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1.
Elife ; 92020 08 13.
Article in English | MEDLINE | ID: covidwho-2155738

ABSTRACT

As of 1 May 2020, there had been 6808 confirmed cases of COVID-19 in Australia. Of these, 98 had died from the disease. The epidemic had been in decline since mid-March, with 308 cases confirmed nationally since 14 April. 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 that contribute to individual country experiences of COVID-19, such as the intensity and timing of public health interventions, will assist in the next stage of response planning globally. We describe how the epidemic and public health response unfolded in Australia up to 13 April. We estimate that the effective reproduction number was likely below one in each Australian state since mid-March and forecast that clinical demand would remain below capacity thresholds over the forecast period (from mid-to-late April).


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Pandemics , Pneumonia, Viral/epidemiology , Adolescent , Adult , Age Distribution , Aged , Aged, 80 and over , Australia/epidemiology , COVID-19 , Child , Child, Preschool , Communicable Disease Control/methods , Communicable Disease Control/organization & administration , Communicable Disease Control/statistics & numerical data , Coronavirus Infections/prevention & control , Female , Forecasting , Geography, Medical , Hospitalization/statistics & numerical data , Humans , Infant , Infant, Newborn , Male , Middle Aged , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Public Health , Quarantine , SARS-CoV-2 , Travel , Young Adult
3.
Nat Commun ; 12(1): 6266, 2021 11 01.
Article in English | MEDLINE | ID: covidwho-1493105

ABSTRACT

During 2020, Victoria was the Australian state hardest hit by COVID-19, but was successful in controlling its second wave through aggressive policy interventions. We calibrated a detailed compartmental model of Victoria's second wave to multiple geographically-structured epidemic time-series indicators. We achieved a good fit overall and for individual health services through a combination of time-varying processes, including case detection, population mobility, school closures, physical distancing and face covering usage. Estimates of the risk of death in those aged ≥75 and of hospitalisation were higher than international estimates, reflecting concentration of cases in high-risk settings. We estimated significant effects for each of the calibrated time-varying processes, with estimates for the individual-level effect of physical distancing of 37.4% (95%CrI 7.2-56.4%) and of face coverings of 45.9% (95%CrI 32.9-55.6%). That the multi-faceted interventions led to the dramatic reversal in the epidemic trajectory is supported by our results, with face coverings likely particularly important.


Subject(s)
COVID-19/epidemiology , COVID-19/prevention & control , Epidemics , Adolescent , Adult , COVID-19/transmission , Hospitalization , Humans , Middle Aged , Models, Theoretical , Physical Distancing , SARS-CoV-2 , Schools , Victoria , Young Adult
4.
Epidemics ; 37: 100517, 2021 12.
Article in English | MEDLINE | ID: covidwho-1482585

ABSTRACT

INTRODUCTION: As of 3rd June 2021, Malaysia is experiencing a resurgence of COVID-19 cases. In response, the federal government has implemented various non-pharmaceutical interventions (NPIs) under a series of Movement Control Orders and, more recently, a vaccination campaign to regain epidemic control. In this study, we assessed the potential for the vaccination campaign to control the epidemic in Malaysia and four high-burden regions of interest, under various public health response scenarios. METHODS: A modified susceptible-exposed-infectious-recovered compartmental model was developed that included two sequential incubation and infectious periods, with stratification by clinical state. The model was further stratified by age and incorporated population mobility to capture NPIs and micro-distancing (behaviour changes not captured through population mobility). Emerging variants of concern (VoC) were included as an additional strain competing with the existing wild-type strain. Several scenarios that included different vaccination strategies (i.e. vaccines that reduce disease severity and/or prevent infection, vaccination coverage) and mobility restrictions were implemented. RESULTS: The national model and the regional models all fit well to notification data but underestimated ICU occupancy and deaths in recent weeks, which may be attributable to increased severity of VoC or saturation of case detection. However, the true case detection proportion showed wide credible intervals, highlighting incomplete understanding of the true epidemic size. The scenario projections suggested that under current vaccination rates complete relaxation of all NPIs would trigger a major epidemic. The results emphasise the importance of micro-distancing, maintaining mobility restrictions during vaccination roll-out and accelerating the pace of vaccination for future control. Malaysia is particularly susceptible to a major COVID-19 resurgence resulting from its limited population immunity due to the country's historical success in maintaining control throughout much of 2020.


Subject(s)
COVID-19 , Epidemiological Models , Humans , Malaysia/epidemiology , SARS-CoV-2 , Vaccination
5.
Med J Aust ; 215(9): 427-432, 2021 11 01.
Article in English | MEDLINE | ID: covidwho-1389702

ABSTRACT

OBJECTIVES: To analyse the outcomes of COVID-19 vaccination by vaccine type, age group eligibility, vaccination strategy, and population coverage. DESIGN: Epidemiologic modelling to assess the final size of a COVID-19 epidemic in Australia, with vaccination program (Pfizer, AstraZeneca, mixed), vaccination strategy (vulnerable first, transmitters first, untargeted), age group eligibility threshold (5 or 15 years), population coverage, and pre-vaccination effective reproduction number ( Reffv¯ ) for the SARS-CoV-2 Delta variant as factors. MAIN OUTCOME MEASURES: Numbers of SARS-CoV-2 infections; cumulative hospitalisations, deaths, and years of life lost. RESULTS: Assuming Reffv¯ = 5, the current mixed vaccination program (vaccinating people aged 60 or more with the AstraZeneca vaccine and people under 60 with the Pfizer vaccine) will not achieve herd protection unless population vaccination coverage reaches 85% by lowering the vaccination eligibility age to 5 years. At Reffv¯ = 3, the mixed program could achieve herd protection at 60-70% population coverage and without vaccinating 5-15-year-old children. At Reffv¯ = 7, herd protection is unlikely to be achieved with currently available vaccines, but they would still reduce the number of COVID-19-related deaths by 85%. CONCLUSION: Vaccinating vulnerable people first is the optimal policy when population vaccination coverage is low, but vaccinating more socially active people becomes more important as the Reffv¯ declines and vaccination coverage increases. Assuming the most plausible Reffv¯ of 5, vaccinating more than 85% of the population, including children, would be needed to achieve herd protection. Even without herd protection, vaccines are highly effective in reducing the number of deaths.


Subject(s)
COVID-19 Vaccines/immunology , COVID-19/prevention & control , Immunity, Herd , Mass Vaccination/organization & administration , SARS-CoV-2/pathogenicity , Adolescent , Adult , Age Factors , Australia/epidemiology , BNT162 Vaccine , COVID-19/epidemiology , COVID-19/immunology , COVID-19/virology , COVID-19 Vaccines/administration & dosage , Child , Child, Preschool , Computer Simulation , Humans , Immunogenicity, Vaccine , Mass Vaccination/statistics & numerical data , Middle Aged , Models, Immunological , SARS-CoV-2/genetics , SARS-CoV-2/immunology , Vaccination Coverage/organization & administration , Vaccination Coverage/statistics & numerical data , Young Adult
6.
Paediatr Respir Rev ; 39: 32-39, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1322314

ABSTRACT

Mathematical modelling has played a pivotal role in understanding the epidemiology of and guiding public health responses to the ongoing coronavirus disease of 2019 (COVID-19) pandemic. Here, we review the role of epidemiological models in understanding evolving epidemic characteristics, including the effects of vaccination and Variants of Concern (VoC). We highlight ways in which models continue to provide important insights, including (1) calculating the herd immunity threshold and evaluating its limitations; (2) verifying that nascent vaccines can prevent severe disease, infection, and transmission but may be less efficacious against VoC; (3) determining optimal vaccine allocation strategies under efficacy and supply constraints; and (4) determining that VoC are more transmissible and lethal than previously circulating strains, and that immune escape may jeopardize vaccine-induced herd immunity. Finally, we explore how models can help us anticipate and prepare for future stages of COVID-19 epidemiology (and that of other diseases) through forecasts and scenario projections, given current uncertainties and data limitations.


Subject(s)
COVID-19 Vaccines/supply & distribution , COVID-19/epidemiology , COVID-19/prevention & control , Communicable Disease Control/organization & administration , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Humans , Models, Theoretical , Pandemics/prevention & control , Pneumonia, Viral/virology , SARS-CoV-2
7.
Paediatr Respir Rev ; 35: 64-69, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-608740

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.


Subject(s)
Coronavirus Infections/epidemiology , Decision Making , Models, Theoretical , Pneumonia, Viral/epidemiology , Public Health , Betacoronavirus , COVID-19 , Coronavirus Infections/physiopathology , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Data Collection , Humans , Pandemics/prevention & control , Pneumonia, Viral/physiopathology , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , SARS-CoV-2 , Severity of Illness Index
8.
Paediatr Respir Rev ; 35: 57-60, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-603916

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.


Subject(s)
Coronavirus Infections/epidemiology , Health Policy , Models, Theoretical , Pneumonia, Viral/epidemiology , Policy Making , Betacoronavirus , COVID-19 , COVID-19 Vaccines , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Developing Countries , Epidemiologic Methods , Humans , Models, Economic , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , Public Health , Public Policy , SARS-CoV-2 , Travel , Viral Vaccines/therapeutic use
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