MyLibrary.bib

@misc{BactDatingBayesianInference,
  title = {{{BactDating}}: {{Bayesian}} Inference of Ancestral Dates on Bacterial Phylogenetic Trees},
  shorttitle = {{{BactDating}}},
  abstract = {BactDating is a R package to perform Bayesian inference of ancestral dates on bacterial phylogenetic trees.},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/8Z5GZFGC/BactDating.html},
  howpublished = {https://xavierdidelot.github.io/BactDating/},
  language = {en}
}

@article{basuEstimatingInfectionFatality2020,
  title = {Estimating {{The Infection Fatality Rate Among Symptomatic COVID}}-19 {{Cases~In The United States}}},
  author = {Basu, Anirban},
  year = {2020},
  month = may,
  pages = {10.1377/hlthaff.2020.00455},
  publisher = {{Health Affairs}},
  issn = {0278-2715},
  doi = {10.1377/hlthaff.2020.00455},
  abstract = {Knowing the infection fatality rate (IFR) of SARS-CoV and SARS-CoV-2 infections is essential for the fight against the COVID-19 pandemic. Using data through April 20, 2020, we fit a statistical model to COVID-19 case fatality rates over time at the US county level to estimate the COVID-19 IFR among symptomatic cases (IFR-S) as time goes to infinity. The IFR-S in the US was estimated to be 1.3\% (95\% central credible interval: 0.6\% to 2.1\%). County-specific rates varied from 0.5\% to 3.6\%. The overall IFR for COVID-19 should be lower when we account for cases that remain and recover without symptoms. When used with other estimating approaches, our model and our estimates can help disease and policy modelers to obtain more accurate predictions for the epidemiology of the disease and the impact of alternative policy levers to contain this pandemic. The model could also be used with future epidemics to get an early sense of the magnitude of symptomatic infection at the population-level before more direct estimates are available. Substantial variation across patient demographics likely exists and should be the focus of future studies. [Editor's Note: This Fast Track Ahead Of Print article is the accepted version of the peer-reviewed manuscript. The final edited version will appear in an upcoming issue of Health Affairs.]},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/EES43SAA/Basu - 2020 - Estimating The Infection Fatality Rate Among Sympt.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/YF2NUV9P/hlthaff.2020.html},
  journal = {Health Affairs}
}

@article{dehningInferringChangePoints2020,
  title = {Inferring Change Points in the Spread of {{COVID}}-19 Reveals the Effectiveness of Interventions},
  author = {Dehning, Jonas and Zierenberg, Johannes and Spitzner, F. Paul and Wibral, Michael and Neto, Joao Pinheiro and Wilczek, Michael and Priesemann, Viola},
  year = {2020},
  month = may,
  publisher = {{American Association for the Advancement of Science}},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.abb9789},
  abstract = {{$<$}p{$>$}As COVID-19 is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies. A major challenge for short-term forecasts is the assessment of key epidemiological parameters and how they change when first interventions show an effect. By combining an established epidemiological model with Bayesian inference, we analyze the time dependence of the effective growth rate of new infections. Focusing on COVID-19 spread in Germany, we detect change points in the effective growth rate that correlate well with the times of publicly announced interventions. Thereby, we can quantify the effect of interventions, and we can incorporate the corresponding change points into forecasts of future scenarios and case numbers. Our code is freely available and can be readily adapted to any country or region.{$<$}/p{$>$}},
  copyright = {Copyright \textcopyright{} 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/5F7A6GIB/Dehning et al. - 2020 - Inferring change points in the spread of COVID-19 .pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/JPRIIXTA/science.html},
  journal = {Science},
  language = {en}
}

@article{fauverCoasttocoastSpreadSARSCoV22020,
  title = {Coast-to-Coast Spread of {{SARS}}-{{CoV}}-2 in the {{United States}} Revealed by Genomic Epidemiology},
  author = {Fauver, Joseph R. and Petrone, Mary E. and Hodcroft, Emma B. and Shioda, Kayoko and Ehrlich, Hanna Y. and Watts, Alexander G. and Vogels, Chantal B. F. and Brito, Anderson F. and Alpert, Tara and Muyombwe, Anthony and Razeq, Jafar and Downing, Randy and Cheemarla, Nagarjuna R. and Wyllie, Anne L. and Kalinich, Chaney C. and Ott, Isabel and Quick, Josh and Loman, Nicholas J. and Neugebauer, Karla M. and Greninger, Alexander L. and Jerome, Keith R. and Roychoundhury, Pavitra and Xie, Hong and Shrestha, Lasata and Huang, Meei-Li and Pitzer, Virginia E. and Iwasaki, Akiko and Omer, Saad B. and Khan, Kamran and Bogoch, Isaac and Martinello, Richard A. and Foxman, Ellen F. and Landry, Marie-Louise and Neher, Richard A. and Ko, Albert I. and Grubaugh, Nathan D.},
  year = {2020},
  month = mar,
  pages = {2020.03.25.20043828},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.03.25.20043828},
  abstract = {{$<$}p{$>$}Since its emergence and detection in Wuhan, China in late 2019, the novel coronavirus SARS-CoV-2 has spread to nearly every country around the world, resulting in hundreds of thousands of infections to date. The virus was first detected in the Pacific Northwest region of the United States in January, 2020, with subsequent COVID-19 outbreaks detected in all 50 states by early March. To uncover the sources of SARS-CoV-2 introductions and patterns of spread within the U.S., we sequenced nine viral genomes from early reported COVID-19 patients in Connecticut. Our phylogenetic analysis places the majority of these genomes with viruses sequenced from Washington state. By coupling our genomic data with domestic and international travel patterns, we show that early SARS-CoV-2 transmission in Connecticut was likely driven by domestic introductions. Moreover, the risk of domestic importation to Connecticut exceeded that of international importation by mid-March regardless of our estimated impacts of federal travel restrictions. This study provides evidence for widespread, sustained transmission of SARS-CoV-2 within the U.S. and highlights the critical need for local surveillance.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/FYBA3SL4/Fauver et al. - 2020 - Coast-to-coast spread of SARS-CoV-2 in the United .pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/IL5P9IFY/2020.03.25.html},
  journal = {medRxiv},
  language = {en}
}

@article{gargHospitalizationRatesCharacteristics2020,
  title = {Hospitalization {{Rates}} and {{Characteristics}} of {{Patients Hospitalized}} with {{Laboratory}}-{{Confirmed Coronavirus Disease}} 2019 \textemdash{} {{COVID}}-{{NET}}, 14 {{States}}, {{March}} 1\textendash{}30, 2020},
  author = {Garg, Shikha},
  year = {2020},
  volume = {69},
  issn = {0149-21951545-861X},
  doi = {10.15585/mmwr.mm6915e3},
  abstract = {The Coronavirus Disease 2019\textendash{}Associated Hospitalization Surveillance Network (COVID-NET) was implemented to produce robust, weekly, age-stratified COVID-19\textendash{}associated hospitalization rates.},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/QEWK9JCU/Garg - 2020 - Hospitalization Rates and Characteristics of Patie.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/FY3S4M8Y/mm6915e3.html},
  journal = {MMWR. Morbidity and Mortality Weekly Report},
  language = {en-us}
}

@article{gonzalez-reicheIntroductionsEarlySpread2020,
  title = {Introductions and Early Spread of {{SARS}}-{{CoV}}-2 in the {{New York City}} Area},
  author = {{Gonzalez-Reiche}, Ana S. and Hernandez, Matthew M. and Sullivan, Mitchell and Ciferri, Brianne and Alshammary, Hala and Obla, Ajay and Fabre, Shelcie and Kleiner, Giulio and Polanco, Jose and Khan, Zenab and Alburquerque, Bremy and van de Guchte, Adriana and Dutta, Jayeeta and Francoeur, Nancy and Melo, Betsaida Salom and Oussenko, Irina and Deikus, Gintaras and Soto, Juan and Sridhar, Shwetha Hara and Wang, Ying-Chih and Twyman, Kathryn and Kasarskis, Andrew and Altman, Deena Rose and Smith, Melissa and Sebra, Robert and Aberg, Judith and Krammer, Florian and {Garcia-Sarstre}, Adolfo and Luksza, Marta and Patel, Gopi and {Paniz-Mondolfi}, Alberto and Gitman, Melissa and Sordillo, Emilia Mia and Simon, Viviana and van Bakel, Harm},
  year = {2020},
  month = apr,
  pages = {2020.04.08.20056929},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.04.08.20056929},
  abstract = {{$<$}p{$>$}New York City (NYC) has emerged as one of the epicenters of the current SARS-CoV2 pandemic. To identify the early events underlying the rapid spread of the virus in the NYC metropolitan area, we sequenced the virus causing COVID19 in patients seeking care at the Mount Sinai Health System. Phylogenetic analysis of 84 distinct SARS-CoV2 genomes indicates multiple, independent but isolated introductions mainly from Europe and other parts of the United States. Moreover, we find evidence for community transmission of SARS-CoV2 as suggested by clusters of related viruses found in patients living in different neighborhoods of the city.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/8XDK6RS7/Gonzalez-Reiche et al. - 2020 - Introductions and early spread of SARS-CoV-2 in th.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/LUMEC2Z2/2020.04.08.html},
  journal = {medRxiv},
  language = {en}
}

@article{gorbalenyaSpeciesSevereAcute2020,
  title = {The Species {{Severe}} Acute Respiratory Syndrome-Related Coronavirus : Classifying 2019-{{nCoV}} and Naming It {{SARS}}-{{CoV}}-2},
  shorttitle = {The Species {{Severe}} Acute Respiratory Syndrome-Related Coronavirus},
  author = {Gorbalenya, Alexander E. and Baker, Susan C. and Baric, Ralph S. and {de Groot}, Raoul J. and Drosten, Christian and Gulyaeva, Anastasia A. and Haagmans, Bart L. and Lauber, Chris and Leontovich, Andrey M. and Neuman, Benjamin W. and Penzar, Dmitry and Perlman, Stanley and Poon, Leo L. M. and Samborskiy, Dmitry V. and Sidorov, Igor A. and Sola, Isabel and Ziebuhr, John and {Coronaviridae Study Group of the International Committee on Taxonomy of Viruses}},
  year = {2020},
  month = apr,
  volume = {5},
  pages = {536--544},
  publisher = {{Nature Publishing Group}},
  issn = {2058-5276},
  doi = {10.1038/s41564-020-0695-z},
  abstract = {The present outbreak of a coronavirus-associated acute respiratory disease called coronavirus disease 19 (COVID-19) is the third documented spillover of an animal coronavirus to humans in only two decades that has resulted in a major epidemic. The Coronaviridae Study Group (CSG) of the International Committee on Taxonomy of Viruses, which is responsible for developing the classification of viruses and taxon nomenclature of the family Coronaviridae, has assessed the placement of the human pathogen, tentatively named 2019-nCoV, within the Coronaviridae. Based on phylogeny, taxonomy and established practice, the CSG recognizes this virus as forming a sister clade to the prototype human and bat severe acute respiratory syndrome coronaviruses (SARS-CoVs) of the species Severe acute respiratory syndrome-related coronavirus, and designates it as SARS-CoV-2. In order to facilitate communication, the CSG proposes to use the following naming convention for individual isolates: SARS-CoV-2/host/location/isolate/date. While the full spectrum of clinical manifestations associated with SARS-CoV-2 infections in humans remains to be determined, the independent zoonotic transmission of SARS-CoV and SARS-CoV-2 highlights the need for studying viruses at the species level to complement research focused on individual pathogenic viruses of immediate significance. This will improve our understanding of virus\textendash{}host interactions in an ever-changing environment and enhance our preparedness for future outbreaks.},
  copyright = {2020 The Author(s), under exclusive licence to Springer Nature Limited},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/R3KJWXJ2/Gorbalenya et al. - 2020 - The species Severe acute respiratory syndrome-rela.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/SWH2QQ95/s41564-020-0695-z.html},
  journal = {Nature Microbiology},
  language = {en},
  number = {4}
}

@article{griffithColliderBiasUndermines2020,
  title = {Collider Bias Undermines Our Understanding of {{COVID}}-19 Disease Risk and Severity},
  author = {Griffith, Gareth and Morris, Tim T and Tudball, Matt and Herbert, Annie and Mancano, Giulia and Pike, Lindsey and Sharp, Gemma C and Palmer, Tom M and Davey Smith, George and Tilling, Kate and Zuccolo, Luisa and Davies, Neil M and Hemani, Gibran},
  year = {2020},
  month = jan,
  pages = {2020.05.04.20090506},
  doi = {10.1101/2020.05.04.20090506},
  abstract = {Observational data on COVID-19 including hypothesised risk factors for infection and progression are accruing rapidly. Here, we highlight the challenge of interpreting observational evidence from non-random samples of the population, which may be affected by collider bias. We illustrate these issues using data from the UK Biobank in which individuals tested for COVID-19 are highly selected for a wide range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the sampling mechanisms that leave aetiological studies of COVID-19 infection and progression particularly susceptible to collider bias. We also describe several tools and strategies that could help mitigate the effects of collider bias in extant studies of COVID-19 and make available a web app for performing sensitivity analyses. While bias due to non-random sampling should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis research has been conducted using the UK Biobank Resource under Application Number 16729. The Medical Research Council (MRC) and the University of Bristol support the MRC Integrative Epidemiology Unit [MC\_UU\_12013/1, MC\_UU\_12013/9, MC\_UU\_00011/1]. NMD is supported by a Norwegian Research Council Grant number 295989. GH is supported by the Wellcome Trust and Royal Society [208806/Z/17/Z].Author DeclarationsAll relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.YesAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll analysis was performed on UK Biobank datahttps://github.com/explodecomputer/covid\_ascertainment},
  journal = {medRxiv}
}

@misc{InfluenzaSurveillanceSystem2020,
  title = {U.{{S}}. {{Influenza Surveillance System}}: {{Purpose}} and {{Methods}} | {{CDC}}},
  shorttitle = {U.{{S}}. {{Influenza Surveillance System}}},
  year = {2020},
  month = feb,
  abstract = {Overview of Influenza Surveillance in the United States - CDC},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/H6DQQ6ZV/overview.html},
  howpublished = {https://www.cdc.gov/flu/weekly/overview.htm},
  language = {en-us}
}

@techreport{kaashoekCOVID19PositiveCases2020,
  title = {{{COVID}}-19 {{Positive Cases}}, {{Evidence}} on the {{Time Evolution}} of the {{Epidemic}} or {{An Indicator}} of {{Local Testing Capabilities}}? {{A Case Study}} in the {{United States}}},
  shorttitle = {{{COVID}}-19 {{Positive Cases}}, {{Evidence}} on the {{Time Evolution}} of the {{Epidemic}} or {{An Indicator}} of {{Local Testing Capabilities}}?},
  author = {Kaashoek, Justin and Santillana, Mauricio},
  year = {2020},
  month = apr,
  address = {{Rochester, NY}},
  institution = {{Social Science Research Network}},
  doi = {10.2139/ssrn.3574849},
  abstract = {The novel SARS-CoV-2 coronavirus, first identified in Wuhan (Hubei), China, in December 2019, has spread to more than 180 countries and caused over 1,700,000 cases of COVID-19 worldwide to date. In an effort to limit human-to-human contact and slow the transmission of COVID-19, the disease caused by this novel coronavirus, the United States have implemented a collection of shelter-in-place public health interventions. To monitor if these interventions are working and to determine when people may go back to (perhaps a new) business as usual requires reliable monitoring systems that provide an accurate real-time picture of the trajectory of the epidemic outbreak. Here, we present evidence that our current healthcare-based monitoring systems, aimed at detecting the new daily number of COVID-19-positive individuals across the US, may be better at tracking the local testing (detection) capabilities than at monitoring the time evolution of the outbreak. This suggests that other data sources are necessary to inform (real-time) critical decisions about when to stop (and perhaps when to restart) shelter-in-place mitigation strategies.},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/Y7HYHG8Z/papers.html},
  keywords = {COVID-19,COVID-19 testing,Disease surveillance},
  language = {en},
  number = {ID 3574849},
  type = {{{SSRN Scholarly Paper}}}
}

@article{lourencoFundamentalPrinciplesEpidemic2020,
  title = {Fundamental Principles of Epidemic Spread Highlight the Immediate Need for Large-Scale Serological Surveys to Assess the Stage of the {{SARS}}-{{CoV}}-2 Epidemic},
  author = {Lourenco, Jose and Paton, Robert and Ghafari, Mahan and Kraemer, Moritz and Thompson, Craig and Simmonds, Peter and Klenerman, Paul and Gupta, Sunetra},
  year = {2020},
  month = mar,
  pages = {2020.03.24.20042291},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.03.24.20042291},
  abstract = {{$<$}p{$>$}The spread of a novel pathogenic infectious agent eliciting protective immunity is typically characterised by three distinct phases: (I) an initial phase of slow accumulation of new infections (often undetectable), (II) a second phase of rapid growth in cases of infection, disease and death, and (III) an eventual slow down of transmission due to the depletion of susceptible individuals, typically leading to the termination of the (first) epidemic wave. Before the implementation of control measures (e.g. social distancing, travel bans, etc) and under the assumption that infection elicits protective immunity, epidemiological theory indicates that the ongoing epidemic of SARS-CoV-2 will conform to this pattern. Here, we calibrate a susceptible-infected-recovered (SIR) model to data on cumulative reported SARS-CoV-2 associated deaths from the United Kingdom (UK) and Italy under the assumption that such deaths are well reported events that occur only in a vulnerable fraction of the population. We focus on model solutions which take into consideration previous estimates of critical epidemiological parameters such as the basic reproduction number (R0), probability of death in the vulnerable fraction of the population, infectious period and time from infection to death, with the intention of exploring the sensitivity of the system to the actual fraction of the population vulnerable to severe disease and death. Our simulations are in agreement with other studies that the current epidemic wave in the UK and Italy in the absence of interventions should have an approximate duration of 2-3 months, with numbers of deaths lagging behind in time relative to overall infections. Importantly, the results we present here suggest the ongoing epidemics in the UK and Italy started at least a month before the first reported death and have already led to the accumulation of significant levels of herd immunity in both countries. There is an inverse relationship between the proportion currently immune and the fraction of the population vulnerable to severe disease. This relationship can be used to determine how many people will require hospitalisation (and possibly die) in the coming weeks if we are able to accurately determine current levels of herd immunity. There is thus an urgent need for investment in technologies such as virus (or viral pseudotype) neutralization assays and other robust assays which provide reliable read-outs of protective immunity, and for the provision of open access to valuable data sources such as blood banks and paired samples of acute and convalescent sera from confirmed cases of SARS-CoV-2 to validate these. Urgent development and assessment of such tests should be followed by rapid implementation at scale to provide real-time data. These data will be critical to the proper assessment of the effects of social distancing and other measures currently being adopted to slow down the case incidence and for informing future policy direction.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/9CETW3QE/Lourenco et al. - 2020 - Fundamental principles of epidemic spread highligh.pdf},
  journal = {medRxiv},
  language = {en}
}

@article{lourencoFundamentalPrinciplesEpidemic2020a,
  title = {Fundamental Principles of Epidemic Spread Highlight the Immediate Need for Large-Scale Serological Surveys to Assess the Stage of the {{SARS}}-{{CoV}}-2 Epidemic},
  author = {Lourenco, Jose and Paton, Robert and Ghafari, Mahan and Kraemer, Moritz and Thompson, Craig and Simmonds, Peter and Klenerman, Paul and Gupta, Sunetra},
  year = {2020},
  month = mar,
  pages = {2020.03.24.20042291},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.03.24.20042291},
  abstract = {{$<$}p{$>$}The spread of a novel pathogenic infectious agent eliciting protective immunity is typically characterised by three distinct phases: (I) an initial phase of slow accumulation of new infections (often undetectable), (II) a second phase of rapid growth in cases of infection, disease and death, and (III) an eventual slow down of transmission due to the depletion of susceptible individuals, typically leading to the termination of the (first) epidemic wave. Before the implementation of control measures (e.g. social distancing, travel bans, etc) and under the assumption that infection elicits protective immunity, epidemiological theory indicates that the ongoing epidemic of SARS-CoV-2 will conform to this pattern. Here, we calibrate a susceptible-infected-recovered (SIR) model to data on cumulative reported SARS-CoV-2 associated deaths from the United Kingdom (UK) and Italy under the assumption that such deaths are well reported events that occur only in a vulnerable fraction of the population. We focus on model solutions which take into consideration previous estimates of critical epidemiological parameters such as the basic reproduction number (R0), probability of death in the vulnerable fraction of the population, infectious period and time from infection to death, with the intention of exploring the sensitivity of the system to the actual fraction of the population vulnerable to severe disease and death. Our simulations are in agreement with other studies that the current epidemic wave in the UK and Italy in the absence of interventions should have an approximate duration of 2-3 months, with numbers of deaths lagging behind in time relative to overall infections. Importantly, the results we present here suggest the ongoing epidemics in the UK and Italy started at least a month before the first reported death and have already led to the accumulation of significant levels of herd immunity in both countries. There is an inverse relationship between the proportion currently immune and the fraction of the population vulnerable to severe disease. This relationship can be used to determine how many people will require hospitalisation (and possibly die) in the coming weeks if we are able to accurately determine current levels of herd immunity. There is thus an urgent need for investment in technologies such as virus (or viral pseudotype) neutralization assays and other robust assays which provide reliable read-outs of protective immunity, and for the provision of open access to valuable data sources such as blood banks and paired samples of acute and convalescent sera from confirmed cases of SARS-CoV-2 to validate these. Urgent development and assessment of such tests should be followed by rapid implementation at scale to provide real-time data. These data will be critical to the proper assessment of the effects of social distancing and other measures currently being adopted to slow down the case incidence and for informing future policy direction.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/A3IZ6B23/Lourenco et al. - 2020 - Fundamental principles of epidemic spread highligh.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/UIVL7YMP/2020.03.24.html},
  journal = {medRxiv},
  language = {en}
}

@article{luEstimatingPrevalenceCOVID192020,
  title = {Estimating the {{Prevalence}} of {{COVID}}-19 in the {{United States}}: {{Three Complementary Approaches}}},
  shorttitle = {Estimating the {{Prevalence}} of {{COVID}}-19 in the {{United States}}},
  author = {Lu, Fred S. and Nguyen, Andrew and Link, Nick and Santillana, Mauricio},
  year = {2020},
  month = apr,
  abstract = {Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the week to week burden of COVID-19. Unfortunately, a lack of systematic testing across the United States (US) due to equipment shortages and varying testing strategies has hindered the usefulness of the available positive COVID-19 case counts. 
We introduce three complementary approaches aimed at estimating the prevalence of COVID-19 in each state in the US as well as in New York City. Instead of relying on an estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources.
Across our three approaches, there is a consistent conclusion that estimated state-level COVID-19 case counts usually vary from 10 to 100 times greater than the official positive test counts. Nationally, our lowest and highest estimates of COVID-19 cases in the US from March 1, 2020 to April 4, 2020 are 2.7 and 8.3 million (9 to 27 times greater). These estimates are to be compared to the cumulative confirmed cases of about 311,000 as of April 4th. Our approaches demonstrate the value of leveraging existing influenza-like-illness surveillance systems for measuring the burden of new diseases that share symptoms with influenza-like-illnesses. Our methods may prove useful in assessing the burden of COVID-19 in other countries with comparable influenza surveillance systems.},
  copyright = {open},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/U35MASVA/Lu et al. - 2020 - Estimating the Prevalence of COVID-19 in the Unite.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/PJ7CMPRJ/42660046.html},
  language = {en\_US},
  note = {Accepted: 2020-04-20T16:43:37Z}
}

@article{luForecastingFluActivity2020,
  title = {Forecasting {{Flu Activity}} in the {{United States}}: {{Benchmarking}} an {{Endemic}}-{{Epidemic Beta Model}}},
  shorttitle = {Forecasting {{Flu Activity}} in the {{United States}}},
  author = {Lu, Junyi and Meyer, Sebastian},
  year = {2020},
  month = feb,
  volume = {17},
  publisher = {{Multidisciplinary Digital Publishing Institute  (MDPI)}},
  doi = {10.3390/ijerph17041381},
  abstract = {Accurate prediction of flu activity enables health officials to plan disease prevention and allocate treatment resources. A promising forecasting approach is to adapt the well-established endemic-epidemic modeling framework to time series of infectious ...},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/2M3W6H5N/Lu and Meyer - 2020 - Forecasting Flu Activity in the United States Ben.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/YRZ5SP8B/PMC7068443.html},
  journal = {International Journal of Environmental Research and Public Health},
  language = {en},
  number = {4},
  pmid = {32098038}
}

@article{mirzaInfluenzaNegativeInfluenzaLikeIllness2020,
  title = {Influenza-{{Negative Influenza}}-{{Like Illness}} ({{fnILI}}) {{Z}}-{{Score}} as a {{Proxy}} for {{Incidence}} and {{Mortality}} of {{COVID}}-19},
  author = {Mirza, Fatima N. and Malik, Amyn A. and Omer, Saad B.},
  year = {2020},
  month = apr,
  pages = {2020.04.22.20075770},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.04.22.20075770},
  abstract = {{$<$}p{$>$}Though ideal for determining the burden of disease, SARS-CoV2 test shortages preclude its implementation as a robust surveillance system in the US. We correlated the use of the derivative influenza-negative influenza-like illness (fnILI) z-score from the CDC as a proxy for incident cases and disease-specific deaths. For every unit increase of fnILI z-score, the number of cases increased by 70.2 (95\%CI[5.1,135.3]) and number of deaths increased by 2.1 (95\%CI[1.0,3.2]). FnILI data may serve as an accurate outcome measurement to track the spread of the and allow for informed and timely decision-making on public health interventions.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/H89GL3PR/Mirza et al. - 2020 - Influenza-Negative Influenza-Like Illness (fnILI) .pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/RWKQPJ2Y/2020.04.22.html},
  journal = {medRxiv},
  language = {en}
}

@article{NeartermForecastsInfluenzalike2019,
  title = {Near-Term Forecasts of Influenza-like Illness: {{An}} Evaluation of Autoregressive Time Series Approaches},
  shorttitle = {Near-Term Forecasts of Influenza-like Illness},
  year = {2019},
  month = jun,
  volume = {27},
  pages = {41--51},
  publisher = {{Elsevier}},
  issn = {1755-4365},
  doi = {10.1016/j.epidem.2019.01.002},
  abstract = {Seasonal influenza in the United States is estimated to cause 9\textendash{}35 million illnesses annually, with resultant economic burden amounting to \$47-\$150 bi\ldots{}},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/ZZEZI5FR/2019 - Near-term forecasts of influenza-like illness An .pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/LMVLGNL2/S1755436518301336.html},
  journal = {Epidemics},
  language = {en}
}

@misc{NovelCoronavirusPatients,
  title = {A {{Novel Coronavirus}} from {{Patients}} with {{Pneumonia}} in {{China}}, 2019 | {{NEJM}}},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/87VX3R68/NEJMoa2001017.html},
  howpublished = {https://www.nejm.org/doi/10.1056/NEJMoa2001017}
}

@article{rothanEpidemiologyPathogenesisCoronavirus2020,
  title = {The Epidemiology and Pathogenesis of Coronavirus Disease ({{COVID}}-19) Outbreak},
  author = {Rothan, Hussin A. and Byrareddy, Siddappa N.},
  year = {2020},
  month = may,
  volume = {109},
  pages = {102433},
  issn = {0896-8411},
  doi = {10.1016/j.jaut.2020.102433},
  abstract = {Coronavirus disease (COVID-19) is caused by SARS-COV2 and represents the causative agent of a potentially fatal disease that is of great global public health concern. Based on the large number of infected people that were exposed to the wet animal market in Wuhan City, China, it is suggested that this is likely the zoonotic origin of COVID-19. Person-to-person transmission of COVID-19 infection led to the isolation of patients that were subsequently administered a variety of treatments. Extensive measures to reduce person-to-person transmission of COVID-19 have been implemented to control the current outbreak. Special attention and efforts to protect or reduce transmission should be applied in susceptible populations including children, health care providers, and elderly people. In this review, we highlights the symptoms, epidemiology, transmission, pathogenesis, phylogenetic analysis and future directions to control the spread of this fatal disease.},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/J4QVVBCN/Rothan and Byrareddy - 2020 - The epidemiology and pathogenesis of coronavirus d.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/ESYRC2YR/S0896841120300469.html},
  journal = {Journal of Autoimmunity},
  keywords = {Coronavirus,COVID-19,Pathogenesis,Pneumonia,Wuhan city},
  language = {en}
}

@article{SevereAcuteRespiratory2020,
  title = {Severe Acute Respiratory Syndrome Coronavirus 2 Isolate {{Wuhan}}-{{Hu}}-1, Complete Genome},
  year = {2020},
  month = mar,
  copyright = {Public domain},
  keywords = {insd,rna,Severe acute respiratory syndrome coronavirus 2},
  language = {en-US},
  lccn = {MN908947.3},
  note = {\{:itemType: dataset\}}
}

@article{spaederMultitieredTimeseriesModelling2012,
  title = {A Multi-Tiered Time-Series Modelling Approach to Forecasting Respiratory Syncytial Virus Incidence at the Local Level},
  author = {Spaeder, M. C. and Fackler, J. C.},
  year = {2012},
  month = apr,
  volume = {140},
  pages = {602--607},
  publisher = {{Cambridge University Press}},
  issn = {1469-4409, 0950-2688},
  doi = {10.1017/S0950268811001026},
  abstract = {Respiratory syncytial virus (RSV) is the most common cause of documented viral respiratory infections, and the leading cause of hospitalization, in young children. We performed a retrospective time-series analysis of all patients aged {$<$}18 years with laboratory-confirmed RSV within a network of multiple affiliated academic medical institutions. Forecasting models of weekly RSV incidence for the local community, inpatient paediatric hospital and paediatric intensive-care unit (PICU) were created. Ninety-five percent confidence intervals calculated around our models' 2-week forecasts were accurate to {$\pm$}9{$\cdot$}3, {$\pm$}7{$\cdot$}5 and {$\pm$}1{$\cdot$}5 cases/week for the local community, inpatient hospital and PICU, respectively. Our results suggest that time-series models may be useful tools in forecasting the burden of RSV infection at the local and institutional levels, helping communities and institutions to optimize distribution of resources based on the changing burden and severity of illness in their respective communities.},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/K9YAXILY/Spaeder and Fackler - 2012 - A multi-tiered time-series modelling approach to f.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/5VY4WGRW/core-reader.html},
  journal = {Epidemiology \& Infection},
  keywords = {Modelling,respiratory infections,respiratory syncytial virus},
  language = {en},
  number = {4}
}

@article{vandorpEmergenceGenomicDiversity2020,
  title = {Emergence of Genomic Diversity and Recurrent Mutations in {{SARS}}-{{CoV}}-2},
  author = {{van Dorp}, Lucy and Acman, Mislav and Richard, Damien and Shaw, Liam P. and Ford, Charlotte E. and Ormond, Louise and Owen, Christopher J. and Pang, Juanita and Tan, Cedric CS and Boshier, Florencia AT},
  year = {2020},
  pages = {104351},
  publisher = {{Elsevier}},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/GZ59X8ZX/S1567134820301829.html},
  journal = {Infection, Genetics and Evolution}
}

@misc{WHODirectorGeneralRemarks,
  title = {{{WHO Director}}-{{General}}'s Remarks at the Media Briefing on 2019-{{nCoV}} on 11 {{February}} 2020},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/9IKQSPFC/who-director-general-s-remarks-at-the-media-briefing-on-2019-ncov-on-11-february-2020.html},
  howpublished = {https://www.who.int/dg/speeches/detail/who-director-general-s-remarks-at-the-media-briefing-on-2019-ncov-on-11-february-2020},
  language = {en}
}

@misc{WHOWhatPandemic,
  title = {{{WHO}} | {{What}} Is a Pandemic?},
  publisher = {{World Health Organization}},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/DDI5UILJ/en.html},
  howpublished = {http://www.who.int/csr/disease/swineflu/frequently\_asked\_questions/pandemic/en/},
  journal = {WHO}
}

@article{wuNewCoronavirusAssociated2020,
  title = {A New Coronavirus Associated with Human Respiratory Disease in {{China}}},
  author = {Wu, Fan and Zhao, Su and Yu, Bin and Chen, Yan-Mei and Wang, Wen and Song, Zhi-Gang and Hu, Yi and Tao, Zhao-Wu and Tian, Jun-Hua and Pei, Yuan-Yuan and Yuan, Ming-Li and Zhang, Yu-Ling and Dai, Fa-Hui and Liu, Yi and Wang, Qi-Min and Zheng, Jiao-Jiao and Xu, Lin and Holmes, Edward C. and Zhang, Yong-Zhen},
  year = {2020},
  month = mar,
  volume = {579},
  pages = {265--269},
  publisher = {{Nature Publishing Group}},
  issn = {1476-4687},
  doi = {10.1038/s41586-020-2008-3},
  abstract = {Phylogenetic and metagenomic analyses of the complete viral genome of a new coronavirus from the family Coronaviridae reveal that the virus is closely related to a group of SARS-like coronaviruses found in bats in China.},
  copyright = {2020 The Author(s)},
  file = {/home/skynet2/snap/zotero-snap/common/Zotero/storage/CZ9YH8WN/Wu et al. - 2020 - A new coronavirus associated with human respirator.pdf;/home/skynet2/snap/zotero-snap/common/Zotero/storage/6LMMGP37/s41586-020-2008-3.html},
  journal = {Nature},
  language = {en},
  number = {7798}
}