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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 , Humans , Malaysia/epidemiology , SARS-CoV-2 , Vaccination
5.
Aust N Z J Public Health ; 45(5): 430-436, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1450514

ABSTRACT

OBJECTIVE: To investigate the admission characteristics and hospital outcomes for patients admitted with lower respiratory tract infections (LRTI) in Northern Queensland. METHODS: We perform a retrospective analysis of the data covering an 11-year period, 2006-2016. Length of hospital stay (LOS) is modelled by negative binomial regression and heterogeneous effects are checked using interaction terms. RESULTS: A total of 11,726 patients were admitted due to LRTI; 2,430 (20.9%) were of Indigenous descent. We found higher hospitalisations due to LRTI for Indigenous than non-Indigenous patients, with a disproportionate increase in hospitalisations occurring during winter. The LOS for Indigenous patients was higher by 2.5 days [95%CI: -0.15; 5.05] than for non-Indigenous patients. The average marginal effect of 17.5 [95%CI: 15.3; 29.7] implies that the LOS for a patient, who was admitted to ICU, was higher by 17.5 days. CONCLUSIONS: We highlighted the increased burden of LRTIs experienced by Indigenous populations, with this information potentially being useful for enhancing community-level policy making. Implications for public health: Future guidelines can use these results to make recommendations for preventative measures in Indigenous communities. Improvements in engagement and partnership with Indigenous communities and consumers can help increase healthcare uptake and reduce the burden of respiratory diseases.


Subject(s)
Hospitalization , Respiratory Tract Infections , Humans , Length of Stay , Queensland/epidemiology , Respiratory Tract Infections/epidemiology , Retrospective Studies
6.
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 ( R eff v ¯ ) 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 R eff v ¯ = 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 R eff v ¯ = 3, the mixed program could achieve herd protection at 60-70% population coverage and without vaccinating 5-15-year-old children. At R eff v ¯ = 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 R eff v ¯ declines and vaccination coverage increases. Assuming the most plausible R eff v ¯ 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 , 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
7.
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
8.
Lancet Reg Health West Pac ; 14: 100211, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1309328

ABSTRACT

BACKGROUND: COVID-19 initially caused less severe outbreaks in many low- and middle-income countries (LMIC) compared with many high-income countries, possibly because of differing demographics, socioeconomics, surveillance, and policy responses. Here, we investigate the role of multiple factors on COVID-19 dynamics in the Philippines, a LMIC that has had a relatively severe COVID-19 outbreak. METHODS: We applied an age-structured compartmental model that incorporated time-varying mobility, testing, and personal protective behaviors (through a "Minimum Health Standards" policy, MHS) to represent the first wave of the Philippines COVID-19 epidemic nationally and for three highly affected regions (Calabarzon, Central Visayas, and the National Capital Region). We estimated effects of control measures, key epidemiological parameters, and interventions. FINDINGS: Population age structure, contact rates, mobility, testing, and MHS were sufficient to explain the Philippines epidemic based on the good fit between modelled and reported cases, hospitalisations, and deaths. The model indicated that MHS reduced the probability of transmission per contact by 13-27%. The February 2021 case detection rate was estimated at ~8%, population recovered at ~9%, and scenario projections indicated high sensitivity to MHS adherence. INTERPRETATION: COVID-19 dynamics in the Philippines are driven by age, contact structure, mobility, and MHS adherence. Continued compliance with low-cost MHS should help the Philippines control the epidemic until vaccines are widely distributed, but disease resurgence may be occurring due to a combination of low population immunity and detection rates and new variants of concern.

9.
Travel Med Infect Dis ; 40: 101988, 2021.
Article in English | MEDLINE | ID: covidwho-1071979

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.


Subject(s)
COVID-19/epidemiology , Communicable Diseases, Imported/epidemiology , Models, Statistical , Air Travel/statistics & numerical data , China/epidemiology , Humans , Italy/epidemiology , Population Surveillance , Risk , SARS-CoV-2/isolation & purification , Travel/statistics & numerical data
12.
Proc Biol Sci ; 287(1932): 20201405, 2020 08 12.
Article in English | MEDLINE | ID: covidwho-711780

ABSTRACT

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions 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, would 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. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (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.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Immunity, Herd , Models, Theoretical , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , COVID-19 , Child , Coronavirus Infections/immunology , Coronavirus Infections/prevention & control , Disease Eradication , Family Characteristics , Humans , Pandemics/prevention & control , Pneumonia, Viral/immunology , Pneumonia, Viral/prevention & control , Schools , Seroepidemiologic Studies
13.
Front Public Health ; 8: 241, 2020.
Article in English | MEDLINE | ID: covidwho-613125

ABSTRACT

COVID-19 is not only a global pandemic and public health crisis; it has also severely affected the global economy and financial markets. Significant reductions in income, a rise in unemployment, and disruptions in the transportation, service, and manufacturing industries are among the consequences of the disease mitigation measures that have been implemented in many countries. It has become clear that most governments in the world underestimated the risks of rapid COVID-19 spread and were mostly reactive in their crisis response. As 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.


Subject(s)
COVID-19/economics , Civil Defense , Disease Outbreaks/economics , Internationality , Public Health/economics , Humans , SARS-CoV-2 , Unemployment
14.
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
15.
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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