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Background: The UK was one of the countries worst affected by the COVID-19 pandemic in Europe. A strict lockdown from early 2021 combined with an aggressive vaccination programme enabled a gradual easing of lockdown measures to be introduced whilst both deaths and reported case numbers reduced to less than 3% of their peak. The emergence of the Del...
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Context 1
... the results shown in Figures 3-5, it can be seen that varying immunity length has a larger impact on case number projections than varying vaccination and recovered immunity protection within their likely ranges. Therefore, potential lockdown scenarios were explored with differing immunity length assumptions, as shown in Table 7. Figure 6a,b simulate the effects of a Government policy which reacts to daily known cases rising above 50,000 by increasing lockdown levels by 20%. The 20% is a theoretical number which could be made up of a number of different measures, e.g., self-isolation restrictions, masks, number limits. ...
Context 2
... a 12-month immunity length, a 2-month return to the 40% lockdown level would be required, starting at a similar time. Figure 6b projects that for a 5-month immunity length, the 50,000-case threshold will be breached in July and continuing lockdown at the July levels would reduce the peak daily numbers to 250,000 before they drop down in November 2021. ...
Context 3
... 10b shows the susceptible percentages for the three immunity scenarios with lockdown interventions implemented. For all scenarios, lockdowns as illustrated in Figure 6 are required to reduce daily known cases below 50,000. These have the effect of slowing the susceptible percentage reduction by reducing case numbers and hence generating less recovered immunity. ...
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Citations
... Modeling studies have become increasingly important in public health during 2020/2021, with policymakers recognizing their potential for predicting and analyzing the course of the COVID-19 pandemic and facilitating comparisons of interventions and policy decisions (Brereton and Pedercini 2021). The objective of this study was to assess the impact of various meteorological, environmental, and temporal variables on the incidence of SARS-CoV-2 in a representative city of a middle-income country. ...
Three years have passed since the outbreak of Coronavirus Disease 2019 (COVID-19) brought the world to standstill. In most countries, the restrictions have ended, and the immunity of the population has increased; however, the possibility of new dangerous variants emerging remains. Therefore, it is crucial to develop tools to study and forecast the dynamics of future pandemics. In this study, a generalized additive model (GAM) was developed to evaluate the impact of meteorological and environmental variables, along with pandemic-related restrictions, on the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Córdoba, Argentina. The results revealed that mean temperature and vegetation cover were the most significant predictors affecting SARS-CoV-2 cases, followed by government restriction phases, days of the week, and hours of sunlight. Although fine particulate matter (PM2.5) and NO2 were less related, they improved the model’s predictive power, and a 1-day lag enhanced accuracy metrics. The models exhibited strong adjusted coefficients of determination (R2adj) but did not perform as well in terms of root-mean-square error (RMSE). This suggests that the number of cases may not be the primary variable for controlling the spread of the disease. Furthermore, the increase in positive cases related to policy interventions may indicate the presence of lockdown fatigue. This study highlights the potential of data science as a management tool for identifying crucial variables that influence epidemiological patterns and can be monitored to prevent an overload in the healthcare system.
... The model does not differentiate between different types of vaccines or differences in vaccine efficacy, though this may be important to consider for future work. Nor does the model differentiate between the effectiveness of infection-induced immunity against vaccination immunity, as suggested in [27] [], but rather assumes that the recovered and vaccinated population may become susceptible again after 180 days (6 months) [28,29] in the absence of an immune-escaping variant. Therefore, the effects of vaccination are formulated with the purpose of decreasing the level of hospitalisations and fatalities, and to achieve 'heard immunity' (i.e.,~70% of the population with immune response either from vaccination or recovery from previous infection as defined by WHO) such that the likelihood of mutation and infection is decreased. ...
Globally, the COVID-19 pandemic bought devastating impacts to multiple economic sectors, with a major downfall observed in the tourism sector owing to explicit travel bans on foreign and domestic tourism. In Nelson Mandela Bay (NMB), South Africa, tourism plays an important role; however, negative effects from the pandemic and resulting restrictions has left the sector dwindling and in need of a path to recovery. Working together with local government and stakeholders, this study applied system dynamics modelling to investigate the impacts of COVID-19 on coastal tourism in NMB to provide decision-support and inform tourism recovery strategies. Through model analysis, a suite of management interventions was tested under two ‘what-if’ scenarios, with reference to the business-as-usual governance response scenario. Scenario one specifically aimed to investigate a desirable tourism recovery strategy assuming governance control, whereas scenario two investigated a scenario where the effects of governance responses were impeded on by the exogenous effects from the virus. Results suggest that uncertainty remained prevalent in the trajectory of the infection rate as well as in associated trends in tourism; however, through the lifting of travel restrictions and the continual administration of vaccines, a path to recovery was shown to be evident.
... There are 54 simulation papers using SDM, accounting for 14.5% of all selected research, where one paper used a simple SIR model (Pornphol & Chittayasothorn, 2020), one paper used a SIRD model (Ibarra-Vega, 2020), two papers used a classic SEIR model (Kumar, Priya, & Srivastava, 2021;Yusoff & Izhan, 2020) and seven papers constructed a SEIRD model (Abdolhamid et al., 2021;Khairulbahri, 2021;Liu et al., 2021;Mutanga et al., 2021;Struben, 2020;Sy et al., 2021;. In the modified papers, new states such as pre-symptomatic (Rahmandad et al., 2021), asymptomatic (Fair et al., 2021;Sy et al., 2020), symptomatic Fair et al., 2021), quarantined Kumar, Viswakarma, et al., 2021;Qian et al., 2021), isolated (Niwa et al., 2020), hospitalized or in treatment Qian et al., 2021;Rahmandad et al., 2021) and vaccinated (Brereton & Pedercini, 2021;Suphanchaimat, Tuangratananon, et al., 2021) were introduced into the models. In addition, without providing particular application cases, three papers built conceptual macro-level SDMs to understand the emergence of COVID-19 and system resilience and vulnerability in response to public health emergencies, respectively (Kontogiannis, 2021;Wang & Mansouri, 2021). ...
... Based on simulated results in Canada, without appropriate NPIs, a majority of the country's population might contract the disease, which would collapse the health system and consequently lead to even higher mortality (Ogden et al., 2020). Simulation studies in other countries suggested possible epidemic rebounds or a new wave spike if quarantine or social distancing (Brereton & Pedercini, 2021;Rice et al., 2020) were lifted prematurely. However, the pandemic will continue to batter the economy if stringent NPIs are not lifted (Ghaffarzadegan & Rahmandad, 2020). ...
This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent‐based model (ABM) and discrete event simulation (DES), and their hybrids in COVID‐19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID‐19 transmission dynamics, 204 evaluated both pharmaceutical and non‐pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID‐19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID‐19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio‐economic systems involved.
... Alternatively they could book an appointment at a walk-in or drive-through test site. However, under-reporting of SARS-CoV-2 cases is likely, and could be up to 50% [14]. We have therefore taken this into account in our modelling approach. ...
... The number removed are taken from all individuals who have ever been recorded as infected since the beginning of the pandemic, except for those that died. We are therefore assuming infected individuals retain immunity for the remaining duration of the pandemic, and we are assuming that there is no under-reporting of cases (which is certainly not true, especially during the first wave of the pandemic) [14,29]. The first of these assumptions leads to an overestimate of the removed category on 24th May 2021, and the second (likely more questionable) assumption leads to an underestimate of the removed category. ...
... This means that the number of susceptible individuals is likely to be overestimated. To account for this, we considered the case where the number of removed individuals in each class are doubled, representing an under-reporting of 50% [14]. This reduces the number in each respective susceptible class for the initial conditions of our model. ...
Background
From January to May 2021 the alpha variant (B.1.1.7) of SARS-CoV-2 was the most commonly detected variant in the UK. Following this, the Delta variant (B.1.617.2) then became the predominant variant. The UK COVID-19 vaccination programme started on 8th December 2020. Prior to the Delta variant, most vaccine effectiveness studies focused on the alpha variant. We therefore aimed to estimate the effectiveness of the BNT162b2 (Pfizer-BioNTech) and the ChAdOx1 nCoV-19 (Oxford-AstraZeneca) vaccines in preventing symptomatic and asymptomatic infection with respect to the Delta variant in a UK setting.
Methods
We used anonymised public health record data linked to infection data (PCR) using the Combined Intelligence for Population Health Action resource. We then constructed an SIR epidemic model to explain SARS-CoV-2 infection data across the Cheshire and Merseyside region of the UK. Vaccines were assumed to be effective after 21 days for 1 dose and 14 days for 2 doses.
Results
We determined that the effectiveness of the Oxford-AstraZeneca vaccine in reducing susceptibility to infection is 39% (95% credible interval [34, 43]) and 64% (95% credible interval [61, 67]) for a single dose and a double dose respectively. For the Pfizer-BioNTech vaccine, the effectiveness is 20% (95% credible interval [10, 28]) and 84% (95% credible interval [82, 86]) for a single-dose and a double dose respectively.
Conclusion
Vaccine effectiveness for reducing susceptibility to SARS-CoV-2 infection shows noticeable improvement after receiving two doses of either vaccine. Findings also suggest that a full course of the Pfizer-BioNTech provides the optimal protection against infection with the Delta variant. This reinforces the need to complete the full course programme to maximise individual protection and reduce transmission.
... Unsurprisingly, disease models using the ubiquitous susceptible-infected-recovered (SIR) or susceptible-exposed-infected-recovered (SEIR) frameworks are featured in two papers (Bärwolff [3]; Brereton and Pedercini [4]). Such density-based dynamic models enable the evolving nature of 'what if' health-management scenarios to be tested over a period of time from the safety of a numerical playground. ...
The preambles in many of the articles in this Special Issue have highlighted how COVID-19 has affected, and is continuing to affect, the way that individuals, groups, organisations and countries operate [...]
