mteb/CovidQA huggingface dataset:
Abstract: To understand the time-dependent risk of infection on a cruise ship, the Diamond Princess, I estimated the incidence of infection with novel coronavirus (COVID-19). The epidemic curve of a total of 199 confirmed cases was drawn, classifying individuals into passengers with and without close contact and crew members. A backcalculation method was employed to estimate the incidence of infection. The peak time of infection was seen for the time period from 2 to 4 February 2020, and the incidence has abruptly declined afterwards. The estimated number of new infections among passengers without close contact was very small from 5 February on which a movement restriction policy was imposed. Without the intervention from 5 February, it was predicted that the cumulative incidence with and without close contact would have been as large as 1373 (95% CI: 570, 2176) and 766 (95% CI: 587, 946) cases, respectively, while these were kept to be 102 and 47 cases, respectively. Based on an
analysis of illness onset data on board, the risk of infection among passengers without close contact was considered to be very limited. Movement restriction greatly reduced the number of infections from 5 February onwards.
Text: An outbreak of novel coronavirus disease (COVID-19) has occurred on a cruise ship, the Diamond Princess [1] . The primary case remains unknown, but the index case, defined as the first identified case, is a passenger who started coughing from 19 January 2020 on board, disembarking the ship in Hong Kong on 25 January. As the case was diagnosed on 1 February, the ship was requested to remain in the ocean near Yokohama from 3 February onwards. Subsequently, the movement of all passengers was restricted on board from 5 February, for a matter of 14 days of quarantine. Out of a total of 3711 persons (consisting of 2666 passengers and 1045 crew members), 199 symptomatic cases have been diagnosed on board as of 24 February, and additional asymptomatic infections and symptomatic cases after disembarkation have also been reported.
One of the critical issues in infectious disease epidemiology is that the time of infection event is seldom directly observable. For this reason, the time of infection needs to be statistically estimated, employing a backcalculation method [2] . Using a sophisticated statistical model with doubly intervalcensored likelihood and right truncation with an exponential growth of cases, the mean incubation period has been estimated to be about 5.0 days [3] . To understand the time-dependent risk of infection throughout the course of outbreak and estimate the effectiveness of the quarantine measure from 5 to 19 February 2020, I aimed to estimate the incidence of infection with COVID-19 and also predict the likely number of infections prevented by the quarantine measure.
I analyzed the epidemic curve, ct, on day t, illustrated by the number of confirmed cases by the date of illness onset. The confirmatory diagnosis was made, using the reverse transcriptase polymerase chain reaction (RT-PCR). The date of illness onset was defined as the first date of fever. In addition to the date of illness onset, cases were classified by contact history inside the cabin and also by the type of membership, i.e., crew or passenger. Close contact was defined as having at least one cabinmate who was confirmed by RT-PCR.

We estimate the number of cases by time of infection, it. Using the probability mass function of the incubation period of length s, fs, the incidence of infection is known to satisfy
where E(.) represents the expected value. As for fs, it is known that the mean and standard deviation are 5.0 and 3.0 days, respectively, best fitted by lognormal distribution [3] . Employing a step function, the incidence of infection was statistically estimated via a maximum likelihood method. The estimation was implemented independently by the history of contact and type of membership.

Regarding the real-time forecasting, we employed the so-called Richards model, an analogue to the generalized logistic model [4, 5] :
where is the cumulative incidence on day t, Z is the cumulative incidence at the end of the outbreak, s is the parameter that governs the flexibility of the logistic curve, a is the early growth rate of cases and ti is the inflection point of the cumulative incidence curve. Assuming that the cumulative incidence is Gaussian distributed, four unknown parameters were estimated. The Richards model was fitted to two different datasets, i.e., (i) the dataset of the entire course of the epidemic and (ii) the dataset by 4 February 2020. The latter dataset corresponds to the time period without any impact of movement restriction that was in place from 5 February onwards. Figure 1 shows the epidemic curve by contact history and type of membership. The highest incidence of illness onset was observed on 7 February. The epidemic curve in a latter half period was dominated by crew members whose movement was not strictly controlled due to the need to continue service on the ship. The second
dominating group was passengers with close contact history. The last illness onset date on board of a passenger without close contact was on 14 February. Estimating the incidence of infection, the peak incidence was identified for the period from 2 to 4 February among passengers both with and without close contact (Figure 2 ). The incidence of infection abruptly dropped after 5 February, the date of movement restriction. Among passengers without close contact, the incidence was estimated to be zero, except for 8-10 February 2020, during which 0.98 persons (95% confidence intervals (CI): 0, 7.74) per day were estimated to have been infected. The epidemic peak among crew members was seen for the period from 8 to 10 February 2020. Figure 3 compares the cumulative incidence with and without movement restriction policy from 5 February. In the presence of intervention, the cumulative incidence among passengers with and without close contact and crew members were 102, 47 and 48 cases,
respectively, as of 24 February 2020. These were well realized by the Richards model. Without intervention from 5 February onwards, it was predicted that the cumulative incidence with and without close contact would have been 1373 (95% CI: 570, 2176) and 766 (95% CI: 587, 946) cases, respectively.
A large outbreak of COVID-19 occurred on a cruise ship. Estimating the incidence, the peak time of infection was shown to have been from 2 to 4 February, and the incidence abruptly declined afterwards. The estimated number of new infections among passengers without close contact was very small from 5 February, on which the movement restriction policy was imposed, and at most there was, on average, one case of infection per day from 8 to 10 February. Other than continued exposure among crew members, the estimated incidence in this study indicates that the movement restriction policy from 5 February 2020 was highly successful in greatly reducing the number of secondary transmissions on board. Based on an analysis of illness onset data on board (and before the disembarkation of a large number of passengers), the risk of infection among passengers without close contact was considered to be very limited Among disembarked passengers, symptomatic cases have started to be reported on the
ground in and outside of Japan. In particular, cases arising from passengers without close contact indicate a possible pathway of infection via mechanisms that were not covered by the abovementioned analysis that relied on symptomatic cases. Although the transmission via direct human-to-human contact was prevented by movement restrictions, the role of other modes of transmission, e.g., environmental and asymptomatic transmissions, should be further explored.
The author declares no conflict of interest.
The Extent of Transmission of Novel Coronavirus in Wuhan, China, 2020

https://doi.org/10.3390/jcm9020330

SHA: 919c524f19f79213e6f81aa38502c70287d273dc

Authors: Nishiura, Hiroshi; Jung, Sung-mok; Linton, Natalie M.; Kinoshita, Ryo; Yang, Yichi; Hayashi, Katsuma; Kobayashi, Tetsuro; Yuan, Baoyin; Akhmetzhanov, Andrei R.
Date: 2020
DOI: 10.3390/jcm9020330
License: cc-by

Abstract: A cluster of pneumonia cases linked to a novel coronavirus (2019-nCoV) was reported by China in late December 2019. Reported case incidence has now reached the hundreds, but this is likely an underestimate. As of 24 January 2020, with reports of thirteen exportation events, we estimate the cumulative incidence in China at 5502 cases (95% confidence interval: 3027, 9057). The most plausible number of infections is in the order of thousands, rather than hundreds, and there is a strong indication that untraced exposures other than the one in the epidemiologically linked seafood market in Wuhan have occurred.
Text: Since the announcement of a cluster of pneumonia cases of unknown etiology in Wuhan, Hubei Province, China, was made on 31 December 2019, many rapid virological, clinical, and epidemiological research responses have taken place [1, 2] . The causative agent of the pneumonia is suggested to be a novel coronavirus (2019-nCoV) of the same lineage (but genetically distinct) from the coronavirus causing severe acute respiratory syndrome (SARS) [1] . Cases in the initial cluster reported a common exposure-a seafood market in Wuhan where wild animals were served at a restaurant-indicating that a point-source zoonotic (animal-to-human) route was likely the main mode of transmission for those cases [2] .
Although early reports from Wuhan [3] stated that (i) there were only tens of cases in the cluster and (ii) no human-to-human transmission was directly observed, the scientific community was alert to the possibility that the novel coronavirus would spread to other geographic locations-including other countries-via direct human-to-human transmission. In early January, the outbreak began to escalate rapidly with hundreds of cases now confirmed along with the presence of a few household clusters [4] [5] [6] [7] .
As of 24 January 2020, the cumulative incidence in China is 830 cases, of which 549 cases were diagnosed in Hubei, 26 in Beijing, 20 in Shanghai, and 53 in Guangdong. Additionally, twenty-six deaths have been linked to the outbreak [6, 8] , and thirteen cases were exported to Japan, Singapore, South Korea, Taiwan, Thailand, Vietnam and the United States as of 22 January 2020. Considering that enhanced surveillance has been underway in these importing countries, case ascertainment has been perhaps better in exported case data.
Using a spatial back-calculation method and analyzing exported cases, we estimate the cumulative incidence of 2019-nCoV cases in China in real time, allowing us to update and discuss the extent of transmission at the source. Table 1 shows the incidence of exported cases by date of hospitalization and report. Due to the initial difficulty of diagnosis in the absence of established primer for polymerase chain reaction testing, the time lag between hospitalization and reporting was longer for early cases compared with that of more recent cases. Among the seven locations reporting importation, the total volume of inbound passengers from China was m = 63.1 million per year in 2017 [9] , of which 100q = 2.1% were from Wuhan [10] , a home of n = 19.0 million people as the catchment population of Wuhan airport. Two other locations with confirmed cases, i.e., Macau and Hong Kong, were excluded from the analysis, because it is commutable by land transporation and the first case in Hong Kong was
indeed not via airtravel. As we already know from elsewhere [11] [12] [13] , given the observed cumulative count of c exported cases, we have a balance equation of the cumulative risk of infection:
where T is the sum of incubation and infectious periods, and here is assumed to be 3.2 and 9.3 days [14] , respectively, assuming that these periods are similar to those of other coronaviruses, and thus, T = 12.5 days. The estimated incidence in China is then given bypn. With an ad-hoc assumption that the data are generated following the binomial sampling process among travelers from Wuhan, the cumulative incidence is then estimated using a maximum likelihood method. Table 1 also shows the estimated incidence in China. The first exportation event in Thailand suggests 423 cases with the upper confidence limit of 1863 cases. The estimated cumulative incidence has grown as additional cases have been reported. As of 24 January 2020, with reports of thirteen exportation events, the cumulative incidence in China is estimated at 5502 cases (95% confidence interval: 3027, 9057).
Our latest estimate is comparable to a preliminary report posted by a research group at Imperial College London (ICL) on their own homepage on 22 January 2020 [26] that estimated the incidence based on three importation events at 4000 cases (95% CI: 1000, 9700). Possible reasons for the slight difference include (i) the number of travelers in the previous study was derived from airline passenger data [27] and (ii) the assumed length of T was different. Two other estimates have also been published: a preliminary study by a Northeastern University group estimated 1250 cases (95% CI: 350, 3000) as of 17 January 2020 [28] and a University of Hong Kong group estimated 1343 cases (95% CI: 547, 3446) as of 17 January 2020 [29] . The former study from the United States assumes that the catchment area population is 10 million (we use 11.1 million).
The number of reported 2019-nCoV infections continues to grow as surveillance and detection methods improve. Our estimate and others [26, 28, 29] agree that the actual number of cases is likely in the order of thousands, rather than hundreds, and there is a strong indication that untraced exposures other than that of the originally linked seafood market in Wuhan have occurred. Such exposures are expected to include human-to-human transmission, but the levels of transmissibility have yet to be quantified. It is still plausible that a substantial number of human infections arose from animal-to-human exposures, such as was the case during the first outbreak of highly pathogenic influenza (H7N9) in China, 2013, and the human-to-human transmissibility has yet to be quantified in an explicit manner.
Despite initially restricting what information on the outbreak was shared publicly, the Chinese government has begun to respectfully provide updates on the situation on a daily basis. This encourages the real-time release of information by means of regularly updated situation reports, including epidemiological information with dates of exposure, illness onset, and hospitalization among cases.
For researchers to be able to contribute to control efforts by improving situation awareness via an explicit risk assessment, it is crucial that detailed epidemiological data are posted to a public domain in real-time. Such datasets should include not only a deidentified line list of cases but also updates on the infection status of traced contacts. Information on exposure period and illness onset can assist with the estimation of important natural history parameters such as the incubation period. It is critical for the public health community and the public at large to understand more about the process of case ascertainment, including the current case definition and reporting system mechanisms. 

The authors declare no conflicts of interest.
Epidemiological research priorities for public health control of the ongoing global novel coronavirus (2019-nCoV) outbreak

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7029449/

SHA: 90de2d957e1960b948b8c38c9877f9eca983f9eb

Authors: Cowling, Benjamin J; Leung, Gabriel M
Date: 2020-02-13
DOI: 10.2807/1560-7917.es.2020.25.6.2000110
License: cc-by
Abstract: Infections with 2019-nCoV can spread from person to person, and in the earliest phase of the outbreak the basic reproductive number was estimated to be around 2.2, assuming a mean serial interval of 7.5 days [2]. The serial interval was not precisely estimated, and a potentially shorter mean serial interval would have corresponded to a slightly lower basic reproductive number. Control measures and changes in population behaviour later in January should have reduced the effective reproductive number. However, it is too early to estimate whether the effective reproductive number has been reduced to below the critical threshold of 1 because cases currently being detected and reported would have mostly been infected in mid- to late-January. Average delays between infection and illness onset have been estimated at around 5–6 days, with an upper limit of around 11-14 days [2,5], and delays from illness onset to laboratory confirmation added a further 10 days on average [2].
Text: It is now 6 weeks since Chinese health authorities announced the discovery of a novel coronavirus (2019-nCoV) [1] causing a cluster of pneumonia cases in Wuhan, the major transport hub of central China. The earliest human infections had occurred by early December 2019, and a large wet market in central Wuhan was linked to most, but not all, of the initial cases [2] . While evidence from the initial outbreak investigations seemed to suggest that 2019-nCoV could not easily spread between humans [3] , it is now very clear that infections have been spreading from person to person [2] . We recently estimated that more than 75,000 infections may have occurred in Wuhan as at 25 January 2020 [4] , and increasing numbers of infections continue to be detected in other cities in mainland China and around the world. A number of important characteristics of 2019-nCoV infection have already been identified, but in order to calibrate public health responses we need improved information on
transmission dynamics, severity of the disease, immunity, and the impact of control and mitigation measures that have been applied to date.
Infections with 2019-nCoV can spread from person to person, and in the earliest phase of the outbreak the basic reproductive number was estimated to be around 2.2, assuming a mean serial interval of 7.5 days [2] . The serial interval was not precisely estimated, and a potentially shorter mean serial interval would have corresponded to a slightly lower basic reproductive number. Control measures and changes in population behaviour later in January should have reduced the effective reproductive number. However, it is too early to estimate whether the effective reproductive number has been reduced to below the critical threshold of 1 because cases currently being detected and reported would have mostly been infected in mid-to late-January. Average delays between infection and illness onset have been estimated at around 5-6 days, with an upper limit of around 11-14 days [2, 5] , and delays from illness onset to laboratory confirmation added a further 10 days on average [2] .
Chains of transmission have now been reported in a number of locations outside of mainland China. Within the coming days or weeks it will become clear whether sustained local transmission has been occurring in other cities outside of Hubei province in China, or in other countries. If sustained transmission does occur in other locations, it would be valuable to determine whether there is variation in transmissibility by location, for example because of different behaviours or control measures, or because of different environmental conditions. To address the latter, virus survival studies can be done in the laboratory to confirm whether there are preferred ranges of temperature or humidity for 2019-nCoV transmission to occur.
In an analysis of the first 425 confirmed cases of infection, 73% of cases with illness onset between 12 and 22 January reported no exposure to either a wet market or another person with symptoms of a respiratory illness [2] . The lack of reported exposure to another ill person could be attributed to lack of awareness or recall bias, but China's health minister publicly warned that pre-symptomatic transmission could be occurring [6] . Determining the extent to which asymptomatic or pre-symptomatic transmission might be occurring is an urgent priority, because it has direct implications for public health and hospital infection control. Data on viral shedding dynamics could help in assessing duration of infectiousness. For severe acute respiratory syndrome-related coronavirus (SARS-CoV), infectivity peaked at around 10 days after illness onset [7] , consistent with the peak in viral load at around that time [8] . This allowed control of the SARS epidemic through prompt detection of
cases and strict isolation. For influenza virus infections, virus shedding is highest on the day of illness onset and relatively higher from shortly before symptom onset until a few days after onset [9] . To date, transmission patterns of 2019-nCoV appear more similar to influenza, with contagiousness occurring around the time of symptom onset, rather than SARS.
Transmission of respiratory viruses generally happens through large respiratory droplets, but some respiratory viruses can spread through fine particle aerosols [10] , and indirect transmission via fomites can also play a role. Coronaviruses can also infect the human gastrointestinal tract [11, 12] , and faecal-oral transmission might also play a role in this instance. The SARS-CoV superspreading event at Amoy Gardens where more than 300 cases were infected was attributed to faecal-oral, then airborne, spread through pressure differentials between contaminated effluent pipes, bathroom floor drains and flushing toilets [13] . The first large identifiable superspreading event during the present 2019-nCoV outbreak has apparently taken place on the Diamond Princess cruise liner quarantined off the coast of Yokohama, Japan, with at least 130 passengers tested positive for 2019-nCoV as at 10 February 2020 [14] . Identifying which modes are important for 2019-nCoV transmission would inform
the importance of personal protective measures such as face masks (and specifically which types) and hand hygiene.
The first human infections were identified through a surveillance system for pneumonia of unknown aetiology, and all of the earliest infections therefore had Modelling studies incorporating healthcare capacity and processes pneumonia. It is well established that some infections can be severe, particularly in older adults with underlying medical conditions [15, 16] , but based on the generally mild clinical presentation of 2019-nCoV cases detected outside China, it appears that there could be many more mild infections than severe infections. Determining the spectrum of clinical manifestations of 2019-nCoV infections is perhaps the most urgent research priority, because it determines the strength of public health response required. If the seriousness of infection is similar to the 1918/19 Spanish influenza, and therefore at the upper end of severity scales in influenza pandemic plans, the same responses would be warranted for 2019-nCoV as for the most severe influenza pandemics. If,
however, the seriousness of infection is similar to seasonal influenza, especially during milder seasons, mitigation measures could be tuned accordingly.
Beyond a robust assessment of overall severity, it is also important to determine high risk groups. Infections would likely be more severe in older adults, obese individuals or those with underlying medical conditions, but there have not yet been reports of severity of infections in pregnant women, and very few cases have been reported in children [2] .