Impact of climate and land use/land cover changes on malaria incidence in the Ecuadorian Amazon.

Malaria transmission is influenced by climate and land use/land cover change (LULC). This study examines the impact of climate and LULC on malaria risk in the Ecuadorian Amazon. Weekly malaria surveillance data between 2008 and 2019 from Ecuador's Ministry of Public Health were combined with hydrometeorological and LULC data. Cross-correlation analyses identified time lags. Bayesian spatiotemporal models estimated annual LULC rates of change (ARC) by census area and assessed the effects on Plasmodium vivax and Plasmodium falciparum incidence. ARC for the five land cover classes (forest, agriculture, urban, shrub vegetation, water) ranged from -1 to 4% with agriculture increasing across areas. Forest and shrub vegetation ARC were significantly associated with both Plasmodium vivax and Plasmodium falciparum. Temperature and terrestrial water content showed consistent negative relationships with both species. Precipitation had varying effects on Plasmodium vivax (null) and Plasmodium falciparum (increase) incidence. Shrubs and forest expansion, increased temperature, and terrestrial water content reduced malaria incidence, while increased precipitation had varying effects. Relationships between malaria, LULC, and climate are complex, influencing risk profiles. These findings aid decision-making and guide further research in the region.

Study protocol: improving response to malaria in the Amazon through identification of inter-community networks and human mobility in border regions of Ecuador, Peru and Brazil.

INTRODUCTION: Understanding human mobility's role in malaria transmission is critical to successful control and elimination. However, common approaches to measuring mobility are ill-equipped for remote regions such as the Amazon. This study develops a network survey to quantify the effect of community connectivity and mobility on malaria transmission. METHODS: We measure community connectivity across the study area using a respondent driven sampling design among key informants who are at least 18 years of age. 45 initial communities will be selected: 10 in Brazil, 10 in Ecuador and 25 in Peru. Participants will be recruited in each initial node and administered a survey to obtain data on each community's mobility patterns. Survey responses will be ranked and the 2-3 most connected communities will then be selected and surveyed. This process will be repeated for a third round of data collection. Community network matrices will be linked with each country's malaria surveillance system to test the effects of mobility on disease risk. ETHICS AND DISSEMINATION: This study protocol has been approved by the institutional review boards of Duke University (USA), Universidad San Francisco de Quito (Ecuador), Universidad Peruana Cayetano Heredia (Peru) and Universidade Federal Minas Gerais (Brazil). Results will be disseminated in communities by the end of the study.

Network Profile: Improving Response to Malaria in the Amazon through Identification of Inter-Community Networks and Human Mobility in Border Regions of Ecuador, Peru, and Brazil.

Objectives: Understanding human mobility's role on malaria transmission is critical to successful control and elimination. However, common approaches to measuring mobility are ill-equipped for remote regions such as the Amazon. This study develops a network survey to quantify the effect of community connectivity and mobility on malaria transmission. Design: A community-level network survey. Setting: We collect data on community connectivity along three river systems in the Amazon basin: the Pastaza river corridor spanning the Ecuador-Peru border; and the Amazon and Javari river corridors spanning the Brazil-Peru border. Participants: We interviewed key informants in Brazil, Ecuador, and Peru, including from indigenous communities: Shuar, Achuar, Shiwiar, Kichwa, Ticuna, and Yagua. Key informants are at least 18 years of age and are considered community leaders. Primary outcome: Weekly, community-level malaria incidence during the study period. Methods: We measure community connectivity across the study area using a respondent driven sampling design. Forty-five communities were initially selected: 10 in Brazil, 10 in Ecuador, and 25 in Peru. Participants were recruited in each initial node and administered a survey to obtain data on each community's mobility patterns. Survey responses were ranked and the 2-3 most connected communities were then selected and surveyed. This process was repeated for a third round of data collection. Community network matrices will be linked with eadch country's malaria surveillance system to test the effects of mobility on disease risk. Findings: To date, 586 key informants were surveyed from 126 communities along the Pastaza river corridor. Data collection along the Amazon and Javari river corridors is ongoing. Initial results indicate that network sampling is a superior method to delineate migration flows between communities. Conclusions: Our study provides measures of mobility and connectivity in rural settings where traditional approaches are insufficient, and will allow us to understand mobility's effect on malaria transmission.

A dengue outbreak in a rural community in Northern Coastal Ecuador: An analysis using unmanned aerial vehicle mapping.

Dengue is recognized as a major health issue in large urban tropical cities but is also observed in rural areas. In these environments, physical characteristics of the landscape and sociodemographic factors may influence vector populations at small geographic scales, while prior immunity to the four dengue virus serotypes affects incidence. In 2019, a rural northwestern Ecuadorian community, only accessible by river, experienced a dengue outbreak. The village is 2-3 hours by boat away from the nearest population center and comprises both Afro-Ecuadorian and Indigenous Chachi households. We used multiple data streams to examine spatial risk factors associated with this outbreak, combining maps collected with an unmanned aerial vehicle (UAV), an entomological survey, a community census, and active surveillance of febrile cases. We mapped visible water containers seen in UAV images and calculated both the green-red vegetation index (GRVI) and household proximity to public spaces like schools and meeting areas. To identify risk factors for symptomatic dengue infection, we used mixed-effect logistic regression models to account for the clustering of symptomatic cases within households. We identified 55 dengue cases (9.5% of the population) from 37 households. Cases peaked in June and continued through October. Rural spatial organization helped to explain disease risk. Afro-Ecuadorian (versus Indigenous) households experience more symptomatic dengue (OR = 3.0, 95%CI: 1.3, 6.9). This association was explained by differences in vegetation (measured by GRVI) near the household (OR: 11.3 95% 0.38, 38.0) and proximity to the football field (OR: 13.9, 95% 4.0, 48.4). The integration of UAV mapping with other data streams adds to our understanding of these dynamics.

Malaria Transmission and Spillover across the Peru-Ecuador Border: A Spatiotemporal Analysis.

Border regions have been implicated as important hot spots of malaria transmission, particularly in Latin America, where free movement rights mean that residents can cross borders using just a national ID. Additionally, rural livelihoods largely depend on short-term migrants traveling across borders via the Amazon's river networks to work in extractive industries, such as logging. As a result, there is likely considerable spillover across country borders, particularly along the border between Peru and Ecuador. This border region exhibits a steep gradient of transmission intensity, with Peru having a much higher incidence of malaria than Ecuador. In this paper, we integrate 13 years of weekly malaria surveillance data collected at the district level in Peru and the canton level in Ecuador, and leverage hierarchical Bayesian spatiotemporal regression models to identify the degree to which malaria transmission in Ecuador is influenced by transmission in Peru. We find that increased case incidence in Peruvian districts that border the Ecuadorian Amazon is associated with increased incidence in Ecuador. Our results highlight the importance of coordinated malaria control across borders.

Semiparametric Modeling of Daily Ammonia Levels in Naturally Ventilated Caged-Egg Facilities.

Ammonia concentration (AMC) in poultry facilities varies depending on different environmental conditions and management; however, this is a relatively unexplored subject in Colombia (South America). The objective of this study was to model daily AMC variations in a naturally ventilated caged-egg facility using generalized additive models. Four sensor nodes were used to record AMC, temperature, relative humidity and wind speed on a daily basis, with 10 minute intervals for 12 weeks. The following variables were included in the model: Heat index, Wind, Hour, Location, Height of the sensor to the ground level, and Period of manure accumulation. All effects included in the model were highly significant (p<0.001). The AMC was higher during the night and early morning when the wind was not blowing (0.0 m/s) and the heat index was extreme. The average and maximum AMC were 5.94±3.83 and 31.70 ppm, respectively. Temperatures above 25°C and humidity greater than 80% increased AMC levels. In naturally ventilated caged-egg facilities the daily variations observed in AMC primarily depend on cyclic variations of the environmental conditions and are also affected by litter handling (i.e., removal of the bedding material).

Efecto de la temperatura ambiente en la temperatura superficial de zonas negras y blancas del pelaje de un hato de vacas holstein en el departamento de Antioquia, Colombia / Effect of ambient temperature on the surface temperature of black and white areas of the coat in a herd of Holstein cows in the department of Antioquia, Colombia / Efeito da temperatura ambiente na temperatura superficial de zonas negras e brancas da pelagem de um rebanho de vacas holstein no estado de Antioquia, Colômbia

Resumen El artículo reporta la evaluación del efecto de la temperatura ambiente, la humedad relativa, la radiación solar y la velocidad del cuerpo bajo el índice THSW en la temperatura superficial de áreas blancas y negras del pelaje de vacas holstein. Se utilizó información de 5 vacas holstein en producción de la finca El Recreo, ubicada en el municipio de Abejorral, Colombia. Se les tomó la temperatura superficial cada 2 h durante 15 días en 10 sitios diferentes: línea dorsal anterior, media y posterior, flanco derecho e izquierdo, pecho, cuello derecho e izquierdo, vulva y glándula mamaria, con termómetro infrarrojo. Para evaluar el efecto de la temperatura ambiente en la corporal se utilizó un índice que involucra la temperatura, la humedad, la radiación y la velocidad del viento (THSW) y para el análisis estadístico se utilizó un modelo mixto aditivo generalizado suavizado. El THSW encontrado fue de 10 y 27 °C. Las áreas muestreadas tuvieron una diferencia en la temperatura; las de color negro fueron las de mayor temperatura superficial. Oscilaron entre 32,5 y 35,8 °C para zonas blancas, y entre 34,5 y 40,5 °C para zonas negras. Los mayores valores de temperatura superficial para ambas zonas en todos los puntos se presentaron a mayores valores de THSW. Se concluye que el índice THSW tiene efecto diferenciado en la temperatura superficial corporal de acuerdo con el color del pelaje; los puntos negros fueron los que presentaron mayores temperaturas.

Abstract The article evaluates the effect of ambient temperature, relative humidity, solar radiation, and wind speed according to the THSW index on the surface temperature of the white and black areas of the coat of Holstein cows. The study used information of five Holstein dairy cows from the El Recreo farm, located in the municipality of Abejorral, Colombia. Surface temperature was measured with an infrared thermometer every 2 h during 15 days in 10 different sites: anterior, middle, and posterior dorsal line, right and left flank, chest, right and left neck, vulva, and mammary gland. To evaluate the effect of ambient temperature on body temperature, an index that involves temperature, humidity, solar radiation, and wind speed (THSW) was used, and for statistical analysis, a mixed generalized additive model. THSW was 10 and 27 °C. The sampled areas had a difference in temperature; being the black spots that had higher surface temperature. Temperature oscillated between 32.5 and 35.8 °C for white areas, and between 34.5 and 40.5 °C for black areas. Higher surface temperature values for both zones at all points had higher THSW values. It is concluded that the THSW index has a differentiated effect on body surface temperature according to the color of the coat, the black spots having higher temperatures.

Resumo O artigo reporta a avaliação do efeito da temperatura ambiente, a umidade relativa, a radiação solar e a velocidade do corpo sob o índice THSW na temperatura superficial de áreas brancas e negras da pelagem de vacas Holstein. Utilizou-se informação de 5 vacas Holstein em produção do sítio El Recreo, situada no município de Abejorral, Colomba. Tomou-se a temperatura superficial destes a cada 2 horas durante 15 dias em 10 lugares diferentes: linha dorsal anterior, media e posterior, flanco direito e esquerdo, peito, pescoço lado direito e esquerdo, vulva e glândula mamária, com termômetro infravermelho. Para avaliar o efeito da temperatura ambiente na corporal utilizou-se um índice que envolve a temperatura, a umidade, a radiação e a velocidade do vento (THSW) e para a análise estatístico utilizou-se um modelo misto aditivo generalizado suavizado. O THSW encontrado foi de 10 e 27 °C. As áreas amostradas tiveram uma diferença na temperatura; as de cor negra foram as de maior temperatura superficial. Oscilaram entre 32,5 e 35,8 °C para zonas brancas, e entre 34,5 e 40,5 °C para zonas negras. Os maiores valores de temperatura superficial para ambas zonas em todos os pontos, se apresentaram a maiores valores de THSW. Conclui-se que o índice THSW tem efeito diferenciado na temperatura superficial corporal de acordo com a cor da pelagem; os pontos negros foram os que apresentaram maiores temperaturas.

Modelación del crecimiento de pollitas Lohmann LSL con redes neuronales y modelos de regresión no lineal / Modeling of growth in Lohmann LSL pullets with neural networks and nonlinear regression models

Objetivo. Modelar la curva del crecimiento de aves de la línea Lohmann LSL utilizando modelos no lineales (MNL), no lineales mixtos (MNLM) y redes neuronales artificiales (RNA). Materiales y métodos. Periódicamente se pesaron 33 aves en promedio, desde el día 21 al 196 de vida para un total de 558 registros individuales de peso. En el ajuste de la curva de crecimiento se utilizaron los modelos: no lineal de Von Bertalanffy (MNL), no lineal Mixto de Von Bertalanffy (MNLM) y redes neuronales artificiales (RNA). Los modelos se compararon con coeficiente de correlación y medidas de precisión cuadrado medio del error (CME), desviación media absoluta (MAD) y porcentaje de la media absoluta del error (MAPE). Resultados. Los valores de correlación entre los datos reales y estimados, fueron 0.999, 0.990 y 0.986 para MNLM, RNA y MNL respectivamente. El modelo más preciso con base en los criterios MAPE, MAD y CME fue el MNLM, seguido por la RNA. La gráfica de predicción generada la RNA es similar a la del MNLM. La RNA presentó un desempeño superior al MLN. Conclusiones. El mejor modelo para la predicción de curvas de crecimiento de aves comerciales de la línea Lohmman LSL hasta los 196 días de edad, con múltiples mediciones por animal en el tiempo, fue el MNLM. La RNA presentó un desempeño superior al MNL.

Objective. Modeling the pullet growth curve of the Lohmann LSL line, by using nonlinear model (MNL), nonlinear mixed model (MNLM) and artificial neural networks (ANN). Materials and methods. An average of 33 birds, were weighed from day 21 to 196 of life for 558 individual weight records. To adjust the growth curve the following models were used: nonlinear Von Bertalanffy (MNL), nonlinear mixed Von Bertalanffy (MNLM) and artificial neural networks (RNA). The models were compared with a correlation coefficient and precision measurements: mean square error (MSE), Mean Absolute Deviation (MAD) and the mean absolute percentage error (MAPE). Results. Correlation values, between actual and estimated data, were 0.999, 0.990 and 0.986 for MNLM, RNA and MNL respectively. The most accurate model based on the MAPE, MAD and CME criteria was MNLM followed by RNA. The prediction graph for RNA was similar to MNLM. The RNA performance was higher than MLN. Conclusions. The best model for the prediction of growth curves of commercial Lohmman LSL birds to 196 days of age, was the MNLM, with multiple measurements per animal at the time. RNA performance was higher MLN.

Using the distributed-delay model to predict egg production in laying hens / Uso del modelo de distribución con retardo para predecir la producción de huevos en gallinas ponedoras / Uso do modelo de distribuição com atraso para predizer a produção de ovos de galinhas poedeiras

Background: using mathematical models to characterize and estimate egg production curves is of great importance for assessing the productive efficiency of hens. These models can be used in identifying and modeling real-time factors affecting animal production and implementing corrective measures to minimize its effect. Objective: we compared the ability to model and adjust the egg production curve in hens using the distributed-Delay model versus the Adams-Bell and Lokhorst models. Methods: 225 records of weekly production of Hy Line Brown (62 data), Lohmann LSL (54 data), Isa Brown (54 data), and Lohmann Brown (55 data) were used. All analyzed flocks were raised at Hacienda La Montaña Farm, owned and managed by the University of Antioquia (Colombia). Models used were Adams-Bell, Lokhorst and Delay; all were validated and contrasted by Durbin-Watson statistic, MAD, determination (R²) and correlation (r) coefficients. Results: the Delay and Lokhorst models resulted in R² values greater than 0.8 and r-values greater than 0.9 (p<0.01). For the Lohmann Brown curve, the Adams-Bell model had the lowest R2 value (0.81), while the Lokhorst and Delay models resulted in the highest R² value for the Isa Brown curve (1.0). The Delay model fit the curve (28 and 40 for the k parameter; 63 and 64 for the DEL parameter). The Hy Line Brown curve presented a high number of irregularities, generating great difficulty for adjustment with the evaluated models. Conclusion: Delay and Lokhorst models are efficient for predicting egg production curve of the bird strains tested. Unlike the Adams-Bell and Lokhorst models, goodness of fit of the Delay model could be increased by including physiological relationships and supply/demand of resources as input variables, which would allow the model to fit the fluctuations observed in the production curves.

Antecedentes: los modelos matemáticos permiten caracterizar y estimar las curvas de producción de huevos, siendo de gran importancia para la evaluación de la eficiencia productiva de las gallinas, posibilitando identificar factores que afecten la producción animal y aplicar correctivos para minimizar su efecto. Objetivo: se comparó la capacidad para ajustar la curva de producción de huevos utilizando el modelo de distribución con retardo (Delay) y los modelos Adams-Bell y Lokhorst. Métodos: se utilizaron 225 datos de registros semanales de producción de cuatrolíneas: Hy Line Brown (62 datos), Lohmann LSL (54 datos), Isa Brown (54 datos), y Lohmann Brown (55 datos). Los lotes analizados pertenecieron a la Hacienda La Montaña, de la Universidad de Antioquia (Colombia). Los modelos fueron validados y contrastados con MAD, el coeficiente de determinación (R²) y de correlación (r), y el estadístico Durbin-Watson. Resultados: los modelos Delay y Lokhorst presentaron valores de R² superiores a 0,8 y valores de r superiores a 0,9 (p<0,01). El modelo Adams-Bell para la curva Lohmann Brown obtuvo el menor valor de r (0,81), mientras que los modelos Delay y Lokhorst presentaron el valor más alto de R² (1,0) para la curva de Isa Brown. El modelo Delay se ajustó a la curva, con valores de 28 y 40 para el parámetro k, y de 63 y 64 para el parámetro DEL. La curva de la línea Hy line Brown presentó gran cantidad de irregularidades (altibajos), generando mayor dificultad para ser ajustada con los modelos evaluados. Conclusión: los modelos Delay y Lokhorst son eficientes para predecir la curva de producción de huevos de aves de las estirpes probadas. La bondad de ajuste del modelo Delay podría aumentarse mediante la inclusión de otras variables de entrada tales como las relaciones fisiológicas, relaciones de oferta y demanda de recursos, y variables ambientales, posibilitando que el modelo Delay se ajuste a las fluctuaciones de las curvas.

Antecedentes: os modelos matemáticos para caracterizar e estimar curvas de produção de ovos são de grande importância para avaliar a eficiência produtiva de galinhas poedeiras. Estes possibilitam identificar os fatores que afetam a produção animal e aplicar os corretivos para minimizar seus efeitos. Objetivo: comparar a capacidade de ajustar a curva de produção de ovos utilizando o modelo de distribuição com atraso (Delay) e os modelos Adams-Bell e Lokhorst. Métodos: foram utilizados 225 dados de registros de produção semanal de quatro linhas de galinhas poedeiras: Hy Line Brown (62 dados), Lohmann LSL (54 dados), Isa Brown (54 dados) e Lohmann Brown (55 dados). Os lotes testados pertenceram à Fazenda La Montaña da Universidade de Antioquia (Colômbia). Os modelos foram validados e comparados com MAD, coeficiente de determinação (R²) e de correlação (r), e estatística de Durbin-Watson. Resultados: os modelos Delay e Lokhorst tiveram valores de R² superiores a 0,8 e de r superiores a 0,9 (p<0,01). O modelo de Adams-Bell para a curva na linha Lohmann Brown teve o menor valor de r (0,81), enquanto os modelos Delay e Lokhorst apresentaram o maior valor de R² (1,0) para a curva na linha Isa Brown. O modelo de atraso foi ajustado para a curva, com valores de 28 e 40 para o parâmetro k, e 63 e 64 para o parâmetro DEL. A curva da linha Hy line Brown apresentou muitas irregularidades (solavancos) gerando maior dificuldade para ser ajustada pelos modelos. Conclusão: os modelos Delay e Lokhorst são eficientes na previsão de curvas produção de ovos de aves das linhas testadas. A bondade de ajustar com o modelo de atraso pode ser melhorada com a inclusão de variáveis de entrada adicionais, tais como relações fisiológicas, relações de oferta, demanda de recursos e as variáveis ambientais. Permitindo que o modelo Delay ajuste as flutuações da curva.