1887
Research Open Access
Like 0

Abstract

Background

Population-level mathematical models of outbreaks typically assume that disease transmission is not impacted by population density (‘frequency-dependent’) or that it increases linearly with density (‘density-dependent’).

Aim

We sought evidence for the role of population density in SARS-CoV-2 transmission.

Methods

Using COVID-19-associated mortality data from England, we fitted multiple functional forms linking density with transmission. We projected forwards beyond lockdown to ascertain the consequences of different functional forms on infection resurgence.

Results

COVID-19-associated mortality data from England show evidence of increasing with population density until a saturating level, after adjusting for local age distribution, deprivation, proportion of ethnic minority population and proportion of key workers among the working population. Projections from a mathematical model that accounts for this observation deviate markedly from the current status quo for SARS-CoV-2 models which either assume linearity between density and transmission (30% of models) or no relationship at all (70%). Respectively, these classical model structures over- and underestimate the delay in infection resurgence following the release of lockdown.

Conclusion

Identifying saturation points for given populations and including transmission terms that account for this feature will improve model accuracy and utility for the current and future pandemics.

Loading

Article metrics loading...

/content/10.2807/1560-7917.ES.2021.26.49.2001809
2021-12-09
2022-06-30
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2021.26.49.2001809
Loading
Loading full text...

Full text loading...

/deliver/fulltext/eurosurveillance/26/49/eurosurv-26-49-4.html?itemId=/content/10.2807/1560-7917.ES.2021.26.49.2001809&mimeType=html&fmt=ahah

References

  1. Anderson RM, May RM, Regulatory Processes. Regulation and stability of host parasite population interactions. I. Regulatory processes. J Anim Ecol. 1978;47(1):219-49.  https://doi.org/10.2307/3933 
  2. May RM, Anderson RM. Regulation and stability of host parasite population interactions. II. Destabilizing processes. J Anim Ecol. 1978;47(1):249-68.  https://doi.org/10.2307/3934 
  3. Dalziel BD, Kissler S, Gog JR, Viboud C, Bjørnstad ON, Metcalf CJE, et al. Urbanization and humidity shape the intensity of influenza epidemics in U.S. cities. Science. 2018;362(6410):75-9.  https://doi.org/10.1126/science.aat6030  PMID: 30287659 
  4. Kulu H, Dorey P. Infection rates from Covid-19 in Great Britain by geographical units: A model-based estimation from mortality data. Health Place. 2021;67:102460.  https://doi.org/10.1016/j.healthplace.2020.102460  PMID: 33418438 
  5. Rajan KB, Dhana K, Barnes LL, Aggarwal NT, Evans LE, McAninch EA, et al. Strong effects of population density and social characteristics on distribution of COVID-19 infections in the United States. medRxiv. 2020.2005.2008.20073239. Preprint. https://doi.org/10.1101/2020.05.08.20073239
  6. Sy KTL, White LF, Nichols BE. Population density and basic reproductive number of COVID-19 across United States counties. medRxiv. 2020.2006.2012.20130021. Preprint. https://doi.org/10.1371/journal.pone.0249271
  7. Rocklöv J, Sjödin H, Wilder-Smith A. COVID-19 outbreak on the Diamond Princess cruise ship: estimating the epidemic potential and effectiveness of public health countermeasures. J Travel Med. 2020;27(3):taaa030.  https://doi.org/10.1093/jtm/taaa030  PMID: 32109273 
  8. Pequeno P, Mendel B, Rosa C, Bosholn M, Souza JL, Baccaro F, et al. Air transportation, population density and temperature predict the spread of COVID-19 in Brazil. PeerJ. 2020;8:e9322.  https://doi.org/10.7717/peerj.9322  PMID: 32547889 
  9. Prats-Uribe A, Paredes R, Prieto-Alhambra D. Ethnicity, comorbidity, socioeconomic status, and their associations with COVID-19 infection in England: a cohort analysis of UK Biobank data. medRxiv. 2020.2005.2006.20092676. Preprint. https://doi.org/10.1101/2020.05.06.20092676
  10. Russell TW, Hellewell J, Jarvis CI, van Zandvoort K, Abbott S, Ratnayake R, et al. Estimating the infection and case fatality ratio for coronavirus disease (COVID-19) using age-adjusted data from the outbreak on the Diamond Princess cruise ship, February 2020. Euro Surveill. 2020;25(12):2000256.  https://doi.org/10.2807/1560-7917.ES.2020.25.12.2000256  PMID: 32234121 
  11. The Lancet. The plight of essential workers during the COVID-19 pandemic. Lancet. 2020;395(10237):1587.  https://doi.org/10.1016/S0140-6736(20)31200-9  PMID: 32446399 
  12. Chaudhry R, Dranitsaris G, Mubashir T, Bartoszko J, Riazi S. A country level analysis measuring the impact of government actions, country preparedness and socioeconomic factors on COVID-19 mortality and related health outcomes. EClinicalMedicine. 2020;25:100464.  https://doi.org/10.1016/j.eclinm.2020.100464  PMID: 32838237 
  13. Cheng VC-C, Wong SC, Chuang VW, So SY, Chen JH, Sridhar S, et al. The role of community-wide wearing of face mask for control of coronavirus disease 2019 (COVID-19) epidemic due to SARS-CoV-2. J Infect. 2020;81(1):107-14.  https://doi.org/10.1016/j.jinf.2020.04.024  PMID: 32335167 
  14. Office for National Statistics (ONS). Estimates of the population for the UK, England and Wales, Scotland and Northern Ireland. Newport: ONS; 2020. Available from: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/populationestimatesforukenglandandwalesscotlandandnorthernireland
  15. Ministry of Housing Communities and Local Government. National statistics. Local authority district summaries. English Indices of deprivation 2019. London: Ministry of Housing Communities and Local Government; 2019. Available from: https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019
  16. Office for National Statistics (ONS). DC2101EW - Ethnic group by sex by age. NOMIS Official Labour Market Statistics. Newport: ONS; 2011. Available from: https://www.nomisweb.co.uk/census/2011/dc2101ew
  17. Office for National Statistics (ONS). Population profiles for local authorities in England – Key workers. Newport: ONS; 2020. Available from: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/articles/populationprofilesforlocalauthoritiesinengland/2020-12-14
  18. Goodrich B, Gabry J, Ali I, Brilleman S. rstanarm: Bayesian applied regression modeling via Stan. R package version 2.21.1; 2020. Available from: https://mc-stan.org/rstanarm
  19. Vehtari A, Gelman A, Gabry J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat Comput. 2017;27(5):1413-32.  https://doi.org/10.1007/s11222-016-9696-4 
  20. Heesterbeek JAP, Metz JAJ. The saturating contact rate in marriage- and epidemic models. J Math Biol. 1993;31(5):529-39.  https://doi.org/10.1007/BF00173891  PMID: 8336087 
  21. Office for National Statistics (ONS). Deaths involving COVID-19 by local area and deprivation. 1 March and 31 July 2020 edition of this dataset. Newport; ONS; 2020. Available from: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/deathsinvolvingcovid19bylocalareaanddeprivation
  22. Metcalf CJE, Morris DH, Park SW. Mathematical models to guide pandemic response. Science. 2020;369(6502):368-9.  https://doi.org/10.1126/science.abd1668  PMID: 32703861 
  23. Bartlett MS. Measles periodicity and community size. J R Stat Soc [Ser A]. 1957;120(1):48-70.  https://doi.org/10.2307/2342553 
  24. Lloyd-Smith JO, Cross PC, Briggs CJ, Daugherty M, Getz WM, Latto J, et al. Should we expect population thresholds for wildlife disease? Trends Ecol Evol. 2005;20(9):511-9.  https://doi.org/10.1016/j.tree.2005.07.004  PMID: 16701428 
  25. Donnelly CA, Woodroffe R, Cox DR, Bourne J, Gettinby G, Le Fevre AM, et al. Impact of localized badger culling on tuberculosis incidence in British cattle. Nature. 2003;426(6968):834-7.  https://doi.org/10.1038/nature02192  PMID: 14634671 
  26. Drew DA, Nguyen LH, Steves CJ, Menni C, Freydin M, Varsavsky T, et al. Rapid implementation of mobile technology for real-time epidemiology of COVID-19. Science. 2020;368(6497):1362-7.  https://doi.org/10.1126/science.abc0473  PMID: 32371477 
/content/10.2807/1560-7917.ES.2021.26.49.2001809
Loading

Data & Media loading...

Supplementary data

Submit comment
Close
Comment moderation successfully completed
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error