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1.
Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-21266969

Résumé

Like other congregate living settings, military basic training has been subject to outbreaks of COVID-19. We sought to identify improved strategies for preventing outbreaks in this setting using an agent-based model of a hypothetical cohort of trainees on a U.S. Army post. Our analysis revealed unique aspects of basic training that require customized approaches to outbreak prevention, which draws attention to the possibility that customized approaches may be necessary in other settings, too. In particular, we showed that introductions by trainers and support staff may be a major vulnerability, given that those individuals remain at risk of community exposure throughout the training period. We also found that increased testing of trainees upon arrival could actually increase the risk of outbreaks, given the potential for false-positive test results to lead to susceptible individuals becoming infected in group isolation and seeding outbreaks in training units upon release. Until an effective transmission-blocking vaccine is adopted at high coverage by individuals involved with basic training, need will persist for non-pharmaceutical interventions to prevent outbreaks in military basic training. Ongoing uncertainties about virus variants and breakthrough infections necessitate continued vigilance in this setting, even as vaccination coverage increases. Significance StatementCOVID-19 has presented enormous disruptions to society. Militaries are not immune to these disruptions, with outbreaks in those settings posing threats to national security. We present a simulation model of COVID-19 outbreaks in a U.S. Army basic training setting to inform improved approaches to prevention there. Counterintuitively, we found that outbreak risk is driven more by virus introductions from trainers than the large number of trainees, and that outbreak risk is highly sensitive to false-positive results during entry testing. These findings suggest practical ways to improve prevention of COVID-19 outbreaks in basic training and, as a result, maintain the flow of new soldiers into the military. This work highlights the need for bespoke modeling to inform prevention in diverse institutional settings.

2.
Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-21261425

Résumé

Accurate tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been critical in efforts to control its spread. The accuracy of molecular tests for SARS-CoV-2 has been assessed numerous times, usually in reference to a gold standard diagnosis. One major disadvantage of that approach is the possibility of error due to inaccuracy of the gold standard, which is especially problematic for evaluating testing in a real-world surveillance context. We used an alternative approach known as Bayesian latent class modeling (BLCM), which circumvents the need to designate a gold standard by simultaneously estimating the accuracy of multiple tests. We applied this technique to a collection of 1,716 tests of three types applied to 853 individuals on a university campus during a one-week period in October 2020. We found that reverse transcriptase polymerase chain reaction (RT-PCR) testing of saliva samples performed at a campus facility had higher sensitivity (median: 0.923; 95% credible interval: 0.732-0.996) than RT-PCR testing of nasal samples performed at a commercial facility (median: 0.859; 95% CrI: 0.547-0.994). The reverse was true for specificity, although the specificity of saliva testing was still very high (median: 0.993; 95% CrI: 0.983-0.999). An antigen test was less sensitive and specific than both of the RT-PCR tests. These results suggest that RT-PCR testing of saliva samples at a campus facility can be an effective basis for surveillance screening to prevent SARS-CoV-2 transmission in a university setting.

3.
Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-21258238

Résumé

Since the start of the COVID-19 pandemic, there has been interest in using wastewater monitoring as an approach for disease surveillance. A significant uncertainty that would improve interpretation of wastewater monitoring data is the intensity and timing with which individuals shed RNA from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into wastewater. By combining wastewater and case surveillance data sets from a university campus during a period of heightened surveillance, we inferred that individual shedding of RNA into wastewater peaks on average six days (50% uncertainty interval (UI): 6 - 7; 95% UI: 4 - 8) following infection, and that wastewater measurements are highly overdispersed (negative binomial dispersion parameter, k = 0.39 (95% credible interval: 0.32 - 0.48)). This limits the utility of wastewater surveillance as a leading indicator of secular trends in SARS-CoV-2 transmission during an epidemic, and implies that it could be most useful as an early warning of rising transmission in areas where transmission is low or clinical testing is delayed or of limited capacity.

4.
Estee Y Cramer; Evan L Ray; Velma K Lopez; Johannes Bracher; Andrea Brennen; Alvaro J Castro Rivadeneira; Aaron Gerding; Tilmann Gneiting; Katie H House; Yuxin Huang; Dasuni Jayawardena; Abdul H Kanji; Ayush Khandelwal; Khoa Le; Anja Muehlemann; Jarad Niemi; Apurv Shah; Ariane Stark; Yijin Wang; Nutcha Wattanachit; Martha W Zorn; Youyang Gu; Sansiddh Jain; Nayana Bannur; Ayush Deva; Mihir Kulkarni; Srujana Merugu; Alpan Raval; Siddhant Shingi; Avtansh Tiwari; Jerome White; Neil F Abernethy; Spencer Woody; Maytal Dahan; Spencer Fox; Kelly Gaither; Michael Lachmann; Lauren Ancel Meyers; James G Scott; Mauricio Tec; Ajitesh Srivastava; Glover E George; Jeffrey C Cegan; Ian D Dettwiller; William P England; Matthew W Farthing; Robert H Hunter; Brandon Lafferty; Igor Linkov; Michael L Mayo; Matthew D Parno; Michael A Rowland; Benjamin D Trump; Yanli Zhang-James; Samuel Chen; Stephen V Faraone; Jonathan Hess; Christopher P Morley; Asif Salekin; Dongliang Wang; Sabrina M Corsetti; Thomas M Baer; Marisa C Eisenberg; Karl Falb; Yitao Huang; Emily T Martin; Ella McCauley; Robert L Myers; Tom Schwarz; Daniel Sheldon; Graham Casey Gibson; Rose Yu; Liyao Gao; Yian Ma; Dongxia Wu; Xifeng Yan; Xiaoyong Jin; Yu-Xiang Wang; YangQuan Chen; Lihong Guo; Yanting Zhao; Quanquan Gu; Jinghui Chen; Lingxiao Wang; Pan Xu; Weitong Zhang; Difan Zou; Hannah Biegel; Joceline Lega; Steve McConnell; VP Nagraj; Stephanie L Guertin; Christopher Hulme-Lowe; Stephen D Turner; Yunfeng Shi; Xuegang Ban; Robert Walraven; Qi-Jun Hong; Stanley Kong; Axel van de Walle; James A Turtle; Michal Ben-Nun; Steven Riley; Pete Riley; Ugur Koyluoglu; David DesRoches; Pedro Forli; Bruce Hamory; Christina Kyriakides; Helen Leis; John Milliken; Michael Moloney; James Morgan; Ninad Nirgudkar; Gokce Ozcan; Noah Piwonka; Matt Ravi; Chris Schrader; Elizabeth Shakhnovich; Daniel Siegel; Ryan Spatz; Chris Stiefeling; Barrie Wilkinson; Alexander Wong; Sean Cavany; Guido Espana; Sean Moore; Rachel Oidtman; Alex Perkins; David Kraus; Andrea Kraus; Zhifeng Gao; Jiang Bian; Wei Cao; Juan Lavista Ferres; Chaozhuo Li; Tie-Yan Liu; Xing Xie; Shun Zhang; Shun Zheng; Alessandro Vespignani; Matteo Chinazzi; Jessica T Davis; Kunpeng Mu; Ana Pastore y Piontti; Xinyue Xiong; Andrew Zheng; Jackie Baek; Vivek Farias; Andreea Georgescu; Retsef Levi; Deeksha Sinha; Joshua Wilde; Georgia Perakis; Mohammed Amine Bennouna; David Nze-Ndong; Divya Singhvi; Ioannis Spantidakis; Leann Thayaparan; Asterios Tsiourvas; Arnab Sarker; Ali Jadbabaie; Devavrat Shah; Nicolas Della Penna; Leo A Celi; Saketh Sundar; Russ Wolfinger; Dave Osthus; Lauren Castro; Geoffrey Fairchild; Isaac Michaud; Dean Karlen; Matt Kinsey; Luke C. Mullany; Kaitlin Rainwater-Lovett; Lauren Shin; Katharine Tallaksen; Shelby Wilson; Elizabeth C Lee; Juan Dent; Kyra H Grantz; Alison L Hill; Joshua Kaminsky; Kathryn Kaminsky; Lindsay T Keegan; Stephen A Lauer; Joseph C Lemaitre; Justin Lessler; Hannah R Meredith; Javier Perez-Saez; Sam Shah; Claire P Smith; Shaun A Truelove; Josh Wills; Maximilian Marshall; Lauren Gardner; Kristen Nixon; John C. Burant; Lily Wang; Lei Gao; Zhiling Gu; Myungjin Kim; Xinyi Li; Guannan Wang; Yueying Wang; Shan Yu; Robert C Reiner; Ryan Barber; Emmanuela Gaikedu; Simon Hay; Steve Lim; Chris Murray; David Pigott; Heidi L Gurung; Prasith Baccam; Steven A Stage; Bradley T Suchoski; B. Aditya Prakash; Bijaya Adhikari; Jiaming Cui; Alexander Rodriguez; Anika Tabassum; Jiajia Xie; Pinar Keskinocak; John Asplund; Arden Baxter; Buse Eylul Oruc; Nicoleta Serban; Sercan O Arik; Mike Dusenberry; Arkady Epshteyn; Elli Kanal; Long T Le; Chun-Liang Li; Tomas Pfister; Dario Sava; Rajarishi Sinha; Thomas Tsai; Nate Yoder; Jinsung Yoon; Leyou Zhang; Sam Abbott; Nikos I Bosse; Sebastian Funk; Joel Hellewell; Sophie R Meakin; Katharine Sherratt; Mingyuan Zhou; Rahi Kalantari; Teresa K Yamana; Sen Pei; Jeffrey Shaman; Michael L Li; Dimitris Bertsimas; Omar Skali Lami; Saksham Soni; Hamza Tazi Bouardi; Turgay Ayer; Madeline Adee; Jagpreet Chhatwal; Ozden O Dalgic; Mary A Ladd; Benjamin P Linas; Peter Mueller; Jade Xiao; Yuanjia Wang; Qinxia Wang; Shanghong Xie; Donglin Zeng; Alden Green; Jacob Bien; Logan Brooks; Addison J Hu; Maria Jahja; Daniel McDonald; Balasubramanian Narasimhan; Collin Politsch; Samyak Rajanala; Aaron Rumack; Noah Simon; Ryan J Tibshirani; Rob Tibshirani; Valerie Ventura; Larry Wasserman; Eamon B O'Dea; John M Drake; Robert Pagano; Quoc T Tran; Lam Si Tung Ho; Huong Huynh; Jo W Walker; Rachel B Slayton; Michael A Johansson; Matthew Biggerstaff; Nicholas G Reich.
Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-21250974

Résumé

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naive baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance StatementThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

5.
Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-21250489

Résumé

BackgroundChildhood immunisation services have been disrupted by COVID-19. WHO recommends considering outbreak risk using epidemiological criteria when deciding whether to conduct preventive vaccination campaigns during the pandemic. MethodsWe used 2-3 models per infection to estimate the health impact of 50% reduced routine vaccination coverage and delaying campaign vaccination for measles, meningococcal A and yellow fever vaccination in 3-6 high burden countries per infection. ResultsReduced routine coverage in 2020 without catch-up vaccination may increase measles and yellow fever disease burden in the modelled countries. Delaying planned campaigns may lead to measles outbreaks and increases in yellow fever burden in some countries. For meningococcal A vaccination, short term disruptions in 2020 are unlikely to have a significant impact. ConclusionThe impact of COVID-19-related disruption to vaccination programs varies between infections and countries. FundingBill & Melinda Gates Foundation and Gavi, the Vaccine Alliance Impact statementRoutine and campaign vaccination disruption in 2020 may lead to measles outbreaks and yellow fever burden increases in some countries, but is unlikely to greatly increase meningococcal A burden. SummaryO_ST_ABSBackgroundC_ST_ABSChildhood immunisation services have been disrupted by the COVID-19 pandemic. WHO recommends considering outbreak risk using epidemiological criteria when deciding whether to conduct preventive vaccination campaigns during the pandemic. MethodsWe used 2-3 models per infection to estimate the health impact of 50% reduced routine vaccination coverage in 2020 and delay of campaign vaccination from 2020 to 2021 for measles vaccination in Bangladesh, Chad, Ethiopia, Kenya, Nigeria, and South Sudan, for meningococcal A vaccination in Burkina Faso, Chad, Niger, and Nigeria, and for yellow fever vaccination in the Democratic Republic of Congo, Ghana, and Nigeria. Our counterfactual comparative scenario was sustaining immunisation services at coverage projections made prior to COVID-19 (i.e. without any disruption). FindingsReduced routine vaccination coverage in 2020 without catch-up vaccination may lead to an increase in measles and yellow fever disease burden in the modelled countries. Delaying planned campaigns in Ethiopia and Nigeria by a year may significantly increase the risk of measles outbreaks (both countries did complete their supplementary immunisation activities (SIAs) planned for 2020). For yellow fever vaccination, delay in campaigns leads to a potential disease burden rise of >1 death per 100,000 people per year until the campaigns are implemented. For meningococcal A vaccination, short term disruptions in 2020 are unlikely to have a significant impact due to the persistence of direct and indirect benefits from past introductory campaigns of the 1 to 29-year-old population, bolstered by inclusion of the vaccine into the routine immunisation schedule accompanied by further catch-up campaigns. InterpretationThe impact of COVID-19-related disruption to vaccination programs varies between infections and countries. Planning and implementation of campaigns should consider country and infection-specific epidemiological factors and local immunity gaps worsened by the COVID-19 pandemic when prioritising vaccines and strategies for catch-up vaccination. FundingBill & Melinda Gates Foundation and Gavi, the Vaccine Alliance

6.
Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-20210211

Résumé

BackgroundThe COVID-19 pandemic has induced unprecedented reductions in human mobility and social contacts throughout the world. Because dengue virus (DENV) transmission is strongly driven by human mobility, behavioral changes associated with the pandemic have been hypothesized to impact dengue incidence. By discouraging human contact, COVID-19 control measures have also disrupted dengue vector control interventions, the most effective of which require entry into homes. MethodWe used an agent-based model with a realistic treatment of human mobility and vector control to investigate how and why dengue incidence could differ under a lockdown scenario with a proportion of the population sheltered at home. ResultWe found that a lockdown in which 70% of the population sheltered at home led to a small average increase in cumulative DENV infections of up to 10%, depending on the time of year lockdown occurred. Lockdown had a more pronounced effect on the spatial distribution of DENV infections, with higher incidence under lockdown in regions with high mosquito abundance. Transmission was also more focused in homes following lockdown. The proportion of people infected in their own home rose from 54% under normal conditions to 66% under lockdown, and the household secondary attack rate rose from 0.109 to 0.128, a 17% increase. When we considered that lockdown measures could disrupt regular, city-wide vector control campaigns, the increase in incidence was more pronounced than with lockdown alone, especially if lockdown occurred at the optimal time for vector control. DiscussionOur results indicate that an unintended outcome of COVID-19 control measures may be to adversely alter the epidemiology of dengue. This observation has important implications for an improved understanding of dengue epidemiology and effective application of dengue vector control. When coordinating public health responses during a syndemic, it is important to monitor multiple infections and understand that an intervention against one disease may exacerbate another.

7.
Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-20225409

Résumé

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

8.
Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-20179960

Résumé

In the United States, schools closed in March 2020 due to COVID-19 and began reopening in August 2020, despite continuing transmission of SARS-CoV-2. In states where in-person instruction resumed at that time, two major unknowns were the capacity at which schools would operate, which depended on the proportion of families opting for remote instruction, and adherence to face-mask requirements in schools, which depended on cooperation from students and enforcement by schools. To determine the impact of these conditions on the statewide burden of COVID-19 in Indiana, we used an agent-based model calibrated to and validated against multiple data types. Using this model, we quantified the burden of COVID-19 on K-12 students, teachers, their families, and the general population under alternative scenarios spanning three levels of school operating capacity (50%, 75%, and 100%) and three levels of face-mask adherence in schools (50%, 75%, and 100%). Under a scenario in which schools operated remotely, we projected 45,579 (95% CrI: 14,109-132,546) infections and 790 (95% CrI: 176-1680) deaths statewide between August 24 and December 31. Reopening at 100% capacity with 50% face-mask adherence in schools resulted in a proportional increase of 42.9 (95% CrI: 41.3-44.3) and 9.2 (95% CrI: 8.9-9.5) times that number of infections and deaths, respectively. In contrast, our results showed that at 50% capacity with 100% face-mask adherence, the number of infections and deaths were 22% (95% CrI: 16%-28%) and 11% (95% CrI: 5%-18%) higher than the scenario in which schools operated remotely. Within this range of possibilities, we found that high levels of school operating capacity (80-95%) and intermediate levels of face-mask adherence (40-70%) resulted in model behavior most consistent with observed data. Together, these results underscore the importance of precautions taken in schools for the benefit of their communities.

9.
Preprint Dans Anglais | bioRxiv | ID: ppbiorxiv-106625

Résumé

The COVID-19 pandemic has challenged diagnostic systems globally. Expanding testing capabilities to conduct population-wide screening for COVID-19 requires innovation in diagnostic services at both the molecular and industrial scale. No report to-date has considered the complexity of laboratory infrastructure in conjunction with the available molecular assays to offer a standardised solution to testing. Here we present CONTAIN. A modular biosafety level 2+ laboratory optimised for automated RT-qPCR COVID-19 testing based on a standard 40ft shipping container. Using open-source liquid-handling robots and RNA extraction reagents we demonstrate a reproducible workflow for RT-qPCR COVID-19 testing. With five OT2 liquid handlers, a single CONTAIN unit reaches a maximum daily testing capacity of 2400 tests/day. We validate this workflow for automated RT-qPCR testing, using both synthetic SARS-CoV-2 samples and patient samples from a local NHS hospital. Finally, we discuss the suitability of CONTAIN and its flexibility in a range of diagnostic testing scenarios including high-density urban environments and mobile response units. Visual abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/106625v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@18acad6org.highwire.dtl.DTLVardef@10ae5f1org.highwire.dtl.DTLVardef@7e34d3org.highwire.dtl.DTLVardef@1be3815_HPS_FORMAT_FIGEXP M_FIG C_FIG

10.
Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-20076018

Résumé

The COVID-19 pandemic has forced societies across the world to resort to social distancing to slow the spread of the SARS-CoV-2 virus. Due to the economic impacts of social distancing, there is growing desire to relax these measures. To characterize a range of possible strategies for control and to understand their consequences, we performed an optimal control analysis of a mathematical model of SARS-CoV-2 transmission. Given that the pandemic is already underway and controls have already been initiated, we calibrated our model to data from the US and focused our analysis on optimal controls from May 2020 through December 2021. We found that a major factor that differentiates strategies that prioritize lives saved versus reduced time under control is how quickly control is relaxed once social distancing restrictions expire in May 2020. Strategies that maintain control at a high level until summer 2020 allow for tapering of control thereafter and minimal deaths, whereas strategies that relax control in the short term lead to fewer options for control later and a higher likelihood of exceeding hospital capacity. Our results also highlight that the potential scope for controlling COVID-19 until a vaccine is available depends on epidemiological parameters about which there is still considerable uncertainty, including the basic reproduction number and the effectiveness of social distancing. In light of those uncertainties, our results do not constitute a quantitative forecast and instead provide a qualitative portrayal of possible outcomes from alternative approaches to control.

11.
Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-20036582

Résumé

By March 2020, COVID-19 led to thousands of deaths and disrupted economic activity worldwide. As a result of narrow case definitions and limited capacity for testing, the number of unobserved SARS-CoV-2 infections during its initial invasion of the US remains unknown. We developed an approach for estimating the number of unobserved infections based on data that are commonly available shortly after the emergence of a new infectious disease. The logic of our approach is, in essence, that there are bounds on the amount of exponential growth of new infections that can occur during the first few weeks after imported cases start appearing. Applying that logic to data on imported cases and local deaths in the US through March 12, we estimated that 22,876 (95% posterior predictive interval: 7,451 - 53,044) infections occurred in the US by this date. By comparing the models predictions of symptomatic infections to local cases reported over time, we obtained daily estimates of the proportion of symptomatic infections detected by surveillance. This revealed that detection of symptomatic infections decreased throughout February as exponential growth of infections outpaced increases in testing. Between February 21 and March 12, we estimated an increase in detection of symptomatic infections, which was strongly correlated (median: 0.97, 95% PPI: 0.85 - 0.98) with increases in testing. These results suggest that testing was a major limiting factor in assessing the extent of SARS-CoV-2 transmission during its initial invasion of the US. Significance StatementCountries across the world observed dramatic rises in COVID-19 cases and deaths in March 2020. In the United States, delays in the availability of diagnostic testing prompted questions about the extent of unobserved community transmission. Using a simulation model informed by reported cases and deaths, we estimated that tens of thousands of people were infected by the time a national emergency was declared on March 13. Our results indicate that fewer than 20% of locally acquired, symptomatic infections in the US were detected over a period of a month. The existence of a large, unobserved reservoir of infection argues for the necessity of large-scale social distancing that went into effect to mitigate the impacts of SARS-CoV-2 on the US.

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