College campuses are at risk of becoming COVID-19 superspreaders for their entire county


College campuses are at risk of becoming COVID-19 superspreaders for their entire county, according to a new vast study which shows the striking danger of the first two weeks of school in particular.

Looking at 30 campuses across the nation with the highest amount of reported cases, experts saw that over half of the institutions had spikes – at their peak – which were well above 1,000 coronavirus cases per 100,000 people per week within the first two weeks of class.

In some colleges, one in five students had been infected with the virus by the end of the fall term. Four institutions had over 5,000 cases.

In 17 of the campuses monitored, a new computer model developed by scientists at Stanford University shows outbreaks translated directly into peaks of infection within their home counties.

Out today, the team’s research—published in the peer-reviewed journal Computer Methods in Biomechanics and Biomedical Engineering—crucially shows, however, that tight outbreak management, for example the immediate transition from in person to all online learning, can reduce the peaks within about two weeks.

Lead author Hannah Lu, from Stanford’s Energy Resources Engineering program, says the incidence levels of 1,000 cases per 100,000 people per week—when compared to the first and second waves of the pandemic with peak incidences of 70 to 150—means colleges are at real risk of developing an extreme incidence of COVID-19.

“Policy makers often use an incidence of 50 COVID-19 cases per 100,000 people per week as a threshold for high risk counties, states, or countries. All 30 institutions in our study exceeded this value, three even by two orders of that magnitude,” she states.

“The number of students who had become infected just throughout the fall is more than twice of the national average since the beginning of the outbreak of 5.3%, with 17.3 million reported cases at a population of 328.2 million.

“At the University of Notre Dame, for instance, all 12,607 students were tested before the beginning of class and only nine had tested positive. Less than two weeks into the term, the seven-day incidence was 3083, with a reproduction number R0 of 3.29.

“However,” she adds, “with around 90 reported deaths nationwide, mainly college employees and not students, the campus-related death rate of 0.02% remains well below the average death rate of COVID-19.”

Members of the research team used advanced modelling, which assesses the real-time epidemiology of the COVID-19 outbreak using an SEIR (susceptible, exposed, infectious, and recovered) model to map how the disease spread across the campuses.

They drew COVID-19 case reports from 30 publicly available college dashboards across the United States throughout the fall of 2020. These institutions were either teaching in person, online or a hybrid of both.

They selected colleges for which case numbers are reported on a daily basis and the total cumulative case number exceeded 100.

During this time window, the nationwide number of new cases had dropped below 50,000 per day.

A limitation of this study is that the true on-campus student population was often unreported and had to be approximated by the total fall quarter enrollment. “This likely underestimates of the real maximum incidence and the fraction of on-campus students that have been affected by the virus,” the authors state.

Senior author, Ellen Kuhl, adds: “Strikingly, these local campus outbreaks rapidly spread across the entire county and triggered a peak in new infections in neighbouring communities in more than half of the cases.

“It is becoming increasingly clear that these initial college outbreaks are unrelated to the national outbreak dynamics. Instead, they are independent local events driven by campus reopening and inviting students back to campus.

“Our results confirm the widespread fear in early fall that colleges could become the new hot spots of COVID-19 transmission. But, at the same time, college administrators should be applauded for their rapid responses to successfully manage local outbreaks.”

All reported campuses pursued regular surveillance testing, weekly or even twice per week, combined with aggressive test-trace-isolate strategies.

“The majority of colleges and universities were able to rapidly manage their outbreaks and suppress campus-wide infections, while the neighbouring communities were less successful in controlling the spread of the virus. As a result, for most institutions, the outbreak dynamics remained manageable throughout the entire fall of 2020 with narrow spikes of less than 300 cases per day,” Lu states.

The team believes that this methodology, in combination with continuing online learning, is the best way to prevent college sites from becoming the major hub of the disease.

“Our study suggests that tight test-trace-isolate strategies, flexible transition to online instruction, and-most importantly-compliance with local regulations will be critical to ensure a safe campus reopening after the winter break,” she added.

Professor Kuhl concludes: “We anticipate that the most important aspect upon campus reopening within the coming weeks will be the human factor. Unfortunately, the fall term has shown that the best of all strategies can become meaningless if people do not follow the recommendations.”

As of July 2020, the cumulative confirmed cases of COVID-19 worldwide have exceeded 17.4 million with over 572 thousand dead. There are 22 countries with more than 100,000 confirmed cases of as of July 14, 2020. The high transmissibility of the SARS-CoV-2 virus has substantially changed people’s hygiene habits, social relations, and forms of work and schooling during and after the pandemic [1]. In the absence of pharmaceutical intervention measures, public policies such as city lockdowns and workplace and school closures can mitigate the spread of disease, though with substantial economic and societal costs. The indecision regarding restarting the economy and stopping the pandemic has resulted in a wave of outbreaks in many countries [2].

Understanding the characteristics of the COVID-19 transmission process is crucial in finding a middle ground between restoring economic and societal order and controlling the pandemic. Previous research has shown that COVID-19 can be infectious pre-symptomatically [3], i.e., the virus is transmissive even without symptom onset. Finding out the incubation period’s duration and the virus reproducibility during the incubation period and shortly after symptom onset but before hospitalization is thus an urgent necessity [4].

Considering the incubation period, as of Jan. 26, the mean and median were 5 and 4.75 days (obtained by 125 patients) [5]. Confirmed cases reported from Jan. 4 to Feb. 24 showed a median incubation period of 5.1 days (obtained from 181 patients) [6]. By Jan. 22, using 425 patients, the mean incubation period was 5.2 days, and [7]. Reference [8] gave a shorter incubation period of 4.2 days, inferring that COVID-19 is more infectious than initially estimated. As of Mar. 31, the mean incubation time is estimated as 8.0 with a standard deviation of 4.75 [9]. Through a renewal process, the estimated median of the incubation period is 8.1 days, which is longer than other studies [10]. The mean and median of the incubation periods were 5.84 and 5.0 days via bootstrap for groups with an age of ≥40, and they otherwise demonstrated a significant difference [11]. By meta-analysis, the incubation period was modeled with a lognormal distribution, and the mean and median were 5.8 and 5.1 days [12].

The transmission rate is defined as the probability that an infection occurs among susceptible people within a specific group. It is an important index for providing an indication of how social interactions are related to transmission risk. Nine reports were listed in [13], showing a rate of 35% (95% CI 27–44), depending on infection caused by different contact methods.

One of the most important indices for infectious disease is the basic reproductive number. Numerous studies are devoted to its estimate. It is estimated to be 2.2 [14], which is higher than SARS-COV and MERS-CoV [15]. More estimates for the basic reproduction number are 4.7–6.6 [8], 2.24–3.58 [16], 3.77 (95% CI 3.51–4.05) [5], and 3.60 (3.49–3.84) [17]. The effective reproduction number is changing with time; it changed from 2.35 (1.15–4.77) to 1.05 (0.41–2.39) due to lockdown in Wuhan within 1 week [18].

The best-known model within infectious disease epidemiology is the SEIR (susceptible-exposed-infectious-recovered) model with different generalization. These models are utilized at the population level for the proportion of each state at given time, aiming to investigating the strategic decisions or effectiveness of the mitigation measures. For illustration, effective containment can explains the subexponential growth in China [19], and effects of containment measures in Italy are also analyzed by an SEIR-like model [17]. More results can be found [20–27].

Clinical investigations may suffer from a limited sample size and biased sampling from the population, leading to geometrical or demographic-dependent results. Different samples and different methods also lead to different results for data analysis and estimates. Simulation of disease spread and mitigation policies require a precise setting of incubation period [19, 28]. Metapopulation disease transmission models require a prerequisite setting of the transmission rate during social gathering events to predict disease spreading range [18, 29, 30]. For a better estimate of the reproduction number, a real data sample is a crucial ingredient. However, it is difficult to collect. Considering the demand of investigating the properties and modeling of COVID-19, fine data extracted from informative line-list records can provide supporting evidence for the existing results and solid foundation for further study.

In this work, we estimate the parameters of concern from a large scale epidemiological line-list database, which contains the contact history and epidemiological timelines of 9,120 confirmed COVID-19 cases in China [31]. The duration of the incubation period and the details of close contacts and contact scenarios are extracted from the line-list. Spreading trees are reconstructed from the potential transmission pairs in the line-list data set. Hidden in the line-list records of confirmed cases, we have collected 421 chains of spreading with a total confirmed cases number of 1,140. We fit proper distributions to the incubation period as well as scale of close contact. The reproducibility is presented by the spreading tree, which can be referred to as the effective reproduction number under strict containment measures in China.

The incubation distribution is fitted by Weibull distribution with a mean and median of 7.83 and 7 days, respectively; this is in agreement with [9]. Larger data size and longer observation period tend to result in larger incubation period, which is coincidence with the long tailed nature of Weibull distribution. For the secondary attack rate, there are much fewer results due to the lack of data. We have obtained 412 close contact events to investigate the transmission rate. It is revealed that the relationship between the contact scale and transmission rate is not strongly related no matter if it is a linear or nonlinear relation. Moreover, the contact scale is fitted by Lognormal distribution, and the empirical distribution of transmission rate is also given. Finally, the reproducibility of COVID-19 under strict containment measures is investigated by the multiple-generation spreading structure, revealing the effectiveness of the containment measures in China. The key contributions of our work are those that aim for a better understanding of the properties of COVID-19 spread.

The rest of the paper is organized as follows. Section 2 describes the data and methods. Section 3 reports the empirical analysis and models fitted. Section 4 discusses the implications of results and provides an explanation based on branching process and the necessity of ultra-strict prevention measures.

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More information: Computer Methods in Biomechanics and Biomedical … 0255842.2020.1869221


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