Determinants of Aedes mosquito density as an indicator of arbovirus transmission risk in three sites affected by co-circulation of globally spreading arboviruses in Colombia, Ecuador and Argentina.

BACKGROUND: The global impact of Zika virus in Latin America has drawn renewed attention to circulating mosquito-borne viruses in this region, such as dengue and chikungunya. Our objective was to assess socio-ecological factors associated with Aedes mosquito vector density as a measure of arbovirus transmission risk in three cities of potentially recent Zika virus introduction: Ibagué, Colombia; Manta, Ecuador; and Posadas, Argentina, in order to inform disease mitigation strategies. METHODS: We sampled Aedes mosquito populations in a total of 1086 households, using indoor and peridomestic mosquito collection methods, including light traps, resting traps, traps equipped with chemical attractant and aspirators. For each sampled household, we collected socio-economic data using structured questionnaires and data on microenvironmental conditions using iButton data loggers. RESULTS: A total of 3230 female Aedes mosquitoes were collected, of which 99.8% were Aedes aegypti and 0.2% were Aedes albopictus. Mean female Aedes mosquito density per household was 1.71 (standard deviation: 2.84). We used mixed-effects generalized linear Poisson regression analyses to identify predictors of Aedes density, using month, neighborhood and country as random-effects variables. Across study sites, the number of household occupants [incidence rate ratio (IRR): 1.08, 95% confidence interval (CI): 1.01-1.14], presence of entry points for mosquitoes into the household (IRR: 1.51, 95% CI: 1.30-1.76) and presence of decorative vegetation (IRR: 1.52, 95% CI: 1.22-1.88) were associated with higher Aedes density; while being in the highest wealth tertile of household wealth (IRR: 0.78, 95% CI: 0.66-0.92), knowledge of how arboviruses are transmitted (IRR: 0.94, 95% CI: 0.89-1.00) and regular emptying of water containers by occupants (IRR: 0.79, 95% CI: 0.67-0.92) were associated with lower Aedes density. CONCLUSIONS: Our study addresses the complexities of arbovirus vectors of global significance at the interface between human and mosquito populations. Our results point to several predictors of Aedes mosquito vector density in countries with co-circulation of multiple Aedes-borne viruses, and point to modifiable risk factors that may be useful for disease prevention and control.

Entomological characterization of Aedes mosquitoes and arbovirus detection in Ibagué, a Colombian city with co-circulation of Zika, dengue and chikungunya viruses.

BACKGROUND: Dengue, Zika and chikungunya are arboviruses of significant public health importance that are transmitted by Aedes aegypti and Aedes albopictus mosquitoes. In Colombia, where dengue is hyperendemic, and where chikungunya and Zika were introduced in the last decade, more than half of the population lives in areas at risk. The objective of this study was to characterize Aedes spp. vectors and study their natural infection with dengue, Zika and chikungunya in Ibagué, a Colombian city and capital of the department of Tolima, with case reports of simultaneous circulation of these three arboviruses. METHODS: Mosquito collections were carried out monthly between June 2018 and May 2019 in neighborhoods with different levels of socioeconomic status. We used the non-parametric Friedman, Mann-Whitney and Kruskal-Wallis tests to compare mosquito density distributions. We applied logistic regression analyses to identify associations between mosquito density and absence/presence of breeding sites, and the Spearman correlation coefficient to analyze the possible relationship between climatic variables and mosquito density. RESULTS: We collected Ae. aegypti in all sampled neighborhoods and found for the first time Ae. albopictus in the city of Ibagué. A greater abundance of mosquitoes was collected in neighborhoods displaying low compared to high socioeconomic status as well as in the intradomicile compared to the peridomestic space. Female mosquitoes predominated over males, and most of the test females had fed on human blood. In total, four Ae. aegypti pools (3%) were positive for dengue virus (serotype 1) and one pool for chikungunya virus (0.8%). Interestingly, infected females were only collected in neighborhoods of low socioeconomic status, and mostly in the intradomicile space. CONCLUSIONS: We confirmed the co-circulation of dengue (serotype 1) and chikungunya viruses in the Ae. aegypti population in Ibagué. However, Zika virus was not detected in any mosquito sample, 3 years after its introduction into the country. The positivity for dengue and chikungunya viruses, predominance of mosquitoes in the intradomicile space and the high proportion of females fed on humans highlight the high risk for arbovirus transmission in Ibagué, but may also provide an opportunity for establishing effective control strategies.

Prenatal hepatitis B screening, and hepatitis B burden among children, in Ontario: a descriptive study.

BACKGROUND: Ontario is 1 of 5 provinces that immunize adolescents for hepatitis B virus (HBV), despite the World Health Organization recommendation for universal birth dose vaccination. One rationale for not vaccinating at birth is that universal prenatal screening and related interventions prevent vertical transmission. The aims of our study were to evaluate the uptake and epidemiology of prenatal HBV screening, and to determine the number of children in Ontario with a diagnosis of HBV before adolescent vaccination. METHODS: We extracted data from ICES, Public Health Ontario and Better Outcomes & Registry Network (BORN) Ontario databases. We assessed prenatal screening uptake and prevalence of prenatal hepatitis B surface antigen (HBsAg) from 2012 to 2016, as well as subsequent hepatitis B e-antigen (HBeAg) and HBV DNA testing and percent positivity. We used age and region to subcategorize the results. In a separate unlinked analysis, we evaluated the number of children positive for HBV aged 0-11 years who were born in Ontario from 2003 to 2013. RESULTS: From 2012 to 2016, 93% of pregnant women were screened for HBV, with an HBsAg prevalence of 0.6%. Prevalence of HBsAg increased with age, peaking at older than 45 years at 3%. North Toronto had the highest overall prevalence of 1.5%, whereas northern Ontario had the lowest. Of women who were HBsAg positive, HBeAg and HBV DNA tests were subsequently ordered in 13% and 38%, respectively. Of children born in Ontario between 2003 and 2013, 139 of 23 759 tested positive for HBV. INTERPRETATION: Prenatal HBV screening is not universal and subsequent evaluation is poor, limiting optimal intervention and possibly contributing to some Ontario-born children being given a diagnosis of HBV before age 12 years. These findings underscore the limitations of the province's adolescent vaccination strategy.

COVID-19: Adaptation of a model to predicting healthcare resources needs in Valle del Cauca, Colombia / COVID-19: Predicción de las necesidades de recursos sanitarios en el Valle del Cauca, Colombia

Abstract Background: Valle del Cauca is the region with the fourth-highest number of COVID-19 cases in Colombia (>50,000 on September 7, 2020). Due to the lack of anti-COVID-19 therapies, decision-makers require timely and accurate data to estimate the incidence of disease and the availability of hospital resources to contain the pandemic. Methods: We adapted an existing model to the local context to forecast COVID-19 incidence and hospital resource use assuming different scenarios: (1) the implementation of quarantine from September 1st to October 15th (average daily growth rate of 2%); (2-3) partial restrictions (at 4% and 8% growth rates); and (4) no restrictions, assuming a 10% growth rate. Previous scenarios with predictions from June to August were also presented. We estimated the number of new cases, diagnostic tests required, and the number of available hospital and intensive care unit (ICU) beds (with and without ventilators) for each scenario. Results: We estimated 67,700 cases by October 15th when assuming the implementation of a quarantine, 80,400 and 101,500 cases when assuming partial restrictions at 4% and 8% infection rates, respectively, and 208,500 with no restrictions. According to different scenarios, the estimated demand for reverse transcription-polymerase chain reaction tests ranged from 202,000 to 1,610,600 between September 1st and October 15th. The model predicted depletion of hospital and ICU beds by September 20th if all restrictions were to be lifted and the infection growth rate increased to 10%. Conclusion: Slowly lifting social distancing restrictions and reopening the economy is not expected to result in full resource depletion by October if the daily growth rate is maintained below 8%. Increasing the number of available beds provides a safeguard against slightly higher infection rates. Predictive models can be iteratively used to obtain nuanced predictions to aid decision-making

Resumen Introducción: Valle del Cauca es el departamento con el cuarto mayor número de casos de COVID-19 en Colombia (>50,000 en septiembre 7, 2020). Debido a la ausencia de tratamientos efectivos para COVID-19, los tomadores de decisiones requieren de acceso a información actualizada para estimar la incidencia de la enfermedad, y la disponibilidad de recursos hospitalarios para contener la pandemia. Métodos: Adaptamos un modelo existente al contexto local para estimar la incidencia de COVID-19, y la demanda de recursos hospitalarios en los próximos meses. Para ello, modelamos cuatro escenarios hipotéticos: (1) el gobierno local implementa una cuarentena desde el primero de septiembre hasta el 15 de octubre (asumiendo una tasa promedio de infecciones diarias del 2%); (2-3) se implementan restricciones parciales (tasas de infección del 4% y 8%); (4) se levantan todas las restricciones (tasa del 10%). Los mismos escenarios fueron previamente evaluados entre julio y agosto, y los resultados fueron resumidos. Estimamos el número de casos nuevos, el número de pruebas diagnósticas requeridas, y el numero de camas de hospital y de unidad de cuidados intensivos (con y sin ventilación) disponibles, para cada escenario. Resultados: El modelo estimó 67,700 casos a octubre 15 al asumir la implementación de una nueva cuarentena, 80,400 y 101,500 al asumir restricciones parciales al 4 y 8% de infecciones diarias, respectivamente, y 208,500 al asumir ninguna restricción. La demanda por pruebas diagnósticas (de reacción en cadena de la polimerasa) fue estimada entre 202,000 y 1,610,600 entre septiembre 1 y octubre 15, a través de los diferentes escenarios evaluados. El modelo estimó un agotamiento de camas de cuidados intensivos para septiembre 20 al asumir una tasa de infecciones del 10%. Conclusión: Se estima que el levantamiento paulatino de las restricciones de distanciamiento social y la reapertura de la economía no debería causar el agotamiento de recursos hospitalarios si la tasa de infección diaria se mantiene por debajo del 8%. Sin embargo, incrementar la disponibilidad de camas permitiría al sistema de salud ajustarse rápidamente a potenciales picos inesperados de infecciones nuevas. Los modelos de predicción deben ser utilizados de manera iterativa para depurar las predicciones epidemiológicas y para proveer a los tomadores de decisiones con información actualizada.

COVID-19: Adaptation of a model to predicting healthcare resources needs in Valle del Cauca, Colombia.

BACKGROUND: Valle del Cauca is the region with the fourth-highest number of COVID-19 cases in Colombia (>50,000 on September 7, 2020). Due to the lack of anti-COVID-19 therapies, decision-makers require timely and accurate data to estimate the incidence of disease and the availability of hospital resources to contain the pandemic. METHODS: We adapted an existing model to the local context to forecast COVID-19 incidence and hospital resource use assuming different scenarios: (1) the implementation of quarantine from September 1st to October 15th (average daily growth rate of 2%); (2-3) partial restrictions (at 4% and 8% growth rates); and (4) no restrictions, assuming a 10% growth rate. Previous scenarios with predictions from June to August were also presented. We estimated the number of new cases, diagnostic tests required, and the number of available hospital and intensive care unit (ICU) beds (with and without ventilators) for each scenario. RESULTS: We estimated 67,700 cases by October 15th when assuming the implementation of a quarantine, 80,400 and 101,500 cases when assuming partial restrictions at 4% and 8% infection rates, respectively, and 208,500 with no restrictions. According to different scenarios, the estimated demand for reverse transcription-polymerase chain reaction tests ranged from 202,000 to 1,610,600 between September 1st and October 15th. The model predicted depletion of hospital and ICU beds by September 20th if all restrictions were to be lifted and the infection growth rate increased to 10%. CONCLUSION: Slowly lifting social distancing restrictions and reopening the economy is not expected to result in full resource depletion by October if the daily growth rate is maintained below 8%. Increasing the number of available beds provides a safeguard against slightly higher infection rates. Predictive models can be iteratively used to obtain nuanced predictions to aid decision-making.

INTRODUCCIÓN: Valle del Cauca es el departamento con el cuarto mayor número de casos de COVID-19 en Colombia (>50,000 en septiembre 7, 2020). Debido a la ausencia de tratamientos efectivos para COVID-19, los tomadores de decisiones requieren de acceso a información actualizada para estimar la incidencia de la enfermedad, y la disponibilidad de recursos hospitalarios para contener la pandemia. MÉTODOS: Adaptamos un modelo existente al contexto local para estimar la incidencia de COVID-19, y la demanda de recursos hospitalarios en los próximos meses. Para ello, modelamos cuatro escenarios hipotéticos: (1) el gobierno local implementa una cuarentena desde el primero de septiembre hasta el 15 de octubre (asumiendo una tasa promedio de infecciones diarias del 2%); (2-3) se implementan restricciones parciales (tasas de infección del 4% y 8%); (4) se levantan todas las restricciones (tasa del 10%). Los mismos escenarios fueron previamente evaluados entre julio y agosto, y los resultados fueron resumidos. Estimamos el número de casos nuevos, el número de pruebas diagnósticas requeridas, y el numero de camas de hospital y de unidad de cuidados intensivos (con y sin ventilación) disponibles, para cada escenario. RESULTADOS: El modelo estimó 67,700 casos a octubre 15 al asumir la implementación de una nueva cuarentena, 80,400 y 101,500 al asumir restricciones parciales al 4 y 8% de infecciones diarias, respectivamente, y 208,500 al asumir ninguna restricción. La demanda por pruebas diagnósticas (de reacción en cadena de la polimerasa) fue estimada entre 202,000 y 1,610,600 entre septiembre 1 y octubre 15, a través de los diferentes escenarios evaluados. El modelo estimó un agotamiento de camas de cuidados intensivos para septiembre 20 al asumir una tasa de infecciones del 10%. Conclusión: Se estima que el levantamiento paulatino de las restricciones de distanciamiento social y la reapertura de la economía no debería causar el agotamiento de recursos hospitalarios si la tasa de infección diaria se mantiene por debajo del 8%. Sin embargo, incrementar la disponibilidad de camas permitiría al sistema de salud ajustarse rápidamente a potenciales picos inesperados de infecciones nuevas. Los modelos de predicción deben ser utilizados de manera iterativa para depurar las predicciones epidemiológicas y para proveer a los tomadores de decisiones con información actualizada.