A review of mathematical model-based scenario analysis and interventions for COVID-19.
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ABSTRACT:
SUBMITTER: Padmanabhan R
PROVIDER: S-EPMC8314871 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In this article, a qualitative analysis of the mathematical model of novel corona virus named COVID-19 under nonsingular derivative of fractional order is considered. The concerned model is composed of two compartments, namely, healthy and infected. Under the new nonsingular derivative, we, first of all, establish some sufficient conditions for existence and uniqueness of solution to the model under consideration. Because of the dynamics of the phenomenon when described by a mathematical model, its existence must be guaranteed. Therefore, via using the classical fixed point theory, we establish the required results. Also, we present the results of stability of Ulam's type by using the tools of nonlinear analysis. For the semianalytical results, we extend the usual Laplace transform coupled with Adomian decomposition method to obtain the approximate solutions for the corresponding compartments of the considered model. Finally, in order to support our study, graphical interpretations are provided to illustrate the results by using some numerical values for the corresponding parameters of the model.
| S-EPMC7273369 | biostudies-literature
Project description:The determinants underlying the heterogeneity of coronavirus disease 2019 (COVID-19) remain to be elucidated. To systemically analyze the immunopathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, we built a multicompartment mathematical model based on immunological principles and typical COVID-19-related characteristics. This model integrated the trafficking of immune cells and cytokines among the secondary lymphoid organs, peripheral blood and lungs. Our results suggested that early-stage lymphopenia was related to lymphocyte chemotaxis, while prolonged lymphopenia in critically ill patients was associated with myeloid-derived suppressor cells. Furthermore, our model predicted that insufficient SARS-CoV-2-specific naïve T/B cell pools and ineffective activation of antigen-presenting cells (APCs) would cause delayed immunity activation, resulting in elevated viral load, low immunoglobulin level, etc. Overall, we provided a comprehensive view of the dynamics of host immunity after SARS-CoV-2 infection that enabled us to understand COVID-19 heterogeneity from systemic perspective.
| S-EPMC9116108 | biostudies-literature
Project description:Prior to vaccination or drug treatment, non-pharmaceutical interventions were almost the only way to control the coronavirus disease 2019 (COVID-19) epidemic. After vaccines were developed, effective vaccination strategies became important. The prolonged COVID-19 pandemic has caused enormous economic losses worldwide. As such, it is necessary to estimate the economic effects of control policies, including non-pharmaceutical interventions and vaccination strategies. We estimated the costs associated with COVID-19 according to different vaccination rollout speeds and social distancing levels and investigated effective control strategies for cost minimization. Age-structured mathematical models were developed and used to study disease transmission epidemiology. Using these models, we estimated the actual costs due to COVID-19, considering costs associated with medical care, lost wages, death, vaccination, and gross domestic product (GDP) losses due to social distancing. The lower the social distancing (SD) level, the more important the vaccination rollout speed. SD level 1 was cost-effective under fast rollout speeds, but SD level 2 was more effective for slow rollout speeds. If the vaccine rollout rate is fast enough, even implementing SD level 1 will be cost effective and can control the number of critically ill patients and deaths. If social distancing is maintained at level 2 at the beginning and then relaxed when sufficient vaccinations have been administered, economic costs can be reduced while maintaining the number of patients with severe symptoms below the intensive care unit (ICU) capacity. Korea has wellequipped medical facilities and infrastructure for rapid vaccination, and the public's desire for vaccination is high. In this case, the speed of vaccine supply is an important factor in controlling the COVID-19 epidemic. If the speed of vaccination is fast, it is possible to maintain a low level of social distancing without a significant increase in the number of deaths and hospitalized patients with severe symptoms, and the corresponding costs can be reduced.
| S-EPMC9512395 | biostudies-literature
Project description: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.
| S-EPMC8341708 | biostudies-literature
Project description:ObjectivesTo investigate the impact of targeted vaccination strategies on morbidity and mortality due to COVID-19, as well as on the incidence of SARS-CoV-2, in India.DesignMathematical modelling.SettingsIndian epidemic of COVID-19 and vulnerable population.Data sourcesCountry-specific and age-segregated pattern of social contact, case fatality rate and demographic data obtained from peer-reviewed literature and public domain.ModelAn age-structured dynamical model describing SARS-CoV-2 transmission in India incorporating uncertainty in natural history parameters was constructed.InterventionsComparison of different vaccine strategies by targeting priority groups such as keyworkers including healthcare professionals, individuals with comorbidities (24-60 years old) and all above 60.Main outcome measuresIncidence reduction and averted deaths in different scenarios, assuming that the current restrictions are fully lifted as vaccination is implemented.ResultsThe priority groups together account for about 18% of India's population. An infection-preventing vaccine with 60% efficacy covering all these groups would reduce peak symptomatic incidence by 20.6% (95% uncertainty intervals (UI) 16.7-25.4) and cumulative mortality by 29.7% (95% CrI 25.8-33.8). A similar vaccine with ability to prevent symptoms (but not infection) will reduce peak incidence of symptomatic cases by 10.4% (95% CrI 8.4-13.0) and cumulative mortality by 32.9% (95% CrI 28.6-37.3). In the event of insufficient vaccine supply to cover all priority groups, model projections suggest that after keyworkers, vaccine strategy should prioritise all who are >60 and subsequently individuals with comorbidities. In settings with weakest transmission, such as sparsely populated rural areas, those with comorbidities should be prioritised after keyworkers.ConclusionsAn appropriately targeted vaccination strategy would witness substantial mitigation of impact of COVID-19 in a country like India with wide heterogeneity. 'Smart vaccination', based on public health considerations, rather than mass vaccination, appears prudent.
| S-EPMC8257292 | biostudies-literature
Project description:This study highlights the major players in the global food balance, potential implications of COVID-19 on global food supply, and SDG-2 (zero hunger). It found that developing countries, fifteen from Africa followed by ten from Latin America, six from Oceania, and four from Asia, are the most vulnerable to changes cereal supply shocks. It concludes that the current pandemic is likely to cause transitory food insecurity across such vulnerable countries. The effects of the pandemic on food security (SDG-2) may persist longer as a combined effect of economic slowdown and increase in poverty, limiting food supply and access beyond 2020.
| S-EPMC7374119 | biostudies-literature
Project description:BackgroundThe dynamics of vaccination against SARS-CoV-2 are complicated by age-dependent factors, changing levels of infection, and the relaxation of non-pharmaceutical interventions (NPIs) as the perceived risk declines, necessitating the use of mathematical models. Our aims were to use epidemiological data from the UK together with estimates of vaccine efficacy to predict the possible long-term dynamics of SARS-CoV-2 under the planned vaccine rollout.MethodsIn this study, we used a mathematical model structured by age and UK region, fitted to a range of epidemiological data in the UK, which incorporated the planned rollout of a two-dose vaccination programme (doses 12 weeks apart, protection onset 14 days after vaccination). We assumed default vaccine uptake of 95% in those aged 80 years and older, 85% in those aged 50-79 years, and 75% in those aged 18-49 years, and then varied uptake optimistically and pessimistically. Vaccine efficacy against symptomatic disease was assumed to be 88% on the basis of Pfizer-BioNTech and Oxford-AstraZeneca vaccines being administered in the UK, and protection against infection was varied from 0% to 85%. We considered the combined interaction of the UK vaccination programme with multiple potential future relaxations (or removals) of NPIs, to predict the reproduction number (R) and pattern of daily deaths and hospital admissions due to COVID-19 from January, 2021, to January, 2024.FindingsWe estimate that vaccination alone is insufficient to contain the outbreak. In the absence of NPIs, even with our most optimistic assumption that the vaccine will prevent 85% of infections, we estimate R to be 1·58 (95% credible intervals [CI] 1·36-1·84) once all eligible adults have been offered both doses of the vaccine. Under the default uptake scenario, removal of all NPIs once the vaccination programme is complete is predicted to lead to 21 400 deaths (95% CI 1400-55 100) due to COVID-19 for a vaccine that prevents 85% of infections, although this number increases to 96 700 deaths (51 800-173 200) if the vaccine only prevents 60% of infections. Although vaccination substantially reduces total deaths, it only provides partial protection for the individual; we estimate that, for the default uptake scenario and 60% protection against infection, 48·3% (95% CI 48·1-48·5) and 16·0% (15·7-16·3) of deaths will be in individuals who have received one or two doses of the vaccine, respectively.InterpretationFor all vaccination scenarios we investigated, our predictions highlight the risks associated with early or rapid relaxation of NPIs. Although novel vaccines against SARS-CoV-2 offer a potential exit strategy for the pandemic, success is highly contingent on the precise vaccine properties and population uptake, both of which need to be carefully monitored.FundingNational Institute for Health Research, Medical Research Council, and UK Research and Innovation.
| S-EPMC7972312 | biostudies-literature
Project description:We consider a general mathematical model for protein subunit vaccine with a focus on the MF59-adjuvanted spike glycoprotein-clamp vaccine for SARS-CoV-2, and use the model to study immunological outcomes in the humoral and cell-mediated arms of the immune response from vaccination. The mathematical model is fit to vaccine clinical trial data. We elucidate the role of Interferon-γ and Interleukin-4 in stimulating the immune response of the host. Model results, and results from a sensitivity analysis, show that a balance between the TH1 and TH2 arms of the immune response is struck, with the TH1 response being dominant. The model predicts that two-doses of the vaccine at 28 days apart will result in approximately 85% humoral immunity loss relative to peak immunity approximately 6 months post dose 1.
| S-EPMC9911981 | biostudies-literature
Project description:COVID-19 vaccines are supposed to be critical measure for ending the pandemic. Governments had to decide on the type of vaccine to provide for their population. In this decision-making process, cost-effectiveness analysis is considered a helpful tool. This study is a cost-effectiveness analysis utilized to calculate the incremental cost per averted disability-adjusted life year (DALY) by vaccination compared to no vaccination for different COVID-19 vaccines. The incremental cost-effectiveness ratio (ICER) for a vaccination with COVID-19 vaccines was estimated at 6.2 to 121.2 USD to avert one DALY and 566.8 to 10,957.7 USD per one death. The lowest and highest ICERs belong to Ad26.COV2.S and CoronaVac, respectively. Considering the scenario of Iran, vaccines that are recommended include ad26.cov2.s, chadox1-S, rAd26-S + rAd5-S, and BNT162b2 in the order of recommendation.
| S-EPMC8777749 | biostudies-literature
Project description:ObjectivesWe aim to estimate the impact of various mitigation strategies on COVID-19 transmission in a US jail beyond those offered in national guidelines.DesignWe developed a stochastic dynamic transmission model of COVID-19.SettingOne anonymous large urban US jail.ParticipantsSeveral thousand staff and incarcerated individuals.InterventionsThere were four intervention phases during the outbreak: the start of the outbreak, depopulation of the jail, increased proportion of people in single cells and asymptomatic testing. These interventions were implemented incrementally and in concert with one another.Primary and secondary outcome measuresThe basic reproduction ratio, R 0 , in each phase, as estimated using the next generation method. The fraction of new cases, hospitalisations and deaths averted by these interventions (along with the standard measures of sanitisation, masking and social distancing interventions).ResultsFor the first outbreak phase, the estimated R 0 was 8.44 (95% credible interval (CrI): 5.00 to 13.10), and for the subsequent phases, R 0,phase 2 =3.64 (95% CrI: 2.43 to 5.11), R 0,phase 3 =1.72 (95% CrI: 1.40 to 2.12) and R 0,phase 4 =0.58 (95% CrI: 0.43 to 0.75). In total, the jail's interventions prevented approximately 83% of projected cases, hospitalisations and deaths over 83 days.ConclusionsDepopulation, single celling and asymptomatic testing within jails can be effective strategies to mitigate COVID-19 transmission in addition to standard public health measures. Decision makers should prioritise reductions in the jail population, single celling and testing asymptomatic populations as additional measures to manage COVID-19 within correctional settings.
| S-EPMC7893212 | biostudies-literature