a quantitative approach to detect high risk outbreaks of independent and coinfective SIR dynamics on three empirical networks: a school, a conference and a hospital contact network. This measurement is based on the k-means clustering method and identifies proper samples for calculating the mean outbreak size and the outbreak probability. Then we systematically study the impact of different temporal correlations on high risk outbreaks over the original and differently shuffled counterparts of each network. We observe that, on the one hand, in the coinfection process, randomization of the sequence of the events increases the mean outbreak size of high-risk cases. On the other hand, these correlations do not have a consistent effect on the independent infection dynamics, and can either decrease or increase this mean. Randomization of the daily pattern correlations has no strong impact on the size of the outbreak in either the coinfection or the independent spreading cases. We also observe that an increase in the mean outbreak size does not always coincide with an increase in the outbreak probability; therefore, we argue that merely considering the mean outbreak size of all realizations may lead us into falsely estimating the outbreak risks. Our results suggest that some sort of contact randomization in the organizational level in schools, events or hospitals might help to suppress the spreading dynamics while the risk of an outbreak is high.

Long-term dynamics of Norovirus transmission in Japan, 2005–2019

Norovirus continues to evolve, adjusting its pathogenesis and transmissibility. In the present study, we systematically collected datasets on Norovirus outbreaks in Japan from 2005 to 2019 and analyzed time-dependent changes in the asymptomatic ratio, the probability of virus detection, and the probability of infection given exposure. Reports of 1,728 outbreaks were published, and feces from all involved individuals, including those with asymptomatic infection, were tested for virus in 434 outbreaks. We found that the outbreak size did not markedly change over this period, but the variance in outbreak size increased during the winter (November–April). Assuming that natural history parameters did not vary over time, the asymptomatic ratio, the probability of virus detection, and the probability of infection given exposure were estimated to be 18.6%, 63.3% and 84.5%, respectively. However, a model with time-varying natural history parameters yielded better goodness-of-fit and suggested that the asymptomatic ratio varied by year. The asymptomatic ratio was as high as 25.8% for outbreaks caused by genotype GII.4 noroviruses. We conclude that Norovirus transmissibility has not changed markedly since 2005, and that yearly variation in the asymptomatic ratio could potentially be explained by the circulating dominant genotype.

Model-based projections for COVID-19 outbreak size and student-days lost to closure in Ontario childcare centres and primary schools

There is a pressing need for evidence-based scrutiny of plans to re-open childcare centres during the COVID-19 pandemic. Here we developed an agent-based model of SARS-CoV-2 transmission within a childcare centre and households. Scenarios varied the student-to-educator ratio (15:2, 8:2, 7:3), family clustering (siblings together versus random assignment) and time spent in class. We also evaluated a primary school setting (with student-educator ratios 30:1, 15:1 and 8:1), including cohorts that alternate weekly. In the childcare centre setting, grouping siblings significantly reduced outbreak size and student-days lost. We identify an intensification cascade specific to classroom outbreaks of respiratory viruses with presymptomatic infection. In both childcare and primary school settings, each doubling of class size from 8 to 15 to 30 more than doubled the outbreak size and student-days lost (increases by factors of 2–5, depending on the scenario. Proposals for childcare and primary school reopening could be enhanced for safety by switching to smaller class sizes and grouping siblings.

SARS-CoV-2 transmission and control in a hospital setting: an individual-based modelling study

Development of strategies for mitigating the severity of COVID-19 is now a top public health priority. We sought to assess strategies for mitigating the COVID-19 outbreak in a hospital setting via the use of non-pharmaceutical interventions. We developed an individual-based model for COVID-19 transmission in a hospital setting. We calibrated the model using data of a COVID-19 outbreak in a hospital unit in Wuhan. The calibrated model was used to simulate different intervention scenarios and estimate the impact of different interventions on outbreak size and workday loss. The use of high-efficacy facial masks was shown to be able to reduce infection cases and workday loss by 80% (90% credible interval (CrI): 73.1–85.7%) and 87% (CrI: 80.0–92.5%), respectively. The use of social distancing alone, through reduced contacts between healthcare workers, had a marginal impact on the outbreak. Our results also indicated that a quarantine policy should be coupled with other interventions to achieve its effect. The effectiveness of all these interventions was shown to increase with their early implementation. Our analysis shows that a COVID-19 outbreak in a hospital's non-COVID-19 unit can be controlled or mitigated by the use of existing non-pharmaceutical measures.

Evaluation of SARS-CoV-2 transmission mitigation strategies on a university campus using an agent-based network model

Universities are faced with decisions on how to resume campus activities while mitigating SARS-CoV-2 risk. To provide guidance for these decisions, we developed an agent-based network model of SARS-CoV-2 transmission to assess the potential impact of strategies to reduce outbreaks. The model incorporates important features related to risk at the University of California San Diego. We found that structural interventions for housing (singles only) and instructional changes (from in-person to hybrid with class size caps) can substantially reduce R0, but masking and social distancing are required to reduce this to at or below 1. Within a risk mitigation scenario, increased frequency of asymptomatic testing from monthly to twice weekly has minimal impact on average outbreak size (1.1-1.9), but substantially reduces the maximum outbreak size and cumulative number of cases. We conclude that an interdependent approach incorporating risk mitigation, viral detection, and public health intervention is required to mitigate risk.

An open-access database of infectious disease transmission trees to explore superspreader epidemiology

Historically, emerging and re-emerging infectious diseases have caused large, deadly, and expensive multi-national outbreaks. Often outbreak investigations aim to identify who infected whom by reconstructing the outbreak transmission tree, which visualizes transmission between individuals as a network with nodes representing individuals and branches representing transmission from person to person. We compiled a database of 383 published, standardized transmission trees consisting of 16 directly-transmitted diseases ranging in size from 2 to 286 cases. For each tree and disease we calculated several key statistics, such as outbreak size, average number of secondary infections, the dispersion parameter, and the number of superspreaders. We demonstrated the potential utility of the database through short analyses addressing questions about superspreader epidemiology for a variety of diseases, including COVID-19. First, we compared the frequency and contribution of superspreaders to onward transmission across diseases. COVID-19 outbreaks had significantly fewer superspreaders than outbreaks of SARS and MERS and a dispersion parameter between that of SARS and MERS. Across diseases the presence of more superspreaders was associated with greater outbreak size. Second, we further examined how early spread impacts tree size. Generally, trees sparked by a superspreader had larger outbreak sizes than those trees not sparked by a superspreader, and this trend was significant for COVID-19 trees. Third, we investigated patterns in how superspreaders are infected. Across trees with more than one superspreader, we found support for the theory that superspreaders generate other superspreaders, even when controlling for number of secondary infections. In sum, our findings put the role of superspreading to COVID-19 transmission in perspective with that of SARS and MERS and suggest an avenue for further research on the generation of superspreaders. These data have been made openly available to encourage reuse and further scientific inquiry.Author SummaryPublic health investigations often aim to identify who infected whom, or the transmission tree, during outbreaks of infectious diseases. These investigations tend to be resource intensive but valuable as they contain epidemiological information, including the average number of infections caused by each individual and the variation in this number. To date, there remains no standardized format nor comprehensive database of infectious disease transmission trees. To fill this gap, we standardized and compiled more than 350 published transmission trees for 16 directly-transmitted diseases into a database that is publicly available. In this paper, we give an overview of the database construction process, as well as a demonstration of the types of questions that the database can be used to answer related to superspreader epidemiology. For example, we show that COVID-19 outbreaks have fewer superspreaders than outbreaks of SARS and MERS. We also find support for the theory that superspreaders generate other superspreaders. In the future, this database can be used to answer other outstanding questions in the field of epidemiology.