Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles.

BACKGROUND: With enough advanced notice, dengue outbreaks can be mitigated. As a climate-sensitive disease, environmental conditions and past patterns of dengue can be used to make predictions about future outbreak risk. These predictions improve public health planning and decision-making to ultimately reduce the burden of disease. Past approaches to dengue forecasting have used seasonal climate forecasts, but the predictive ability of a system using different lead times in a year-round prediction system has been seldom explored. Moreover, the transition from theoretical to operational systems integrated with disease control activities is rare. METHODS AND FINDINGS: We introduce an operational seasonal dengue forecasting system for Vietnam where Earth observations, seasonal climate forecasts, and lagged dengue cases are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to 6 months ahead. Bayesian spatiotemporal models were fit to 19 years (2002-2020) of dengue data at the province level across Vietnam. A superensemble of these models then makes probabilistic predictions of dengue incidence at various future time points aligned with key Vietnamese decision and planning deadlines. We demonstrate that the superensemble generates more accurate predictions of dengue incidence than the individual models it incorporates across a suite of time horizons and transmission settings. Using historical data, the superensemble made slightly more accurate predictions (continuous rank probability score [CRPS] = 66.8, 95% CI 60.6-148.0) than a baseline model which forecasts the same incidence rate every month (CRPS = 79.4, 95% CI 78.5-80.5) at lead times of 1 to 3 months, albeit with larger uncertainty. The outbreak detection capability of the superensemble was considerably larger (69%) than that of the baseline model (54.5%). Predictions were most accurate in southern Vietnam, an area that experiences semi-regular seasonal dengue transmission. The system also demonstrated added value across multiple areas compared to previous practice of not using a forecast. We use the system to make a prospective prediction for dengue incidence in Vietnam for the period May to October 2020. Prospective predictions made with the superensemble were slightly more accurate (CRPS = 110, 95% CI 102-575) than those made with the baseline model (CRPS = 125, 95% CI 120-168) but had larger uncertainty. Finally, we propose a framework for the evaluation of probabilistic predictions. Despite the demonstrated value of our forecasting system, the approach is limited by the consistency of the dengue case data, as well as the lack of publicly available, continuous, and long-term data sets on mosquito control efforts and serotype-specific case data. CONCLUSIONS: This study shows that by combining detailed Earth observation data, seasonal climate forecasts, and state-of-the-art models, dengue outbreaks can be predicted across a broad range of settings, with enough lead time to meaningfully inform dengue control. While our system omits some important variables not currently available at a subnational scale, the majority of past outbreaks could be predicted up to 3 months ahead. Over the next 2 years, the system will be prospectively evaluated and, if successful, potentially extended to other areas and other climate-sensitive disease systems.

Sex, vaccination status, and comorbidities influence long COVID persistence.

BACKGROUND: There is interest in the public health impact of Long COVID, defined as symptoms that persist or begin after SARS-CoV-2 infection. We aimed to identify demographic and clinical risk factors associated with Long COVID over time in an Upper Middle-Income Country (UMIC) and potential biomarkers predictive of symptom trajectory. METHODS: Prospective cohort study of adults with mild SARS-COV-2 during the Omicron period. We tracked symptom persistence and IgG antibody titers against the spike S1 subunit. RESULTS: Of 383 participants, 276 had confirmed SARS-CoV-2 infection. Long COVID persisted for ≥ two months in 21 % and ≥ 12 months in 5 %. The most common symptoms were fatigue, upper respiratory symptoms, and myalgia/arthralgia: 15 % had fatigue for ≥ one month, 10 % for ≥ two months, and 5 % ≥ three months. Upper respiratory symptoms lasted ≥ one month in 17 %, ≥ two months in 7 %, and ≥ three months in 3 %. Fully 9 % reported myalgia/arthralgia lasting ≥ one month, 6 % ≥ two months, and 4 % ≥ three months. Risk factors for symptom persistence included female sex, not being fully vaccinated, and comorbidities. Participants experiencing persistent fatigue had lower anti-S1 IgG titers. CONCLUSIONS: In this population, symptom persistence declined after the acute phase, but 5 % of participants did not fully recover. Even in a population that was almost fully vaccinated, women, individuals with comorbidities, and the few remaining people who were unvaccinated were at greater risk for Long COVID. Immunoglobulins may have utility as a biomarker of Long COVID fatigue in this population.

Factores determinantes de tabaquismo en mujeres embarazadas atendidas en instituciones públicas de la Provincia de Tucumán, Argentina / Determinants of Smoking in Pregnant Women treated in Public Institutions in the Province of Tucumán, Argentina

Introducción: El tabaquismo es uno de los principales problemas de Salud Pública prevenibles en el mundo. En Tucumán no existe información sobre grupos específicos como el de las embarazadas. El objetivo fue estimar la prevalencia de tabaquismo en mujeres embarazadas y sus factores determinantes en Tucumán. Métodos: La Población objetivo fueron mujeres embarazadas que concurrieron a las instituciones seleccionadas. Se utilizó la biblioteca "lme4" del software R. Se realizó análisis bivariado utilizando el test de Chi cuadrado o de Fisher según corresponda. Mediante regresión logística se determinó las variables que presentan asociación. Resultados: La prevalencia de vida de tabaquismo fue 41.5%. El 25.3% fumaban al momento de enterarse que estaban embarazadas, de ellas 86.2% abandonaron el tabaquismo en algún momento del embarazo. Nivel de instrucción de la embarazada y de la pareja, normas en el hogar y no tener conocimiento del daño que produce fueron algunas de las variables que mostraron asociación con ser fumadoras. Conclusiones: En este estudio se reporta una prevalencia de tabaquismo en mujeres embarazadas del 25%. Además presenta factores determinantes en grupos poblacionales específicos sobre los cuales hay escasa información en nuestro ámbito.

Determinants of Smoking in Pregnant Women treated in Public Institutions in the Province of Tucumán, Argentina

Introduction: Smoking is one of the main preventable Public Health problems in the world. In Tucumán there is no information about specific groups such as pregnant women. The objective of this study was to estimate the prevalence of smoking in pregnant women and its determining factors in Tucumán. Methods: The target population were pregnant women who attended the selected institutions. The "lme4" library of the R software was used. Bivariate analysis was performed using the Chi-square or Fisher test as appropriate. Logistic regression was used to determine the variables with association. Results: The lifetime prevalence of smoking was 41.5%. 25.3% of women were smokers when they found out they were pregnant, 86.2% of them quit smoking at some point during their pregnancy. The educational level of the pregnant woman and her partner, the household rules and not being aware of the damage produced by smoking were some of the variables associated with being smokers. Conclusions: In this study, smoking prevalence in pregnant women was reported to be 25%. It also presents determining factors in specific population groups about which there is scarce information in our area.

Análisis por series temporales de la incidencia de varicela y el impacto de la implementación de la vacuna en Tucumán / Time series analysis of the chickenpox rate and the impact of vaccine implementation in Tucumán

La varicela es una de las enfermedades inmunoprevenibles más comunes. En 1998 la Organización Mundial de la Salud recomendó incorporar la vacuna a los programas nacionales de vacunación. Argentina lo hizo en 2015. El objetivo de este trabajo fue analizar la serie temporal de varicela durante 2005-2019 y evaluar el impacto de la vacuna sobre su incidencia en Tucumán tras la implementación. MÉTODOS: Se llevó a cabo un estudio observacional de tipo ecológico con fuentes de datos secundarias. Los casos de varicela fueron los consignados por el Sistema Nacional de Vigilancia de Salud. Se describió la serie temporal de casos notificados de varicela para Tucumán y se construyeron modelos aditivos generalizados (GAM) utilizando una distribución binomial negativa. Se verificó el impacto de la vacuna tomando el período 2005-2014, se construyó un modelo GAM y se pronosticó el comportamiento más probable luego de la implementación. Se evaluó el impacto comparando las tasas con sus intervalos de confianza entre lo pronosticado y lo observado. RESULTADOS: Tucumán notificó 82 810 casos durante 2005-2019. La tasa anual varió entre 1,66 por 1000 habitantes (2019) y 6,04 por 1000 habitantes (2007). La serie presentó estacionalidad y en los últimos años una tendencia decreciente. Se observó una disminución significativa de la tasa de incidencia tras la implementación de la vacuna. DISCUSIÓN: El presente trabajo evidenció el impacto de una política pública como la vacuna