RESUMO
The widespread, and in many countries unprecedented, use of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic has highlighted the need for mathematical models which can estimate the impact of these measures while accounting for the highly heterogeneous risk profile of COVID-19. Models accounting either for age structure or the household structure necessary to explicitly model many NPIs are commonly used in infectious disease modelling, but models incorporating both levels of structure present substantial computational and mathematical challenges due to their high dimensionality. Here we present a modelling framework for the spread of an epidemic that includes explicit representation of age structure and household structure. Our model is formulated in terms of tractable systems of ordinary differential equations for which we provide an open-source Python implementation. Such tractability leads to significant benefits for model calibration, exhaustive evaluation of possible parameter values, and interpretability of results. We demonstrate the flexibility of our model through four policy case studies, where we quantify the likely benefits of the following measures which were either considered or implemented in the UK during the current COVID-19 pandemic: control of within- and between-household mixing through NPIs; formation of support bubbles during lockdown periods; out-of-household isolation (OOHI); and temporary relaxation of NPIs during holiday periods. Our ordinary differential equation formulation and associated analysis demonstrate that multiple dimensions of risk stratification and social structure can be incorporated into infectious disease models without sacrificing mathematical tractability. This model and its software implementation expand the range of tools available to infectious disease policy analysts.
Assuntos
COVID-19 , Doenças Transmissíveis , COVID-19/epidemiologia , COVID-19/prevenção & controle , Controle de Doenças Transmissíveis/métodos , Humanos , Pandemias/prevenção & controle , Políticas , SARS-CoV-2RESUMO
We explore the spatial and temporal spread of the novel SARS-CoV-2 virus under containment measures in three European countries based on fits to data of the early outbreak. Using data from Spain and Italy, we estimate an age dependent infection fatality ratio for SARS-CoV-2, as well as risks of hospitalization and intensive care admission. We use them in a model that simulates the dynamics of the virus using an age structured, spatially detailed agent based approach, that explicitly incorporates governmental interventions and changes in mobility and contact patterns occurred during the COVID-19 outbreak in each country. Our simulations reproduce several of the features of its spatio-temporal spread in the three countries studied. They show that containment measures combined with high density are responsible for the containment of cases within densely populated areas, and that spread to less densely populated areas occurred during the late stages of the first wave. The capability to reproduce observed features of the spatio-temporal dynamics of SARS-CoV-2 makes this model a potential candidate for forecasting the dynamics of SARS-CoV-2 in other settings, and we recommend its application in low and lower-middle income countries which remain understudied.
Assuntos
COVID-19/epidemiologia , Simulação por Computador , COVID-19/mortalidade , COVID-19/transmissão , COVID-19/virologia , Busca de Comunicante , Surtos de Doenças , Hospitalização/estatística & dados numéricos , Humanos , Unidades de Terapia Intensiva/estatística & dados numéricos , Itália/epidemiologia , Admissão do Paciente/estatística & dados numéricos , Distanciamento Físico , Risco , SARS-CoV-2/isolamento & purificação , Espanha/epidemiologia , Reino Unido/epidemiologiaRESUMO
This paper aims to test and illustrate the utility and extensibility of an existing model, SimNCD (Simulation of NonCommunicable Diseases). It also proposes a way to include questionnaires - widely used in epidemiology - in the individual's reasoning mechanism in order to identify his/her attitude and personal choices. SimNCD is a formal agent-based model. It helps researchers and health practitioners study and simulate the complex dynamics of noncommunicable diseases. It models individuals that evolve within a social network, and behave while engaging in activities offered by their physical environment. The literature strongly supports the influence of the individual's behavioral choices on their health, particularly, the acquirement and maintainability of noncommunicable diseases. Therefore, we propose to extend SimNCD in order to acquire the agents with a reasoning process that allows them to choose the activities to practice. Thus, we model their attitude via preferences that are modeled based on the available literature and expressed with the linguistic 2-tuple method. Our solution also employs a multi-attribute decision-making method. We specify the proposed solution in the study of childhood obesity and use it to predict children's corpulence variations in different scenarios.
Assuntos
Modelos Teóricos , Doenças não Transmissíveis/epidemiologia , Criança , Feminino , Humanos , Masculino , Fatores de RiscoRESUMO
INTRODUCTION: Preserving function and independence to perform activities of daily living (ADL) is critical for patients and carers to manage the burden of care and improve quality of life. In children living with rare diseases, video recording ADLs offer the opportunity to collect the patients' experience in a real-life setting and accurately reflect treatment effectiveness on outcomes that matter to patients and families. AREAS COVERED: We reviewed the measurement of ADL in pediatric rare diseases and the use of video to develop at-home electronic clinical outcome assessments (eCOA) by leveraging smartphone apps and artificial intelligence-based analysis. We broadly searched PubMed using Boolean combinations of the following MeSH terms 'Rare Diseases,' 'Quality of Life,' 'Activities of Daily Living,' 'Child,' 'Video Recording,' 'Outcome Assessment, Healthcare,' 'Intellectual disability,' and 'Genetic Diseases, Inborn.' Non-controlled vocabulary was used to include human pose estimation in movement analysis. EXPERT OPINION: Broad uptake of video eCOA in drug development is linked to the generation of technical and clinical validation evidence to confidently assess a patient's functional abilities. Software platforms handling video data must align with quality regulations to ensure data integrity, security, and privacy. Regulatory flexibility and optimized validation processes should facilitate video eCOA to support benefit/risk drug assessment.
Assuntos
Atividades Cotidianas , Inteligência Artificial , Aplicativos Móveis , Avaliação de Resultados em Cuidados de Saúde , Qualidade de Vida , Doenças Raras , Smartphone , Gravação em Vídeo , Humanos , Criança , Doenças Raras/terapia , Resultado do TratamentoRESUMO
BACKGROUND: A few studies have assessed the epidemiological impact and the cost-effectiveness of COVID-19 vaccines in settings where most of the population had been exposed to SARS-CoV-2 infection. METHODS: We conducted a cost-effectiveness analysis of COVID-19 vaccine in Kenya from a societal perspective over a 1.5-year time frame. An age-structured transmission model assumed at least 80% of the population to have prior natural immunity when an immune escape variant was introduced. We examine the effect of slow (18 months) or rapid (6 months) vaccine roll-out with vaccine coverage of 30%, 50% or 70% of the adult (>18 years) population prioritising roll-out in those over 50-years (80% uptake in all scenarios). Cost data were obtained from primary analyses. We assumed vaccine procurement at US$7 per dose and vaccine delivery costs of US$3.90-US$6.11 per dose. The cost-effectiveness threshold was US$919.11. FINDINGS: Slow roll-out at 30% coverage largely targets those over 50 years and resulted in 54% fewer deaths (8132 (7914-8373)) than no vaccination and was cost saving (incremental cost-effectiveness ratio, ICER=US$-1343 (US$-1345 to US$-1341) per disability-adjusted life-year, DALY averted). Increasing coverage to 50% and 70%, further reduced deaths by 12% (810 (757-872) and 5% (282 (251-317) but was not cost-effective, using Kenya's cost-effectiveness threshold (US$919.11). Rapid roll-out with 30% coverage averted 63% more deaths and was more cost-saving (ICER=US$-1607 (US$-1609 to US$-1604) per DALY averted) compared with slow roll-out at the same coverage level, but 50% and 70% coverage scenarios were not cost-effective. INTERPRETATION: With prior exposure partially protecting much of the Kenyan population, vaccination of young adults may no longer be cost-effective.
Assuntos
Vacinas contra COVID-19 , COVID-19 , COVID-19/prevenção & controle , Análise Custo-Benefício , Humanos , Quênia/epidemiologia , SARS-CoV-2 , Adulto JovemRESUMO
INTRODUCTION: The high proportion of SARS-CoV-2 infections that have remained undetected presents a challenge to tracking the progress of the pandemic and estimating the extent of population immunity. METHODS: We used residual blood samples from women attending antenatal care services at three hospitals in Kenya between August 2020 and October 2021and a validated IgG ELISA for SARS-Cov-2 spike protein and adjusted the results for assay sensitivity and specificity. We fitted a two-component mixture model as an alternative to the threshold analysis to estimate of the proportion of individuals with past SARS-CoV-2 infection. RESULTS: We estimated seroprevalence in 2,981 women; 706 in Nairobi, 567 in Busia and 1,708 in Kilifi. By October 2021, 13% of participants were vaccinated (at least one dose) in Nairobi, 2% in Busia. Adjusted seroprevalence rose in all sites; from 50% (95%CI 42-58) in August 2020, to 85% (95%CI 78-92) in October 2021 in Nairobi; from 31% (95%CI 25-37) in May 2021 to 71% (95%CI 64-77) in October 2021 in Busia; and from 1% (95% CI 0-3) in September 2020 to 63% (95% CI 56-69) in October 2021 in Kilifi. Mixture modelling, suggests adjusted cross-sectional prevalence estimates are underestimates; seroprevalence in October 2021 could be 74% in Busia and 72% in Kilifi. CONCLUSIONS: There has been substantial, unobserved transmission of SARS-CoV-2 in Nairobi, Busia and Kilifi Counties. Due to the length of time since the beginning of the pandemic, repeated cross-sectional surveys are now difficult to interpret without the use of models to account for antibody waning.
Assuntos
COVID-19 , Complicações Infecciosas na Gravidez , Anticorpos Antivirais , COVID-19/epidemiologia , Estudos Transversais , Feminino , Hospitais , Humanos , Imunoglobulina G , Quênia/epidemiologia , Gravidez , Cuidado Pré-Natal , Encaminhamento e Consulta , SARS-CoV-2 , Estudos Soroepidemiológicos , Glicoproteína da Espícula de CoronavírusRESUMO
Policy decisions on COVID-19 interventions should be informed by a local, regional and national understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. Epidemic waves may result when restrictions are lifted or poorly adhered to, variants with new phenotypic properties successfully invade, or infection spreads to susceptible subpopulations. Three COVID-19 epidemic waves have been observed in Kenya. Using a mechanistic mathematical model, we explain the first two distinct waves by differences in contact rates in high and low social-economic groups, and the third wave by the introduction of higher-transmissibility variants. Reopening schools led to a minor increase in transmission between the second and third waves. Socioeconomic and urbanrural population structure are critical determinants of viral transmission in Kenya.
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COVID-19/epidemiologia , COVID-19/transmissão , COVID-19/virologia , Teste de Ácido Nucleico para COVID-19 , Controle de Doenças Transmissíveis , Epidemias , Humanos , Incidência , Quênia/epidemiologia , Modelos Biológicos , Estudos Soroepidemiológicos , Classe Social , Fatores SocioeconômicosRESUMO
Policymakers in Africa need robust estimates of the current and future spread of SARS-CoV-2. We used national surveillance PCR test, serological survey and mobility data to develop and fit a county-specific transmission model for Kenya up to the end of September 2020, which encompasses the first wave of SARS-CoV-2 transmission in the country. We estimate that the first wave of the SARS-CoV-2 pandemic peaked before the end of July 2020 in the major urban counties, with 30-50% of residents infected. Our analysis suggests, first, that the reported low COVID-19 disease burden in Kenya cannot be explained solely by limited spread of the virus, and second, that a 30-50% attack rate was not sufficient to avoid a further wave of transmission.