RESUMO
Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. We assess current methodological approaches to these issues via a case study of the 2010-2019 cholera epidemic in Haiti. We consider three dynamic models developed by expert teams to advise on vaccination policies. We evaluate previous methods used for fitting these models, and we demonstrate modified data analysis strategies leading to improved statistical fit. Specifically, we present approaches for diagnosing model misspecification and the consequent development of improved models. Additionally, we demonstrate the utility of recent advances in likelihood maximization for high-dimensional nonlinear dynamic models, enabling likelihood-based inference for spatiotemporal incidence data using this class of models. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.
Assuntos
Cólera , Haiti/epidemiologia , Cólera/epidemiologia , Cólera/transmissão , Cólera/prevenção & controle , Humanos , Biologia Computacional/métodos , Epidemias/estatística & dados numéricos , Epidemias/prevenção & controle , Modelos Epidemiológicos , Política de Saúde , Funções Verossimilhança , Processos Estocásticos , Modelos EstatísticosRESUMO
BACKGROUND: Although live attenuated monovalent human rotavirus vaccine (Rotarix) efficacy has been characterized through randomized studies, its effectiveness, especially in non-clinical settings, is less clear. In this study, we estimate the impact of childhood Rotarix® vaccination on community rotavirus prevalence. METHODS: We analyse 10 years of serial population-based diarrhoea case-control study, which also included testing for rotavirus infection (n = 3430), and 29 months of all-cause diarrhoea active surveillance from a child cohort (n = 376) from rural Ecuador during a period in which Rotarix vaccination was introduced. We use weighted logistic regression from the case-control data to assess changes in community rotavirus prevalence (both symptomatic and asymptomatic) and all-cause diarrhoea after the vaccine was introduced. We also assess changes in all-cause diarrhoea rates in the child cohort (born 2008-13) using Cox regression, comparing time to first all-cause diarrhoea case by vaccine status. RESULTS: Overall, vaccine introduction among age-eligible children was associated with a 82.9% reduction [95% confidence interval (CI): 49.4%, 94.2%] in prevalence of rotavirus in participants without diarrhoea symptoms and a 46.0% reduction (95% CI: 6.2%, 68.9%) in prevalence of rotavirus infection among participants experiencing diarrhoea. Whereas all age groups benefited, this reduction was strongest among the youngest age groups. For young children, prevalence of symptomatic diarrhoea also decreased in the post-vaccine period in both the case-control study (reduction in prevalence for children <1 year of age = 69.3%, 95% CI: 8.7%, 89.7%) and the cohort study (reduction in hazard for receipt of two Rotarix doses among children aged 0.5-2 years = 57.1%, 95% CI: 16.6, 77.9%). CONCLUSIONS: Rotarix vaccination may suppress transmission, including asymptomatic transmission, in low- and middle-income settings. It was highly effective among children in a rural community setting and provides population-level benefits through indirect protection among adults.
Assuntos
Infecções por Rotavirus , Rotavirus , Adulto , Idoso , Estudos de Casos e Controles , Criança , Pré-Escolar , Estudos de Coortes , Equador/epidemiologia , Humanos , Lactente , Prevalência , Infecções por Rotavirus/epidemiologia , Infecções por Rotavirus/prevenção & controle , População Rural , VacinaçãoRESUMO
Predicting arbovirus re-emergence remains challenging in regions with limited off-season transmission and intermittent epidemics. Current mathematical models treat the depletion and replenishment of susceptible (non-immune) hosts as the principal drivers of re-emergence, based on established understanding of highly transmissible childhood diseases with frequent epidemics. We extend an analytical approach to determine the number of 'skip' years preceding re-emergence for diseases with continuous seasonal transmission, population growth and under-reporting. Re-emergence times are shown to be highly sensitive to small changes in low R0 (secondary cases produced from a primary infection in a fully susceptible population). We then fit a stochastic Susceptible-Infected-Recovered (SIR) model to observed case data for the emergence of dengue serotype DENV1 in Rio de Janeiro. This aggregated city-level model substantially over-estimates observed re-emergence times either in terms of skips or outbreak probability under forward simulation. The inability of susceptible depletion and replenishment to explain re-emergence under 'well-mixed' conditions at a city-wide scale demonstrates a key limitation of SIR aggregated models, including those applied to other arboviruses. The predictive uncertainty and high skip sensitivity to epidemiological parameters suggest a need to investigate the relevant spatial scales of susceptible depletion and the scaling of microscale transmission dynamics to formulate simpler models that apply at coarse resolutions.