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61: Association of BCG vaccination policy with prevalence and mortality of COVID-19
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Posted 06 Apr 2020

Association of BCG vaccination policy with prevalence and mortality of COVID-19
19,927 downloads medRxiv epidemiology

Giovanni Sala, Rik Chakraborti, Atsuhiko Ota, Tsuyoshi Miyakawa

There is some evidence that tuberculosis vaccine bacillus Calmette-Guerin (BCG) has non-specific beneficial effects against non-related infections. Here, we examined the possible association between BCG vaccination with prevalence and mortality by COVID-19 by using publicly available data of COVID-19 in 199 countries/regions and the BCG World Atlas. By using linear regression modeling, we found that the number of total cases and deaths per one million population were significantly associated with the countrys policy concerning BCG vaccine administration. The amount of variance in cases and deaths explained by BCG vaccination policy ranged between 12.5% and 38%. Importantly, this effect remained significant after controlling for the countrys life expectancy and the average temperature in February and March 2020, which themselves are significantly correlated with the cases and deaths indices, respectively. By contrast, the ratio between deaths and cases was weakly affected. This latter outcome suggested that BCG vaccination may have hindered the overall spread of the virus or progression of the disease rather than reducing mortality rates (i.e., deaths/cases ratio). Finally, by roughly dividing countries into three categories showing high, middle, or low growth rate of the cases, we found a highly significant difference between the slope categories among the BCG groups, suggesting that the time since the onset of the spread of the virus was not a major confounding factor. While this retrospective epidemiological study potentially suffers from a number of unknown confounding factors, these associations support the idea that BCG vaccination may provide protection against SARS-CoV-2, which, together with its proven safety, encourages consideration of further detailed epidemiological studies, large-scale clinical trials on the efficacy of this vaccine on COVID-19, and/or re-introduction of BCG vaccination practice in the countries which are currently devoid of the practice.

62: Time course quantitative detection of SARS-CoV-2 in Parisian wastewaters correlates with COVID-19 confirmed cases
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Posted 17 Apr 2020

Time course quantitative detection of SARS-CoV-2 in Parisian wastewaters correlates with COVID-19 confirmed cases
19,870 downloads medRxiv epidemiology

Sebastien Wurtzer, Vincent Marechal, Jean-Marie Mouchel, Yvon Maday, Remy Teyssou, Elise Richard, Jean Luc Almayrac, Laurent Moulin

SARS-CoV-2 is the etiological agent of COVID-19. Most of SARS-CoV-2 carriers are assumed to exhibit no or mild non-specific symptoms. Thus, they may contribute to the rapid and mostly silent circulation of the virus among humans. Since SARS-CoV-2 can be detected in stool samples it has recently been proposed to monitor SARS-CoV-2 in wastewaters (WW) as a complementary tool to investigate virus circulation in human populations. In the present work we assumed that the quantification of SARS-CoV-2 genomes in wastewaters should correlate with the number of symptomatic or non-symptomatic carriers. To test this hypothesis, we performed a time-course quantitative analysis of SARS-CoV-2 by RT-qPCR in raw wastewater samples collected from several major wastewater treatment plant (WWTP) of the Parisian area. The study was conducted from March 5 to April 23 2020, therefore including the lockdown period in France (since March 17 2020). We confirmed that the increase of genome units in raw wastewaters accurately followed the increase of human COVID-19 cases observed at the regional level. Of note, the viral genomes could be detected before the beginning of the exponential growth of the epidemic. As importantly, a marked decrease in the quantities of genomes units was observed concomitantly with the reduction in the number of new COVID-19 cases which was an expected consequence of the lockdown. As a conclusion, this work suggests that a quantitative monitoring of SARS-CoV-2 genomes in wastewaters should bring important and additional information for an improved survey of SARS-CoV-2 circulation at the local or regional scale.

63: COVID-19 epidemic in Malaysia: Impact of lock-down on infection dynamics
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Posted 11 Apr 2020

COVID-19 epidemic in Malaysia: Impact of lock-down on infection dynamics
19,790 downloads medRxiv epidemiology

Naomie Salim, Weng Howe Chan, Shuhaimi Mansor, Nor Erne Nazira Bazin, Safiya Amaran, Ahmad Athif Mohd Faudzi, Anazida Zainal, Sharin Hazlin Huspi, Eric Jiun Hooi Khoo, Shaekh Mohammad Shithil

COVID-19 epidemic in Malaysia started as a small wave of 22 cases in January 2020 through imported cases. It was followed by a bigger wave mainly from local transmissions resulting in 651 cases. The following wave saw unexpectedly three digit number of daily cases following a mass gathering urged the government to choose a more stringent measure. A limited lock-down approach called Movement Control Order (MCO) was immediately initiated to the whole country as a way to suppress the epidemic trajectory. The lock-down causes a major socio-economic disruption thus the ability to forecast the infection dynamic is urgently required to assist the government on timely decisions. Limited testing capacity and limited epidemiological data complicate the understanding of the future infection dynamic of the COVID-19 epidemic. Three different epidemic forecasting models was used to generate forecasts of COVID-19 cases in Malaysia using daily reported cumulative case data up until 1st April 2020 from the Malaysia Ministry of Health. The forecasts were generated using a Curve Fitting Model with Probability Density Function and Skewness Effect, the SIR Model, and a System Dynamic Model. Method one based on curve fitting with probability density function estimated that the peak will be on 19th April 2020 with an estimation of 5,637 infected persons. Method two based on SIR Model estimated that the peak will be on 20th - 31st May 2020 if Movement Contro (MCO) is in place with an estimation of 630,000 to 800,000 infected persons. Method three based on System Dynamic Model estimated that the peak will be on 17th May 2020 with an estimation of 22,421 infected persons. Forecasts from each of model suggested the epidemic may peak between middle of April to end of May 2020. Keywords: COVID-19, Infection dynamic, Prediction Modeling, SIR, System Learning, Lock-down

64: Investigating the Impact of Asymptomatic Carriers on COVID-19 Transmission
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Posted 20 Mar 2020

Investigating the Impact of Asymptomatic Carriers on COVID-19 Transmission
19,176 downloads medRxiv epidemiology

Jacob B Aguilar, Jeremy Faust, Lauren M. Westafer, Juan B. Gutierrez

Coronavirus disease 2019 (COVID-19) is a novel human respiratory disease caused by the SARS-CoV-2 virus. Asymptomatic carriers of the virus display no clinical symptoms but are known to be contagious. Recent evidence reveals that this sub-population, as well as persons with mild disease, are a major contributor in the propagation of COVID-19. The asymptomatic sub-population frequently escapes detection by public health surveillance systems. Because of this, the currently accepted estimates of the basic reproduction number ([R]0) of the disease are inaccurate. It is unlikely that a pathogen can blanket the planet in three months with an [R]0 in the vicinity of 3, as reported in the literature (1-6). In this manuscript, we present a mathematical model taking into account asymptomatic carriers. Our results indicate that an initial value of the effective reproduction number could range from 5.5 to 25.4, with a point estimate of 15.4, assuming mean parameters. The first three weeks of the model exhibit exponential growth, which is in agreement with average case data collected from thirteen countries with universal health care and robust communicable disease surveillance systems; the average rate of growth in the number of reported cases is 23.3% per day during this period.

65: Modeling COVID-19 epidemics in an Excel spreadsheet: Democratizing the access to first-hand accurate predictions of epidemic outbreaks
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Posted 27 Mar 2020

Modeling COVID-19 epidemics in an Excel spreadsheet: Democratizing the access to first-hand accurate predictions of epidemic outbreaks
18,929 downloads medRxiv epidemiology

Mario Moisés Alvarez, Everardo González-González, Grissel Trujillo-de Santiago

COVID-19, the first pandemic of this decade and the second in less than 15 years, has harshly taught us that viral diseases do not recognize boundaries; however, they truly do discriminate between aggressive and mediocre containment responses. We present a simple epidemiological model that is amenable to implementation in Excel spreadsheets and sufficiently accurate to reproduce observed data on the evolution of the COVID-19 pandemics in different regions (i.e., Italy, Spain, and New York City (NYC)). We also show that the model can be adapted to closely follow the evolution of COVID-19 in any large city by simply adjusting two parameters related to (a) population density and (b) aggressiveness of the response from a society/government to epidemics. Moreover, we show that this simple epidemiological simulator can be used to assess the efficacy of the response of a government/society to an outbreak. The simplicity and accuracy of this model will greatly contribute to democratizing the availability of knowledge in societies regarding the extent of an epidemic event and the efficacy of a governmental response.

66: Phylogenetic analysis of SARS-CoV-2 in the Boston area highlights the role of recurrent importation and superspreading events
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Posted 25 Aug 2020

Phylogenetic analysis of SARS-CoV-2 in the Boston area highlights the role of recurrent importation and superspreading events
18,305 downloads medRxiv epidemiology

Jacob E. Lemieux, Katherine J. Siddle, Bennett M. Shaw, Christine Loreth, Stephen Schaffner, Adrianne Gladden-Young, Gordon Adams, Timelia Fink, Chris H Tomkins-Tinch, Lydia A Krasilnikova, Katherine C Deruff, Melissa Rudy, Matthew R Bauer, Kim A. Lagerborg, Erica Normandin, Sinéad B Chapman, Steven K. Reilly, Melis N Anahtar, Aaron E Lin, Amber Carter, Cameron Myhrvold, Molly Kemball, Suschma Chaluvadi, Caroline Cusick, Katelyn Flowers, Anna Neumann, Felecia Cerrato, Maha Farhat, Damien Slater, Jason B. Harris, John Branda, David Hooper, Jessie M. Gaeta, Travis P. Baggett, James O'Connell, Andreas Gnirke, Tami D. Lieberman, Anthony Philippakis, Meagan Burns, Catherine Brown, Jeremy Luban, Edward T Ryan, Sarah E. Turbett, Regina C. LaRocque, William P. Hanage, Glen Gallagher, Lawrence C Madoff, Sandra Smole, Virginia M. Pierce, Eric S Rosenberg, Pardis C Sabeti, Daniel J. Park, Bronwyn L MacInnis

SARS-CoV-2 has caused a severe, ongoing outbreak of COVID-19 in Massachusetts with 111,070 confirmed cases and 8,433 deaths as of August 1, 2020. To investigate the introduction, spread, and epidemiology of COVID-19 in the Boston area, we sequenced and analyzed 772 complete SARS-CoV-2 genomes from the region, including nearly all confirmed cases within the first week of the epidemic and hundreds of cases from major outbreaks at a conference, a nursing facility, and among homeless shelter guests and staff. The data reveal over 80 introductions into the Boston area, predominantly from elsewhere in the United States and Europe. We studied two superspreading events covered by the data, events that led to very different outcomes because of the timing and populations involved. One produced rapid spread in a vulnerable population but little onward transmission, while the other was a major contributor to sustained community transmission, including outbreaks in homeless populations, and was exported to several other domestic and international sites. The same two events differed significantly in the number of new mutations seen, raising the possibility that SARS-CoV-2 superspreading might encompass disparate transmission dynamics. Our results highlight the failure of measures to prevent importation into MA early in the outbreak, underscore the role of superspreading in amplifying an outbreak in a major urban area, and lay a foundation for contact tracing informed by genetic data.

67: Estimating the early death toll of COVID-19 in the United States
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Posted 18 Apr 2020

Estimating the early death toll of COVID-19 in the United States
18,209 downloads medRxiv epidemiology

Daniel Weinberger, Ted Cohen, Forrest Crawford, Farzad Mostashari, Don Olson, Virginia E. Pitzer, Nicholas Reich, Marcus Russi, Lone Simonsen, Annie Watkins, Cecile Viboud

Background Efforts to track the severity and public health impact of the novel coronavirus, COVID-19, in the US have been hampered by testing issues, reporting lags, and inconsistency between states. Evaluating unexplained increases in deaths attributed to broad outcomes, such as pneumonia and influenza (P&I) or all causes, can provide a more complete and consistent picture of the burden caused by COVID-19. Methods We evaluated increases in the occurrence of deaths due to P&I above a seasonal baseline (adjusted for influenza activity) or due to any cause across the United States in February and March 2020. These estimates are compared with reported deaths due to COVID-19 and with testing data. Results There were notable increases in the rate of death due to P&I in February and March 2020. In a number of states, these deaths pre-dated increases in COVID-19 testing rates and were not counted in official records as related to COVID-19. There was substantial variability between states in the discrepancy between reported rates of death due to COVID-19 and the estimated burden of excess deaths due to P&I. The increase in all-cause deaths in New York and New Jersey is 1.5-3 times higher than the official tally of COVID-19 confirmed deaths or the estimated excess death due to P&I. Conclusions Excess P&I deaths provide a conservative estimate of COVID-19 burden and indicate that COVID-19-related deaths are missed in locations with inadequate testing or intense pandemic activity.

68: A Gaussian model for the time development of the Sars-Cov-2 corona pandemic disease. Predictions for Germany made on March 30, 2020
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Posted 02 Apr 2020

A Gaussian model for the time development of the Sars-Cov-2 corona pandemic disease. Predictions for Germany made on March 30, 2020
17,748 downloads medRxiv epidemiology

Reinhard Schlickeiser, F. Schlickeiser

For Germany it is predicted that the first wave of the corona pandemic disease reaches its maximum of new infections on April 11th, 2020 [Formula] days with 90 percent confidence. With a delay of about 7 days the maximum demand on breathing machines in hospitals occurs on April 18th, 2020 [Formula] days. The first pandemic wave ends in Germany end of May 2020. The predictions are based on the assumption of a Gaussian time evolution well justified by the central limit theorem of statistics. The width and the maximum time and thus the duration of this Gaussian distribution are determined from a statistical{chi} 2-fit to the observed doubling times before March 28, 2020.

69: The serial interval of COVID-19 from publicly reported confirmed cases
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Posted 23 Feb 2020

The serial interval of COVID-19 from publicly reported confirmed cases
17,725 downloads medRxiv epidemiology

Zhanwei Du, Xiaoke Xu, Ye Wu, Lin Wang, Benjamin J Cowling, Lauren Ancel Meyers

Short AbstractWe estimate the distribution of serial intervals for 468 confirmed cases of COVID-19 reported in 93 Chinese cities by February 8, 2020. The mean and standard deviation are 3.96 (95% CI 3.53-4.39) and 4.75 (95% CI 4.46-5.07) days, respectively, with 12.6% of reports indicating pre-symptomatic transmission. One sentence summaryWe estimate the distribution of serial intervals for 468 confirmed cases of COVID-19 reported in 93 Chinese cities by February 8, 2020.

70: Impact of seasonal forcing on a potential SARS-CoV-2 pandemic
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Posted 17 Feb 2020

Impact of seasonal forcing on a potential SARS-CoV-2 pandemic
17,054 downloads medRxiv epidemiology

Richard A Neher, Robert Dyrdak, Valentin Druelle, Emma B Hodcroft, Jan Albert

A novel coronavirus (SARS-CoV-2) first detected in Wuhan, China, has spread rapidly since December 2019, causing more than 80,000 confirmed infections and 2,700 fatalities (as of Feb 27, 2020). Imported cases and transmission clusters of various sizes have been reported globally suggesting a pandemic is likely. Here, we explore how seasonal variation in transmissibility could modulate a SARS-CoV-2 pandemic. Data from routine diagnostics show a strong and consistent seasonal variation of the four endemic coronaviruses (229E, HKU1, NL63, OC43) and we parameterize our model for SARS-CoV-2 using these data. The model allows for many subpopulations of different size with variable parameters. Simulations of different scenarios show that plausible parameters result in a small peak in early 2020 in temperate regions of the Northern Hemisphere and a larger peak in winter 2020/2021. Variation in transmission and migration rates can result in substantial variation in prevalence between regions. While the uncertainty in parameters is large, the scenarios we explore show that transient reductions in the incidence rate might be due to a combination of seasonal variation and infection control efforts but do not necessarily mean the epidemic is contained. Seasonal forcing on SARS-CoV-2 should thus be taken into account in the further monitoring of the global transmission. The likely aggregated effect of seasonal variation, infection control measures, and transmission rate variation is a prolonged pandemic wave with lower prevalence at any given time, thereby providing a window of opportunity for better preparation of health care systems.

71: Initial real world evidence for lower viral load of individuals who have been vaccinated by BNT162b2
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Posted 08 Feb 2021

Initial real world evidence for lower viral load of individuals who have been vaccinated by BNT162b2
16,673 downloads medRxiv epidemiology

Ella Petter, Orna Mor, Neta Zuckerman, Danit Oz-Levi, Asaf Younger, Dvir Aran, Yaniv Erlich

One of the key questions regarding COVID19 vaccines is whether they can reduce viral shedding. To date, Israel vaccinated substantial parts of the adult population, which enables extracting real world signals. The vaccination rollout started on Dec 20th 2020, utilized mainly the BNT162b2 vaccine, and focused on individuals who are 60 years or older. By now, more than 75% of the individuals of this age group have been at least 14 days after the first dose, compared to 25% of the individuals between ages 40-60 years old. Here, we traced the Ct value distribution of 16,297 positive qPCR tests in our lab between Dec 1st to Jan 31st that came from these two age groups. As we do not have access to the vaccine status of each test, our hypothesis was that if vaccines reduce viral load, we should see a difference in the Ct values between these two age groups in late January but not before. Consistent with this hypothesis, until Jan 15th, we did not find any statistically significant differences in the average Ct value between the groups. In stark contrast, our results in the last two weeks of January show a significant weakening in the average Ct value of 60+ individuals to the 40-60 group. To further corroborate these results, we also used a series nested linear models to explain the Ct values of the positive tests. This analysis favored a model that included an interaction between age and the late January time period, consistent with the effect of vaccination. We then used demographic data and the daily vaccination rates to estimate the effect of vaccination on viral load reduction. Our estimate suggests that vaccination reduces the viral load by 1.6x to 20x in individuals who are positive for SARS-CoV-2. This estimate might improve after more individuals receive the second dose. Taken together, our findings indicate vaccination is not only important for individual's protection but can reduce transmission.

72: Contacts in context: large-scale setting-specific social mixing matrices from the BBC Pandemic project
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Posted 19 Feb 2020

Contacts in context: large-scale setting-specific social mixing matrices from the BBC Pandemic project
15,970 downloads medRxiv epidemiology

Pietra Klepac, Adam J. Kucharski, Andrew JK Conlan, Stephen Kissler, Maria L Tang, Hannah Fry, Julia R Gog

Social mixing patterns are crucial in driving transmission of infectious diseases and informing public health interventions to contain their spread. Age-specific social mixing is often inferred from surveys of self-recorded contacts which by design often have a very limited number of participants. In addition, such surveys are rare, so public health interventions are often evaluated by considering only one such study. Here we report detailed population contact patterns for United Kingdom based self-reported contact data from over 36,000 volunteers that participated in the massive citizen science project BBC Pandemic. The amount of data collected allows us generate fine-scale age-specific population contact matrices by context (home, work, school, other) and type (conversational or physical) of contact that took place. These matrices are highly relevant for informing prevention and control of new outbreaks, and evaluating strategies that reduce the amount of mixing in the population (such as school closures, social distancing, or working from home). In addition, they finally provide the possibility to use multiple sources of social mixing data to evaluate the uncertainty that stems from social mixing when designing public health interventions.

73: Vaccine effectiveness after 1st and 2nd dose of the BNT162b2 mRNA Covid-19 Vaccine in long-term care facility residents and healthcare workers - a Danish cohort study
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Posted 09 Mar 2021

Vaccine effectiveness after 1st and 2nd dose of the BNT162b2 mRNA Covid-19 Vaccine in long-term care facility residents and healthcare workers - a Danish cohort study
15,842 downloads medRxiv epidemiology

Ida Rask Moustsen-Helms, Hanne-Dorthe Emborg, Jens Nielsen, Katrine Finderup Nielsen, Tyra Krause, Kaare Molbak, Karina Lauenborg Moeller, Ann-Sofie Nicole Berthelsen, Palle Valentiner-Branth

Abstract Background At the end of 2020, Denmark launched an immunization program against SARS-CoV-2. The Danish health authorities prioritized persons currently living in long-term care facilities (LTCF residents) and frontline healthcare workers (HCW) as the first receivers of vaccination. Here we present preliminary population based vaccine effectiveness (VE) estimates in these two target groups. Methods The study was designed as a retrospective registry- and population-based observational cohort study including all LTCF residents and all HWC. The outcome was a polymerase chain reaction confirmed SARS-CoV-2, and VE was estimated for different periods following first and second dose. We used Poisson and Cox regressions to estimate respectively crude and calendar time-adjusted VE for the BNT162b2 mRNA Covid-19 Vaccine from Pfizer/BioNTech with 95% confidence intervals (CI) for vaccinated versus unvaccinated. Results A total of 39,040 LTCF residents (median age at first dose; 84 years, Interquartile range (IQR): 77-90) and 331,039 HCW (median age at first dose; 47 years, IQR: 36-57) were included. Among LTCF residents, 95.2% and 86.0% received first and second dose from 27 December 2020 until 18 February 2021, for HWC the proportion was 27.8% and 24.4%. During a median follow-up of 53 days , there were 488 and 5,663 confirmed SARS-CoV-2 cases in the unvaccinated groups, whereas there were 57 and 52 in LTCF residents and HCW within the first 7 days after the second dose and 27 and 10 cases beyond seven days of second dose. No protective effect was observed for LTCF residents after first dose. In HCW, VE was 17% (95% CI; 4-28) in the > 14 days after first dose (before second dose). Furthermore, the VE in LTCF residents at day 0-7 of second dose was 52% (95% CI; 27-69) and 46% (95% CI; 28-59) in HCW. Beyond seven days of second dose, VE increased to 64% (95% CI; 14-84) and 90% (95% CI; 82-95) in the two groups, respectively. Conclusion The results were promising regarding the VE both within and beyond seven days of second vaccination with the BNT162b2 mRNA Covid-19 Vaccine currently used in many countries to help mitigate the global SARS-CoV-2 pandemic. Impact of the research: So far, observational studies of the real-word effectiveness of the mRNA Vaccine BNT162b2 has been limited to the period after the administration of the first dose. This is the first report to date to present vaccine effectiveness (VE) estimates after the second BNT162b2 mRNA Covid-19 Vaccine. We estimated a VE of 52% and 46% in LTCF residents and HCW within seven days, which increased to 64% and 90% in the two groups respectively beyond seven days of immunization. These findings supports maintaining a two-dose schedule of the BNT162b2 mRNA Covid-19 Vaccine.

74: Transmission interval estimates suggest pre-symptomatic spread of COVID-19
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Posted 06 Mar 2020

Transmission interval estimates suggest pre-symptomatic spread of COVID-19
15,771 downloads medRxiv epidemiology

Lauren C. Tindale, Michelle Coombe, Jessica E Stockdale, Emma S. Garlock, Wing Yin Venus Lau, Manu Saraswat, Yen-Hsiang Brian Lee, Louxin Zhang, Dongxuan Chen, Jacco Wallinga, Caroline Colijn

BackgroundAs the COVID-19 epidemic is spreading, incoming data allows us to quantify values of key variables that determine the transmission and the effort required to control the epidemic. We determine the incubation period and serial interval distribution for transmission clusters in Singapore and in Tianjin. We infer the basic reproduction number and identify the extent of pre-symptomatic transmission. MethodsWe collected outbreak information from Singapore and Tianjin, China, reported from Jan.19-Feb.26 and Jan.21-Feb.27, respectively. We estimated incubation periods and serial intervals in both populations. ResultsThe mean incubation period was 7.1 (6.13, 8.25) days for Singapore and 9 (7.92, 10.2) days for Tianjin. Both datasets had shorter incubation periods for earlier-occurring cases. The mean serial interval was 4.56 (2.69, 6.42) days for Singapore and 4.22 (3.43, 5.01) for Tianjin. We inferred that early in the outbreaks, infection was transmitted on average 2.55 and 2.89 days before symptom onset (Singapore, Tianjin). The estimated basic reproduction number for Singapore was 1.97 (1.45, 2.48) secondary cases per infective; for Tianjin it was 1.87 (1.65, 2.09) secondary cases per infective. ConclusionsEstimated serial intervals are shorter than incubation periods in both Singapore and Tianjin, suggesting that pre-symptomatic transmission is occurring. Shorter serial intervals lead to lower estimates of R0, which suggest that half of all secondary infections should be prevented to control spread.

75: A meta-analysis on the role of children in SARS-CoV-2 in household transmission clusters
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Posted 30 Mar 2020

A meta-analysis on the role of children in SARS-CoV-2 in household transmission clusters
15,658 downloads medRxiv epidemiology

Yanshan Zhu, Conor J. Bloxham, Katina D. Hulme, Jane E Sinclair, Zhen Wei Marcus Tong, Lauren E. Steele, Ellesandra C. Noye, Jiahai Lu, Yao Xia, Keng Yih Chew, Janessa Pickering, Charles Gilks, Asha C. Bowen, Kirsty R Short

The role of children in the spread of SARS-CoV-2 remains highly controversial. To address this issue, we performed a meta-analysis of the published literature on household SARS-CoV-2 transmission clusters (n=213 from 12 countries). Only 8 (3.8%) transmission clusters were identified as having a paediatric index case. Asymptomatic index cases were associated with a lower secondary attack in contacts than symptomatic index cases (estimate risk ratio [RR], 0.17; 95% confidence interval [CI], 0.09-0.29). To determine the susceptibility of children to household infections the secondary attack rate (SAR) in paediatric household contacts was assessed. The secondary attack rate in paediatric household contacts was lower than in adult household contacts (RR, 0.62; 95% CI, 0.42-0.91). These data have important implications for the ongoing management of the COVID-19 pandemic, including potential vaccine prioritization strategies.

76: Patterns of COVID-19 pandemic dynamics following deployment of a broad national immunization program
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Posted 09 Feb 2021

Patterns of COVID-19 pandemic dynamics following deployment of a broad national immunization program
15,591 downloads medRxiv epidemiology

Hagai Rossman, Smadar Shilo, Tomer Meir, Malka Gorfine, Uri Shalit, Eran Segal

Studies on the real-life impact of the BNT162b2 vaccine, recently authorized for the prevention of coronavirus disease 2019 (COVID-19), are urgently needed. Here, we analysed the temporal dynamics of the number of new COVID-19 cases and hospitalization in Israel following a rapid vaccination campaign initiated on December 20th, 2020. We conducted a retrospective descriptive analysis of data originating from the Israeli Ministry of Health (MOH) from March 2020 to February 2021. In order to distill the possible effect of the vaccinations from other factors, including a third lockdown imposed in Israel on January 2021, we compared the time-dependent changes in number of COVID-19 cases and hospitalizations between (1) individuals aged 60 years and older, eligible to receive the vaccine earlier, and younger age groups; (2) the latest lockdown (which was imposed in parallel to the vaccine rollout) versus the previous lockdown, imposed on September 2020; (3) early-vaccinated cities compared to late-vaccinated cities; and (4) early-vaccinated geographical statistical areas (GSAs) compared to late-vaccinated GSAs. In mid-January, the number of COVID-19 cases and hospitalization started to decline, with a larger and earlier decrease among older individuals, followed by younger age groups, by the order in which they were prioritized for vaccination. This fast and early decline in older individuals was more evident in early-vaccinated compared to late-vaccinated cities. Such a pattern was not observed in the previous lockdown. Our analysis demonstrates evidence for the real-life impact of a national vaccination campaign in Israel on the pandemic dynamics. We believe that our findings have major public health implications in the struggle against the COVID-19 pandemic, including the publics perception of the need for and benefit of nationwide vaccination campaigns. More studies aimed at assessing the effectiveness and impact of vaccination both on the individual and on the population level, with longer followup, are needed.

77: Beyond R0: the importance of contact tracing when predicting epidemics
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Posted 12 Feb 2020

Beyond R0: the importance of contact tracing when predicting epidemics
15,580 downloads medRxiv epidemiology

Laurent Hébert-Dufresne, Benjamin M. Althouse, Samuel V. Scarpino, Antoine Allard

The basic reproductive number -- R0 -- is one of the most common and most commonly misapplied numbers in public health. Although often used to compare outbreaks and forecast pandemic risk, this single number belies the complexity that two different pathogens can exhibit, even when they have the same R0 [1-3]. Here, we show how to predict outbreak size using estimates of the distribution of secondary infections, leveraging both its average R0 and the underlying heterogeneity. To do so, we reformulate and extend a classic result from random network theory [4] that relies on contact tracing data to simultaneously determine the first moment (R0) and the higher moments (representing the heterogeneity) in the distribution of secondary infections. Further, we show the different ways in which this framework can be implemented in the data-scarce reality of emerging pathogens. Lastly, we demonstrate that without data on the heterogeneity in secondary infections for emerging infectious diseases like COVID-19, the uncertainty in outbreak size ranges dramatically. Taken together, our work highlights the critical need for contact tracing during emerging infectious disease outbreaks and the need to look beyond R0 when predicting epidemic size.

78: Estimation of COVID-2019 burden and potential for international dissemination of infection from Iran
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Posted 25 Feb 2020

Estimation of COVID-2019 burden and potential for international dissemination of infection from Iran
14,766 downloads medRxiv epidemiology

Ashleigh Tuite, Isaac Bogoch, Ryan Sherbo, Alexander Watts, David Fisman, Kamran Khan

The Coronavirus Disease 2019 (COVID-19) epidemic began in Wuhan, China in late 2019 and continues to spread globally, with exported cases confirmed in 28 countries at the time of writing. During the interval between February 19 and 23, 2020, Iran reported its first 43 cases with eight deaths. Three exported cases originating in Iran were identified, suggesting a underlying burden of disease in that country than is indicated by reported cases. A large epidemic in Iran could further fuel global dissemination of COVID-19. We sought to estimate COVID-19 outbreak size in Iran based on known exported case counts and air travel links between Iran and other countries, and to anticipate where infections originating in Iran may spread to next. We assessed interconnectivity between Iran and other countries using using International Air Transport Association (IATA) data. We used the methods of Fraser et al. to estimate the size of the underlying epidemic that would result in cases being observed in the United Arab Emirates (UAE), Lebanon, and Canada. Time at risk estimates were based on a presumed 6 week epidemic age, and length of stay data for visitors to Iran derived from the United Nations World Tourism Organization (UNWTO). We evaluated the relationship between the strength of travel links with Iran, and destination country rankings on the Infectious Disease Vulnerability Index (IDVI), a validated metric that estimates the capacity of a country to respond to an infectious disease outbreak. Scores range between 0-1, with higher scores reflecting greater capacity to manage infectious outbreaks. UAE, Lebanon, and Canada ranked 3rd, 21st, and 31st, respectively, for outbound air travel volume from Iran in February 2019. We estimated that 18,300 (95% confidence interval: 3770 to 53,470) COVID-19 cases would have had to occur in Iran, assuming an outbreak duration of 1.5 months in the country, in order to observe these three internationally exported cases reported at the time of writing. Results were robust under varying assumptions about undiagnosed case numbers in Syria, Azerbaijan and Iraq. Even if it were assumed that all cases were identified in all countries with certainty, the "best case" outbreak size was substantial (1820, 95% CI: 380-5320 cases), and far higher than reported case counts. Given the low volumes of air travel to countries with identified cases of COVID-19 with origin in Iran (such as Canada), it is likely that Iran is currently experiencing a COVID-19 epidemic of significant size for such exportations to be occurring. This is concerning, both for public health in Iran itself, and because of the high likelihood for outward dissemination of the epidemic to neighbouring countries with lower capacity to respond to infectious diseases epidemics.

79: Assessment of the risk of SARS-CoV-2 reinfection in an intense re-exposure setting
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Posted 26 Aug 2020

Assessment of the risk of SARS-CoV-2 reinfection in an intense re-exposure setting
14,613 downloads medRxiv epidemiology

Laith J Abu-Raddad, Hiam Chemaitelly, Joel A Malek, Ayeda A Ahmed, Yasmin A. Mohamoud, Shameem Younuskunju, Houssein H. Ayoub, Zaina Al Kanaani, Abdullatif Al Khal, Einas Al Kuwari, Adeel A Butt, Peter Coyle, Andrew Jeremijenko, Anvar Hassan Kaleeckal, Ali Nizar Latif, Riyazuddin Mohammad Shaik, Hanan F. Abdul Rahim, HADI M. YASSINE, Mohamed Ghaith Al Kuwari, Hamad Eid Al Romaihi, Sheikh Mohammad Al Thani, Roberto Bertollini

Background: Reinfection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is debated. We assessed risk and incidence rate of documented SARS-CoV-2 reinfection in a large cohort of laboratory-confirmed cases in Qatar. Methods: All SARS-CoV-2 laboratory-confirmed cases with at least one PCR positive swab that is [≥]45 days after a first-positive swab were individually investigated for evidence of reinfection, and classified as showing strong, good, some, or weak/no evidence for reinfection. Viral genome sequencing of the paired first-positive and reinfection viral specimens was conducted to confirm reinfection. Risk and incidence rate of reinfection were estimated. Results: Out of 133,266 laboratory-confirmed SARS-CoV-2 cases, 243 persons (0.18%) had at least one subsequent positive swab [≥]45 days after the first-positive swab. Of these, 54 cases (22.2%) had strong or good evidence for reinfection. Median time between first and reinfection swab was 64.5 days (range: 45-129). Twenty-three of the 54 cases (42.6%) were diagnosed at a health facility suggesting presence of symptoms, while 31 (57.4%) were identified incidentally through random testing campaigns/surveys or contact tracing. Only one person was hospitalized at time of reinfection, but still with mild infection. No deaths were recorded. Viral genome sequencing confirmed four out of 12 cases with available genetic evidence. Risk of reinfection was estimated at 0.01% (95% CI: 0.01-0.02%) and incidence rate of reinfection was estimated at 0.36 (95% CI: 0.28-0.47) per 10,000 person-weeks. Conclusions: SARS-CoV-2 reinfection can occur but is a rare phenomenon suggestive of a strong protective immunity against reinfection that lasts for at least a few months post primary infection.

80: Loss of smell and taste in combination with other symptoms is a strong predictor of COVID-19 infection
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Posted 07 Apr 2020

Loss of smell and taste in combination with other symptoms is a strong predictor of COVID-19 infection
14,457 downloads medRxiv epidemiology

Cristina Menni, Ana M Valdes, Maxim Freidin, Sajaysurya Ganesh, Julia Sarah El-Sayed Moustafa, Alessia Visconti, Pirro Hysi, Ruth C. Bowyer, Massimo Mangino, Mario Falchi, Jonathan Wolf, Claire J Steves, Timothy Spector

ImportanceA strategy for preventing further spread of the ongoing COVID-19 epidemic is to detect infections and isolate infected individuals without the need of extensive bio-specimen testing. ObjectivesHere we investigate the prevalence of loss of smell and taste among COVID-19 diagnosed individuals and we identify the combination of symptoms, besides loss of smell and taste, most likely to correspond to a positive COVID-19 diagnosis in non-severe cases. DesignCommunity survey. Setting and ParticipantsSubscribers of RADAR COVID-19, an app that was launched for use among the UK general population asking about COVID-19 symptoms. Main ExposureLoss of smell and taste. Main Outcome MeasuresCOVID-19. ResultsBetween 24 and 29 March 2020, 1,573,103 individuals reported their symptoms via the app; 26% reported suffering from one or more symptoms of COVID-19. Of those, n=1702 reported having had a RT-PCR COVID-19 test and gave full report on symptoms including loss of smell and taste; 579 were positive and 1123 negative. In this subset, we find that loss of smell and taste were present in 59% of COVID-19 positive individuals compared to 18% of those negative to the test, yielding an odds ratio (OR) of COVID-19 diagnosis of OR[95%CI]=6.59[5.25; 8.27], P= 1.90x10-59. We also find that a combination of loss of smell and taste, fever, persistent cough, fatigue, diarrhoea, abdominal pain and loss of appetite is predictive of COVID-19 positive test with sensitivity 0.54[0.44; 0.63], specificity 0.86[0.80; 0.90], ROC-AUC 0.77[0.72; 0.82] in the test set, and cross-validation ROC-AUC 0.75[0.72; 0.77]. When applied to the 410,598 individuals reporting symptoms but not formally tested, our model predicted that 13.06%[12.97%;13.15] of these might have been already infected by the virus. Conclusions and RelevanceOur study suggests that loss of taste and smell is a strong predictor of having been infected by the COVID-19 virus. Also, the combination of symptoms that could be used to identify and isolate individuals includes anosmia, fever, persistent cough, diarrhoea, fatigue, abdominal pain and loss of appetite. This is particularly relevant to healthcare and other key workers in constant contact with the public who have not yet been tested for COVID-19. Key pointsO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIThe spread of COVID-19 can be reduced by identifying and isolating infected individuals but it is not possible to test everyone and priority has been given in most countries to individuals presenting symptoms of the disease. C_LIO_LICOVID-19 symptoms, such as fever, cough, aches, fatigue are common in many other viral infections C_LIO_LIThere is therefore a need to identify symptom combinations that can rightly pinpoint to infected individuals C_LI What this study addsO_LIAmong individuals showing symptoms severe enough to be given a COVID-19 RT-PCR test in the UK the prevalence of loss of smell (anosmia) was 3-fold higher (59%) in those positive to the test than among those negative to the test (18%). C_LIO_LIWe developed a mathematical model combining symptoms to predict individuals likely to be COVID-19 positive and applied this to over 400,000 individuals in the general population presenting some of the COVID-19 symptoms. C_LIO_LIWe find that [~]13% of those presenting symptoms are likely to have or have had a COVID-19 infection. The proportion was slightly higher in women than in men but is comparable in all age groups, and corresponds to 3.4% of those who filled the app report. C_LI

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