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This page is to keep up to date with the latest literature on COVID19. Anyone is welcome to add new papers, particularly modelling papers or those that might inform modelling. For each paper please add a link and a couple of sentences summarising the paper. Since things are changing so fast, it's also helpful to put the date the paper was put up, rather than just the year. If you have come across papers that look interesting but haven't had time to read them, leave them in the list at the top so that others can read them and move them down with summarising sentences. You can also add new headings if your paper doesn't fit into the existing categories.

To save duplicated effort, if you start reading a paper from the "Papers to read" section, please change the label from "UNREAD" to "being read by xxx", before moving it down the page with your summary once you have finished.

Papers to read

Finding R0/Early spread

Current patterns of transmission

Estimating case numbers

Evaluating strategies

  • Quilty, Bill, et al. (LSHTM) "Effectiveness of airport screening at deteccting travellers infected with novel coronavirus (2019-nCoV)." The Lancet Global Health (06/02/2020).
    • Analysed airport screening, finding that screening at arrival would likely miss the vast majority of incoming infected travellers
  • Keeling, Matt J., T. Deirdre Hollingsworth, and Jonathan M. Read. "The Efficacy of Contact Tracing for the Containment of the 2019 Novel Coronavirus (COVID-19)." medRxiv (17/02/2020).
    • With contact tracing less than 1 in 5 cases will generate any subsequent untraced cases, but there is an average of 36.1 individuals (95th percentiles 0-182) traced per case.
  • Hellewell, Joel, et al. (LSHTM) "Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts." The Lancet Global Health (28/02/2020).
    • Looked at feasibility of controlling COVID through continued contact tracing, estimating that if R0=2.5, then to control the majority of outbreaks, more than 70% of contacts would need to be traced.
  • Clifford, Sam, et al. (LSHTM) "Interventions targeting air travellers early in the pandemic may delay local outbreak of SARS-COV-2" medRxiv (28/02/2020).
    • Syndromic screening and traveller sensitisation in combination could delay outbreaks in yet unaffected countries and support local containment efforts, but only if infected traveller numbers are very low.
  • Prem, Kiesha, et al. (LSHTM) "The effect of control strategies that reducce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China" medRxiv (12/03/2020).
    • Potential impact of social distancing intervention measures in Wuhan, and what might happen when the interventions stop.
    • Changes in mixing patterns may have contributed to reducing the number of infections in mid-2020 by 92% (interquartile range: 66-97%). There are benefits to sustaining these measures until April in terms of reducing the height of the peak, overall epidemic size in mid-2020 and probability that a second peak may occur after return to work.
  • Cowling, Benjamin J, et al. "Impact assessment of non-pharmaceutical interventions against COVID-19 and influenza in Hong Kong: an observational study" medRxiv (16/03/2020)
    • Measuring the impact of social distancing in Hong Kong through two telephone surveys, comparing to estiamtes of the daily effective reproduction number for COVID and influenza A.
    • Influenza reduced: 44% reduction in transmissibility in the community and 33% reduction in transmissibility based on paediatric hospitalisation rates. in the earlier survey 74.5% of adult population wore masks when going out, rising to 97.5% and 61.3% avoided going to crowded places, rising to 90.2%.
  • Jombart, Thibaut, et al. (LSHTM) "Forecasting critical care bed requirements for COVID-19 patients in England". CMMID repository (22/03/2020).
    • Estimating critical care bed needs up to 31st March 2020.
    • "These results imply that unless transmissibility is strongly reduced in the coming days, ICU/HDU capacity for COVID-19 in England (in January 2020: 4,123 critical beds for adults, 312 in paediatrics) may be challenged by the end of March, without even considering capacity requirements for other conditions."
  • Ferguson et al. (Imperial group) "Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand" (16/03/2020)
    • The modelling provided directly to the SAGE group (other evidence can be found here), seems to have influenced decisions a lot
    • Uses a detailed IBM originally made for flu, changing some parameters for COVID (I think - they don't put a lot of actual detail in). Considers whether we want to "mitigate" (reduce R0, but not below 1) or "suppress" (reduce R0 below 1) and concludes that all the mitigate strategies lead to running out of critical care beds. This model finds that only by using very severe interventions can we stay below the critical care beds required, and finishes with the suggestion that we could oscillate between more and less severe cases, based on something like the number of critical care beds available.
  • Kissler, Stephen, et al. "Social distancing strategies for curbing the COVID-19 epidemic" medRxiv(24/03/2020)
    • Assessed that one-time interventions will be insufficient to maintain COVID-19 prevalence within the critical care capacity of the United States. Intermittent distancing measures may be the only way to avoid overwhelming critical care capacity while building population immunity.
    • Model is an adapted SEIR model with three tracks, each of differing disease severity (asymptomatic & mild; hospitalised; critical care).

  • Peak, Corey M., et al. "Modeling the Comparative Impact of Individual Quarantine vs. Active Monitoring of Contacts for the Mitigation of COVID-19." medRxiv (08/03/2020).
    • Using a branching process model. Need to quarantine at least 3/4s of infected contacts.
  • Ferretti, Luca, et al.( Christophe Fraser group) "Quantifying dynamics of SARS-CoV-2 transmission suggests that epidemic control and avoidance is feasible through instantaneous digital contact tracing". medRxiv (12/03/2020)
    • "We show that first-degree instantaneous contact tracing, informing users when they can move safely or when to seek medical help and avoid vulnerable individuals, has the potential to stop the spread of the epidemic if used by a sufficiently large number of people with reasonable fidelity."
  • Rossberg and Knell "How will this continue? Modelling interactions between the COVID-19 pandemic and policy responses" (medRxiv, 01/04/2020).
    • A “management strategy evaluation” approach. Uses a matrix population model to simulate disease spread, takes key infection parameter estimates from Ferguson 2020 paper (e.g. 4.6 days to go from exposure to infectiousness; 20% assumed to be asymptomatic; 30% of hospitalized cases require critical care etc).
    • Assumes that policy interventions reduce infection rates by d. d changes according to various rules (i.e. imposition of a disease control policy if the case numbers exceed a certain threshold/relaxation of policy when case numbers below a certain threshold). Variation in policy compliance is also incorporated in d.
    • Key conclusions: “To be effective [at minimising overall mortality], the frequency of policy revisions should be comparable to the doubling time of the outbreak”, and “any decline of the population’s compliance through time has a comparatively weak effect on cumulative mortality” (but this is assuming that the government keeps changing policies, and this can make up for the declining compliance).

Evaluating mixing

  • Pepe, Emanuele et al. "COVID-19 outbreak response: first assessment of mobility changes in Italy following lockdown" (updating daily, accessed 25/03/2020) and on medRxiv
    • Using mobility data from Cuebiq, which provides anonymous location data from smartphones. Various measurements of mobility between regions, and a loose estimate of networks of contacts by saying a contact is being within 50m of another person over a 1 hour period. "In some provinces (Milano, Piacenza, Rimini, and others), the average network degree has dropped about 30% with respect to the pre-outbreak period." In the summary they say "The restrictions in mobility, closure of public spaces and the enhancement of smart/remote working, led to an average reduction of potential encounters of 8% during week 2 and almost 19% during week 3" but I can't find this in the actual results.
  • Kraemer, Moritz et al. ""The effect of human mobility and control measures on the COVID-19 epidemic in China"(25/03/2020)
    • "use real-time mobility data, crowdsourced line-list data of cases with reported travel history, and timelines of reporting changes to identify early shifts in the epidemiological dynamics of the COVID-19 epidemic in China, from an epidemic driven by frequent importations to local transmission"
    • "We find that the magnitude of the early epidemic (total number of cases until February 10, 2020) outside of Wuhan is remarkably well predicted by the volume of human movement out of Wuhan alone"
    • Tests the contribution of the epidemic in Wuhan to seeding epidemics elsewhere in China through use of a naïve COVID-19 GLM model of daily case counts.
  • Chris Jarvis, Kevin van Zandvoort et al. "Impact of physical distance measures on transmission in the UK" medRxiv (03/04/2020)
    • Survey of contact patterns of representative sample of UK adults (18+) post-lockdown. Found a 73% reduction in the average daily number of contacts observed per participant (from 10.8 to 2.9).

Hospital capacity

  • Verhagen et al (Oxford and Denmark) "Mapping hospital demand: demographics, spatial variation, and the risk of "hospital deserts" during COVID-19 in England and Wales" OSF preprints (21/03/2020)
    • They take the hospitalisation estimates from the Ferguson paper and put it together with age distributions in the various regions of England and Wales, and the number of beds and critical care beds available. It's unclear to me what number's they're actually using from the Ferguson paper - the peak hospitalisation, or total over the epidemic. It should also depend how long people are hospitalised for.
    • The result is that London is much better off than more coastal regions, because there are generally more beds and a younger population.

Biological parameters

Sources of data