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1.
PLoS Comput Biol ; 17(12): e1009697, 2021 12.
Artigo em Inglês | MEDLINE | ID: mdl-34898617

RESUMO

For the control of COVID-19, vaccination programmes provide a long-term solution. The amount of available vaccines is often limited, and thus it is crucial to determine the allocation strategy. While mathematical modelling approaches have been used to find an optimal distribution of vaccines, there is an excessively large number of possible allocation schemes to be simulated. Here, we propose an algorithm to find a near-optimal allocation scheme given an intervention objective such as minimization of new infections, hospitalizations, or deaths, where multiple vaccines are available. The proposed principle for allocating vaccines is to target subgroups with the largest reduction in the outcome of interest. We use an approximation method to reconstruct the age-specific transmission intensity (the next generation matrix), and express the expected impact of vaccinating each subgroup in terms of the observed incidence of infection and force of infection. The proposed approach is firstly evaluated with a simulated epidemic and then applied to the epidemiological data on COVID-19 in the Netherlands. Our results reveal how the optimal allocation depends on the objective of infection control. In the case of COVID-19, if we wish to minimize deaths, the optimal allocation strategy is not efficient for minimizing other outcomes, such as infections. In simulated epidemics, an allocation strategy optimized for an outcome outperforms other strategies such as the allocation from young to old, from old to young, and at random. Our simulations clarify that the current policy in the Netherlands (i.e., allocation from old to young) was concordant with the allocation scheme that minimizes deaths. The proposed method provides an optimal allocation scheme, given routine surveillance data that reflect ongoing transmissions. This approach to allocation is useful for providing plausible simulation scenarios for complex models, which give a more robust basis to determine intervention strategies.


Assuntos
Algoritmos , Vacinas contra COVID-19/uso terapêutico , COVID-19/prevenção & controle , SARS-CoV-2 , Vacinação/métodos , Fatores Etários , COVID-19/epidemiologia , COVID-19/imunologia , Vacinas contra COVID-19/provisão & distribuição , Biologia Computacional , Simulação por Computador , Alocação de Recursos para a Atenção à Saúde/métodos , Alocação de Recursos para a Atenção à Saúde/estatística & dados numéricos , Humanos , Vacinação em Massa/métodos , Vacinação em Massa/estatística & dados numéricos , Países Baixos/epidemiologia , Pandemias/prevenção & controle , Pandemias/estatística & dados numéricos , SARS-CoV-2/imunologia , Vacinação/estatística & dados numéricos
2.
J Travel Med ; 27(8)2020 12 23.
Artigo em Inglês | MEDLINE | ID: mdl-32830853
3.
Lancet Glob Health ; 8(9): e1132-e1141, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32673577

RESUMO

BACKGROUND: COVID-19 has the potential to cause substantial disruptions to health services, due to cases overburdening the health system or response measures limiting usual programmatic activities. We aimed to quantify the extent to which disruptions to services for HIV, tuberculosis, and malaria in low-income and middle-income countries with high burdens of these diseases could lead to additional loss of life over the next 5 years. METHODS: Assuming a basic reproduction number of 3·0, we constructed four scenarios for possible responses to the COVID-19 pandemic: no action, mitigation for 6 months, suppression for 2 months, or suppression for 1 year. We used established transmission models of HIV, tuberculosis, and malaria to estimate the additional impact on health that could be caused in selected settings, either due to COVID-19 interventions limiting activities, or due to the high demand on the health system due to the COVID-19 pandemic. FINDINGS: In high-burden settings, deaths due to HIV, tuberculosis, and malaria over 5 years could increase by up to 10%, 20%, and 36%, respectively, compared with if there was no COVID-19 pandemic. The greatest impact on HIV was estimated to be from interruption to antiretroviral therapy, which could occur during a period of high health system demand. For tuberculosis, the greatest impact would be from reductions in timely diagnosis and treatment of new cases, which could result from any prolonged period of COVID-19 suppression interventions. The greatest impact on malaria burden could be as a result of interruption of planned net campaigns. These disruptions could lead to a loss of life-years over 5 years that is of the same order of magnitude as the direct impact from COVID-19 in places with a high burden of malaria and large HIV and tuberculosis epidemics. INTERPRETATION: Maintaining the most critical prevention activities and health-care services for HIV, tuberculosis, and malaria could substantially reduce the overall impact of the COVID-19 pandemic. FUNDING: Bill & Melinda Gates Foundation, Wellcome Trust, UK Department for International Development, and Medical Research Council.


Assuntos
Infecções por Coronavirus/epidemiologia , Países em Desenvolvimento , Infecções por HIV/prevenção & controle , Acessibilidade aos Serviços de Saúde , Malária/prevenção & controle , Pandemias , Pneumonia Viral/epidemiologia , Tuberculose/prevenção & controle , COVID-19 , Infecções por HIV/epidemiologia , Infecções por HIV/mortalidade , Humanos , Malária/epidemiologia , Malária/mortalidade , Modelos Teóricos , Tuberculose/epidemiologia , Tuberculose/mortalidade
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