COVID-19 research is moving as fast as the pandemic itself. We are now rewriting the rulebook for much of the way we do things in our daily lives. For scientists, we have always relied on the peer-review process to keep work as accurate and high-quality as possible. However, in the current environment the process is too slow.

My group is writing important analyses of COVID-19, with an Australian perspective, which may be very valuable right now, but have limited value in one month, after peer review is completed. We have also read about the frustration with the government “black-boxing” its models. We at AITHM have therefore decided to make all our preprints publically available on this site (see below).

Emma McBryde
MB BS, MBios, FRACP, PhD
Professor of Infectious Diseases Modelling and Epidemiology
Australian Institute of Tropical Health and Medicine
James Cook University, Townsville, Australia

Reports:

Webinars:

Change in outbreak epicentre and its impact on the important risks of COVID-19 progression: A modelling study

Authors: Oyelola A Adegboye^a,b, Adeshina I Adekunle^b, Anton Pak^b, Ezra Gayawanc, Denis HY. Leung^d, Diana P. Rojas^b, Faiz Elfaki^e, Emma S. McBryde^b and Damon P. Eisen^b

^a Public Health & Tropical Medicine, College of Public Health, Medical and Veterinary Sciences, James Cook University, Australia

^b Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia

^c Biostatistics and Spatial Statistics Research Group, Department of Statistics, Federal University of Technology, Akure, Nigeria

^d School of Economics, Singapore Management University, Singapore, Singapore

^e Department of Mathematics, Statistics and Physics, Qatar University, Doha, Qatar

Abstract: 

Background

The outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that was first detected in the city of Wuhan, China has now spread to every inhabitable continent, but now the attention has shifted from China to other epicentres. This study explored early assessment of the influence of spatial proximities and travel patterns from Italy on the further spread of SARS-CoV-2 worldwide.

Methods

Using data on the number of confirmed cases of COVID-19 and air travel data between countries, we applied a stochastic meta-population model to estimate the global spread of COVID-19. Pearson's correlation, semi-variogram, and Moran's Index were used to examine the association and spatial autocorrelation between the number of COVID-19 cases and travel influx (and arrival time) from the source country.

Results

We found significant negative association between disease arrival time and number of cases imported from Italy (r = −0.43, p = 0.004) and significant positive association between the number of COVID-19 cases and daily travel influx from Italy (r = 0.39, p = 0.011). Using bivariate Moran's Index analysis, we found evidence of spatial interaction between COVID-19 cases and travel influx (Moran's I = 0.340). Asia-Pacific region is at higher/extreme risk of disease importation from the Chinese epicentre, whereas the rest of Europe, South-America and Africa are more at risk from the Italian epicentre.

Conclusion

We showed that as the epicentre changes, the dynamics of SARS-CoV-2 spread change to reflect spatial proximities.

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Time for a clear national COVID-19 strategy

Published online by MJA: 1st February 2021

Authors: James M Trauer^1,2, Ben J Marais^3,4, Romain Ragonnet¹, Julian Savulescu^5,6 and Emma McBryde⁷

¹ Monash University, Melbourne, VIC

² Alfred Hospital, Melbourne. VIC

³ Marie Bashir Institute for infectious Diseases and Biosecurity, Sydney, NSW

⁴ Children’s Hospital at Westmead, Sydney, NSW

⁵ University of Oxford, Oxford, United Kingdom

⁶ University of Melbourne, Melbourne, VIC

⁷ Australian Institute of Tropical Health and Medicine, Townsville QLD

Abstract:

Pandemic responses across the world have been highly reactive. However, there remain only three strategic options to managing coronavirus disease 2019 (COVID‐19): mitigation, suppression and elimination (Box).2 With the promise of efficacious new vaccines, mitigation is appropriately not considered as part of Australia’s national strategy. However, our stated goal of achieving “no community transmission” remains poorly defined and risks missing important distinctions between elimination and suppression.

Effective elimination is dependent both on getting to zero local cases and then staying there, with any new transmission chains immediately halted. All jurisdictions of Australia have now achieved elimination over significant periods, even without articulating this as their strategy. By comparison to suppression, greater relaxation of restrictions may well be allowable under an elimination approach if vigilance is maintained, as New Zealand has demonstrated.4 Although the challenges of ensuring quarantine of returning travellers are well recognised, this is an essential aspect of maintaining elimination and increases in importance as distancing restrictions are eased.

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Does high public trust amplifly compliance with stringent covid-19 government health guidelines? a multi-country analysis using data from 102,627 individuals

Published online by Dove Press: 26th Jan 2021

Authors: Anton Pak¹, Emma McBryde¹ and Oyelola Adegboye^1,2

¹Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD, Australia

²Public Health & Tropical Medicine, College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, QLD, Australia

Abstract:

Purpose: To examine how public trust mediates the people's adherence to levels of stringent government health policies and to establish if these effects vary across the political regimes.

Methods: This study utilizes data from two large-scale surveys: the global behaviors and perceptions at the onset of COVID-19 pandemic and the Oxford COVID-19 Government Response Tracker (OxCGRT). Linear regression models were used to estimate the effects of public trust and strictness of restriction measures on people's compliance level. The model accounted for individual and daily variations in country-level stringency of preventative measures. Differences in the dynamics between public trust, the stringent level of government health guidelines and policy compliance were also examined among countries based on political regimes.

Results: We find strong evidence of the increase in compliance due to the imposition of stricter government restrictions. The examination of heterogeneous effects suggests that high public trust in government and the perception of its truthfulness double the impact of policy restrictions on public compliance. Among political regimes, higher levels of public trust significantly increase the predicted compliance as stringency level rises in authoritarian and democratic countries.

Conclusion: This study highlights the importance of public trust in government and its institutions during public health emergencies such as the COVID-19 pandemic. Our results are relevant and help understand why governments need to address the risks of non-compliance among low trusting individuals to achieve the success of the containment policies.

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The spatio-temporal epidemic dynamics of COVID-19 outbreak in Africa

Published online by Cambridge University Press: 2nd September 2020

Authors: Ezra Gayawan^1,2*, Olushina O Awe^3,4, Bamidele M Oseni¹, Ikemefuna C. Uzochukwu⁵, Adeshina Adekunle⁶, Gbemisola Samuel⁷, Damon P Eisen^6,8 and Oyelola A Adegboye⁶

¹ Department of Statistics, Federal University of Technology, Akure, Nigeria
² Population Study Center (NEPO), Universidade Estadual de Campinas, Campinas, Brazil
³ Department of Mathematics, Anchor University, Lagos, Nigeria
⁴ Institute of Mathematics and Statistics, Federal University of Bahia (UFBA), Salvador, Brazil
⁵ Faculty of Pharmaceutical Sciences, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
⁶ Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia
⁷ Department of Demography and Social Statistics, Covenant University, Ota, Nigeria
⁸ College of Medicine and Dentistry, James Cook University, Townsville, Australia

* Corresponding author: egayawan@futa.edu.ng

Abstract:

Corona virus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first detected in the city of Wuhan, China in December 2019. Although, the disease appeared in Africa later than other regions, it has now spread to virtually all countries on the continent. We provide early spatio-temporal dynamics of COVID-19 within the first 62 days of the disease’s appearance on the African continent. We used a two-parameter hurdle Poisson model to simultaneously analyze the zero counts and the frequency of occurrence. We investigate the effects of important healthcare capacities including hospital beds and number of medical doctors in the different countries. The results show that cases of the pandemic vary geographically across Africa with notably high incidence in neighboring countries particularly in West and North Africa. The burden of the disease (per 100,000) mostly impacted Djibouti, Tunisia, Morocco and Algeria. Temporally, during the first 4 weeks, the burden was highest in Senegal, Egypt and Mauritania, but by mid-April it shifted to Somalia, Chad, Guinea, Tanzania, Gabon, Sudan, and Zimbabwe. Currently, Namibia, Angola, South Sudan, Burundi and Uganda have the least burden. These findings could be useful in guiding epidemiological interventions and the allocation of scarce resources based on heterogeneity of the disease patterns.

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Key questions for Modelling COVID-19 Exit Strategies

21st July 2020

Authors: Robin N Thompson, T Deirdre Hollingsworth, Valerie Isham, Daniel Arribas-Bel, Ben Ashby, Tom Britton, Peter Challenor, Lauren H K Chappell, Hannah Clapham, Nik J Cunniffe, A Philip Dawid, Christl A Donnelly, Rosalind M Eggo, Sebastian Funk, Nigel Gilbert, Julia R Gog, Paul Glendinning, William S Hart, Hans Heesterbeek, Thomas House, Matt Keeling, Istvan Z Kiss, Mirjam E Kretzschmar, Alun L Lloyd, Emma S McBryde, James M McCaw, Joel C Miller, Trevelyan J McKinley, Martina Morris, Philip D O'Neill, Carl A B Pearson, Kris V Parag, Lorenzo Pellis, Juliet R C Pulliam, Joshua V Ross, Michael J Tidesley, Gianpaolo Scalia Tomba, Bernard W Silverman, Claudio J Struchiner, Pieter Trapman, Cerian R Webb, Denis Mollison, Olivier Restif

Abstract:

Combinations of intense non-pharmaceutical interventions (“lockdowns”) were introduced in countries worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement lockdown exit strategies that allow restrictions to be relaxed while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute “Models for an exit strategy” workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, will allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. The roadmap requires a global collaborative effort from the scientific community and policy-makers, and is made up of three parts: i) improve estimation of key epidemiological parameters; ii) understand sources of heterogeneity in populations; iii) focus on requirements for data collection, particularly in Low-to-Middle-Income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

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Superspreaders, asymptomatics and COVID-19 elimination

MJA published online: 3rd July 2020

Authors: David Kault¹

¹ James Cook University, Australia 

There are features of  covid-19 that would make us expect that it would be difficult to eliminate. It is more infectious than many diseases and there are silent or asymptomatic cases who can pass on the virus. However, despite these features, there is another aspect of this disease that makes elimination easier than would otherwise be anticipated. Most infections are due to relatively rare superspreaders. Therefore when the disease has been reduced to very small numbers, those remaining may well not include any superspreaders and so the disease is likely to die out. This paper gives results from using a combination of advanced algebra and computer simulation to estimate how long it will take before there are no silent cases left and the disease will have been eliminated. Elimination will have occurred with a probability of 99.6%, if there is no new diagnosis of a symptomatic case for 5 or 6 weeks.

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Modelling insights into the COVID-19 pandemic

ScienceDirect published online: 30th June 2020

Authors: Michael T Meehan^a, Diana P Rojas^b, Adeshina I Adekunle^a, Oyelola A Adegboye^a, Jamie M Caldwell^c, Evelyn Turek^d, Bridget M Williams^d, Ben J Marais^e, James M Trauer^d, Emma S McBryde^a

^a Australian Institute of Tropical Health and Medicine, James Cook University, Australia

^b College of Public Health, Medical and Veterinary Sciences, James Cook University, Australia

^c Department of Biology, University of Hawaii at Manoa, Australia

^d Epidemiological Modelling Unit, School of Public Health and Preventive Medicine, Monash University, Australia

^e The Marie Bashir Institute for Infectious Diseases and Biosecurity (MBI), University of Sydney, Sydney, Australia

Abstract:

Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that was declared a pandemic by the World Health Organization on 11th March, 2020. Response to this ongoing pandemic requires extensive collaboration across the scientific community in an attempt to contain its impact and limit further transmission. Mathematical modelling has been at the forefront of these response efforts by: (1) providing initial estimates of the SARS-CoV-2 reproduction rate, R₀ (of approximately 2–3); (2) updating these estimates following the implementation of various interventions (with significantly reduced, often sub-critical, transmission rates); (3) assessing the potential for global spread before significant case numbers had been reported internationally; and (4) quantifying the expected disease severity and burden of COVID-19, indicating that the likely true infection rate is often orders of magnitude greater than estimates based on confirmed case counts alone. In this review, we highlight the critical role played by mathematical modelling to understand COVID-19 thus far, the challenges posed by data availability and uncertainty, and the continuing utility of modelling-based approaches to guide decision making and inform the public health response.

†Unless otherwise stated, all bracketed error margins correspond to the 95% credible interval (CrI) for reported estimates.

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Role of modelling in COVID-19 policy development

ScienceDirect published online: 18th June 2020

Authors: Emma S McBryde^a, Michael T Meehan^a, Oyelola A Adegboye^a, Adeshina I Adekunle^a, Jamie M Caldwell^b, Anton Pak^a, Diana P Rojas^c, Bridget M Williams^d, James M Trauer^d

^a Australian Institute of Tropical Health and Medicine, James Cook University, Australia

^b Centre of Excellence for Coral Reef Studies, James Cook University, Australia

^c Department of Biology, University of Hawaii at Manoa, United States of America

^d Epidemiological Modelling Unit, School of Public Health and Preventive Medicine, Monash University, Australia

Abstract:

Abstract

Models have played an important role in policy development to address the COVID-19 outbreak from its emergence in China to the current global pandemic. Early projections of international spread influenced travel restrictions and border closures. Model projections based on the virus’s infectiousness demonstrated its pandemic potential, which guided the global response to and prepared countries for increases in hospitalisations and deaths. Tracking the impact of distancing and movement policies and behaviour changes has been critical in evaluating these decisions. Models have provided insights into the epidemiological differences between higher and lower income countries, as well as vulnerable population groups within countries to help design fit-for-purpose policies. Economic evaluation and policies have combined epidemic models and traditional economic models to address the economic consequences of COVID-19, which have informed policy calls for easing restrictions. Social contact and mobility models have allowed evaluation of the pathways to safely relax mobility restrictions and distancing measures. Finally, models can consider future end-game scenarios, including how suppression can be achieved and the impact of different vaccination strategies.

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Is Nigeria really on top of COVID-19? Message from effective reproduction number

20th May 2020

Cambridge University Press published online: 5th August 2020

Authors: Adeshina I Adekunle^1,2, Oyelola Adegboye¹, Ezra Gayawan³, Emma McBryde¹

¹ Australia Institute of Tropical Health and Medicine, James Cook University, Townsville
² Decision and Modelling Science, Victoria University, Melbourne
³ Biostatistics and Spatial Statistics Research Group, Department of Statistics, Federal University of Technology, Akure.

Abstract:

Following the importation of Covid-19 into Nigeria on the 27 February 2020 and then the outbreak, the question is: how do we anticipate the progression of the ongoing epidemics following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population. In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of Covid-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R_0). This also enables us to estimate the daily transmission rate (β) that determines the effect of social distancing. We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on Covid-19 symptomatic and death cases from 27 February 2020 and 7 May 2020. In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 1.78. Most importantly, the R(t) is strictly greater than one from April 13 till 7 May 2020. The R_0 is estimated to be 2.42 with credible interval: (2.37, 2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below one. Also, the estimated fractional reported symptomatic cases are between 10 to 50%. Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

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Stepping out of lockdown should start with school re-openings while maintaining distancing measures. Insights from mixing matrices and mathematical models

13th May 2020

Authors: Emma S McBryde¹, James M Trauer², Adeshina Adekunle¹, Romain Ragonnet², Michael T Meehan¹

¹ Australian Tropical Health and Medicine, James Cook University
² Monash University

Abstract:

Australia is one of a few countries which has managed to control its first COVID-19 epidemic. Australia is seeing less than 20 new cases per day in May 2020. This is a positive outcome but makes estimation of current effective reproduction numbers difficult. Australia, like much of the world is poised to step out of lockdown and looking at which measures to relax first.
We use age-based contact matrices, calibrated to Chinese data on reproduction numbers and difference in infectiousness and susceptibility of children to generate next generation matrices (NGMs) for Australia. These matrices have a spectral radius of 2.49, which is hence our estimated basic reproduction number for Australia. The effective reproduction number (Reff) for Australia during the April/May lockdown period is estimated by other means to be around 0.8. We simulate the impact of lockdown on the NGM by first applying observations through Google Mobility Report for Australia and school attendances. Applying macro-distancing to the NGM leads to a spectral radius of 1.76. We estimate that the further reduction of the reproduction number to current levels of Reff = 0.8 is achieved by a micro-distancing factor of 0.26. That is, in a given location, people are 26% as likely as usual to have an effective contact with another person.
We apply both macro and micro-distancing to the NGMs to examine the impact of different exit strategies. We find that reopening schools is estimated to reduce Reff from 0.8 to 0.78. This is because increase in school contact is offset by decrease in home contact. The NGMs all estimate that adults aged 30-50 are responsible for the majority of transmission. We also find that micro-distancing is critically important to maintain Reff <1. There is considerable uncertainty in these estimates and a sensitivity and uncertainty analysis is presented.

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Supporting online materials

Developing next generation matrices from existing data

13th May 2020

The estimates of transmission rates in each sector (i.e., home, school, work and other locations) and the overall reproduction number are derived from baseline estimates of the daily, age-specific contact rates between individuals of different age groups. These contact rates are provided by the analysis in Prem et al.(1) where data from population-based contact diaries in eight European countries were projected to generate contact intensities for 144 other countries using Bayesian modelling techniques. The inferred values 𝑐𝑎𝑎′ give the number of (pre-COVID-19) typical daily contacts an individual of age 𝑎′ makes with an individual of age 𝑎. In the dataset, age bands are separated into 5 year age groups and contacts are further divided into four locations: work, home, school and other.

To estimate the transmission capacity associated with these contacts we convert the contact intensity matrices to next-generation matrices, 𝐾, whose elements, 𝑘𝑎𝑎′, give the number of new infections of age 𝑎 generated by individuals of age 𝑎′. As a first step, we compute an unscaled next-generation matrix 𝐾̅ by weighting the elements of the contact matrix 𝑐𝑎𝑎′ by the age-dependent relative susceptibility (𝜎𝑎) and infectivity (𝛽𝑎) of individuals in the population and the distribution of susceptible (𝑠𝑎) and total (𝑛𝑎) individuals in each age group. In particular, the elements, 𝑘̅
𝑎𝑎′, of the unscaled next-generation matrix (NGM), 𝐾̅, are given by

𝑘̅𝑎𝑎′=𝜎𝑎𝑠𝑎𝑐𝑎𝑎′𝛽𝑎′𝑛𝑎′.

Here 𝜎𝑎 is the relative susceptibility to infection for an individual in age group 𝑎 and 𝛽𝑎 is their corresponding transmissibility once infected. Since the population is entirely susceptible upon first introduction of the infection such that 𝑠𝑎=𝑛𝑎.

For symmetry, we assume that the age-dependent susceptibility and transmissibility profiles are equal equivalent, i.e., 𝜎𝑎=𝛽𝑎, and are given by the following parametric equation:

𝜎𝑎=1−𝜎rel2tanh(𝑏(𝑎−𝑐))+1+𝜎rel2

where 𝜎rel is approximately equal to the relative susceptibility between individuals in the youngest (<5) and those in the oldest (>80) age groups. In the following analysis we assume baseline values of 𝜎min = 0.1, 𝑏 = 0.3 and 𝑐 = 27.

We choose values to match the proportion of each age group infected in China (the country used to calibrate the model) and then applied the calibrated values to Australian mixing matrices.

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Early transmission dynamics of novel coronavirus (covid-19) in nigeria

Published by MDPI online: 28th April 2020

Authors: Oyelola Adegboye¹, Adeshina Adekunle¹, Ezra Gayawan²

¹ Australian Institute of Tropical Health and Medicine, James Cook University, Townsville QLD Australia

² Biostatistics and Spatial Statistics Research Group, Department of Statistics, Federal University of Technology, Akure 340271, Nigeria

Employee presenteeism and occupational acquisition of COVID-19

MJA published preprint online: 7th May 2020

MJA published online: 28th June 2020

Author: Damon Eisen

Abstract:

Presenteeism, where SARS-CoV-2 infected workers attend work while symptomatic, contributes to occupational acquisition of COVID-19. This is documented to have occurred in the North West Regional Hospital Outbreak among Tasmanian Health Care workers. It is also likely to be present among a newly recognised Melbourne abattoir outbreak. Infection prevention practices must account for presenteeism.

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Early analysis of the Australian COVID-19 epidemic

30th April 2020

Authors: David J Price^1,2,†, Freya M Shearer^1,†, Michael T Meehan³, Emma McBryde³, Robert Moss¹, Nick Golding⁴, Eamon J Conway², Peter Dawson⁵, Deborah Cromer^6,7, James Wood⁸, Sam Abbott⁹, Jodie McVernon^1,2,10, James M McCaw^1,2,11

¹ Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
² Victorian Infectious Diseases Reference Laboratory Epidemiology Unit at The Peter Doherty Institute for Infection and Immunity, The University of Melbourne and Royal Melbourne Hospital, Melbourne, Australia
³ Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia
⁴ Telethon Kids Institute and Curtin University, Perth, Australia
⁵ Defence Science and Technology, Department of Defence, Australia
⁶ Kirby Institute for Infection and Immunity, University of New South Wales, Sydney, Australia
⁷ School of Mathematics and Statistics, University of New South Wales, Sydney, Australia
⁸ School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia
⁹ Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
¹⁰ Infection and Immunity Theme, Murdoch Children’s Research Institute, The Royal Children’s Hospital, Melbourne, Australia
¹¹ School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
^† These authors contributed equally.
Email: Freya M Shearer (freya.shearer@unimelb.edu.au) and David J Price (david.price1@unimelb.edu.au)

Abstract:

As of 18 April 2020, there had been 6,533 confirmed cases of COVID-19 in Australia [1]. Of these, 67 had died from the disease. The daily count of new confirmed cases was declining. This suggests that the collective actions of the Australian public and government authorities in response to COVID-19 were sufficiently early and assiduous to avert a public health crisis — for now. Analysing factors, such as the intensity and timing public health interventions, that contribute to individual country experiences of COVID-19 will assist in the next stage of response planning globally. Using data from the Australian national COVID-19 database, we describe how the epidemic and public health response unfolded in Australia up to 13 April 2020. We estimate that the effective reproduction number was likely below 1 (the threshold value for control) in each Australian state since mid-March and forecast that hospital ward and intensive care unit occupancy will remain below capacity thresholds over the next two weeks.

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Estimating the case detection rate and temporal variation in transmission of COVID-19 in Australia
Technical Report

14th April 2020

Authors: David J. Price^1,2, Freya M. Shearer², Michael Meehan³, Emma McBryde³, Nick Golding⁴, Jodie McVernon¹, James M. McCaw^1,2,5

¹ Victorian Infectious Diseases Laboratory Epidemiology Unit at The Peter Doherty Institute for Infection and Immunity; The University of Melbourne and Royal Melbourne Hospital, Melbourne, Australia
² Modelling and Simulation Unit, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
³ Australian Institute of Tropical Health & Medicine, James Cook University, Townsville, Australia
⁴ Telethon Kids Institute, Curtin University, Perth, Australia
⁵ School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia

Key messages:

• We adapted and applied the method developed by colleagues at the London School of Hygiene and Tropical Medicine that uses the Case Fatality Rate in a region (adjusted for cases with known outcomes) to provide estimates of the symptomatic case detection rate in Australia. We note that LSHTM added Australia to their analysis on 1 April. The present authors have since updated the analysis, including the ability to estimate a time-dependent detection rate, at national level and for each state/territory.
• As of 9th April 2020, our estimate of the symptomatic case detection rate for Australia is 93%
  (95% CI 77–100%). The corresponding estimates for each state/territory are all greater than
  80% (Figures 1 and 2).
• Analyses were performed to identify temporal changes in the effective reproduction number
  (Reff) during the early course of the COVID-19 pandemic in each Australian state/territory.
• These analyses produced broadly consistent results showing that the effective reproduction
  number is likely less than 1 in NSW, VIC, QLD, SA, and WA as of 5 April 2020 (Figures 3–5). It
  should be noted that these estimates are averaged across the whole of each jurisdiction, and
  may reflect Reff >> 1 in a number of localised settings and Reff << 1 elsewhere.
• Reff is estimated to be above 1 in TAS, which should be interpreted with caution given the
  small cumulative number of cases and the large relative increase in cases recently reported
  (32 cases reported between 10 and 12 April).

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Researchers warn about potential increase in COVID-19 cases and remind Nigerians to practice "physical" distancing this Easter period

11th April 2020

Authors: Oyelola Adegboye¹, Adeshina Adekunle¹, Ezra Gayawan²

¹ Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia
² Federal University of Technology, Akure, Nigeria

Researchers from James Cook University (JCU) in Australia and Federal University of Technology in Akure, Nigeria (FUTA) Drs Oyelola Adegboye, Adeshina Adekunle and Ezra Gayawan have warned that Nigerians should practice “physical” distancing this Easter holiday. They warned that it is important to understand that the travel ban imposed by the Federal Government has prevented or reduced future importations according to their simulation study which found that the effects of the international travel ban imposed by the Australia Government resulted in an 80% reduction in COVID-19 importations. Hence, violating the social distancing intervention strategy could lead to an escalation of cases and all the gains as a result of the international travel ban would be lost.

The outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that emerged in the city of Wuhan, China has now spread to every inhabitable continent, but the attention has now shifted from China to other epicenters. The researchers said, “Although the epidemic trajectory in Nigeria has been slow compared to other countries, in part, due to public health interventions being implemented at the early stage, the government must intensify its effort.” We have seen in the USA where COVID-19 cases jumped from 472 on March 10, 2020 to 425, 889 by April 10, 2020.

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Plain English explainer about the explainer

6th April 2020

Authors: McBryde ES¹, Meehan MT¹, Trauer JM²

.¹Australian Institute of Tropical Health and Medicine, James Cook University
²School of Public Health and Preventive Medicine, Monash University

When we talk about flattening the curve, there are several interpretations of this. We may make the epidemic longer slower and much less peaked, as shown by the first of the blue curves below. Most efforts to curb COVID in Europe have certainly reduced the infectiousness to levels that will slow the peak but go nowhere near eliminating COVID.

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Flattening the curve is not enough, we need to squash it: An explainer using a simple model

30th March 2020

Authors: McBryde ES¹, Meehan MT¹, Trauer JM².

¹ Australian Institute of Tropical Health and Medicine, James Cook University
² School of Public Health and Preventive Medicine, Monash University

The known: COVID-19 has been diagnosed in over 4,000 Australians. Up until mid-March, most were from international travel, but now we are seeing a rise in locally acquired cases.

The new: This study uses a simple transmission dynamic model to demonstrate the difference between moderate changes to the reproduction number and forcing the reproduction number below one.

The implications: Lowering local transmission is becoming important in reducing the transmission of COVID-19. To maintain control of the epidemic, the focus should be on those in the community who do not regard themselves as at risk.

Abstract:

Background: Around the world there are examples of both effective control (e.g., South Korea, Japan) and less successful control (e.g., Italy, Spain, United States) of COVID-19 with dramatic differences in the consequent epidemic curves. Models agree that flattening the curve without controlling the epidemic completely is insufficient and will lead to an overwhelmed health service. A recent model, calibrated for the UK and US, demonstrated this starkly.

Methods: We used a simple compartmental deterministic model of COVID-19 transmission in Australia, to illustrate the dynamics resulting from shifting or flattening the curve versus completely squashing it.

Results: We find that when the reproduction number is close to one, a small decrease in transmission leads to a large reduction in burden (i.e., cases, deaths and hospitalisations), but achieving this early in the epidemic through social distancing interventions also implies that the community will not reach herd immunity.

Conclusions: Australia needs not just to shift and flatten the curve, but to squash it by getting the reproduction number below one. This will require Australia to achieve transmission rates at least two thirds lower than those seen in the most severely affected countries.

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Modelling the impact of COVID-19 upon intensive care services in New South Wales

MJA Published online: 30th March 2020

Authors: Gregory J Fox, James M Trauer and Emma McBryde

The known: COVID-19 has been diagnosed in over 1,000 Australians, with the notification rate being the highest in NSW.

The new: This study applies two statistical models to demonstrate the effect of COVID-19 upon critical care services. Even with limited mitigation, the effect is expected to overwhelm existing ICU
capacity.

The implications: Urgent action is required to reduce transmission of COVID-19, and increase the capacity of critical care services.

Abstract:

Background: The Australian healthcare system faces a mounting burden due to COVID-19. Modelling performed in a comparable population in the United Kingdom anticipates a substantial burden for intensive care departments.

Methods: This analysis uses two approaches to estimating intensive care unit (ICU) bed demands associated with COVID-19 in the context of local health districts in NSW. In the first approach, the findings of an individual-based simulation model undertaken in the United Kingdom (UK) was applied to the age distribution of the NSW population. In the second approach, we developed a compartmental model applied to the NSW population. In both models, we estimated the number of hospitalisations and peak ICU demand at the initial peak of the COVID-19 epidemic, under a number of mitigation strategies.

Results: Applying UK-based model to the NSW population, the peak demand for ICU beds was forecast to be 6,965 ICU beds with an intensive mitigation strategy (797% of the ICU capacity prior to COVID-19). The compartmental model estimated that under a strategy that reduced transmission by one third, at least 5,109 ICU beds would be required (584% of the prior ICU capacity).

Conclusions: The burden upon intensive care services due to COVID-19 was forecast to be immense with both modelling approaches. Strategies to mitigate transmission must be accompanied by substantial increases in the capacity of critical care services in advance of peak demand. Modelling is an important tool to assist policymakers and the public to understand the impacts pandemic diseases.

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Economic consequences of the COVID-19 outbreak: the need for epidemic preparedness

27th March 2020

Published by Frontiers in Public Health: 29th May 2020

Authors: Anton Pak^1,†, Oyelola A Adegboye^1.†*, Adeshina I Adekunle¹, Kazi M Rahman^2,3, Emma S McBryde¹, Damon P Eisen¹

¹Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia

²North Coast Public Health Unit, New South Wales Health, Lismore, NSW 2480, Australia

³The University of Sydney, University Centre for Rural Health, Lismore, NSW 2480, Australia

^†First authors

^*Corresponding author: Oyelola A. Adegboye (oyelola.adegboye@jcu.edu.au)

Keywords: SARS-CoV-2; Global markets; Economy

Abstract

COVID-19 has not only become global pandemic and a public health crisis but also affected the global economy and financial markets. Significant reductions in income, rise of unemployment and disruptions in transportation, service and manufacturing industries are among the consequences of governments’ disease mitigation measures. It has become clear that most governments in the world has underestimated the risks of rapid COVID-19 spread and were mostly reactive in their crisis response. As the disease outbreaks are not likely to disappear in the near future, proactive international actions are required to not only save lives but also protect economic prosperity.

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Delaying the COVID-19 epidemic in Australia: Evaluating the effectiveness of international travel bans

24th March 2020

Authors: Adeshina I. Adekunle^{1, 2*}, Michael Meehan¹, Diana Rojas³, James Trauer⁴, Emma McBryde¹

¹Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia.

²Decision Sciences Program Victoria University, Melbourne, Australia

³College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Australia.

⁴Department of Epidemiology and Biostatistics, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia

corresponding author: adeshina.adekunle@jcu.edu.au

Summary: Following the outbreak of novel Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) or COVID-19 in Wuhan, China late 2019, different countries have put in place interventions such as travel ban, proper hygiene, and social distancing to slow the spread of this novel virus. We evaluated the effects of travel bans in the Australia context and projected the epidemic until May 2020. Our modelling results closely align with observed cases in Australia indicating the need for maintaining or improving on the control measures to slow down the virus.

Introduction: As of 18th March 2020, COVID-19 has caused almost 200,000 cases, 8,000 deaths and spread to over 150 countries (1). Declared a pandemic on 12th March (2), it is clear that the world has lost the opportunity to contain the virus SARS-CoV2 as it managed to for SARS-CoV. With 565 confirmed cases (3) and still counting, COVID-19 now looks certain to cause sustained local transmission within Australia. Therefore, at this time it is reasonable to reflect on the value of travel restrictions imposed to date, and to consider the benefit of ongoing travel restrictions in the coming weeks and months, when community transmission starts to increase. We answer these questions using OAG-travel data and a meta-population model for disease transmission. First we examine the counterfactuals: what would have happened had the travel ban from Wuhan/China not been implemented. Similarly we examine the impacts of bans to other emerging epicentres including Iran, Italy, and South Korea. We then examine the impacts for the future and compare the cases expected through community transmission, and from importation over the next 2 months.

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Change in outbreak epicenter and its impact on the importation risks of COVID-19 progression: a modelling study

15th March 2020

Authors: Oyelola A Adegboye^1*, Adeshina I Adekunle¹, Anton Pak¹, Ezra Gayawan², Denis HY Leung³, Diana P Rojas⁴, Faiz Elfaki⁵, Emma S McBryde¹, Damon P Eisen¹

¹Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Australia
²Biostatistics and Spatial Statistics Research Group, Department of Statistics, Federal University of Technology, Akure, Nigeria.
³School of Economics, Singapore Management University, Singapore, Singapore
⁴College of Public Health. Medical and Veterinary Sciences, James Cook University, Townsville, Australia
⁵Department of Mathematics, Statistics and Physics, Qatar University, Doha, Qatar

*Corresponding author: Oyelola A. Adegboye, Email: oyelola.adegboye@jcu.edu.au, Phone: +61 7 4781 5707.

Abstract:

The outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that originated in the city of Wuhan, China has now spread to every inhabitable continent, but now the attention has shifted from China to other epicenters, especially Italy. This study explored the influence of spatial proximities and travel patterns from Italy on the further spread of SARS-CoV-2 around the globe. We showed that as the epicenter changes, the dynamics of SARS-CoV-2 spread change to reflect spatial proximities.

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The value of early transmission dynamic studies in emerging infectious diseases

The Lancet published online: 11th March 2020

Author: Emma McBryde

The world is braced for a public health emergency of international concern caused by a novel emerging infectious disease, a coronavirus with similarities to severe acute respiratory syndrome coronavirus (SARS-CoV). Person-to-person transmission of SARS-CoV-2, the causative agent of coronavirus disease 2019 (COVID-19), started in December, 2019, in Wuhan, China and has spread to become a global pandemic, with, as of Feb 26, 2020, community transmission in Italy, Iran, and South Korea.

Modelling studies have aided understanding of COVID-19 dynamics from the first announcement of the epidemic and publication of the genetic sequence of the causative virus. Initial phylogenetic analysis of closely related viruses suggested highly linked person-to-person spread of SARS-CoV-2 originating from mid-November to early December, 2019.1^,2 Following this, modellers provided simple calculations that identified a mismatch between reported cases in China and reported importations of cases from travellers. Based on travel volumes, modellers inferred 3 that cases in Wuhan were underestimated by a factor of 40—a crucially important finding. Further calculations, again based on travel volumes, suggested that some countries would be expected to have many more travel-related cases than had been notified, 4 drawing attention to the possibility of undetected cases and community transmission in several countries.

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