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1.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-329911

ABSTRACT

Between June and August 2020, an agent-based model was used to project rates of COVID-19 infection incidence and cases diagnosed as positive from 15 September to 31 October 2020 for 72 geographic settings. Five scenarios were modelled: a baseline scenario where no future changes were made to existing restrictions, and four scenarios representing small or moderate changes in restrictions at two intervals. Post hoc, upper and lower bounds for number of diagnosed Covid-19 cases were compared with actual data collected during the prediction window. A regression analysis with 17 covariates was performed to determine correlates of accurate projections. It was found that the actual data fell within the lower and upper bounds in 27 settings and out of bounds in 45 settings. The only statistically significant predictor of actual data within the predicted bounds was correct assumptions about future policy changes (OR = 15.04;95%CI 2.20-208.70;p = 0.016). Frequent changes in restrictions implemented by governments, which the modelling team was not always able to predict, in part explains why the majority of model projections were inaccurate compared with actual outcomes and supports revision of projections when policies are changed as well as the importance of modelling teams collaborating with policy experts.

2.
BMC Infect Dis ; 22(1): 232, 2022 Mar 07.
Article in English | MEDLINE | ID: covidwho-1731519

ABSTRACT

BACKGROUND: In settings with zero community transmission, any new SARS-CoV-2 outbreaks are likely to be the result of random incursions. The level of restrictions in place at the time of the incursion is likely to considerably affect possible outbreak trajectories, but the probability that a large outbreak eventuates is not known. METHODS: We used an agent-based model to investigate the relationship between ongoing restrictions and behavioural factors, and the probability of an incursion causing an outbreak and the resulting growth rate. We applied our model to the state of Victoria, Australia, which has reached zero community transmission as of November 2020. RESULTS: We found that a future incursion has a 45% probability of causing an outbreak (defined as a 7-day average of > 5 new cases per day within 60 days) if no restrictions were in place, decreasing to 23% with a mandatory masks policy, density restrictions on venues such as restaurants, and if employees worked from home where possible. A drop in community symptomatic testing rates was associated with up to a 10-percentage point increase in outbreak probability, highlighting the importance of maintaining high testing rates as part of a suppression strategy. CONCLUSIONS: Because the chance of an incursion occurring is closely related to border controls, outbreak risk management strategies require an integrated approaching spanning border controls, ongoing restrictions, and plans for response. Each individual restriction or control strategy reduces the risk of an outbreak. They can be traded off against each other, but if too many are removed there is a danger of accumulating an unsafe level of risk. The outbreak probabilities estimated in this study are of particular relevance in assessing the downstream risks associated with increased international travel.


Subject(s)
COVID-19 , COVID-19/epidemiology , COVID-19/prevention & control , Disease Outbreaks/prevention & control , Humans , Longitudinal Studies , SARS-CoV-2 , Victoria/epidemiology
3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-295129

ABSTRACT

Background: Reducing unmet need for modern contraception and expanding access to quality maternal health (MH) services are priorities for improving women’s health and economic empowerment including in Small Island Developing States in the Pacific and Caribbean. We estimated health benefits and return-on-investment for contraceptive and MH interventions to help ensure appropriate prioritization.<br><br>Methods: Contraceptive and MH interventions were scaled linearly from 2022 (following COVID-19 disruptions) to reach zero unmet need for modern contraception and 95% MH intervention coverage by 2030. Five Pacific (Kiribati, Samoa, Solomon Islands, Tonga, and Vanuatu) and four Caribbean (Barbados, Guyana, Jamaica, and Saint Lucia) countries were considered based on population survey availability. Health outcomes were estimated and converted to economic benefits, and compared for business-as-usual (BAU) (coverage maintained) and coverage-targets-achieved scenarios.<br><br>Findings: An additional US$13.4M (US$10.9M-US$16.0M) is needed over 2020-2030 for the five Pacific countries to reach coverage targets (19% more than US$70.5M to maintain BAU). This could avert 126,000 (40%) unintended pregnancies, 2,200 (28%) stillbirths, and 121 (29%) maternal deaths and bring an elevenfold economic benefit of US$149.7M (US$54.5M-US$214.7M) by 2040. For the four Caribbean countries, an additional US$18.8M (US$15.3M-US$22.4M) is needed to reach targets (5% more than US$342.3M to maintain BAU). This could avert 127,000 (23%) unintended pregnancies, 3,600 (23%) stillbirths, and 221 (25%) maternal deaths and bring a twentyfold economic benefit of US$375.4M (US$137.9M-US$540.6M) by 2040.<br><br>Interpretation: Achieving full coverage of contraceptive and maternal health services in the Pacific and Caribbean is likely to be affordable and have high return-on-investment.<br><br>Funding Information: Funding for this study was provided by UNFPA.<br><br>Declaration of Interests: None declared.<br>

4.
PLoS Comput Biol ; 17(7): e1009149, 2021 07.
Article in English | MEDLINE | ID: covidwho-1325366

ABSTRACT

The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America.


Subject(s)
COVID-19 , Models, Biological , SARS-CoV-2 , Systems Analysis , Basic Reproduction Number , COVID-19/etiology , COVID-19/prevention & control , COVID-19/transmission , COVID-19 Testing , COVID-19 Vaccines , Computational Biology , Computer Simulation , Contact Tracing , Disease Progression , Hand Disinfection , Host Microbial Interactions , Humans , Masks , Mathematical Concepts , Pandemics , Physical Distancing , Quarantine , Software
5.
Med J Aust ; 214(2): 79-83, 2021 02.
Article in English | MEDLINE | ID: covidwho-934605

ABSTRACT

OBJECTIVES: To assess the risks associated with relaxing coronavirus disease 2019 (COVID-19)-related physical distancing restrictions and lockdown policies during a period of low viral transmission. DESIGN: Network-based viral transmission risks in households, schools, workplaces, and a variety of community spaces and activities were simulated in an agent-based model, Covasim. SETTING: The model was calibrated for a baseline scenario reflecting the epidemiological and policy environment in Victoria during March-May 2020, a period of low community viral transmission. INTERVENTION: Policy changes for easing COVID-19-related restrictions from May 2020 were simulated in the context of interventions that included testing, contact tracing (including with a smartphone app), and quarantine. MAIN OUTCOME MEASURE: Increase in detected COVID-19 cases following relaxation of restrictions. RESULTS: Policy changes that facilitate contact of individuals with large numbers of unknown people (eg, opening bars, increased public transport use) were associated with the greatest risk of COVID-19 case numbers increasing; changes leading to smaller, structured gatherings with known contacts (eg, small social gatherings, opening schools) were associated with lower risks. In our model, the rise in case numbers following some policy changes was notable only two months after their implementation. CONCLUSIONS: Removing several COVID-19-related restrictions within a short period of time should be undertaken with care, as the consequences may not be apparent for more than two months. Our findings support continuation of work from home policies (to reduce public transport use) and strategies that mitigate the risk associated with re-opening of social venues.


Subject(s)
COVID-19/prevention & control , COVID-19/transmission , Epidemiological Monitoring , Health Policy , Models, Theoretical , Physical Distancing , Quarantine , Contact Tracing/methods , Humans , Mobile Applications , Risk Assessment , SARS-CoV-2 , Smartphone , Victoria/epidemiology
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